Jurnal Teknik Informatika is a peer-reviewed academic journal published by the Department of Informatics, Faculty of Science and Technology, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia. The journal is officially registered with ISSN 2549-7901 (online) and ISSN 1979-9160 (print), and is nationally accredited as Sinta 2, affirming its recognition and quality within Indonesia’s academic landscape. Published twice a year in April and October, it provides open and advance access to scholarly works in the fields of Information and Communication Technology (ICT) and Islamic studies. The journal’s scope encompasses a wide range of topics, including software engineering, system design methodology, artificial intelligence, big data and data mining, human-computer interaction, mobile computing, computational linguistics, cybersecurity, and computer networking. Through its consistent publication and commitment to academic rigor, Jurnal Teknik Informatika serves as an influential platform for researchers, educators, and students to exchange ideas and findings that contribute to the advancement of informatics and computing. Its affiliation with the Indonesian Association of Higher Education in Informatics and Computing (APTIKOM) further enhances its role as a collaborative medium that promotes innovation, interdisciplinary research, and scientific dialogue across Indonesia’s higher education institutions. By bridging academic and technological perspectives, the journal continues to strengthen the national and global discourse in the development of informatics and its applications in various domains.
Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah
Implementation of Augmented Reality in Occupational Health and Safety Practicum Learning to Enhance Student Competence at Politeknik Transportasi Darat Bali
Occupational Health and Safety (OHS) competence is essential for vocational and polytechnic students in transportation-related fields where workplace hazards are frequent and potentially life-threatening. Conventional teaching methods, such as lectures and traditional practicums, often fail to simulate real-life risks or engage students effectively. This study examines the effectiveness of Augmented Reality (AR)-based practicum in enhancing OHS competence by providing realistic and interactive learning environments without exposing students to actual danger. Using a quantitative experimental design with a pretest–posttest control group, the research involved 55 second-semester students of Politeknik Transportasi Darat Bali. Class A (experimental) received AR-based instruction, while Class B (control) followed conventional training. Data were collected through questionnaires measuring knowledge, attitudes, and self-efficacy, and observation checklists assessing practicum performance. Instrument reliability was confirmed (Cronbach’s alpha = 0.879). Results indicated significant improvements in all five dimensions of OHS competence—safety knowledge, hazard recognition, procedural compliance, situational awareness, and safety communication—with posttest mean scores of 85–95 compared to 45–55 in pretests. Statistical analyses showed significant differences (p < 0.05) and strong effect sizes (Cohen’s d = 0.82–1.03). Student satisfaction exceeded 85% in interactivity, realism, and usability. The findings demonstrate that AR-based practicums enhance learning outcomes, motivation, and digital literacy, making AR a promising pedagogical innovation for transportation polytechnic education.
Copyright (c) 2026 Handoko, Ahmad Soimun, Ni Luh Darmayanti https://creativecommons.org/licenses/by-sa/4.0
Detecting Hoax News in Indonesian Language Using Web-Based Multinomial Naïve Bayes
This study addresses the growing problem of hoax news in Indonesia, which has contributed to social conflicts. It aims to develop an effective detection method using the Multinomial Naive Bayes algorithm. The study integrates Indonesian specific text preprocessing and feature engineering within the CRISP-DM framework to enhance classification performance. A dataset of 5,226 news articles (2,612 non-hoax and 2,614 hoax) was collected from kompas.com and turnbackhoax.id. Preprocessing steps included case folding, tokenization, stopword removal, and stemming tailored to the Indonesian language. Feature extraction was performed using the TF-IDF weighting scheme to convert text into numerical representations. The Multinomial Naive Bayes algorithm achieved an average accuracy of 86%, precision of 86%, recall of 86%, and F1 score of 86%, indicating stable and balanced performance. Furthermore, the trained model was successfully deployed using the Flask framework and stored in (pickle/joblib) format, demonstrating its practical applicability in real world systems. The results indicate that the integration of Indonesian specific preprocessing and TF-IDF feature representation significantly supports the effectiveness of the Multinomial Naive Bayes algorithm in detecting hoax news. This study provides a scalable and implementable approach to combating the spread of false information in Indonesian digital media.
Copyright (c) 2026 Fitri Mintarsih, Ivan Ananda Putra, Arini, Victor Amrizal, Hendra Bayu Suseno https://creativecommons.org/licenses/by-sa/4.0
Starlink-Based IoT Network Performance Evaluation for Water Quality Monitoring in Remote Environments
Continuous water quality monitoring in remote and infrastructure-limited regions is constrained by the lack of reliable communication networks to support real-time IoT data transmission. Starlink offers a promising alternative due to its independence from terrestrial infrastructure, yet empirical evidence on its reliability as an end-to-end IoT communication backbone remains limited. This study therefore presents the design, implementation, and empirical network performance evaluation of a Starlink-based IoT system for real-time water quality monitoring. The proposed system integrates an ESP32 microcontroller with pH, total dissolved solids (TDS), and turbidity sensors, transmitting sensor data via a Starlink satellite link to a backend platform using the MQTT protocol with AES-128-GCM application-layer encryption. Received data are processed in Node-RED, stored in InfluxDB, and visualized through a Grafana real-time dashboard. Network performance was evaluated through five independent test iterations under both TCP and UDP transmission modes, measuring latency, jitter, packet loss, and throughput as key indicators of satellite link reliability for continuous IoT data transmission. The results demonstrate stable and reliable satellite connectivity, with latency consistently within 33–36 ms, jitter below 10 ms, zero packet loss across all configurations, and UDP throughput reaching up to 32.8 Mbps. TCP throughput was constrained to approximately 3.4–4.1 Mbps due to congestion control behavior over high-latency satellite links, a finding with direct implications for transport protocol selection in satellite-based IoT deployments. These results confirm that Starlink-based connectivity provides communication quality well in excess of the demands of periodic MQTT-based sensor transmission, demonstrating its feasibility as a reliable communication backbone for IoT-based water quality monitoring in environments where terrestrial infrastructure is unavailable or unreliable.
Copyright (c) 2026 Kasyful Amron, Dany Primanita Kartikasari, Tiara Calista Kusumawardani Atarian, Archie Vian Nizam Efendi, Rayhan Egar Sadtya Nugraha, Maritza Aliyya Devy https://creativecommons.org/licenses/by-sa/4.0
Interactive Mobile IoT Application for Monitoring Etawa Crossbreed (PE) Goat Growth in Rural Communal Farms
The rapid development of communication and information technology has encouraged the adoption of the Internet of Things (IoT) in the agricultural sector, particularly in livestock management. This study presents the development of an interactive mobile IoT application for monitoring the growth of Etawa Crossbreed (PE) goats in rural communal farms. The proposed system integrates a load cell–based digital weighing scale with an IoT module to automatically record livestock weight data and store it in cloud storage, which can be accessed through a mobile application. The application also supports the recording of feed and health history to enable structured growth monitoring. The system was deployed and tested in a communal farm environment involving 20 rural farmers, where the IoT scale measured livestock weight and transmitted data wirelessly to the mobile application. A design thinking approach was employed to ensure usability and suitability for rural users. The usability evaluation using the System Usability Scale (SUS) produced an average score of 83.5, classified as excellent (Grade B), indicating high user acceptance. In addition, technical performance evaluation showed that all 20 monitoring records were successfully transmitted, resulting in a transmission success rate of 100%, with complete and consistent data storage achieving 100% data integrity. Observational latency results indicated that RFID reading required an average of 2.5 seconds, while the weighing and data synchronization process took an average of 7.5 seconds before appearing in the mobile application. These results demonstrate that the proposed system is not only user-friendly but also reliable and capable of supporting near real-time livestock monitoring in communal farming environments.
Copyright (c) 2026 Ariq Cahya Wardhana, Arif Amrulloh, Rifki Adhitama https://creativecommons.org/licenses/by-sa/4.0
Accuracy Evaluation of 2D MediaPipe-Based Pose Estimation for Archery Posture Detection Using N-MPJPE
Archery requires high consistency and precise body posture, where small deviations can affect stability and accuracy. Recently, 2D human pose estimation has become an effective approach for analyzing sports techniques through automatic joint detection. This study proposes a 2D pose estimation system based on the MediaPipe framework to detect eight fundamental phases of archery technique and evaluate accuracy using the Normalized Mean Per Joint Position Error (N-MPJPE) metric. The dataset consists of annotated images representing the eight phases, which serve as ground-truth references. Accuracy is measured by calculating the normalized Euclidean distance between predicted joint positions and ground-truth coordinates across all phases. Experimental results show an average N-MPJPE of 0.71, indicating low joint-position deviation after scale normalization. Compared with prior studies reporting N-MPJPE values between 0.6 and 1.2, the proposed system demonstrates competitive accuracy for real-time 2D pose estimation. These results indicate that the system can reliably capture posture variations across archery phases and provide quantitative feedback on body alignment, making it a practical tool to support athletes and coaches in improving training quality and shooting performance.
Copyright (c) 2026 Muhammad Andhika Bayu Prasetya, Harits Ar Rosyid, M. Zainal Arifin https://creativecommons.org/licenses/by-sa/4.0
Design and Implementation of MCP-Web-Curl: A Model Context Protocol Server for Web and API Access in Agentic Coding Assistants
Many contemporary agentic coding assistants expose large language models through API wrappers but still lack a generic, reliable way to read the live web or invoke arbitrary REST endpoints, when relevant documentation or error explanations fall outside their internal context, these agents often stop rather than extend the search space. To address this gap, this paper presents MCP-Web-Curl, a Node.js/TypeScript based Model Context Protocol (MCP) server and command-line interface that provides LLM oriented tools for browser-based web scraping, REST API requests, Google Custom Search, smart routing over natural language commands, and robust file downloading. MCP-Web-Curl is designed around strict character limits, explicit truncation metadata, and resource blocking so that external calls remain token efficient and predictable for agent planning. Using a design science approach, we elicit requirements from real world agentic coder usage, design a modular architecture, implement the server with Puppeteer and the official MCP SDK, and evaluate it qualitatively through documentation-reading, API inspection, and download scenarios, complemented by independent marketplace reviews on MCPlane, Glama, and related MCP catalogs. The resulting architecture positions MCP-Web-Curl as a reusable blueprint for generic web/API access layers in agentic coding environments.
Copyright (c) 2026 Rayhan Zahwan Saleh, Muharman Lubis https://creativecommons.org/licenses/by-sa/4.0
FinBERT-Based Sentiment Integration in Hybrid CNN– BiLSTM Models For Stock Price Forecasting
This study investigates sentiment-aware deep learning models for short-term stock price forecasting using NVIDIA (NVDA) as a representative high-volatility technology stock. Four architectures—CNN, LSTM, BiLSTM, and a hybrid CNN–BiLSTM—are evaluated under two configurations: without sentiment and with FinBERT-based financial news sentiment integrated as a continuous contextual feature. Historical OHLV data are combined with sentiment information to enable multimodal learning under a controlled experimental setting. The results demonstrate that recurrent architectures consistently outperform convolution-only models, highlighting the importance of temporal dependency modeling in financial time series. Among all configurations, the hybrid CNN–BiLSTM with FinBERT sentiment achieves the best overall performance, yielding the highest R², the lowest MAE and RMSE, and the smallest overfitting gap. Bootstrap-based confidence intervals indicate stable generalization, while Wilcoxon signed-rank tests confirm that the observed performance improvements are statistically significant. The study also presents a near real-time deployment framework with low inference latency, demonstrating practical applicability for decision-support systems. Overall, the findings show that effective alignment between local feature extraction, bidirectional temporal modeling, and contextual sentiment integration is critical for improving stock price. forecasting accuracy and robustness.
Copyright (c) 2026 Mohammad Tyas Pawitra, Lukman Abdurrahman, Hanif Fakhrurroja https://creativecommons.org/licenses/by-sa/4.0
Comparing K-Prototypes and K-Medoids with Catboost for Health Profile Clustering of Pesantren Students
Health screening in pesantren is challenging due to communal living conditions, limited health facilities, and the need for early identification of vulnerable student groups. This study compares the performance of K-Prototypes and K-Medoids clustering for grouping student health profiles and evaluates the use of cluster labels as additional features in a CatBoost classification model. The dataset consists of 1,464 new students from Queen Al Falah Islamic Boarding School in the 2025/2026 academic year, collected through the admission system and analyzed after preprocessing. Clustering is performed using K-Prototypes and K-Medoids with three clusters to support interpretability of nutritional and health profiles. Although two clusters yield higher silhouette values, three clusters provide more meaningful distinctions for practical screening. Classification experiments use CatBoost with an 80:20 stratified train-test split, comparing baseline models and hybrid models that integrate cross-algorithm cluster features. The results show an asymmetric pattern. Adding K-Prototypes features improves K-Medoids target accuracy from 99.66 percent to 100 percent, while adding K-Medoids features slightly decreases K-Prototypes target accuracy from 98.98 percent to 98.63 percent. McNemar test results indicate that these differences are not statistically significant. Overall, the proposed framework supports reliable and interpretable health profile clustering for pesantren student monitoring.
Copyright (c) 2026 Moch. Aghisna Hadzikunnuha, Harits Ar Rosyid, M. Zainal Arifin https://creativecommons.org/licenses/by-sa/4.0
Geographical Indication Classification Based on Stingless Bee Honey Samples Using a Generative Model on Spectral Data for Mobile Device Applications
Stingless bee honey (SBH) is a food product with a simple and fast production process. Honey production is highly dependent on the condition and ability of the bees. Each region in Indonesia has different botanical and environmental conditions, with different entomological characteristics of bee species based on their geographical origin. This honey classification model is part of the formation of Geographical Indications (GI) based on product characteristics. A generative model is used in the pre-processing stage to produce spectrum data with the best grouping (silhouette score > 0.6). The main process discussed in this article is the application of GI classification model of four types of stingless bee honey based on cultivation location. The results of the study with a 400x745 dataset and 4 classes (Lampung, Bogor, Sukabumi, and Rangkas Bitung) showed that the classification model produced an accuracy above 95% with a precision and recall above 0.99. The SBH GI classification application has been successfully built using the scrum methodology and is cloud-based. The application displays the classification results of origin, type (entomological), feed dominance (botanical) and dominant spectrum values. The application has also been tested for feasibility based on the User Acceptance Test with results above 90%.
Copyright (c) 2026 Hata Maulana, Yohanes Aris Purwanto, Diding Suhandy, Sony Hartono Wijaya, Heru Sukoco https://creativecommons.org/licenses/by-sa/4.0
Impact of Wavelet Denoising on LSTM-Based Greeting Sentence Recognition Using the IndSpeech Teldialog SVCR Dataset
Speech signals play a crucial role in human communication, particularly in speech recognition systems. However, speech recognition performance is often compromised by noise in the audio signal. This study aims to examine the effect of wavelet denoising technique on greeting sentence data containing artificial white noise before performing speech recognition using Long Short-Term Memory (LSTM). Mel Frequency Cepstral Coefficient (MFCC) is used as speech feature extraction. The results show that speech recognition accuracy reaches 90% on clean data. Accuracy drops to 51% when tested on data with noise, indicating a significant decrease of 39 percentage points. After applying the wavelet denoising method, accuracy improved using the two best parameter combinations. The combination with the highest SNR value resulted in an improvement of 18 percentage points, while the combination with the highest PESQ value resulted in an improvement of 13 percentage points. These findings indicate that the wavelet denoising method is capable of improving the performance of LSTM-based speech recognition in noisy environments.
Copyright (c) 2026 Shabira Zhillan, Luh Kesuma Wardhani, Nenny Anggraini, Nashrul Hakiem, Imam Marzuki Shofi https://creativecommons.org/licenses/by-sa/4.0
Explainable Ensemble Learning for Urban Flood Risk Mapping in Jakarta Using Multi-Source Geospatial and Hydrometeorological Data
Urban flooding is a frequent hydrometeorological hazard in Indonesia, particularly in Jakarta, driven by rapid urbanization, limited drainage capacity, land cover change, and extreme rainfall. This study develops an explainable ensemble learning framework for urban flood risk mapping in Jakarta using multi-source geospatial and hydrometeorological data, including satellite-based rainfall, topography, land use/land cover, NDVI, and IoT-based river water level observations from 2023–2025. Flood occurrence labels were constructed by integrating municipal flood records with satellite-based inundation data. The framework integrates Random Forest, Gradient Boosting, and XGBoost models, with SHAP applied for interpretability and identification of dominant flood drivers. Model evaluation using ROC-AUC and RMSE indicates that XGBoost achieved the highest performance (AUC = 0.91, RMSE = 0.184), outperforming Random Forest (AUC = 0.87, RMSE = 0.221) and Gradient Boosting (AUC = 0.89, RMSE = 0.203). SHAP analysis identifies rainfall intensity, elevation, proximity to river channels, and built-up area percentage as the most influential factors. Despite uncertainties in flood labeling and the lack of high-resolution drainage data, the results demonstrate the potential of explainable ensemble learning for urban flood risk assessment and resilience planning.
Copyright (c) 2026 Arief Wibowo, Abdul Haris Achadi https://creativecommons.org/licenses/by-sa/4.0
Usability Evaluation of SIMAK at Udayana University Using Outlier Identification with Webuse and Heuristic Methods
Universities require reliable and user-friendly academic information systems to support teaching, learning, and administrative processes. However, the usability of such systems often encounters obstacles that affect user satisfaction and operational efficiency. This study evaluates the usability of the Sistem Informasi Manajemen Akademik (SIMAK) at Udayana University using a combined Webuse questionnaire and Heuristic Evaluation approach. A total of 100 student responses were collected, and outlier identification using standard deviation analysis removed 34 inconsistent responses. This step was essential for preventing inflated Webuse scores and ensuring that the final dataset (n = 66) more accurately reflected typical user experiences.The Webuse results classify all usability dimensions in the “Excellent” category Content Organization & Readability (0.86), Navigation & Links (0.83), User Interface Design (0.84), and Performance & Effectiveness (0.81). However, the heuristic evaluation conducted by four expert evaluators identified 19 moderate to high-severity issues, revealing critical weaknesses in system responsiveness, interface consistency, and error prevention. These contrasting outcomes highlight that high perceived satisfaction does not necessarily align with expert-validated usability standards.The main contribution of this study lies in integrating outlier detection to refine questionnaire-based usability data, resulting in more valid interpretations. The findings offer practical recommendations for improving SIMAK’s performance, interface clarity, and error-handling mechanisms, while providing methodological insights for future usability evaluations.
Copyright (c) 2026 Deppy Librata, Putu Desiana Wulaning Ayu, Roy Rudolf Huizen https://creativecommons.org/licenses/by-sa/4.0
Ensemble Hybrid Recommender System (CBF, CF, KNN, NBC) With Multi-View TF-IDF for Robust Preliminary Medical Diagnosis
Advances in health information technology require intelligent systems capable of supporting rapid and accurate diagnosis. This study proposes a Hybrid Recommender System (HRS) for preliminary medical diagnosis based on electronic medical records. The developed system combines K-Nearest Neighbor and Naïve Bayes Classifier with Multi-View TF-IDF feature representation. A total of 948 doctor-annotated medical records were used in the evaluation using a 10-Fold Cross-Validation scheme to improve the reliability of performance assessment. The results show that the hybrid model provides the best performance with an accuracy of 87.37% and an F1-score of 84.20%, consistently surpassing all comparison methods. These findings confirm that the integration of similarity-based and probabilistic learning can improve the quality of initial diagnosis recommendations in medical decision support systems. Further research will focus on expanding the dataset and clinical validation to ensure the reliability of the system in real-world practice.
Copyright (c) 2026 Jamilah Karaman, Triyanna Widiyaningtyas https://creativecommons.org/licenses/by-sa/4.0
A Computer Vision Approach for Bali Cattle Morphometric Measurement Using Multi-Threshold Segmentation and FIS–CF-Based Classification
Manual morphometric measurement of livestock is time-consuming, stressful to animals, and poses safety risks to handlers. This study presents a computer vision-based system for automatically measuring three key morphometric parameters of Bali cattle—withers height (WH), body length (BL), and chest girth (CG)—in accordance with the Indonesian National Standard (SNI). Images were captured from side and rear perspectives and processed using threshold-based image segmentation in the HSV color space to isolate the cattle contour. Pixel-to-centimeter calibration was performed using a fixed reference marker placed at a known distance of 1.5–2.0 m from the camera. The extracted morphometric values were subsequently fed into a Fuzzy Inference System with Certainty Factor (FIS-CF) for cattle grading and classification. Threshold values ranging from 0.5 to 0.9 were evaluated against manual ground-truth measurements using MAE, RMSE, MAPE, and R². The optimal threshold of 0.9 achieved MAPE values of 9.85% (WH), 6.04% (BL), and 11.49% (CG), representing up to 52% improvement over the lowest threshold. Although R² values remain negative due to limited sample size and non-linear pixel-to-metric variance, a consistent upward trend toward zero confirms measurement improvement with higher thresholds. The proposed method offers a practical, non-invasive alternative to manual measurement, with potential application in precision livestock farming and automated cattle grading systems.
Copyright (c) 2026 Defiana Arnaldy, Kudang Boro Seminar, Muladno, Heru Sukoco, Shelvie Nidya Neyman https://creativecommons.org/licenses/by-sa/4.0
Automatic Detection of Foot Arch Using Clarke's Angle Calculation Through A Web-Integrated System for Children
Foot deformities, particularly flatfeet (pes planus), are frequently observed in children. Although many children exhibit some form of flatfeet, this condition can persist and cause biomechanical issues, pain, and postural misalignments. Early detection and monitoring of foot arch development are critical to preventing long-term complications. However, common diagnostic methods such as wet foot print tests, X-rays, and MRIs are costly, time-consuming, and involve radiation exposure. This study proposes an innovative web-based system that detects foot arch types in children using Clarke’s Angle. The system employs digital image processing to calculate the arch angle, offering a non-invasive, efficient, and cost-effective alternative to existing methods. The system was tested on 180 children aged 2-7 years at two locations in Semarang: Kindergarten & Daycare Habibie Ainun Tlogosari and Integrated Service Post Kemuning. The results from the Clarke’s Angle measurements from the web-based system were compared with the wet foot print test, showing minimal differences and achieving an accuracy rate of 98.89%. This system offers a reliable solution for early detection and is suitable for use in both clinical and community health settings. It provides a faster, more accessible approach to pediatric foot assessment, delivering real-time results and eliminating any waiting time.
Copyright (c) 2026 Shobiha Awwaliyah, Qalbi Rivantona, Alfaturachman Maulana Pahlevi, Dedi Nurcipto, Menik Dwi Kurniatie https://creativecommons.org/licenses/by-sa/4.0
A Real-Time IOT Monitoring and Safety Cutoff System for Electric Vehicle Batteries Using the BLYNK Platform
This paper presents the design and implementation of a 58-volt LiFePO₄ battery voltage management system for electric vehicles, featuring remote monitoring and control via the Blynk application. The system continuously monitors battery voltage levels and enables control through a Solid-State Relay (SSR) connected to a NodeMCU ESP8266 microcontroller. Through the Blynk interface, users can view real-time voltage readings along with the corresponding battery capacity percentage. The NodeMCU ESP8266 demonstrated reliable performance throughout all test phases, maintaining a stable internet connection. The average voltage measurement deviation displayed on the Blynk application was approximately 0.74%. The SSR is configured to disconnect the vehicle’s power supply when the battery voltage reaches around 46.3 volts or when the battery percentage decreases to 0.16%. This disconnection can be triggered manually through the Blynk application or automatically by the SSR. The disconnection process can be carried out through the Blynk application or automatically via the Solid State Relay (SSR). Remote disconnection via the Blynk application serves as an additional safety measure activated when suspicious conditions or activities are detected. This mechanism safeguards the battery from excessive discharge, promoting better performance and extending its service life by cutting off the load at critical limits.
Copyright (c) 2026 Jefri Lianda, Agustiawan, Adam, Johny Custer,Rindilla Antika, Marzuarman https://creativecommons.org/licenses/by-sa/4.0
Integration of YOLOv8 and ResNet-50 to Improve Road Damage Detection Performance
Automatic road damage detection is an important solution for more effective and efficient transportation infrastructure maintenance. This study proposes the implementation of the You Only Look Once version 8 (YOLOv8) method with ResNet50 as a backbone to improve feature extraction capabilities in detecting various types of road damage. The model was trained using a road damage image dataset that has gone through preprocessing and data augmentation stages to enrich image variations. Test results show that the proposed model is able to achieve excellent performance, with an accuracy value of 95.2%, a precision of 0.979, a recall of 0.968, and an F1-score of 0.974. This achievement proves that the integration of YOLOv8 with ResNet50 as a backbone can improve the reliability of the road damage detection system compared to the original model. With this performance, this method has the potential to be applied in a real-time road monitoring system to support more optimal transportation infrastructure maintenance planning.
Copyright (c) 2026 Rendi Andrea Pramana, M. Arief Soeleman https://creativecommons.org/licenses/by-sa/4.0
A Socio-Technical Windows 11 Hardening Framework Integrating Ferret-Windows and CIS Benchmarks
The high level of vulnerability in the Windows operating system requires implementing a hardening strategy that focuses not only on technical security but also on end-user convenience. This study, using a Design Science Research (DSR) methodology, aims to design and evaluate a Windows-based host security configuration by applying 35 parameters developed by integrating the Ferret-Windows open-source tool and the Microsoft Windows 11 Benchmark CIS standard. The research method includes a descriptive study to analyze the effectiveness of the tool and its parameter formulation, and a prescriptive study to formulate a combined configuration. The evaluation was carried out through functional testing and usability measurements using the System Usability Scale (SUS) instrument for 31 end-users (e.g., staff and students) in a public institution context, using the System Usability Scale (SUS) instrument on a standardized test laptop. The results showed that the configured system obtained an average SUS score of 73.63, which falls within the "good" category according to the standard interpretation scale, indicating that the resulting system is usable by most users. These findings indicate that the proposed hardening framework results in a system configuration that achieves acceptable usability without significant user sacrifice. While the study’s limitations include a focus on usability rather than quantitative pre-/post-security validation, it provides a practical contribution, a socio-technical hardening framework adaptable for public institutions.
Copyright (c) 2026 Prasetyo Adi Wibowo Putro, Ray Novita Yasa, Mochammad Latief Reswandana Musonip, Dimas Nugroho Putro, Agry Zharfa https://creativecommons.org/licenses/by-sa/4.0
Handling Class Imbalance in Fan Sentiment Analysis: Naïve Bayes with TF-IDF on Instagram and Twitter
Social media platforms such as Instagram and Twitter serve as major channels for football fans to share opinions and respond to club-related dynamics, including Manchester United. Beyond fan interaction, these platforms play an important role in business, marketing, and information exchange, making efficient text classification essential. This study applies the Naïve Bayes to analyze sentiment toward Manchester United’s performance based on 2,500 Instagram comments and 2,500 Twitter comments. The research process included data cleaning, sentiment labeling, and preprocessing steps. An imbalance in positive, negative, and neutral comments was managed using data balancing techniques to enhance model reliability. Results show that balancing significantly improved performance, with accuracy reaching 83.87% for Instagram and 82.48% for Twitter. Improvements in precision, recall, and F1-score further confirmed Naïve Bayes’ capability to handle complex, noisy, and diverse social media language. The study highlights how dataset size, effective preprocessing, and accurate labeling contributed to performance gains. Overall, Naïve Bayes proved effective for sentiment classification, offering insights into public perception of Manchester United. These findings emphasize its potential for large-scale social media analysis, supporting both academic research and practical applications in digital marketing and fan engagement strategies.
Copyright (c) 2026 Khomsatun Ni’mah, Rakha Arian Archaniga https://creativecommons.org/licenses/by-sa/4.0
Implementation of IoT Technology on MySmartTrash Waste Bank Application
The effectiveness of Waste Bank in addressing national waste management challenges is limited by inherent shortcomings. Conventional processes, which heavily rely on manual labor and record-keeping, often face logistical challenges and inefficiencies that limit the effectiveness of waste banks. This paper presents the MySmartTrash application, a solution that integrates IoT technology to enhance waste management practices through a smart waste bank system. By utilizing IoT-enabled sensors, the application allows users to monitor waste levels in real time, thereby optimizing waste collection processes and promoting effective waste segregation. This study employed IoT Design Methodology and Prototyping. Through a SWOT analysis of existing waste management applications, the research identifies strengths and opportunities for enhancing waste management systems. Usability testing also highlighted the significance of various features. This study offers insights for future research into IoT applications in environmental sustainability and waste management systems.
Copyright (c) 2025 Viva Arifin, Siti Ummi Masruroh, Rizka Amalia Putri, Fitri Mintarsih, Nenny Anggraini, Nurhayati, Dewi Khairani https://creativecommons.org/licenses/by-sa/4.0
Use Case Point Activity-Based Costing and Adjusted Function Point for Software Cost Estimation
Effective software development planning is vital across various industries, and inadequate planning can lead to project failures. A key part of this planning is accurately estimating effort and costs, which is crucial for staying within budget and meeting deadlines. This research compares two methods – Use Case Point Activity Based Costing (with 21 complexity factors) and Adjusted Function Point (with 16 complexity factors) – for estimating costs versus actual values. The analysis reveals that the Use Case Point method had a 23.52% deviation from actual costs, while the Adjusted Function Point method had a 33.35% deviation. These findings provide essential reference points during software project planning, ensuring estimates closely align with actual values based on project-specific attributes. This deviation underscores the importance of precision and selecting appropriate methodologies tailored to each project’s unique characteristics. Ultimately, this research equips businesses and project managers with a robust financial prudence framework and enhances the likelihood of project success.
Copyright (c) 2025 Ahmad Fauzan Haryono, Andria Farhan, Dewi Khairani, Supardi Razak, Fitri Mintarsih https://creativecommons.org/licenses/by-sa/4.0
Uncovering Hidden Themes in Indie Music: Crisp-Dm Guided LDA Topic Modeling on a Kaggle-Based Lyric Generation Dataset
The development of music has produced many works in the form of data, especially lyrical data, which provide insight into the semantic structure of music. This study explores latent thematic patterns in the indie lyric dataset from Kaggle by applying Latent Dirichlet Allocation (LDA), which is the first LDA study of indie music lyrics in the Indonesian context with the interpretation of love, emotional needs, romance, and inner conflict. The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology can be effectively applied to unstructured data, opening up opportunities for better music classification. The methodological stages include business and data understanding, data preparation, modelling, evaluation, and dissemination. In the early stages, the Kaggle dataset implemented Natural Language Processing, which was done with case folding, punctuation removal, stopword removal, stemming, and tokenization. The LDA model is trained by identifying five topics with different interpretations. Visualization in WordClouds, with topic distribution on datasets and title-based topic mapping. This model yielded a coherence value of 0.3044, which indicates limited semantic consistency, which means the words in the topic have a reasonably good relationship, but there is still potential for refinement in subsequent studies. The limitations of this study include the limited size of the dataset, with only 347 rows and slight variation in interpretation. For future research, it is recommended to use larger datasets and more diverse interpretations and apply more machine learning models.
Copyright (c) 2025 Thoyyibah T , Yan Mitha Djaksana https://creativecommons.org/licenses/by-sa/4.0
VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems
Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.
Copyright (c) 2025 Yunita Sartika Sari, Kusrini, Ema Utami, Ferry Wahyu Wibowo https://creativecommons.org/licenses/by-sa/4.0
Sentiment Classification in Imbalanced Data: Trade-Offs Between Metrics and Real-World Relevance
Sentiment analysis plays a crucial role in assessing public perception, particularly in healthcare services like BPJS Kesehatan, Indonesia’s national health insurance program. However, sentiment classification faces a challenge due to class imbalance, where negative feedback dominates positive responses. This study investigates whether sentiment classification should prioritize traditional evaluation or maintain real-world data representation by preserving the original sentiment distribution. Two feature extraction methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), were evaluated using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression with varying maximum feature counts (100–300) to examine the impact of feature dimensionality. Model performance was evaluated using traditional metrics, while sentiment distribution fidelity was assessed by comparing predicted proportions with the dataset. Results show TF-IDF achieves higher precision and recall but fails to capture positive sentiments, leading to a skewed representation of real-world trends, while BoW offers a more balanced distribution with slightly lower accuracy. Paired t-tests and Wilcoxon signed-rank tests confirmed differences in accuracy and recall are significant, but not in precision and sentiment distribution. These findings highlight a trade-off between performance and sentiment diversity, vital in healthcare services and other fields with imbalanced datasets, emphasizing the need to align evaluation metrics with real-world objectives. Future research should investigate advanced models, such as deep learning and transformer-based approaches, to enhance both accuracy and fairness when analyzing imbalanced data.
Copyright (c) 2025 Indra Swanto Ritonga, Wanayumini, Dedy Hartama https://creativecommons.org/licenses/by-sa/4.0
Back-End Development of an Interactive Dashboard with Real-Time API Integration for Chili Plant Monitoring in Precision Agriculture
This research focuses on the development of an interactive web-based dashboard to support a precision agriculture system for chili plants. The primary focus of this research is on the back-end development of the system. The system integrates several internal and external APIs, including the Flask API (internal) for plant disease classification and growth prediction, and the Google Gemini API for the AI-powered chatbot that provides consultation to farmers (external). These features allow farmers to receive automatic disease diagnosis and growth predictions, improving decision-making and crop management. The dashboard also presents weather information, environmental data, and nanobubble data, along with Echarts gauge charts for seven essential metrics: Electrical Conductivity (EC), temperature, humidity, pH, nitrogen, phosphorus, and potassium. Data for the environmental and nanobubble data is retrieved from the ThingSpeak API (external), while weather information is fetched from the OpenWeatherMap API (external). The system was thoroughly tested using Postman to ensure all API endpoints function correctly. The results confirmed that all endpoints responded with status code 200 OK, indicating stable back-end performance. Performance testing showed response times stabilizing at 2000 ms after initial 4500 ms peaks, confirming efficient handling, reliable endpoints, and deployment readiness.
Copyright (c) 2025 Azwar Farrel Wirasena, Hanif Fakhrurroja, Dita Pramesti https://creativecommons.org/licenses/by-sa/4.0
Real-Time Retail Shelf-Stock Detection with YOLOv7
This study developed a real-time shelf stock monitoring system for retail environments, leveraging the You Only Look Once version 7 (YOLOv7) deep learning-based object detection framework. The system effectively addresses the inefficiencies, delays, and errors inherent in manual stock auditing processes. The underlying model was trained on a comprehensive dataset comprising 15,397 annotated object labels across fifteen distinct retail product categories. The fully trained model was then integrated into a web-based platform designed to capture real-time shelf images via a webcam. These captured images undergo automated processing for product detection and counting. The detection results are dynamically displayed on an interactive dashboard and securely stored in a backend database. The system also incorporates voice alerts, which are triggered automatically when stock levels fall below predefined thresholds, thereby facilitating immediate restocking. Experimental validation indicates high performance, with both precision and recall exceeding 96%, and an average processing latency of less than one second per frame. The model achieved an mAP@0.5 of 0.996 and an mAP@0.5:0.95 of 0.86. These findings underscore the system's effectiveness in providing a rapid, accurate, and efficient monitoring solution specifically tailored for small to medium-sized retail businesses. The primary contribution of this research lies in its comprehensive, end-to-end system integration, combining robust YOLOv7-based object detection with real-time web visualization and automated voice alerts, successfully addressing existing gaps in prior implementations.
Copyright (c) 2025 Annies Alquratu SeptriaPS, Ade Silvia Handayani, Nasron Nasron https://creativecommons.org/licenses/by-sa/4.0
Food Image Classification and Recipe Recommendation for South Sumatran Cuisine Using EfficientNetB1
Visual-based food classification and recipe recommendation systems remain underexplored in the context of local culinary traditions. To address this gap, a system was developed using the EfficientNetB1 architecture of Convolutional Neural Networks (CNN), integrated with a Large Language Model (LLM) to generate South Sumatran recipes from food images, adapting suggestions to classification results. The model was trained using transfer learning on eight food ingredient classes selected for their prevalence in local cuisine. It achieved a validation accuracy of 98.2% and a test accuracy of 98%, with average precision, recall, and F1-score all exceeding 98%, indicating consistent and reliable performance. The system was deployed as a web-based application, DapoerKito, allowing users to upload food images, receive classification results, and obtain generated recipe suggestions. LLM-generated recipes are produced instantly, matched to ingredients, and shown in a clear format. These findings demonstrate the value of integrating computer vision and language generation in an AI-based platform that supports usability and cultural relevance. In addition to its technical capabilities, the system contributes to the digital preservation of regional culinary heritage through interactive AI. This CNN–LLM integration offers a novel approach for advancing food AI with diverse ingredients, personalized nutrition, and multilingual support.
Copyright (c) 2025 Farhah Salsabillah, Ade Silvia Handayani, Nurhajar Anugraha https://creativecommons.org/licenses/by-sa/4.0
Public Sentiment and GoTo Stock Price Movement in Indonesia: A Null-Relationship Study Using Naïve Bayes and Non-Parametric Measures
The expiration of the lock-up period for PT GoTo Gojek Tokopedia Tbk's shares led to a sharp stock price decline and public discourse on Twitter. This study aims to examine the statistical relationship between public sentiment and GoTo’s stock price movement in Indonesia. Tweets were classified into positive or negative sentiment using the Naïve Bayes classifier, selected for its computational efficiency on large-scale textual data. The model achieved 70% accuracy, with a precision of 82% and F1-score of 75%. The sentiment polarity was then compared with stock trends across 39 distinct trading periods using four non-parametric statistical tests: Chi-Square (p = 0.6398), Cramer’s V (0.014), Goodman-Kruskal’s Lambda (0.053), and Mann-Whitney U test (p = 0.8994). None of these tests showed a statistically significant association between sentiment polarity and stock price movement. These findings highlight that while public sentiment may reflect short-term public interest, it does not reliably capture the market’s behavioral dynamics—especially in cases of investor decisions driven by broader macroeconomic or institutional factors. Sentiment data, therefore, should be considered as a complementary, rather than primary indicators in stock price analysis.
Copyright (c) 2025 Dita Pramesti, Hanif Fakhrurroja, Rahma Karina M. https://creativecommons.org/licenses/by-sa/4.0
Small Object Detection and Object Counting for Primary Roe Dataset Based on Yolo
This research offers an initial exploration into the effectiveness of three variations of the YOLOv8 model original, trimmed, and YOLOv8n.pt in combination with two distinct datasets characterized by tight and loose distributions of roe, aimed at enhancing small object detection and counting accuracy. Utilizing a primary roe dataset across 776 images, the research systematically compares these model-dataset configurations to identify the most effective combination for precise object detection. The experimental results reveal that the YOLOv8n.pt model combined with the loosely distributed dataset achieves the highest detection performance, with a mean Average Precision (mAP) of 53.86%. This outcome underscores the critical impact of both model selection and data distribution on the detection accuracy in machine learning applications. The findings highlight the importance of tailored model and dataset synergies in optimizing detection tasks, particularly in complex scenarios involving small, densely clustered objects. This research contributes valuable insights into the strategic deployment of neural network architectures for refined object detection challenges.
Copyright (c) 2025 Wahyu Andi Saputra, Nicolaus Euclides Wahyu Nugroho, Dany Candra Febrianto, Andi Prademon Yunus, Muhammad Azrino Gustalika, Yit Hong Choo
Detection of Vulgarity in Anime Character: Implementation of Detection Transformer
Vulgar and pornographic content has become a widespread issue on the internet, appearing in various fields include anime. Vulgar pornographic content in anime is not limited to the sexuality genre; anime from general genres such as action, adventure, and others also contain vulgar visual. The main focus of this research is the implementation of the Detection Transformer (DETR) object detection method to identify vulgar parts of anime characters, particularly female characters. DETR is a deep learning model designed for object detection tasks, adapting the attention mechanism of Transformers. The dataset used consists of 800 images taken from popular anime, based on viewership rankings, which were augmented to a total of 1,689 images. The research involved training models with different backbones, specifically ResNet-50 and ResNet-101, each with dilation convolution applied at different stages. The results show that the DETR model with a ResNet-50 backbone and dilation convolution at stage 5 outperformed other backbones and dilation configurations, achieving a mean Average Precision of 0.479 and of 0.875. The other result is dilated convolution improves small object detection by enlarging the receptive field, applying it in early stages tends to reduce spatial detail and harm performance on medium and large objects. However, the primary focus of this research is not solely on achieving the highest performance but on exploring the potential of transformer-based models, such as DETR, for detecting vulgar content in anime. DETR benefits from its ability to understand spatial context through self-attention mechanisms, offering potential for further development with larger datasets, more complex architectures, or training at larger data scales.
Copyright (c) 2025 Amalia Suciati, Dian Kartika Sari, Andi Prademon Yunus, Nuuraan Rizqy Amaliah