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 (certificate) 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) 2026 Aang Subiyakto, Qurrotul Aini https://creativecommons.org/licenses/by-sa/4.0
Clustering Analysis of E-Learning Readiness in Java Island Indonesia with GIS Visualization
E-learning readiness (e-readiness) is used as a tool to measure the success rate of using ICT in the academic process. Until now, a lot of research on e-readiness has been collected in various regions, especially Java Island, but the data has not been grouped and visualized, so it is difficult to know the level of readiness. The purpose of this study is to group e-readiness data using clustering analysis, then create a GIS-based map of the distribution of e-readiness clusters. To obtain optimal clustering results, researchers used the K-means and PCA combination as a cluster optimization method. The total dataset used is 27 locations' data with 2 parameters selected based on the level of e-learning readiness. Based on the results of the performance analysis using the selected internal clustering validation metrics, specifically the Davies Bouldin Index (DBI) and the average within centroid distance, each metric indicates the best cluster with values of 0.057 and 0.001, respectively. The most optimal cluster formed using the K-means and PCA methods, with a total of three clusters spread across various areas on the island of Java. As for the division of each cluster by its location point, namely, cluster 1 (amounting to 20 locations) for the ready level, cluster 2 (amounting to 4 locations) for the less ready level, and cluster 3 (amounting to 3 locations) for the very ready level.
Copyright (c) 2026 Eva Khudzaeva, Qurrotul Aini, Evy Nurmiati, Ismi Ana Sulasiyah, Ibrahim Shehu Usman https://creativecommons.org/licenses/by-sa/4.0
A Systematic Review of Deep Learning and Computer Vision Methods for Accurate Object Volume Measurement
Precise and efficient object volume measurement is crucial across diverse industrial domains, including logistics, manufacturing, and agriculture, where traditional methods are often labor-intensive and error-prone. DL and CV technologies offer a compelling alternative, providing greater accuracy, speed, and safety through non-contact, real-time, and adaptive solutions. To address existing knowledge gaps, this SLR is believed to be the first to integrate and analyze the three critical dimensions of DL holistically- and CV-based volume measurement: core methodologies, practical applications across various industries, and outstanding challenges, thereby providing a unified and comprehensive understanding of the state of the art that previous, more fragmented reviews have failed to deliver. Regarding methodology, dominant DL techniques include CNNs, Mask R-CNN, and U-Net for segmentation; GANs for 3D model generation; and PointNet/voxel networks for 3D data processing, with sensor integration impacting model architecture. Applications span agriculture, logistics, manufacturing, and construction, demonstrating high accuracy with error rates as low as 0.75% and MAPE typically ranging from 3.2% to 5%. Challenges involve occlusions, diverse environmental conditions, data scarcity, and computational costs. Prioritized research directions include lightweight models, multi-task learning, improved generalization, greater robustness, and explainable AI. Overall, this SLR comprehensively synthesizes current DL and CV methodologies, their practical applications, and future research directions in object volume measurement.
Copyright (c) 2026 Muhamad Achya Arifudin, Kusrini, Andi Sunyoto, Ferry Wahyu Wibowo https://creativecommons.org/licenses/by-sa/4.0
SWOT-Based Strategic Development for Enhancing MSMEs Digital Marketing Performance in Banda Aceh
This study identifies the internal and external factors influencing the digital marketing performance of Micro, Small, and Medium Enterprises (MSMEs) in Banda Aceh to formulate a context-based strategic roadmap for improving digital competitiveness. The research addresses a gap left by previous studies, which focused primarily on individual platform use and sales outcomes, overlooking digital capability management as an applied information systems issue. Using a descriptive qualitative approach, the study involved 9 MSME owners from the culinary, fashion, and service sectors, selected through purposive sampling. Data were collected through semi-structured interviews, digital content observations, marketplace platforms, and document reviews. The analysis used thematic coding consisting of open, axial, and selective coding, with results mapped into a SWOT matrix. Findings indicate that the primary strengths lie in cultural storytelling, adaptability to trends, and customer loyalty. At the same time, the weaknesses include unstructured content management, weak visual branding, limited understanding of analytics, and low cybersecurity awareness. Externally, opportunities arise from government training and digital payment adoption, whereas threats include algorithmic changes and account security risks. The study contributes by repositioning digital marketing as a digital capability management problem and providing strategic guidance for MSME owners, local governments, and supporting institutions to foster sustainable digital transformation in regional contexts.
Copyright (c) 2026 Meutia Fadilla, Hazful Maizi, Friesca Erwan https://creativecommons.org/licenses/by-sa/4.0
Genetic Algorithm–Optimized Clustering for University Promotion Target Recommendation
Competition among higher education institutions demands promotional strategies that are more targeted and data-driven. This study proposes a clustering-based recommendation model for determining university promotion targets by integrating Genetic Algorithm (GA) optimization into three clustering methods: K-means, Fuzzy C-means (FCM), and K-medoids. The dataset consists of 925 student records (cohorts 2021–2023) from the Information Technology Department, with the selected attributes including school origin, NPSN, school location (city and province), and GPA. Clustering performance was evaluated using the Davis-Bouldin Index (DBI) and the Silhouette Coefficient as primary metrics, with intra- and inter-cluster distances as supporting indicators. The results show that GA-K-means achieves the best performance at K = 3, with a DBI of 1.2792 and a Silhouette Coefficient of 0.2876, and the improvement is statistically significant (p < 0.05). GA optimization also improves FCM performance but does not significantly improve K-medoids performance. Although the GA increases computational time by approximately two to three times, the improvement in clustering quality justifies its use in non-real-time decision-support scenarios. The proposed model enables universities to determine promotion targets in a more objective, adaptive, and data-driven manner, supporting strategic decision-making in higher education promotion.
Copyright (c) 2026 Ulla Delfana Rosiani, Clauria Dwi Putri Nabillah, M. Hasan Basri, Ahmadi Yuli Ananta, Yushintia Pramitarini https://creativecommons.org/licenses/by-sa/4.0
From Participation to Legislative Knowledge: A Bibliometric Review of E-Participation Research in Legislative Institutions
This study provides a comprehensive science map of e-Participation research in legislative institutions, addressing the fragmentation and lack of connection between citizen input and internal knowledge management. The purpose is to map the field's intellectual structure, evaluate the traceability of citizen inputs into parliamentary outputs, and identify strategic research gaps. Using the SPAR-4-SLR framework and PICOS-based screening, 41 empirical journal articles were selected from the Scopus database for bibliometric analysis using Bibliometrix and VOSviewer. Findings indicate that the field is dominated by European contexts and grounded in participation theory, institutional implementation, and behavioral adoption models. While recent trends show an increasing focus on advanced analytics and artificial intelligence, existing literature fails to explain how e-Participation mechanisms integrate with internal knowledge management. Specifically, there is no standardized metric to trace citizen input from initial submission to final committee recommendations or legislative amendments. This study contributes by offering an integrated scientific map tailored to legislative settings and proposing the Legislative Knowledge Transformation Model (LKTM). This model provides strategic guidance and practical indicators to strengthen evidence-based and transparent legislation. By establishing a reproducible pipeline for managing participatory data, the research paves the way for comparative validation in developing countries to enhance parliamentary oversight and institutional legitimacy.
Copyright (c) 2026 Rahayu Yuni Susanti, Marimin, Dikky Indrawan, Nur Hasanah https://creativecommons.org/licenses/by-sa/4.0
Application of EfficientNet Transfer Learning with Incremental Fine-Tuning for Road Damage Detection
Image-based road damage detection is an essential component of intelligent infrastructure monitoring systems. However, conventional transfer learning often fails to adapt pre-trained models to domain-specific characteristics such as fine-crack textures, illumination variations, and perspective distortions. This study proposes an EfficientNet-based road damage classification model that leverages incremental fine-tuning and multi-stage data augmentation to enhance feature adaptation and model robustness. The experiments were conducted using the Road Damage Detection dataset from Kaggle, comprising 1,400 labeled images across several road damage classes. The dataset was partitioned into 80:10:10 splits for training, validation, and testing, with stratification. The proposed approach gradually unfreezes EfficientNet layers through a structured incremental fine-tuning schedule while applying staged augmentation to expand data diversity. Experimental results show that the baseline EfficientNet transfer learning model achieved 78.26% accuracy, whereas the proposed model improved performance to 97.10% accuracy, with 97.60% macro precision, 97.20% macro recall, and 97.30% macro F1-score. The results demonstrate that incremental fine-tuning effectively enhances feature adaptation to road damage textures, while multi-stage augmentation improves model robustness. These findings indicate that the proposed approach provides an effective strategy for improving deep-learning-based road damage detection systems in real-world infrastructure monitoring applications.
Copyright (c) 2026 Riki Winanjaya, Abdi Rahim Damanik, Anton Abdulbasah Kamil https://creativecommons.org/licenses/by-sa/4.0
Predicting Consumer Purchase Intention in Informal Retail Using Machine Learning and the Purchase Intention Probability Index (PIPI)
Informal retail remains a growing and predominant form of shopping for many people. However, modern and well-organized supermarkets, using data-driven approaches to attract consumers, have increasingly challenged informal retailers in recent years. This phenomenon presents new challenges, particularly in predicting consumers' purchase intentions given limited, unstructured, and poorly documented data. Therefore, this study aims to develop and evaluate a predictive model for consumer purchase intention in informal retail using machine learning techniques and to introduce the Purchase Intention Probability Index (PIPI) as a probability-based aggregation approach to enhance predictive sensitivity. The study uses the Subsistence Retail Consumer Dataset from Mendeley Data, comprising 281 consumer records with 38 demographic, behavioral, and psychological attributes, with purchase intention as the binary target variable. Three widely used classification algorithms in consumer behavior research (decision tree, random forest, and support vector machine (SVM)) were employed to identify purchase-predictive patterns in the data. Based on these models, the PIPI was developed, which aggregates the highest probabilities from all three models to produce more robust predictions, particularly for small and heterogeneous datasets, and supports cross-model performance evaluation. The results show that the proposed PIPI method achieves the highest recall (1.00), outperforming individual classifiers in detecting purchase intention. This fact indicates that informal retailers can apply machine-learning-based analytics to improve marketing effectiveness and decision-making without requiring advanced technological infrastructure.
Copyright (c) 2026 Gatot Tri Pranoto, Yoga Religia, Dwi Pebrianti https://creativecommons.org/licenses/by-sa/4.0
Mapping Theories, Digital Predictors, and Asian Dominance in Green Purchase Intention Research
Despite growing environmental awareness, consumers often fail to translate this interest into actual green purchases. This systematic review maps the theoretical and empirical evolution of green purchase intention research during the period of rapid digital transformation from 2020 to 2025. This study follows PRISMA guidelines and analyzes 56 empirical studies from five major academic databases, including Scopus and ScienceDirect. The analysis focuses on publication trends, geographical distributions, theoretical frameworks, and key predictors. The review reveals a significant geographical concentration of research in Asia, particularly China, India, and Vietnam, while studies from Western contexts remain limited. Theoretically, while the Theory of Planned Behavior remains the dominant framework, the Stimulus-Organism-Response model is gaining traction as researchers increasingly explore digital contexts. Key findings indicate that attitude and green trust are the most consistent psychological predictors, while social media influence has emerged as a powerful external stimulus. This review is timely, as it captures how digital marketing factors have reshaped traditional behavioral models in the post-2020 period, a shift that earlier reviews did not address. The main contributions include: (1) documenting the paradigm shift from static behavioral models to dynamic, digitally-integrated frameworks; (2) identifying the most robust predictors across diverse contexts; and (3) examining the implications of the geographical research concentration for global theory development. The study concludes by recommending that future research prioritize longitudinal designs, cross-cultural comparisons, and investigations into digital platform features to understand better how green intentions translate into actual sustainable consumption behaviors across different cultural and institutional contexts.
Copyright (c) 2026 Elisa Elisa, Sarwo Edy Handoyo, Haris Maupa https://creativecommons.org/licenses/by-sa/4.0
Copyright (c) 2025 Aang Subiyakto, Qurrotul Aini https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence Integration Strategy in Management Information Systems: A Case Study of Micro, Small, and Medium Enterprises
Digital transformation through the integration of Artificial Intelligence (AI) technology into Management Information Systems (MIS) has become an urgent need for Micro, Small, and Medium Enterprises (MSMEs). The MSME sector in Indonesia plays a vital role in the national economy but faces significant challenges, including limited access to capital and technology. This study aims to develop an effective AI integration strategy to improve MSME operational efficiency. Using a mixed-methods approach combining exploratory and descriptive designs, this study involved 30 MSMEs from various sectors selected through purposive sampling. Data were collected through structured questionnaires, semi-structured interviews, and direct observation, and then analyzed using descriptive statistics, regression analysis, and thematic analysis. The results reveal a paradox: 85% of MSMEs show strong enthusiasm for AI technology while simultaneously facing implementation barriers. A significant gap exists between infrastructure readiness (40%) and human resource readiness (25%). Five critical success factors were identified: human resource readiness (35%), technology infrastructure (25%), financial support (20%), management commitment (15%), and the digital ecosystem (5%). MSMEs that successfully implemented AI achieved operational efficiency improvements of up to 30% and ROI within 12–18 months. This study produced the "MSME-AI Integration Framework" as a practical guide for phased implementation. Strategic recommendations include a phased implementation approach, ongoing digital training programs, government support, and partnerships with technology providers for affordable AI solutions.
Copyright (c) 2026 Emi Sita Eriana, Asep Erlan Maulana https://creativecommons.org/licenses/by-sa/4.0
Evaluation of The Performance of Learning Management System Using IS Success Model
Learning Management Systems (LMSs) have become a crucial element in digital learning at universities. However, not all LMSs demonstrate optimal implementation results. The urgency of this study arises from the need to ensure that the implemented LMS truly has a positive impact on the educational process, enhances the effectiveness of learning interactions, and increases user satisfaction. This study aimed to assess the effectiveness of LMS using the Information System Success Model (ISSM) proposed by Delone and McLean through a quantitative Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach. The variables analyzed included system quality, information quality, service quality, user satisfaction, system usage, and net benefits. The study's population comprised 548 LMS users from universities in Banten Province, including instructors and students who were active in the previous semester. Data were collected through questionnaires distributed via Google Forms and analyzed descriptively and quantitatively using SmartPLS 3.0 software to test the outer and inner models. The results of the study revealed that of the 10 hypotheses tested, 6 were accepted and 4 were rejected. The main findings indicate that system quality and service quality have a positive impact on intention to use and user satisfaction. These findings provide practical contributions for LMS managers in formulating steps to improve system quality, as well as theoretical contributions in developing models to evaluate the success of information systems in education.
Copyright (c) 2026 Reni Haerani, Feby Charlos, Angga Pramadjaya https://creativecommons.org/licenses/by-sa/4.0
Design and Analysis of UI Effectiveness for SDGs Interactive Learning Media Using Heuristic Evaluation
Interactive learning media must be capable of presenting the concept of Sustainable Development Goals (SDGs) in an engaging and easily understandable way for students in support of achieving the Sustainable Development Goals. However, the effectiveness of such media is strongly influenced by user interface (UI) quality, as poor UI design can cause confusion, reduce user engagement, and hinder users' understanding of SDG concepts. This research aims to develop and analyze interactive learning media that support the dissemination of SDG content. The Multimedia Development Life Cycle (MDLC) method was used to develop the interactive learning medium, and the user interface was analyzed using the heuristic evaluation method, with questionnaires distributed to 200 high school students. The results show that the developed interactive learning medium achieved a "High" level of UI effectiveness with an average score of 4.04. The aesthetic and minimalist design principle (HE08) was the indicator that scored highest, indicating that respondents considered the system's visual design well implemented, ensuring users felt comfortable and did not encounter significant usability issues. The principles of error prevention (HE05) and helping users recognize, diagnose, and recover from errors (HE09) received lower scores, highlighting areas that require improvement. Overall, studies have demonstrated that the interactive learning medium improves student engagement and the learning experience with respect to SDG concepts. This research contributes to the development of interactive learning media and guides future research on interface improvement.
Copyright (c) 2026 Deli, Suwarno, Joanntika Lewis, Risna Yunita https://creativecommons.org/licenses/by-sa/4.0
Understanding Online Purchase Intention in Social Commerce: The Roles of Web Quality, User Satisfaction, and Algorithmic Personalization
This study examines how web quality influences online purchase intention through user satisfaction in social commerce, with a specific focus on TikTok Shop. Drawing on the WebQual 4.0 framework and expectation-confirmation theory, the research investigates the effects of usability quality, information quality, and service interaction quality on user satisfaction and how satisfaction subsequently drives online purchase intention. Extending prior WebQual research, this study integrates algorithmic personalization as a moderating mechanism that strengthens the satisfaction–online purchase intention relationship, addressing an aspect largely overlooked in existing social commerce studies. Data were collected from 210 TikTok Shop users in Banjarmasin and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all web quality dimensions significantly enhance user satisfaction, which in turn positively affects online purchase intention. Algorithmic personalization significantly reinforces this relationship, and the model explains 44.6% of the variance in online purchase intention (R² = 0.446). The findings contribute to theory by extending WebQual to algorithm-driven social commerce contexts and offer practical insights for platforms seeking to optimize user experience and personalized recommendation strategies.
Copyright (c) 2026 Megi Nediawan, Asrid Juniar, Liko Noor Rafianto Rahadhian, Muhammad Rayyan Adhiyani, Nasywa Ghaida Salsabila, Paarth Agarwal https://creativecommons.org/licenses/by-sa/4.0
Reviewing The Combination of Case-Based Reasoning and Machine Learning for Improving a Decision Support System
This study investigates the integration of case-based reasoning (CBR) with machine learning (ML) to enhance decision support systems. Due to the inadequate synthesis of empirical data in this domain by previous research, we undertook a systematic review of 46 indexed journal articles published between 2019 and 2024. The review adhered to PRISMA principles to ensure a transparent and rigorous selection and analysis procedure. We analyzed integration architectures and documented performance results, application areas, and persistent implementation challenges. The research indicates that hybrid CBR-ML systems typically surpass single-method systems in accuracy, precision, and flexibility, with an average improvement of approximately 7% over CBR-only systems. Sequential and ensemble approaches typically demonstrate effectiveness, though weighted hybrid designs often achieve superior precision and recall, particularly in complex problem domains such as healthcare and finance where accuracy is critical. Researchers based in Asia authored the majority of the reviewed studies, with contributions from Europe and Africa following. These regions concentrated high-impact applications in healthcare, finance, manufacturing, and environmental management. Notwithstanding these developments, several enduring challenges persist, including substantial computational demands, vulnerability to variations in data quality, and the continuing scarcity of clearly articulated evaluation protocols, which hinder the effective implementation of high-impact applications across these regions. Overall, existing findings suggest that integrating reasoning-oriented approaches with learning-based methods can yield a balanced trade-off between predictive accuracy and interpretability.
Copyright (c) 2026 Hendri Maradona Hendri, Fatchul Arifin, Sri Andayani https://creativecommons.org/licenses/by-sa/4.0
Assessing ERP Adoption Determinants in Oil Palm Plantations Using UTAUT2 and SEM Evidence from PT Menthobi Makmur Lestari
The palm oil plantation industry in Indonesia still faces challenges in efficiency, resource management, and operational sustainability. To overcome these challenges, Enterprise Resource Planning (ERP) systems offer integrated solutions. However, their adoption is often hindered by organizational and individual barriers. This study investigates the factors influencing ERP adoption at PT. Menthobi Makmur Lestari is using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Data were collected from 88 respondents, including managers and staff actively engaged with ERP systems, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that price value, facilitating conditions, and performance expectancy significantly and positively affect behavioral intention, which in turn drives actual system usage. Moderation analysis of age, gender, and Experience showed minimal influence, indicating that demographic characteristics play a limited role in shaping ERP adoption behavior. Instead, organizational support, infrastructure availability, and perceived economic benefits emerged as the dominant enablers of ERP adoption. The findings highlight the critical role of management strategies, training, and system alignment with employee tasks in ensuring successful ERP implementation. The study concludes that ERP adoption in palm oil companies is more strongly determined by organizational readiness than by individual differences.
Copyright (c) 2026 Bimantoro Suryo Budi Sudibyo, Evi Triandini, Dadang Hermawan https://creativecommons.org/licenses/by-sa/4.0
The Influence of Video Marketing and Digital Storytelling on Perceived Increase in Consumer Purchase Intention through Brand Engagement: A Study on MSMEs
This study investigates the influence of video marketing and digital storytelling on consumer purchase intention through brand engagement among MSMEs in Jakarta. The research is grounded in the Theory of Planned Behavior (TPB) and Customer Engagement Theory, which together provide the conceptual framework for understanding how visual and narrative-based digital content affects consumer attitudes, engagement, and behavioral intention. The study is driven by real-world challenges faced by MSMEs, particularly the widespread use of video and storytelling content that often fails to generate meaningful engagement or improve sales due to limited emotional depth, weak narrative authenticity, and low digital literacy among business actors. Using a quantitative approach and SEM–PLS analysis on data obtained from 100 MSME respondents, the findings reveal that both video marketing and digital storytelling significantly enhance brand engagement. However, only video marketing shows a significant direct effect on purchase intention, while digital storytelling demonstrates no significant impact and even indicates a negative tendency. Additionally, brand engagement does not serve as a significant mediator for either content strategy. These results highlight that exposure to video or narrative content alone is insufficient without strong contextual relevance and emotional resonance. The study contributes to the development of TPB and Customer Engagement Theory by showing that engagement and intention require deeper psychological processes beyond content exposure and provides practical insights for MSMEs to produce more authentic, emotionally compelling, and culturally aligned digital content.
Copyright (c) 2026 Frans Sudirjo, Suherlan, Moses Odhiambo Okombo, Syamsuri https://creativecommons.org/licenses/by-sa/4.0
Food Recommendations to Support Unsold Food Marketplace Using Content-Based Filtering
Unsold food is food that has a short shelf life, is intended to be sold, and is still edible. Based on survey results, business owners usually resell unsold food at a discounted price via WhatsApp or posters placed in front of the shop or distribute it to the surrounding community. Meanwhile, consumers buy unsold food by visiting the shop directly. Unsold food promotions via WhatsApp or posters do not reach the wider community. Meanwhile, direct purchases limit the opportunity to buy unsold food from several sellers simultaneously and increase the risk of stockouts, which can lead to wasted food and missed savings for consumers. This study aims to develop an unsold food marketplace integrated with two key features: bargaining and recommendations. Recommendations are generated using content-based filtering with cosine similarity to measure the similarity between the user's purchase history and each unsold food item. The recommendation feature's findings reveal that content-based filtering generates recommendations more in line with user preferences than popularity-based ones. Validation results confirm this finding, demonstrating 100% accuracy in matching the recommended food categories with the ones users have purchased. Meanwhile, during the marketplace validation stage, 15 respondents reported strong acceptance, with average scores of 4.67 out of 5 for usefulness and 4.71 out of 5 for usability. This study highlights how an unsold food marketplace supports consumers with limited budgets, reduces food waste, and increases seller revenue, while its bargaining and recommendation features enhance user satisfaction and engagement, thereby achieving mutual benefits.
Copyright (c) 2026 Susana Limanto, Liliana, Fenny Cahyawati, Daniel Soesanto, Maya Hilda Lestari Louk, Heru Arwoko https://creativecommons.org/licenses/by-sa/4.0
Brand Experience and Brand Loyalty Relationship: A Study Under Attribution Theory Perspective
Research on coffee shop brand loyalty has largely focused on factors like self-congruity, brand equity, and brand trust, while the role of brand experience remains underexplored. Moreover, its link to multidimensional loyalty outcomes and attribution theory has received little attention, especially in emerging markets. This study investigates how brand experience influences brand loyalty dimensions—willingness to pay more (WTPM), word-of-mouth recommendations (WOM), and repurchase intention (RI)—within Indonesian coffee shop brands, using attribution theory to explain the mechanisms. A quantitative approach was used, with 246 Indonesian coffee shop consumers completing a questionnaire. To confirm the assumptions, we applied partial least squares structural equation modeling (PLS-SEM). The findings reveal that brand experience significantly enhances all three factors of brand loyalty: WTPM, WOM, and RI. The research provides a theoretical foundation for understanding how brand experience affects customer loyalty, suggesting future studies could explore similar relationships in other cultural and market contexts. For coffee shop owners, the findings emphasize the need to enhance brand experience through sensory, emotional, and intellectual engagements. Tailoring environments for intellectual activities can help differentiate brands in a competitive market. The study emphasizes how coffee shops serve as social and intellectual hubs. By fostering such environments, coffee shops can enhance their value, offering spaces for cognitive engagement and social interaction.
Copyright (c) 2026 Shely Rizki Hardiana , Muhamad Ridwan https://creativecommons.org/licenses/by-sa/4.0
Development of an ORMAWA Work Program Management Information System Using a User-Centered Design Approach
Student organizations (ORMAWA) at the Faculty of Mathematics and Natural Sciences, Universitas Lambung Mangkurat, face significant challenges in managing their work programs manually. This reliance on paper-based processes and decentralized record-keeping leads to chronic delays, documentation errors, and difficulties in tracking accountability. These issues severely hamper efficient coordination with faculty administrators, complicating timely decision-making and budget monitoring. The primary aim of this research is to develop and implement a web-based work program management information system for ORMAWA using a User-Centered Design (UCD) approach. Specific objectives include providing a centralized digital platform for program submission, approval, and reporting, and significantly enhancing the administrative efficiency and accountability of the entire workflow. The research methodology involved requirements gathering, iterative system design, and implementation using the React JS and Laravel frameworks. Evaluation was conducted through black box testing and User Acceptance Testing (UAT) with 13 ORMAWA administrators. Results demonstrate high user satisfaction (85%) and a substantial 30% efficiency improvement in program submission and reporting processes. The UCD approach was crucial in delivering a system that successfully eliminated redundant administrative tasks and centralized documentation. This study contributes to the application of UCD in developing organizational management systems in higher education, demonstrating how technology can transform traditional administrative workflow.
Copyright (c) 2026 Muhammad Helmi, Mohammad Reza Faisal, Friska Abadi, Dodon Turianto Nugrahadi, Setyo Wahyu Saputro, Deni Sutaji https://creativecommons.org/licenses/by-sa/4.0
Evaluating User Satisfaction in Institutional Repositories through a Combined WebQual 4.0, EUCS, and Importance–Performance Analysis Approach
The repository website of UIN Syarif Hidayatullah Jakarta was designed to provide open access to academic publications produced by students and faculty members. However, it continues to encounter issues concerning interface usability, information accuracy, service personalization, and data access speed. These challenges underscore the necessity for a comprehensive assessment of its quality and the level of user satisfaction. This study aims to identify the key factors influencing user satisfaction, measure overall website performance through the WebQual Index (WQI), and analyze the alignment between user perceptions and expectations using the Importance Performance Analysis (IPA). A quantitative approach was employed, applying the extended WebQual 4.0 model with the inclusion of accuracy and timeliness variables adapted from the End-User Computing Satisfaction (EUCS) framework. Data were collected from 402 undergraduate respondents and analyzed using SPSS through validity, reliability, partial t-tests, and paired sample t-tests. The findings reveal that all independent variables significantly affect user satisfaction, and the website achieved a WQI score of 0.80, indicating a very good quality level. Nevertheless, aspects such as visual appeal and service personalization require improvement. The gap analysis result of 0.35 signifies that current performance has yet to fully meet user expectations. Furthermore, the IPA quadrant mapping provides strategic insights, emphasizing the need to enhance data security and the timeliness of information delivery. This research contributes to both theory and practice by developing an integrated evaluation framework that combines WebQual 4.0, EUCS, and IPA, offering a structured model for assessing user satisfaction within digital academic repositories in higher education institutions.
Copyright (c) 2026 Syopiansyah Jaya Putra, Abdullah Fadli Nurazza, Zainul Arham, Yusuf Durachman, Bayu Waspodo, Ismail Khalil https://creativecommons.org/licenses/by-sa/4.0
Knowledge Management and IT Governance in Higher Education: A Systematic Review and a Proposed Integration Framework
In the digital transformation era, higher education institutions (HEIs) in developing countries struggle to align information technology governance (ITG) with institutional goals because of fragmented knowledge and constrained resources. Knowledge management (KM) can address these challenges by supporting informed decisions, increasing transparency, and fostering organizational learning. This study conducts a systematic literature review (SLR) to examine how KM strategies enhance ITG in HEIs. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures, we screened 3,361 records from IEEE Xplore, Scopus, ACM Digital Library, SpringerLink, Google Scholar, and ResearchGate; 20 studies met the inclusion criteria targeting the KM--ITG nexus in HEIs within developing countries (2020--2025). We applied thematic synthesis to answer two research questions (RQs): (RQ1) How do HEIs employ KM to strengthen ITG? (RQ2) Which strategies integrate KM with ITG? For RQ1, HEIs utilize KM to enhance IT decision-making, improve operational efficiency, accelerate digital transformation, and boost staff capacity. For RQ2, integration strategies comprise tailored governance models, digital platforms, communities of practice (CoPs), maturity assessment frameworks, and leadership-driven approaches. This review maps current KM--ITG practices, identifies enablers and barriers, and highlights the need for context-sensitive frameworks in the Global South. The findings synthesize strategies and practices that can guide the design of knowledge-based ITG models to support sustainable digital innovation in higher education.
Copyright (c) 2026 Domingas Soares, Dana Indra Sensuse, Ni Wayan Trisnawaty, Jacquline Waworundeng 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