Applied Information System and Management (AISM)
Applied Information System and Management (AISM) is a peer-reviewed, scientific journal published by the Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia collaborated (MoU) with the Association for Informatics and Computer Higher Education (APTIKOM), Indonesia. Applied Information System and Management (AISM) aims to publish the research articles focused on the applied issues of Information Systems and its management. Its scope covers the application views of the development, implementation, evaluation, operation, management, policy, and strategy issues of Information Systems. We believe that the contribution of practical research indirectly also becomes a foothold in the development of theoretical (presenting new concepts and techniques) and methodological (reporting experimental experience in the problem-solving process) issues for the development of information systems science. Applied Information System and Management (AISM) is published in English twice a year, (two issues/year, in May and October). AISM has been accredited by National Journal Accreditation (Akreditasi Jurnal Nasional-ARJUNA) in second rank (Sinta 2) of the six accreditation ranks by the Ministry of Education and Culture of the Republic of Indonesia according to Decree No. 177/E/KPT/2024 on 15 October 2024. This accreditation status has been valid for five years, starting in Vol. 6, No. 2 (2023) to Vol. 11, No. 1 (2028). The average time between article submission & publication is around four months.
Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta
Cover depan belakang, pengantar redaksi dan daftar isi
Copyright (c) 2025 Aang Subiyakto, Qurrotul Aini https://creativecommons.org/licenses/by-sa/4.0
Ontology Modeling of The Nymphalidae Family Butterfly on Java Island Using the Methontology
Indonesia, a mega-biodiverse nation with 53 national parks, including 12 on Java Island, hosts an immense variety of flora and fauna, particularly within the Nymphalidae butterfly family. However, the country's vast geographical complexity makes the understanding, classification, and management of information about these butterflies highly challenging, leading to fragmented and difficult-to-process data. This research aims to address this problem by constructing an ontology-based knowledge base to enable effective information reuse, integration, and semantic processing of Nymphalidae butterfly data from Java's national parks. The methodology employed the Methontology framework, which structured the development process into detailed stages: specification, conceptualization, formalization, implementation, and maintenance. The ontology was formally built using the Web Ontology Language (OWL) and the Resource Description Framework (RDF), with development and implementation carried out using the Protégé tool. Data integration and querying were facilitated through the application of semantic web standards, including SPARQL for retrieving information. The results yielded a structured semantic model for Nymphalidae butterflies in Java, comprising 7 classes, 4 object properties, and 4 data type properties. A critical evaluation using the HermiT Reasoner confirmed the ontology's logical consistency, proving the model to be sound. The primary contribution of this study is a validated, interoperable ontology that provides a standardized framework for organizing butterfly information, thereby facilitating better data sharing, integration, and knowledge management for biodiversity conservation and research efforts in Indonesia.
Copyright (c) 2025 Nurbojatmiko Nurbojatmiko, Azizah Nurfauziah Yusri, Ismi Ana Sulasiyah, Winda Wulandari https://creativecommons.org/licenses/by-sa/4.0
Bitcoin Price Forecasting Using Random Forest and On‑Chain Data
Bitcoin’s extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.
Copyright (c) 2025 Samsudin Samsudin, Muhammad Dedi Irawan, Muhammad Irwan Padli Nasution, Raissa Amanda Putri https://creativecommons.org/licenses/by-sa/4.0
An Integrated IT Governance and Project Management Framework for Resource-Constrained Universities in Timor-Leste
This study designed and validated an integrated information technology governance (ITG) and project management strategy for resource-constrained universities in developing countries. A mixed-methods approach combined a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review, three criterion-based elite interviews at a private university in Timor-Leste, and expert validation to refine the model. The framework operationalized ISO/IEC 38500 principles as governance guardrails across the PMBOK 7th Edition performance domains, linking decision rights, escalation paths, and conformance duties to day-to-day delivery routines. Findings indicated that the integration clarified accountability, mitigated the mum effect through time-boxed escalation and red-flag protocols, supported phased low-bandwidth service deployment, and aligned institutional priorities with budget and capacity constraints. This study introduced a governance–execution fit mechanism that made governance actionable in resource-constrained higher education settings. It also provided policy recommendations for university leaders and regulators: formalize an IT Steering Committee (ITSC) by decree, embed ISO/IEC 38500 guardrails into portfolio and project life cycles, mandate lightweight governance artifacts (charters, responsible–accountable–consulted–informed (RACI) matrices, risk registers, and decision logs), and adopt phase-gated funding with targeted capability building. These measures strengthen feasibility, scalability, and strategic adoption across comparable contexts.
Copyright (c) 2025 Ni Wayan Trisnawaty, Teguh Raharjo, Domingas Soares https://creativecommons.org/licenses/by-sa/4.0
Online-to-Offline (O2O) E-Commerce Adoption Using Technology Acceptance Model 3 (TAM 3): The Case of GrabKios
The rapid growth of digital technology has streamlined workflows and accelerated information exchange. However, it has also introduced risks such as data theft and online fraud, which make people hesitant to engage in online transactions. To address these concerns, platforms such as GrabKios have introduced online-to-offline (O2O) solutions, enabling users to order goods online through local agents and pay in cash. While GrabKios shows strong potential, its adoption remains limited, particularly among small kiosk (warung) owners who face difficulties navigating the app. This study examines the factors influencing the use of GrabKios by applying the Technology Acceptance Model 3 (TAM3). A total of 108 respondents participated in the survey. The findings indicate that perceived usefulness significantly influences behavioural intention; however, adoption does not occur unless the app is user-friendly. Usability issues, therefore, remain a significant barrier to broader adoption. These results underscore the importance of intuitive interface design and user-friendly features in promoting the broader adoption of O2O e-commerce applications. This research contributes to the field by extending the application of TAM3 in the O2O context, which remains underexplored. It offers practical implications for developers in designing simpler and more accessible digital platforms.
Copyright (c) 2025 Elwin Jusuf Togatorop, Mardiana Purwaningsih https://creativecommons.org/licenses/by-sa/4.0
Evaluating User Readiness for IoT-Based Smart Home Adoption: A PLS-SEM and IPMA Study Integrating TAM and Technological Readiness Model in the Context of NTB, Indonesia
The swift evolution of Internet of Things (IoT) innovations has significantly accelerated the integration of smart home solutions into everyday life. However, user readiness remains a pivotal aspect for ensuring successful adoption, particularly in developing regions such as Indonesia. This study aimed to evaluate user readiness for IoT-based smart home adoption by integrating the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI) within the socio-cultural context of Nusa Tenggara Barat (NTB) province, Indonesia. A quantitative survey involving 496 respondents was conducted using purposive sampling during the 2025 data collection period. The data were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The results indicate that optimism, innovativeness, and digital literacy have a significant influence on perceived ease of use and perceived usefulness. In contrast, discomfort and insecurity negatively affect user acceptance. Furthermore, perceived ease of use and perceived usefulness were found to predict behavioral intention strongly. The IPMA results further highlight optimism and innovativeness as high-impact yet underperforming areas that require attention. This study contributes theoretically by extending the integration of TAM and TRI in the context of IoT-based smart home adoption, while providing practical insights for policymakers, system designers, and digital transformation stakeholders to design more inclusive and user-aligned smart home strategies. The combined TAM–TRI framework reflects both cognitive and emotional elements that shape user decision-making, thereby strengthening the understanding of technology adoption in developing regions.
Copyright (c) 2025 Wire Bagye, Khairul Imtihan, Maulana Ashari https://creativecommons.org/licenses/by-sa/4.0
Profile Matching in Python to Identify Tourist Destinations for The Development of National Tourism
Tourism has emerged as a key contributor to state revenue and has played a crucial role in national economic recovery following the Covid-19 pandemic, establishing itself as a new engine for the country’s economic growth. In response to this, the government—through the Ministry of Tourism and Creative Economy—is actively promoting development in the tourism sector to enhance numerous tourist attractions in Indonesia, which are renowned for their beauty and recognized globally. However, the Ministry faces several challenges, including the need for technological advancements and the expansion of infrastructure. Addressing these needs will require substantial financial investment to optimize all tourism sites. This study aims to identify 22 tourist attractions across Indonesia using the Profile Matching method in Python programming, based on seven established criteria. The uniqueness about this study is that this research implemented a simple Python script and the tourist attractions are spread across all regions of Indonesia. The results of the study highlight three tourist destinations that warrant government attention for optimization: Lake Maninjau, Rantepao, and Seminyak. To facilitate improvement, several areas require focus, including upgrading access roads, enhancing basic facilities, fostering community participation, developing human resources, and leveraging technology. The analysis using Python demonstrates that the software performs efficiently, processing 22 data points with seven criteria in a mere 0.06 seconds.
Copyright (c) 2025 Meinarini Catur Utami, Qurrotul Aini, Elvi Fetrina, Muhammad Shofian Tsauri, Mella Safitri https://creativecommons.org/licenses/by-sa/4.0
E-Services Quality: An Analysis of Customer's Mobile Banking Services in Sharia—A Systematic Literature Review
In the current digital era, banking services have rapidly evolved thanks to technological advancements, such as mobile banking, which allows customers to conduct transactions easily and efficiently. Studies show that customer perception and satisfaction levels highly depend on service standards, available features, and user experience when using the service. Studies have shown that how customers feel about a service and how satisfied they are with it relies a lot on the quality of the service, the features it offers, and how easy it is to use. A big part of how well financial services on mobile devices do is how good digital services are, particularly in sharia banking that operates in accordance with Sharia principles and regulations. This research aims to gather systematic literature about the level of quality in mobile banking features provided by sharia-compliant banks. To comprehend the connection between customer experience and the effectiveness of e-services in mobile banking apps, the modified e-services quality model is essential. Finding the elements that affect the quality of e-services and consumer happiness, as well as investigating methods for enhancing digital services in compliance with sharia law, are the main goals of this study. It is anticipated that the findings of this study will offer more profound understandings of how Islamic banks may enhance the quality of their mobile banking services.
Copyright (c) 2025 Nia Kumaladewi, Dede Rosyada, Erma Suryani, Nashrul Hakiem https://creativecommons.org/licenses/by-sa/4.0
Cocoa Land Suitability Analysis Using ID3 Spatial Algorithm
Cocoa production in Indonesia encounters ongoing challenges due to declining plantation areas and suboptimal land utilization. This study applies the ID3 Spatial algorithm to evaluate land suitability for cocoa cultivation in Bogor Regency, West Java Province. The methodology integrates nine basic land characteristics, including elevation, drainage, relief, base saturation, cation exchange capacity, soil texture, soil pH, and mineral soil depth, derived from field surveys conducted by BBSDLP. Two classification models were developed and tested using spatial data preprocessing techniques. Model M1 was the baseline approach without constraints, while Model M2 incorporated a minimum planted area threshold of ≥1 ha. The results show notable performance differences between models. Model M2 achieved a reasonable accuracy of 87.27% compared to Model M1’s 29.09%, with relief identified as the root node due to its higher gain value and reduced entropy. Classification results indicate that Bogor Regency’s cocoa cultivation potential comprises 16,443 ha of S2 (moderately suitable) land and 231,018 ha of S3 (marginally suitable) land. The generated land suitability map may provide stakeholders with helpful guidance for identifying potential cultivation areas. The result suggests that artificial intelligence integration, specifically the ID3 spatial algorithm, could improve land suitability evaluation processes, potentially supporting more informed agricultural development decisions.
Copyright (c) 2025 Andi Nurkholis, Ririn Wuri Rahayu, Andrey Ferriyan, Akfina Ni’mawati https://creativecommons.org/licenses/by-sa/4.0
Leveraging Deep Learning for Cultural Preservation: A Mobile Application for Padang Cuisine
Padang cuisine, originating from West Sumatra, Indonesia, is recognized as one of the most widespread traditional food types due to its prevalence in restaurants across the country. Despite the increasing interest in classifying Indonesian food using artificial intelligence, there have been limited studies that have explicitly focused on classifying Padang dishes using deep learning approaches. This study aimed to develop an intelligent mobile application capable of identifying various Padang dishes from images using transfer learning-based convolutional neural networks (CNNs). Four pre-trained CNN architectures—EfficientNetV2M, MobileNetV2, VGG19, and ResNet152V2—were fine-tuned and evaluated on a dataset of Padang food images. This dataset comprised a total of 1,108 images, categorized into nine distinct Padang dishes, collected from both publicly available repositories and original photographs taken for this study. Among these models, ResNet152V2 achieved the best performance after optimization, with a validation loss of 0.4142 and a test accuracy of 91.33%. The optimized model was converted to TensorFlow Lite and deployed as a mobile application, enabling real-time recognition of Padang dishes. This study presented a deep-learning-based mobile solution for recognising nine traditional Padang dishes with high accuracy, demonstrating the potential of AI-driven applications to support culinary heritage preservation and promote cultural tourism in Indonesia.
Copyright (c) 2025 Njoto Benarkah, Vincentius Riandaru Prasetyo, Andreas Bayu Prakarsa https://creativecommons.org/licenses/by-sa/4.0
Evaluating QR Code-Based Attendance System Using User Experience
Efficient attendance tracking and academic schedule management are critical for students and lecturers in higher education. However, many existing systems lack user-friendly interfaces and integrated functionality, resulting in inefficiencies and reduced user satisfaction. This study aims to develop and evaluate the user experience of ‘Attend’, a mobile application designed to address these challenges. The target population consisted of higher education students and lecturers, with 35 student respondents participating in the evaluation phase through interviews and focus group discussions (FGDs) to inform the design of an intuitive and functional interface prototype. The authors conducted usability testing using the Maze App to assess task completion efficiency, followed by a user experience evaluation using the User Experience Questionnaire (UEQ). The results showed that users were able to complete tasks efficiently, although some interface components required refinement. The UEQ scores showed 'Above Average' ratings for attractiveness, efficiency, dependability, and novelty. The app also received 'Good' ratings for perspicuity and stimulation for improving clarity. These findings suggest that Attend effectively meets the needs and expectations of users in academic settings. This study contributes to the development of mobile-based educational tools by demonstrating the importance of incorporating user feedback in the design process to improve usability and user satisfaction.
Copyright (c) 2025 Dani Hamdani, Ari Purno Wahyu Wibowo, Heri Heryono https://creativecommons.org/licenses/by-sa/4.0
Building Resilience Digital Community Ecosystem by Applications Information System and Brand Experience
In the face of rapid technological advancements and socio-economic disruptions, digital community ecosystems must develop resilience to ensure sustainability and adaptability. This study investigates how digital applications, information systems, and brand experience contribute to building resilience in Indonesia's land transportation sector. Using a quantitative approach, data were collected from 350 valid responses through online structured questionnaires targeting users of land transportation services, such as buses, trains, and ride-hailing platforms in Jakarta, Bandung, and Surabaya. The sampling was purposive, involving users with prior experience using digital transportation apps. The data were analyzed using Structural Equation Modeling - Partial Least Squares (SEM-PLS). The findings indicate that system quality and information quality significantly influence brand experience, which in turn affects user engagement. Furthermore, user engagement plays a crucial role in enhancing the resilience of the digital community ecosystem. The study confirms that brand experience partially mediates the effect of system and information quality on engagement. Implications of the study emphasize the importance for transportation providers to prioritize high-quality, user-centric digital services that foster emotional connection and trust, which are essential to sustaining adaptive and resilient community platforms in the face of urban transportation challenges.
Copyright (c) 2025 Harys Kristanto, Savira Arumdini, Muhammad Fardhon, Wahyu Kurniawan https://creativecommons.org/licenses/by-sa/4.0
Revealing User Perception and Mental Model in My Tel-U Apps: A UX and Integrated Method
My Tel-U app user experience and usability were tested using a mixed-methods design with both quantitative and qualitative measurements. User experience was measured quantitatively using the User Experience Questionnaire (UEQ), as well as general satisfaction assessed by the Customer Satisfaction Score (CSAT), and task-based usability testing to evaluate actual user interaction. Thirty participants from different faculties and levels of study at Telkom University joined to experience various digital exposures and acclimate their behaviour to the application. UEQ results indicate overall positive attitudes across all six scales, with Perspicuity, Attractiveness, and Dependability achieving the highest scores. CSAT was 90%, indicating that most users were satisfied or very satisfied with the application. Although task accomplishment increased, usability testing revealed that problems such as slowness, poor navigation organization, and inadequate system feedback persisted. Open-ended questions confirmed similar issues, such as core functionality like grade viewing and attendance tracking, as well as ongoing concerns about performance and reliability. These results indicate that, although the application adheres to functional requirements, user support, response time, and clarity may need improvement in certain aspects. This research contributes to an integrative UX evaluation model by incorporating perceptual, emotional, and behavioural measures. The results provide positive feedback to developers and institutional stakeholders, justifying priorities for alterations that will make the My Tel-U app more accessible, user-friendly, and support long-term usage on the online learning platform.
Copyright (c) 2025 Alif Noorachmad Muttaqin, Muharman Lubis, Dini Handayani, Ikhlas Fuad Zamzami https://creativecommons.org/licenses/by-sa/4.0
Integrating Technology Acceptance and Government Trust to Explain Public Engagement on Social Media: An IPMA-Based Study in Local E-Government Communication
In the digital transformation era, local governments increasingly use social media to foster transparency, disseminate information, and enhance civic participation. However, challenges remain in ensuring consistent public engagement and user satisfaction, especially in regions with limited digital literacy and uneven access to infrastructure. This study investigates the key determinants of user satisfaction, engagement, and continuance intention in local government social media platforms, with a specific focus on Central Lombok, Indonesia, a rural region facing significant digital inclusion gaps. The research combines the Technology Acceptance Model (TAM), which focuses on how easy and useful a system is, with the e-Government Adoption Model (e-GAM), which looks at factors like trust in the government, transparency, how interactive the platform is, and perceived risks. A survey of 557 users was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance Performance Map Analysis (IPMA), showing that user satisfaction is greatly affected by how easy the platform is to use and how much users trust it. Digital literacy, interactivity, and institutional trust further shape these perceptions. Interestingly, perceived usefulness and perceived risk did not show strong direct effects, which was surprising and might be due to regular usage habits and dependence on institutional signals in rural areas. IPMA results indicate that interactivity, user trust, and digital literacy are high-impact yet underperforming areas, warranting strategic attention. The study makes progress in theory by merging behavioral and institutional models and provides practical suggestions for improving two-way communication, building public trust, and encouraging digital skill development to boost meaningful participation in local digital governance.
Copyright (c) 2025 Khairul Imtihan, Muhamad Rodi, Wire Bagye, Baiq Yulia Fitriyani https://creativecommons.org/licenses/by-sa/4.0
Predictive Modeling of Student Dropout Using Academic Data and Machine Learning Techniques
This study's objective is to investigate the performance of a predictive model for students at risk of dropout (DO) by considering several internal criteria of an academic program. This research uses academic information from UIN Syarif Hidayatullah Jakarta and applies the C4.5, Naive Bayes Classification (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) to forecast which students might drop out. The data used consists of 714 student records from Department of Information Systems for the academic year 2010–2015 as training and 2018 as testing data. The research method refers to the SEMMA framework (Sample, Explore, Modify, Model, and Assess) to ensure systematic and accurate data processing. Meanwhile, the internal criteria used are the completed courses, the status of the internship report, and the final project proposal. According to the study's findings, the C4.5 and SVM algorithms get the best accuracy rates of 94.44%, while KNN and NBC come in second and third, respectively, with 93%. The results show that the C4.5 and SVM algorithms work well with academic data. This study provides a substantial contribution to the development of a prediction system for students at risk of dropping out, which can be integrated into data-based applications or dashboards. This solution is expected to help higher education institutions identify students who need further academic support. In addition, this research also opens up opportunities for the progress of more accurate forecasting models through the integration of additional variables such as behavioral or psychological data. With this data-driven approach, higher education institutions can enhance their efficiency in monitoring and preventing student dropouts, thereby supporting a vision of quality and sustainable education.
Copyright (c) 2025 Qurrotul Aini, Elsy Rahajeng, Mufadha Tiohandra, Hamzah Aji Pratama, Jehad Hammad https://creativecommons.org/licenses/by-sa/4.0
Automated Glaucoma Detection and Classification from Large-Scale Fundus Image Dataset Using YOLOv8 and CNN
Glaucoma is a major eye condition that slowly damages the optic nerve and remains one of the top causes of permanent blindness around the world. This study presents an automated framework for early detection and classification of glaucoma using artificial intelligence techniques applied to large-scale retinal fundus image dataset of over 17,000 images. The optic disc (OD) and optic cup (OC) were localized using YOLOv8. Following this, we conducted Region of Interest (ROI) extraction and contour masking to isolate the OD and highlight critical regions for further examination. We extracted essential features, such as the Cup-to-Disc Ratio (CDR), Vertical CDR (VCDR), neuroretinal rim (NRR) thinning, and compliance with the ISNT (Inferior > Superior > Nasal > Temporal) rule, resulting in a detailed tabular dataset. For classification, we applied ML and DL models. YOLOv8 demonstrated superior detection precision and CNN led the classification models with 87.13% accuracy. The proposed method offers a reliable, automated solution that can support large-scale glaucoma screening in clinical settings. This framework has the potential to assist ophthalmologists by improving the speed and accuracy of early glaucoma diagnosis, reducing the risk of vision impairment in affected patients.
Copyright (c) 2025 Sheikh Aminul Islam, Humana Khan, Taslim Taher https://creativecommons.org/licenses/by-sa/4.0
Uncovering The Barriers and Business Impacts of E-Commerce Non-Adoption Among SMEs: A TOE Framework Perspective
This study investigates barriers in technology, organization, and environment that prevent Indonesian SMEs from adopting e-commerce and examines their effects on business sustainability. Using the TOE framework, this research adopts a quantitative cross-sectional method, with data from 320 SMEs analyzed using PLS-SEM. Results reveal that technological and environmental barriers significantly hinder e-commerce adoption, negatively affecting sustainability, while organizational barriers have no significant impact. These findings highlight the need for targeted interventions to address technological and environmental challenges. The study provides valuable insights for policymakers to foster supportive environments, enhance SME competitiveness, and promote digital inclusion. By addressing these barriers, SMEs can access digital markets more effectively, improve performance, and contribute to economic growth. This research underscores the importance of policies supporting SME growth and digital transformation, offering guidance to improve e-commerce adoption and business sustainability in the digital era.
Copyright (c) 2025 Yoga Religia, Yussi Ramawati, Jamaliah Said https://creativecommons.org/licenses/by-sa/4.0
Analysis of Information Technology Governance Priorities at PTKIN Using The COBIT 2019 Framework
Risk management is a part of information technology (IT) governance, enabling organizations to safeguard their information assets effectively and efficiently. It involves identifying potential risks, planning mitigation strategies, and establishing relevant policies. An interview with the Head of Pustipanda at Syarif Hidayatullah State Islamic University (SIU) Jakarta revealed that the university underwent an IT governance audit by the National Cyber and Crypto Agency in 2020, with results indicating the need for significant improvements. Given the critical role of the Academic Information System (AIS) as a strategic IT asset in higher education, effective management is essential. This study aims to evaluate the IT governance maturity of Pustipanda using the COBIT 2019 framework and to identify key governance domains that should be prioritized for improvement. The study focuses on the IT governance of Pustipanda Syarif Hidayatullah SIU Jakarta, selected as a sample from the broader population of PTKIN (State Islamic Religious Colleges) in Indonesia. Using the COBIT 2019 framework, the research identifies 22 IT governance domains prioritized for implementation, with six domains categorized as top priority. These findings provide actionable insights for IT governance enhancement, particularly in academic institutions. The methodology presented can serve as a reference for other PTKIN to assess and prioritize IT governance domains according to their specific organizational needs, supporting the development of more secure, efficient, and well-governed IT environments in higher education institutions.
Copyright (c) 2025 Nur Aeni Hidayah, Junaidi Junaidi, Elvi Fetrina, Suci Ratnawati https://creativecommons.org/licenses/by-sa/4.0
Digital Transformation at Bandung Wholesale Center: The Impact of Information Technology on Sales Performance and Experience
This study examines the impact of digital transformation on sales performance and customer experience in wholesale centers in Bandung, specifically focusing on the adoption of digital inventory management systems, electronic payment platforms, and mobile applications, including the use of TikTok and Instagram for digital marketing. The study aims to analyze how these technologies improve operational efficiency, transaction speed, and customer satisfaction in the wholesale sector. A mixed-method approach was used, combining qualitative and quantitative data. Qualitative data was obtained through in-depth interviews with 10 wholesale center owners and managers, while quantitative data was collected through a survey distributed to 200 customers. Purposive sampling was used to select wholesale centers that had adopted digital technology. The findings show significant improvements in operational efficiency, with inventory checking time reduced by 66.67% (from 12 hours to 4 hours), stock errors reduced by 66.67% (from 15% to 5%), and order fulfillment speed increased by 50% (from 2 days to 1 day). The use of TikTok and Instagram in digital marketing has expanded market reach, increased customer engagement, and raised brand awareness. In addition, electronic payment systems and mobile applications have accelerated transactions, reduced queues, and increased sales volumes. Customers reported higher satisfaction, with ease of transaction increasing by 40.63% and overall satisfaction increasing by 31.43%. This study supports existing literature on the benefits of digitalization in retail and highlights challenges such as investment costs and staff training. These findings offer important insights for the wholesale sector to embrace digital transformation to increase competitiveness in an increasingly digital marketplace.
Copyright (c) 2025 Syaifuddin, Noor Aimi Mohamad Puad https://creativecommons.org/licenses/by-sa/4.0
Challenges of Implementing Knowledge Management System Agribusiness for Aglaonema Farmers
Aglaonema plants have become an important element in people's lives, serving as home decorations and sources of income. Aglaonema cultivation is increasingly popular, opening up business opportunities for farmers. This plant is not just a hobby but a horticultural commodity with high economic value. This research aims to analyze the difficulties faced by Aglaonema farmers, from seedling to marketing. This research uses a constructivist paradigm and qualitative methods, involving 20 informants from among farmers, traders, buyers, extension workers, and farmer organizations. Data were collected through interviews, focus group discussions, and observations, with triangulation to validate the information. Data analysis was conducted using the NVivo application. The research results show that the main difficulties faced by Aglaonema farmers include unpredictable price fluctuations, limited varieties, weak communication among farmers, traditional seedling methods, and a lack of information about seed supply. These findings emphasize the need for information sharing between farmers and stakeholders to improve productivity and quality in agribusiness, in line with consumer demands and technological advancements, which can be implemented through a knowledge management system platform in the Aglaonema agribusiness supply chain.
Copyright (c) 2025 Yuni Sugiarti, Arif Imam Suroso, Irman Hermadi, Euis Sunarti, Fadhilah Bt Mat Yamin https://creativecommons.org/licenses/by-sa/4.0
Design and Analysis of a Flutter-Based Mobile Application for Palm Oil Monitoring
In the current era of digital transformation, it is essential to modernize data processing systems to improve operational efficiency and competitiveness across industries. However, PT Agro Sawit Musi Rawas still relies on outdated manual processes, resulting in reporting delays, inaccurate data, and difficulties in real-time performance monitoring. Therefore, the aim of this research is to design a comprehensive monitoring system specifically tailored to meet the unique operational needs of PT Agro Sawit Musi Rawas. The system criteria were identified through methods such as observations, interviews, and literature reviews, which provided valuable insights into the company’s specific challenges and requirements. The system design incorporates data collection and analysis using the URS (User Requirement Specification) and UML (Unified Modeling Language) frameworks, ensuring it addresses the identified needs effectively. The design and analysis follow a structured and systematic process to develop an information monitoring system that enhances transparency, operational effectiveness, and efficiency. The findings reveal that the implementation of the proposed system can significantly improve reporting speed, data accuracy, and real-time monitoring capabilities. By adopting this monitoring system, PT Agro Sawit Musi Rawas can optimize reporting processes, facilitate real-time performance monitoring, and ultimately strengthen its competitive market position.
Copyright (c) 2025 Ananda Rahmatul Hannan, Ajang Sopandi, Ali Firdaus, Arif Kurniawan https://creativecommons.org/licenses/by-sa/4.0
Development of a User-Personalized Decision Support System for Contraception Method Selection
The number of unmet needs for contraception has not reached the target set by the Indonesian government, while the uneven distribution of health workers, the main source of information for contraception, is still an unresolved problem. The Internet serves as an alternative source of information for contraception selection. However, without personalization, it may lead to inappropriate choices. This study discussed the development of an information system for selecting contraceptives, incorporating a decision support system (DSS), enabling personalized recommendations based on user preferences to assist in determining the appropriate contraceptive method. The functionality of the information system was evaluated using black-box testing, conducted by reproductive health experts, while its usability was assessed based on ISO-9241-11:2018 standards with 25 respondents. The functional evaluation of the system showed that 14 functions successfully passed the testing procedures, while 2 functions failed. The usability evaluation yielded excellent results, with an overall score of 4.52. Based on these findings, the developed information system can serve as a medium for reducing the number of unmet needs for contraception by providing users with contraceptive information tailored to their preferences. Further research needs to enhance system information by integrating user medical reports and user location and evaluating the recommendation-to-selection conversion rate— the extent to which users follow the system’s recommendations when choosing contraceptives.
Copyright (c) 2025 Ahmad Fahmi Karami, Rany Ekawati, Syauqi Muhammad Marier, Purwanti Susantini https://creativecommons.org/licenses/by-sa/4.0
Analysis and Design of Inventory Management Information Systems at PT. XYZ
This study aims to analyze and design an Inventory Management Information System (IMIS) for PT. XYZ, a company that currently relies on manual inventory processes, leading to inefficiencies and operational delays. To address these challenges, the IMIS was developed using the Rapid Application Development (RAD) methodology, which followed three key stages: requirements planning, design workshop, and implementation. In the requirements planning stage, comprehensive user needs and system specifications were gathered. During the user design phase, iterative prototyping and continuous feedback sessions helped refine the system’s interface and functionalities. The rapid construction phase saw the development of core features—such as real-time inventory tracking, automated restocking alerts, and detailed transaction histories—using PHP, HTML, JavaScript, and MySQL. Finally, in the cutover phase, pilot testing demonstrated that the system reduced material search times from 15–30 minutes to nearly instantaneous retrieval, significantly improving inventory accuracy and overall operational efficiency. Further research is needed to evaluate its long-term performance and scalability. This study not only highlights the practical advantages of the RAD-based approach but also offers a promising model for organizations seeking to modernize their inventory management practices.
Copyright (c) 2025 Nia Kumaladewi, Ryan Faatih Firdaus https://creativecommons.org/licenses/by-sa/4.0
Critical Success Factors for IT Risk Management in the Digital Transformation Era: Insights from a Multiple Case Study
The convergence of Information Technology risk management and digital transformation is a vital consideration for contemporary organizations navigating the rapidly changing digital landscape. This research investigates the intersection of these domains, aiming to identify the critical success factors that enable effective Information Technology risk management within the context of digital transformation. Through a Systematic Literature Review, a comprehensive search on Web of Science and Scopus led to the acceptance of 61 peer-reviewed papers published between 2020 and 2024, providing a solid foundation for understanding current trends and best practices. Employing a qualitative multiple case study approach, this study examines the experiences, strategies, and challenges of organizations that have successfully managed Information Technology risks during their digital transformation journeys. Thematic analysis reveals three key critical success factors: executive leadership and support, cross-functional collaboration, and risk-aware decision-making. These findings offer actionable insights for organizations seeking to align their risk management practices with the complexities of digital transformation. By bridging theoretical frameworks with practical insights, this research provides valuable recommendations for organizations to navigate digital transformation securely. Future research could focus on exploring the implementation nuances of these success factors across various industries, such as healthcare, finance, and manufacturing, to deepen our understanding of the intricate relationship between IT risk management and digital transformation in diverse contexts.
Copyright (c) 2025 Dwi Yuniarto https://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine
The Indonesian government's implementation of a booster vaccination program as part of its COVID-19 response has generated diverse public reactions, particularly on social media platforms like Twitter. This study aims to analyze public sentiment regarding booster vaccines by examining Twitter data to understand the prevailing discourse and attitudes toward this policy. The research employs sentiment analysis, a text mining and processing technique, to classify tweets into positive, neutral, and negative categories. The study utilizes the Support Vector Machine (SVM) algorithm, evaluating its performance through a multi-class parameter assessment. Two multi-class strategies, One-against-one (OAO) and One-against-rest (OAR) are combined with various kernels (Sigmoid, Polynomial, and RBF) to identify the most accurate model for sentiment classification. The results show that the OAO method with the RBF kernel achieves the highest accuracy of 96%, outperforming other combinations like OAO with Polynomial (95.2%) and Sigmoid (93.7%) kernels. Similarly, the RBF kernel performs best with 95.5% accuracy in the OAR approach. Using the optimal model, sentiment analysis classifies 49 tweets as positive, 927 as neutral, and 24 as negative, revealing a predominantly neutral public sentiment with limited positive and negative opinions. In conclusion, this study demonstrates the effectiveness of SVM, particularly the OAO method with the RBF kernel, for sentiment analysis of social media data. The findings provide insights into public perceptions of the booster vaccine program, offering policymakers a data-driven basis for designing targeted communication strategies to address concerns and enhance public acceptance.
Copyright (c) 2025 Andi Nurkholis, Styawati Styawati, Syahirul Alim, Hendi Saputra, Andrey Ferriyan https://creativecommons.org/licenses/by-sa/4.0
Integrating Artificial Intelligence in Human Resources Management: A Bibliometric Analysis of Emerging Trends and Influences
This study aims to analyze the integration of Artificial Intelligence (AI) in the field of Human Resource Management (HRM) from 2004 to 2024. By measuring productivity, impact, collaboration and research topics, this research tracks the development of the domain. These findings may provide a foundation for future research on the growth of AI-focused HRM research, lead authors, institutions, and emerging themes. Using the Scopus database, 381 relevant documents were identified and analyzed, showing an increase in publications year on year, with significant growth between 2020 and 2023. This shows the transformative impact of AI on HRM. This analysis reveals rapid growth and international collaboration in AI-enabled HRM research, demonstrating significant potential. Future research could explore specific applications of AI, ethical considerations, and long-term impacts on HRM practices.
Copyright (c) 2025 Dwi Soca Baskara, Suyud Gunadi, Nabil Muttaqin https://creativecommons.org/licenses/by-sa/4.0
Evaluation of The Orthopedic Hospital Website's Performance Using User Acceptance Testing
User Acceptance Testing (UAT) is essential for ensuring a system meets the real-world needs of its users. Unlike traditional testing that prioritizes technical accuracy, UAT focuses on usability and overall user satisfaction. For the Orthopedic Hospital's website, UAT served as the primary evaluation method, targeting its core user groups—patients, staff, and stakeholders. The testing process followed the ISO 9126 software quality standards, evaluating the website across key dimensions such as functionality, reliability, usability, efficiency, maintainability, and portability. A Likert scale was used to capture structured user feedback, providing quantifiable insights into user perceptions. This combined approach allowed for a well-rounded assessment, balancing technical quality with user experience. Results indicated a high satisfaction rate of 92.7%, reflecting strong approval of the website’s design and functionality. However, the evaluation also pointed to areas for enhancement. The online registration process, for example, could be simplified to improve ease of use and task completion. While the results are promising, continued improvement is vital—especially in healthcare, where user experience directly affects patient outcomes and service efficiency. To maintain alignment with evolving user needs and technologies, ongoing UAT cycles should be integrated into the hospital’s digital strategy. Leveraging ISO 9126 standards and user-centered tools like the Likert scale will ensure the website remains effective, accessible, and responsive to its users.
Copyright (c) 2025 Resad Setyadi, Aedah Abd. Rahman, Toni Anwar https://creativecommons.org/licenses/by-sa/4.0
Enterprise Architecture Planning with TOGAF ADM: A Case Study of a Heavy Equipment Rental Service Company
Strategic Information Systems (IS) and Information Technology (IT) planning play a vital role in aligning technological initiatives with business objectives, particularly in asset-intensive industries. This study aims to implement the TOGAF ADM framework to design a strategic IS/IT planning model for a heavy equipment rental company, demonstrating how enterprise architecture can align IT initiatives with business goals. PT. XYZ has not yet implemented an information system strategy for the company’s needs in terms of management, leading to inefficiencies in business operations at PT. XYZ. A qualitative approach was used to obtained through in-depth interviews with company’s owners and managers, observation data of company’s IT asset also collected to describe organization’s current IS/IT landscape and applies the TOGAF ADM phases to design an enterprise architecture that supports business-IT alignment. This study demonstrates how TOGAF ADM can provide a structured methodology for identifying business needs, defining architectural visions, and developing a roadmap for IS/IT improvements. Findings indicate that using EA frameworks such as TOGAF ADM enhances strategic planning capabilities, supports organizational agility, and provides a repeatable model for similar enterprises in the rental or industrial sector. The results offer valuable insights for practitioners and researchers interested in applying enterprise architecture to improve IS/IT strategic planning processes.
Copyright (c) 2025 Rian Safrianto https://creativecommons.org/licenses/by-sa/4.0
Assessing The Acceptance and Trust in Student Information Systems Through a Modified TAM Perspective
The rapid development of information technology has motivated universities to implement technology-based student information systems to enhance the efficiency and effectiveness of student data management. This research seeks to evaluate acceptance and trust in student information systems at universities using a modified version of the Technology Acceptance Model, incorporating perceived trust as an additional variable. The study involved a sample of 200 active university students, with data analyzed using the structural equation modeling approach. Findings from the analysis show that both perceived usefulness and perceived ease of use significantly impact students’ intention to adopt the system, which in turn influences actual system usage. Additionally, perceived trust emerged as a critical factor in reinforcing both the intention to use and the subsequent actual use of the student information system. The results indicate that the intention to use the system acts as an essential mediator in the relationships between students’ perceptions of usefulness, ease of use, trust, and their actual usage behavior. These results have significant implications for universities aiming to improve the adoption of student information systems. Enhancing user experience, building system trust, and ensuring robust security should be prioritized in the development and refinement of such systems. By focusing on these aspects, institutions can foster higher acceptance and sustained usage, leading to more effective student data management and a better overall educational experience.
Copyright (c) 2025 Muhammad Taufik Nur Hidayat, Taqwa Hariguna, Dhanar Intan Surya Saputra https://creativecommons.org/licenses/by-sa/4.0
Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost
Fraud detection in imbalanced datasets, where fraudulent transactions represent a small fraction of total data, presents a major challenge for machine learning models. Traditional classifiers often perform poorly in such scenarios due to their bias toward the majority class. This study investigates the effectiveness of two data augmentation techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Generative Adversarial Networks (CGAN) in improving fraud detection performance. Both methods are applied to balance the dataset, and their impact is evaluated using two classifiers: Random Forest (RF) and XGBoost. The models are tested across three versions of the dataset: the original imbalanced data, the SMOTE-augmented data, and the CGAN-augmented data. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC. Results indicate that both augmentation techniques enhance the models' ability to detect fraudulent transactions compared to the original dataset. Notably, CGAN outperforms SMOTE in terms of recall and F1 score, suggesting its ability to generate more diverse and realistic synthetic samples. While SMOTE creates new samples through interpolation, CGAN uses an adversarial process involving a generator and a discriminator, resulting in more complex data representations. The study also finds that XGBoost combined with CGAN yields the highest performance, effectively capturing intricate fraud patterns. In contrast, SMOTE, though beneficial, shows limited capacity in improving recall. This research highlights the importance of advanced augmentation techniques like CGAN in addressing class imbalance and improving fraud detection systems. It also opens pathways for future exploration of deep learning-based augmentation and classification methods in fraud detection.
Copyright (c) 2025 Sarmini Sarmini, Sunardi Sunardi, Abdul Fadlil https://creativecommons.org/licenses/by-sa/4.0