J-Intech (Journal of Information and Technology) is a journal published by the Research & Community Service Institute (LPPM) of the UBHINUS (Universitas Bhinneka Nusantara), Malang. The scope of this journal includes the fields of Informatics Engineering, Information Systems, and Informatics Management. Its purpose is to accommodate the growing needs in Information Technology development. J-Intech is published four times a year, in March, June, September, December, with ISSN (print): 2303-1425 and ISSN (online): 2580-720X. It also has a DOI: https://doi.org/10.32664/j-intech.
Clustering HIV Screening Data in Teluk Bintuni Using K-Means
Human Immunodeficiency Virus (HIV) remains a major public health challenge in Papua, Indonesia, where geographical barriers and limited resources complicate service delivery. This study applies clustering methods to HIV screening data from 22 Health Service Units (UPK) in Teluk Bintuni Regency during 2024–2025. The final dataset consisted of 29 valid unit-year observations containing variables related to total tests, HIV-positive cases, and sex-disaggregated distributions. Data preprocessing followed the Knowledge Discovery in Database (KDD) framework, including data selection, cleaning, log transformation, normalization, and the construction of derived variables such as positivity rate and gender ratios. Four clustering algorithms were compared, namely K-Means, Hierarchical Clustering, Fuzzy C-Means, and DBSCAN, using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The results indicate that K-Means produced the most stable and interpretable clustering structure, forming three groups of UPKs: intermediate screening units with moderate coverage and low positivity, priority units with limited testing but high positivity rates, and active screening units with the highest testing volume and case detection. These findings reveal heterogeneity in HIV screening performance across UPKs and support differentiated intervention strategies. Units with high apparent positivity but low testing coverage require expanded outreach, field verification, and improved access to HIV screening services, while active screening units should be strengthened through counseling, referral, and follow-up services. This study demonstrates the usefulness of clustering analysis in identifying service gaps and supporting evidence-based HIV intervention planning at the local level.
Copyright (c) 2026 J-INTECH https://creativecommons.org/licenses/by-sa/4.0
Detection and Classification of Organic and Inorganic Waste Using the Yolo Algorithm at Solo Technopark
The advancement of artificial intelligence (AI) technology, particularly in the area of computer vision, has encouraged the use of automatic object detection methods for various needs, including the classification of organic and inorganic waste. The problem of waste management in the Solo Technopark area which is still carried out manually causes the waste sorting process to not run optimally. This research focuses on developing and evaluating the performance of several YOLO models for detecting and classifying organic and inorganic waste types in real-time. The research dataset contains 6,758 waste images categorized into 10 object classes, obtained from Roboflow. The preprocessing stages include annotation, auto-orientation, and image resizing to 640×640 pixels. The dataset is then divided into 70% training data, 20% validation, and 10% testing. This study used three YOLO models, namely YOLOv11, YOLOv12, and YOLOv26 with epoch variations of 10, 30, 50, and 100. Model evaluation was carried out using precision, recall, mAP50, mAP50-95, and inference time metrics. The results showed that the best model was obtained on YOLOv26 epoch 100 with a precision value of 0.92, recall of 0.847, mAP50 of 0.892, mAP50-95 of 0.741, and inference time of 3.0 ms. These findings indicate that the YOLOv26 model has good capabilities in detecting and classifying organic and inorganic waste accurately and quickly, so it has the potential to be used as a basis for developing a real-time waste detection system.
Implementation of K-Means Clustering in a Web-Based Public Complaint System to Improve Efficiency and Transparency
The community complaint system in Sumengko Village still relies on conventional methods such as direct visits to the village office and deliberation forums, making it difficult for village officials to systematically categorize complaints and determine the appropriate handling priorities. This research aims to design and develop a web-based community complaint system integrated with the K-Means Clustering algorithm to automatically group complaints based on the similarity of text characteristics. The system is built using the Laravel framework based on PHP with a MySQL database, and applies text preprocessing stages including case folding, tokenizing, stopword removal, and stemming, followed by TF-IDF weighting to convert the text into a numerical representation before the clustering process is performed. The dataset used consists of 15 complaint documents representing three dominant topics: infrastructure, environment, and health. Validation of the number of clusters using the Elbow Method shows k=3 as the optimal point with the lowest significant SSE value. The clustering results show that the K-Means algorithm converges at the 2nd iteration with the following cluster distribution: C₁ Infrastructure (60%), C₂ Environment (33.3%), and C₃ Health (6.7%), indicating that infrastructure-related complaints are the dominant issue reported by residents. Usability evaluation using the System Usability Scale (SUS) yielded an overall mean score of 79.25, categorized as "Good" with an acceptability level of "Acceptable," confirming that the system is well-received by both village administrators and community members. This system not only serves as a medium for submitting complaints digitally but also as an automatic analysis tool that helps village officials prioritize complaint handling more efficiently and transparently.
Analysis of E-Business Strategy at PT Damri: PEST, SWOT, Porter's Five Forces, and Value Chain Approach
This study aims to analyze the e-business strategy implemented by PT Damri, a state-owned transportation company in Indonesia. Using a qualitative case study approach, data were collected through literature studies and analyzed using PEST, SWOT, Porter's Five Forces, and Value Chain analysis. The results show four key findings: (1) PEST analysis identifies technological factors (4G/5G, cloud computing, IoT) as the primary drivers of digital opportunity, while social barriers (digital divide, low literacy) remain the main challenges; (2) Porter's Five Forces reveals high buyer bargaining power and high competitive rivalry as the most critical external pressures requiring digital differentiation; (3) SWOT analysis positions PT Damri in the SO (Strengths-Opportunities) quadrant, indicating that brand strength and extensive route networks should be leveraged to capitalize on growing smartphone penetration; (4) Value Chain analysis shows that marketing & sales and customer service activities derive the highest benefit from e-business integration. Based on these findings, the study recommends strengthening the digital platform, strategic partnerships with travel and fintech platforms, digital training for employees, and phased digital transformation to minimize disruption.
Development of a Color Analysis Print Cashier System at the Penjilidan Express Tulungagung Using Laravel Framework with the Scrum Framework
The manual and subjective process of determining color printing costs and recording sales transactions at Penjilidan Express, which resulted in inconsistent pricing, recording errors, and reduced operational efficiency. This research focuses on developing a web-based cashier system equipped with a Print Color Analysis feature to support more objective and consistent printing cost estimation and streamline transaction management. The system was developed using the Scrum framework and implemented with the Laravel framework based on the Model-View-Controller (MVC) architecture. The Print Color Analysis feature calculates the percentage of RGB/CMYK color composition from digital files in JPG, PNG, and PDF formats to automatically estimate printing costs. The developed system was evaluated through Black Box Testing across seven core functional features. The results demonstrated that every tested feature performed as expected, achieving a 100% success rate and confirming that the system fulfilled all predefined functional requirements.The developed system is expected to improve the consistency of printing cost calculations, streamline transaction processing, and minimize the risk of errors in sales data recording. Therefore, the proposed system can serve as an effective solution to support operational efficiency, pricing transparency, and business digitalization in printing service companies.
Lexicon-Based Approach for Sentiment Analysis of Technology Students in the City of Tegal toward AI Adoption in Learning
This study aims to analyze the sentiment of technology students in Tegal City toward the adoption of Artificial Intelligence (AI) in learning. The explicit novelty of this research lies in its empirical focus on a localized tech-cluster student cohort within a regional urban area (Tegal City) who display uniquely high frequencies of digital-tool interaction, coupled with a specialized lexicon-based computational framework optimized for Indonesian academic linguistics. The research employs a descriptive quantitative approach combined with text mining techniques. Data were collected through an online survey involving 158 respondents using a questionnaire consisting of closed and open-ended questions. Sentiment analysis was conducted using a lexicon-based method to classify opinions into positive, neutral, and negative categories. The results indicate that students’ experiences with AI are dominated by positive sentiment (56.96%), reflecting its benefits in improving efficiency and understanding of learning materials. However, in terms of future expectations, there is an increase in neutral (44.94%) and negative (8.86%) sentiments, indicating a more critical perspective. The main concerns include overdependence on AI and the accuracy of AI-generated information. This study concludes that AI has significant potential to support learning, but its use should be balanced with the development of critical thinking skills and the improvement of reliable and accurate AI systems.
Development of an Integrated Web-Based Academic and Administrative Information System with E-Learning and Student Interest Recommendation
The advancement of information technology in the education sector has driven the need for digital transformation in managing academic and administrative processes, including in Early Childhood Education (ECE) institutions. However, many ECE institutions, such as BKB PAUD Bougenville, still rely on manual and fragmented systems for managing student data, attendance, assessments, and learning materials, which often leads to inefficiencies, data inconsistencies, and delays in information processing. This study aims to develop an integrated web-based academic and administrative information system that incorporates e-learning features and student interest recommendations to support more effective and structured educational management. The system is developed using the Agile methodology, which emphasizes iterative development, continuous user involvement, and adaptive improvements based on user feedback to ensure alignment with real operational needs. The proposed system integrates several core modules, including student data management, attendance recording, e-learning through digital materials (e-books), assessment processing, and a data-driven student interest recommendation feature. The implementation results indicate that the system is able to improve data management efficiency, facilitate real-time access to academic and administrative information, and reduce the risk of data inconsistencies. In addition, the interest recommendation feature provides practical benefits by assisting teachers in identifying student potential and supporting more informed decision-making processes. Therefore, this research contributes to the development of an integrated, user-friendly, and adaptive digital platform that enhances the quality of academic and administrative services in the PAUD environment.
Evaluation of the Website Quality of a Faculty of Animal Science Using WebQual 4.0 and Importance Performance Map Analysis
The present study seeks to assess the quality of the Faculty of Animal Science website at the University of Papua and determine key areas requiring improvement based on user perceptions using the WebQual 4.0 method, PLS-SEM, and IPMA. Data were collected through an online questionnaire involving 110 respondents consisting of students, lecturers, and educational staff. The findings indicated that all indicators satisfied the standards for both validity and reliability, confirming their suitability for further analysis. The evaluation of the structural model indicated that user satisfaction was significantly and positively influenced by only usability (β = 0.343; t = 2.962; p = 0.003), while no significant effects were identified for information quality and service interaction quality. The Q²predict value of 0.229 suggests that the model has acceptable predictive relevance, while the coefficient of determination (R² = 0.293) indicates that the model demonstrates weak to moderate explanatory power. Furthermore, usability demonstrated the largest practical contribution to user satisfaction with an f² value of 0.091, although the effect size remained within the small category. IPMA results at the construct level identified usability as the highest priority for improvement, with an importance score of 0.343 and a performance score of 59.437. At the indicator level, indicator related to ease of use and navigation, particularly indicators U1 and U3, were identified as the primary areas requiring improvement. Meanwhile, service interaction quality demonstrated relatively high performance and should be maintained, whereas information quality requires gradual enhancement. Therefore, efforts to improve website quality should prioritize usability while continuously improving information quality and service interaction quality to enhance user satisfaction.
Comparison of K-Means and Hierarchical Clustering Using Silhouette Score on BPBD Data, Papua Barat
This study compares K-Means Clustering and Hierarchical Clustering to group disaster-prone areas in Papua Barat Province using official data from UPTD Pusdalops BPBD for the period 2020 to November 11, 2025. The dataset covers seven regencies with three aggregated variables: total floods and tidal floods, other disasters, and earthquakes. The research procedure includes data preprocessing, z-score normalization, determining the optimal number of clusters using the Elbow Method and Silhouette Score, and visualizing results with scatter plots and dendrograms. Both algorithms produced two clusters, with Hierarchical Clustering achieving a higher Silhouette Score of 0.4274 compared to 0.339 for K-Means. The scores indicate moderate clustering quality, reflecting the exploratory nature of the study due to the small dataset. Manokwari Regency formed a distinct cluster due to differing disaster characteristics. The novelty lies in employing official BPBD data and Silhouette Score evaluation to compare the two clustering algorithms, supporting data-driven prioritization for disaster mitigation.
Optimizing Inventory Management Through the Inventory Information System at PT Lintas Cakrawala
The issue of stock management Optimization at PT. Lintas Cakrawala has so far been handled manually; the company lacks a proper inventory information system, which often leads to inaccurate results and increases the likelihood of recording errors, resulting in financial losses for the company. The objective of this research is to analyse system requirements and design a web-based inventory information system as a solution to optimise stock management at PT. Lintas Cakrawala. Furthermore, this research aims to address the issue of data discrepancies between manual records and the physical condition of goods, which can hinder decision-making. The method used in this study is the Waterfall software development model, which comprises the stages of requirements analysis, system design, implementation, testing, and maintenance. Data collection was carried out through observation and a review of documentation relating to the existing system. The performance test results showed that the new system had an average response time of 1.2 seconds for real-time stock tracking. It also cut down the time needed to create inventory reports from about 120 minutes when done manually to just 1.8 minutes with the automated system. These results show a big boost in how well the operations are running and back up how good the new inventory information system is working. This system is expected to minimise human error, prevent stockouts and overstocking, and improve operational efficiency within the warehouse division of PT. Lintas Cakrawala
User Satisfaction Analysis of Streaming Applications Using the Technology Acceptance Model
The rise of information and communication technology has led to the development of several digital platforms, especially in the entertainment industry, such as community-driven streaming apps. One of the growing platforms is Bstation, which provides anime, comics, and game content. However, in its implementation, several issues are still encountered, such as difficulties in finding content due to irrelevant search results, limited content availability and delayed episode updates, as well as suboptimal viewing experiences, including streaming interruptions and audio-video desynchronization. These problems have an impact on decreasing user comfort, usage frequency, and users’ intention to continue using the application. This investigation assesses Bstation application satisfaction levels through Technology Acceptance Model (TAM) application. Employing quantitative questionnaire methodology, study tests key variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU). Although BI and AU correlations appear insignificant, integrated variables clearly impact ATU. Data confirms perceived benefits and ease drive user satisfaction, overshadowing actual usage pattern.
Design and Development of an Internet of Things (IoT)-Based Digital Weighing System Using ESP32 and Load Cell for Rice Milling Machine at PT. Jaring Solusi Persada Tulungagung Branch
The advancement of Internet of Things (IoT) technology in industrial sectors has driven the need for more accurate and integrated automation systems, particularly in production weighing processes. PT. Jaring Solusi Persada Tulungagung Branch required improvements in measurement accuracy, real-time data recording, and monitoring of rice milling operations. This research aims to design and develop an IoT-based digital weighing system using ESP32 and load cell to enhance operational efficiency and reduce weighing errors. The research method applied is Research and Development (R&D) using a Prototyping approach, encompassing hardware design, software development, load cell calibration, and direct system testing in the operational environment. The system utilizes the HX711 module as a signal amplifier for the load cell, with ESP32 as the main microcontroller to process weight data and transmit information in real-time via Bluetooth to a Flutter-based mobile dashboard application. Testing results indicate that the system achieved 0% error rate in weighing accuracy tests across six trials with load variations of 1–20 kg. Actuator response tests showed an average response time of 0.80 seconds with an average overshoot of 0.72% of the target weight, within acceptable tolerance for on-off control systems. IoT integration with real-time monitoring via Bluetooth successfully enabled live weight tracking and production history recording. This system demonstrates significant improvement in work efficiency, minimization of human error, and support for faster and more accurate decision-making in rice milling production
Development of an Android-Based Inventory Application to Improve Stock Management Efficiency
Advances in information technology are driving companies to implement digital systems to improve operational efficiency, particularly in stock and inventory management. PT Interior Indonesia still uses manual stock recording, which results in data inaccuracies, delays in information, and difficulties in monitoring stock availability in real time. This research aims to design and develop an Android-based mobile application capable of supporting structured, accurate, and efficient stock and inventory management. The research method employed is a qualitative approach through observation, interviews, and literature review, utilising the Rapid Application Development (RAD) software development model. The application was developed using the Flutter framework with Firebase Cloud Firestore as the backend database. The results of the study indicate that the developed application is capable of improving the accuracy of stock recording, accelerating the stock management process, and facilitating real-time monitoring of stock availability. Consequently, this application is expected to support operational efficiency and decision-making at PT Interior Indonesia
Optimization of Android Malware Detection Based on Permissions Using LightGBM with Hyperparameter Tuning and Chi-Square Feature Selection
This study aims to develop an efficient Android malware detection framework using the LightGBM algorithm optimized through Chi-Square feature selection and hyperparameter tuning. Although machine learning techniques have been widely applied in Android malware detection, many previous studies primarily focus on classification accuracy while paying limited attention to feature reduction efficiency, computational complexity, and permission-based feature interpretability. Therefore, this study proposes a lightweight permission-based detection framework that integrates Chi-Square feature selection with optimized LightGBM classification to improve detection performance while reducing irrelevant features. The dataset used was obtained from Kaggle and consists of 29,300 Android applications, including 14,630 benign applications and 14,700 malware samples. Each application is represented using 86 binary permission-based features extracted from the AndroidManifest.xml file. The research stages include preprocessing, feature selection, training-testing data splitting, hyperparameter optimization, and model evaluation. Experimental results show that the proposed model achieves an accuracy of 0.96, precision of 0.95, recall of 0.96, F1-score of 0.96, and ROC-AUC of 0.9885. These findings indicate that the proposed framework effectively distinguishes malware from benign applications while maintaining computational efficiency and improving permission-based malware detection interpretability. The novelty of this study lies in the integration of Chi-Square feature selection and Grid Search-optimized LightGBM to develop a lightweight permission-based Android malware detection framework that reduces feature dimensionality while maintaining high detection performance and interpretability.
The Potential Effectiveness of Utilizing Google Ads Location Targeting as a Digital Promotion Strategy for Startupreneur
The post-pandemic digital economy growth opens significant opportunities for culinary startupreneurs in Indonesia; however, limited digital marketing budgets often lead to advertising budget waste. This study aims to analyze the potential utilization of Google Ads' Location Targeting feature as an efficient digital promotion strategy for a donut culinary startupreneur in Karawaci, Tangerang City. The research employs a descriptive quantitative method with a case study approach through the comparison of Google Ads dashboard metrics before and after location radius optimization (A/B Testing). The results indicate that implementing Location Targeting within a 7 km radius from the store location significantly reduced the Cost-Per-Acquisition (CPA) from IDR 37,500 to IDR 8,050 per order (a 78.53% reduction), while simultaneously increasing the Click-Through-Rate (CTR) from 1.5% to 6.8%. The sales conversion increase from 15 to 61 orders confirms that precise geographic targeting within a logistics-relevant radius effectively eliminates advertising budget waste. It can be concluded that Google Ads Location Targeting is an essential survival and escalation strategy for budget-constrained culinary startupreneurs, aligned with the business efficiency principles within the Sustainable Development Goals (SDG) 8 framework.
Implementation of SerpApi-Based Google Scholar Monitoring System for Faculty Publication Evaluation
Evaluating faculty performance based on scientific publication metrics is a crucial tool in higher education quality assurance and accreditation reporting. However, conventional web scraping techniques based on HTML DOM parsing are highly vulnerable to layout changes and anti-bot blocking on Google Scholar. This study aims to develop a web-based Faculty Publication Monitoring System using the Django framework and SerpApi as a stable data extraction solution, following the Waterfall Software Development Life Cycle (SDLC) model. The results demonstrate that the API integration successfully bypasses anti-bot mechanisms with a 100% data extraction accuracy rate within the validated sample, validated through a cross-referencing protocol involving manual checks of 10 randomly sampled faculty profiles against the live Google Scholar database. Furthermore, mass data synchronization for 63 active faculty profiles was securely completed in approximately 180 seconds, while negative testing confirmed successful data normalization of null values. In conclusion, the system enhances institutional administrative efficiency in providing valid publication datasets to support institutional accreditation needs, delivering an estimated time-reduction of over 99%. Given the performance and stability delivered by this architecture, it can be adopted by other institutions facing similar challenges in automated bibliometric monitoring.
Implementation of ESP8266 Deauther Version 2 for Wireless Network Testing Against Deauthentication Attacks at PT Jaring Solusi Persada Tulungagung Branch
The rapid advancement of wireless network technology and the Internet of Things (IoT) ecosystem in the industrial sector carries significant security risks, particularly regarding vulnerabilities within the management frame protocol. This study aims to evaluate the resilience of wireless network infrastructure against communication disruptions at the protocol level, specifically deauthentication attacks, at PT Jaring Solusi Persada (JSP) Tulungagung Branch. Applying a Research and Development (R&D) method with a Prototyping approach , empirical and real-time testing was conducted using a low-power ESP8266 Deauther Version 2 device as a penetration medium across 15 different access points (APs). Quality of Service (QoS) parameters, including throughput, latency, and packet loss, were measured using Wireshark software to analyze network performance degradation in detail. The experimental results reveal that deauthentication attacks inflict a highly destructive impact on vulnerable devices. Out of the 15 tested APs, 6 units (40%) consisting of consumer-grade devices (such as TP-Link, ZTE, and Totolink) were declared vulnerable because they did not support or activate the Management Frame Protection (MFP/IEEE 802.11w) feature. On these vulnerable devices, the attack successfully triggered complete client disconnections, a drastic 70% drop in throughput (from 567 kbps to 170 kbps), extreme latency spikes ranging from 100 to 300 ms, and a packet loss of 15%. Conversely, 9 AP units (60%), dominated by Ruijie Enterprise business-class devices, successfully withstood the attack and maintained stable network connections due to superior hardware specifications and better defense configurations. This study recommends upgrading network security standards to WPA3 and strictly enabling the Protected Management Frames (PMF) feature to ensure the company's operational continuity.
Rotten Apple Detection Using YOLOv12 for Postharvest Quality Sorting
Detecting rotten apples is critical in postharvest quality sorting, as spoiled fruit can accelerate overall decay, shorten shelf life, and lower market value. This study introduces a real-time, edge-deployable object detection method using YOLOv12 to differentiate between fresh and rotten apples in RGB images. The dataset included 2,312 annotated images, with 1,011 fresh apples and 1,301 rotten apples, split into training, validation, and testing sets with an 80:10:10 stratified ratio. To enhance model generalization, data augmentation techniques such as mosaic augmentation, horizontal flipping, rotation, scaling, HSV color jitter, and mixup were applied. The YOLOv12s model was trained with an input resolution of 640 × 640 and evaluated using accuracy, precision, recall, and F1-score. The results from the confusion matrix showed that the model achieved an accuracy of 0.93, precision of 0.91, recall of 0.89, and F1-score of 0.90, indicating that YOLOv12 offers a lightweight and effective framework for rapid apple quality assessment. The primary contribution of this work lies in integrating an attention-focused YOLOv12 detector into a postharvest apple sorting workflow, accompanied by quantitative performance evaluation and robustness analysis under challenging visual conditions.
Web-Based 360 Degree Virtual Tour Video for Promoting Cultural Tourism at Pura Puseh and Pura Desa Batuan
Pura Puseh and Pura Desa Batuan in Gianyar Regency are prestigious National Cultural Heritage sites characterized by ancient Balinese architecture and deep historical roots dating back to 1020 AD. Despite their significance, a reliance on conventional promotional media has resulted in a lack of immersive experiences for tourists, often causing the site to be overlooked as a mere transit point. This study addresses this gap by designing and developing an immersive, web-based Virtual Tour 360 Video platform using the systematic ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The production utilized 360-degree 4K panoramic videos, edited with Adobe Premiere and 3DVista, integrating hotspot navigation and AI-animated characters to enhance storytelling. Technical validation via Black Box Testing on 6 functional test cases (BB-01 to BB-06) resulted in a 100% "Succeed" status, confirming that all features operate seamlessly. Furthermore, usability testing conducted with 30 respondents using the System Usability Scale (SUS) yielded an average score of 83.1, placing the platform in the "Excellent" category. The findings confirm that 360-degree virtual tour technology is an effective and accessible medium for digital cultural heritage promotion, with implications for the development of immersive tourism platforms for other heritage sites.
Implementation of a Plant Disease Identification System using the CNN Algorithm and the Web-Based Django Framework
This study addresses the need for efficient and accessible plant disease identification systems in the era of Agriculture 4.0, where advances in artificial intelligence (AI) and machine learning (ML) support data-driven agricultural practices. The increasing popularity of home gardening highlights challenges faced by users in identifying plant diseases due to limited knowledge and diagnostic tools. Therefore, this research aims to develop a web plant disease detection system using the Django framework and convolutional neural networks (CNNs). The model was trained on a controlled dataset consisting of 57,320 leaf images collected from the PlantVillage and Turmeric Plant Disease datasets. Image preprocessing was applied, including resizing, normalization, and data augmentation such as image rotation, zooming, image inversion and brightness adjustmen. Class imbalance during training was handled using class weighting. The dataset is divided into a training set and a validation set for model development and evaluation. The CNN model achieved an accuracy of 92% on the labeled validation dataset, with a mean F1 score of 0.79 and a weighted mean F1 score of 0.92. For generalization testing, an uncontrolled (wild) dataset consisting of 223 images collected from online sources was used, resulting in an accuracy of 11%, indicating limited real-world generalization due to domain differences. Despite this limitation, the proposed system demonstrates the feasibility of CNN-based plant disease classification in a web application.
Design of an Enterprise Architecture for Monitoring IT Services and Infrastructure Using TOGAF ADM at PT Fratama Kencana Gemilang
The management of information technology infrastructure at PT Fratama Kencana Gemilang currently faces significant operational challenges in its day-to-day operations. This is due to the device monitoring mechanisms currently in place, which remain manual and fragmented across units, resulting in the IT team often only becoming aware of technical issues after receiving user complaints. This reactive approach inevitably hinders company productivity, particularly regarding server services that form the core of the business. Therefore, this study aims to design a more proactive, automated, and integrated enterprise system monitoring architecture using the TOGAF ADM (The Open Group Architecture Framework Architecture Development Method) framework. Through this approach, it is expected that all of the company’s technology assets can be centrally monitored and aligned with long-term strategic business objectives. This research employs a qualitative descriptive approach conducted through direct observation of the existing system infrastructure and in-depth architectural modeling. This design process covers various key domains in a structured manner, ranging from the vision domain, business architecture, information system architecture, to the supporting technology infrastructure. The research results indicate that the proposed open-source-based monitoring system design has successfully met the company’s functional and technical requirements comprehensively. This is evidenced by the results of the expert validation process (expert review), which yielded an average score of 4.5 out of 5.0. These results confirm that the designed system is highly effective in providing real-time and accurate visibility into infrastructure performance. This study concludes that the proposed architecture and resulting blueprint are highly suitable to serve as the primary reference for company management in enhancing the reliability of their IT services. The implementation of this design is expected to accelerate the troubleshooting process and minimize the risk of future system failures.
Evaluating Business Process Management Maturity Using the Business Process Orientation Maturity Model: Evidence from a Coffee MSME in Jember, Indonesia
This study investigates the maturity level of Business Process Management (BPM) in three coffee micro and small enterprises (MSMEs) supported by the Integrated Business Service Center (PLUT) of Jember Regency—BC, PT. FAI, and PT. RKS. These enterprises have demonstrated potential as Go-Digital MSMEs, as indicated by their adoption of digital platforms such as social media, e-commerce, and point-of-sales systems, as well as their participation in digital business facilitation programs. However, current evaluations of their business development remain predominantly focused on financial growth, limiting a comprehensive understanding of their process performance and organizational readiness. This study applies the Business Process Orientation Maturity Model (BPOMM) to assess and compare BPM maturity across nine dimensions. Using a qualitative case study approach based on interviews, observations, and member checking, the findings reveal that all three MSMEs remain at Level 1 (ad hoc), indicating that their business processes are largely unstructured and lack systematic management. The comparative analysis further uncovers differences in organizational structure formalization, the use of key performance indicators (KPIs), the extent of business process documentation, and the level of digital system integration supporting operational processes. This study contributes by contextualizing BPM maturity assessment within digital MSMEs and providing empirically grounded insights into process-related gaps that hinder their readiness for scaling and global market expansion. The findings offer practical implications for improving BPM practices while also suggesting the need for adapting maturity models to better capture the characteristics of digitally transforming MSMEs.
From Small Data to MMM-Style Budget Decisions: Reproducible Conjugate Bayesian Log-Response Regression with ElasticNet Benchmarks and Explainable Budget Scenario Rollouts
Marketing mix modeling (MMM) is often treated as a large-sample, time-series task, but many organizations must make near-term budget decisions with limited historical data. This study develops a reproducible small-sample MMM-style workflow using the public ISLR Advertising.csv dataset (n=200 markets; TV, radio, newspaper spend; sales). Reproducibility is ensured by using a public dataset, fixed random seeds, explicitly stated preprocessing, analysis scripts supplied with the manuscript, and specified Python libraries. The workflow combines predictive benchmarking and prescriptive decision support. First, OLS, Ridge, Lasso, and ElasticNet are evaluated under 5-fold cross-validation and a fixed 80/20 hold-out split. Second, a conjugate Bayesian regression on log(1+spend) features models diminishing returns and yields closed-form posterior and Student-t predictive distributions. Third, for each total budget B, the allocation that maximizes the posterior mean prediction is solved under non-negativity, budget-balance, and observed-maximum channel caps; posterior samples are then evaluated at each optimized allocation to form 90% credible bands for the budget-sales curve. On the fixed split, test RMSE ranges from 1.829 to 1.871, while 100 repeated splits indicate that raw-feature OLS is most accurate on average (mean RMSE 1.671). At B=200, the bounded optimum allocates $147.25k to TV, $49.60k to radio, and $3.15k to newspaper, predicting 18.149 sales. The results suggest that, in small samples, regularization and log-response modeling mainly support stable, interpretable budget recommendations rather than improving point prediction alone.
Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance
Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach integrating clustering, spatial analysis, and classification. The dataset consists of 655 Shopee stores collected on February 18, 2026. K-Means clustering is applied to segment store performance, while spatial analysis examines geographic distribution patterns. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features. The clustering results reveal three distinct store performance groups with a Silhouette Score of 0.6704, indicating a good cluster structure. Although K = 2 produced a higher score (0.7891), K = 3 was selected to provide more meaningful segmentation (low, medium, and high performance). The Random Forest model achieved an accuracy of 79%, with precision, recall, and F1-score demonstrating reliable predictive performance across all classes. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. Spatial analysis indicates that provinces such as West Java and Jakarta dominate high-performance clusters. The findings contribute to hybrid data mining frameworks and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
Development of a Teacher Administrative Information System Digitally-Based Inside the Classroom
The development of information technology encourages educational institutions to undergo digital transformation in administrative management, including student attendance recording. MA Syamsul Jinan is still using a manual attendance system that causes various problems, such as low efficiency, a high potential for recording errors, difficulties in data storage, and the risk of attendance data manipulation. This study aims to develop a web-based digital attendance system as part of a classroom administrative information system to improve the efficiency, accuracy, and transparency of managing student attendance data. The research method used is Research and Development (R&D) with the Waterfall development model which includes requirements analysis, design, implementation, testing, and maintenance. Data were collected through observation, questionnaires, and documentation. System quality evaluation refers to the ISO/IEC 25010 standard with a focus on six aspects, namely functionality suitability, usability, reliability, user satisfaction, maintainability, and portability. The results show that all system features function well, with a usability level of 87.6% which falls into the very good category. The system has reliability, ease of maintenance, and can run optimally on various devices and browsers. Thus, the web-based digital attendance system is declared feasible to implement and capable of improving the quality of educational administration at MA Syamsul Jinan.
FAQ Chatbot for Small Businesses on the Web Using Semantic Search and Response Ranking
Small businesses often handle customer questions through manual replies via chat applications or phone calls, causing repetitive work, delayed responses, and inconsistent information delivery. This study proposes a web-based FAQ chatbot that answers user questions by performing semantic search over an Indonesian FAQ knowledge base and ranking the most relevant response. The chatbot applies a lightweight information retrieval approach using TF-IDF vectorization and cosine similarity to compute the relevance score between the user query and FAQ entries (question and tags). The system then selects the top-ranked FAQ entry and returns its associated answer, meaning the semantic matching is performed at the question-to-question level, not directly between questions and answers. The top results are ranked, and the chatbot returns the best answer along with a confidence score and the top three candidate questions to increase transparency. If the score is below a predefined threshold, the system provides a fallback response and suggests related topics rather than forcing an incorrect answer. The system is implemented as a PHP–MySQL web application with an administrator dashboard that supports secure login, FAQ CRUD management, chat logging, and usage analytics. Functional verification is conducted using black-box testing across main modules, including authentication, FAQ management, chatbot interaction, logging, and analytics dashboards. The expected contribution of this work is a practical and low-cost chatbot solution that can be deployed by small businesses to reduce repetitive customer service workload, accelerate response time, and provide measurable service insights through log-based analytics. Future improvements include expanding the knowledge base, enhancing Indonesian text normalization, and adopting embedding-based retrieval for better semantic matching.
Analyzing Key Success Factors for Driving Repurchasing Conversion in E-CRM: Customer Insights on Gojek Application in Batam
This study examines the determinants of repurchase intention in Gojek’s digital service environment by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), with Customer Relationship Management (CRM) serving as the contextual framework. In this study, CRM is not operationalized as an independent variable but provides the contextual setting for understanding user interactions with the platform. Using a sequential mixed-methods design, Qualitative findings reveal that price sensitivity is the most dominant factor influencing repurchase decisions for key drivers of repurchase intention, followed by usability, functional efficiency, and trust. Quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed that Perceived Ease of Use significantly influences Perceived Usefulness, which shapes Attitude, while Attitude and Perceived Behavioral Control strongly predict Repurchase Intention; Subjective Norms exert a weaker effect. By positioning CRM as a contextual environment rather than a measured construct, this study extends the application of the TAM–TPB framework in digital ride-hailing services and provides practical insights for strengthening customer retention strategies.
Hybrid PSO Feature Selection Correlation and Support Vector Machine Model for Heart Disease Detection
Heart disease remains a major health problem worldwide. The World Health Organization (WHO) reports that in 2022, approximately 19.8 million people died from heart disease, highlighting the need for the implementation of an appropriate early detection model. This study proposes a hybrid SVM–PSO model with correlation-based feature selection, duplicate data handling, and a multi-metric fitness function to enhance classification performance. PSO is employed to optimize the C parameter and RBF kernel of SVM, producing a more robust and balanced model compared to existing approaches. This study uses a heart disease dataset consisting of 1,025 rows with 13 attributes and 1 target variable obtained from the Kaggle repository and republished on the Zenodo platform in 2024. The research stages include Pre-Processing, Standardization, Feature Selection based on Correlation, and evaluation using the 10-Fold Cross Validation technique with Accuracy, precision, recall, and F1-score metrics. The results show that Support Vector Machine (SVM) achieved an Accuracy of 82.80%, Precision of 79.31%, Recall of 91.70%, and an F1-score of 84.88%. After optimization using PSO, the performance improved to an accuracy of 84.46%, precision of 80.54%, recall of 92.72%, and an F1-score of 86.04%. The experimental results indicate performance improvements of 2.00% in accuracy, 1.55% in precision, 1.11% in recall, and 1.37% in F1-score after PSO optimization. These results prove that the applied hybrid approach successfully improved the ability to detect heart disease. Therefore, this study contributes by demonstrating that PSO-based hyperparameter optimization can effectively enhance SVM classification performance for heart disease detection. The proposed model also has practical implications as a decision support tool for early heart disease detection that can assist medical practitioners in improving diagnostic accuracy and supporting preventive treatment strategies.
Web-Based Employee Meeting Attendance System Using SDLC and Laravel 11 at the Ministry of Higher Education, Science, and Technology
The rapid development of information technology has significantly transformed administrative practices within government institutions, encouraging the transition from conventional manual systems to integrated digital platforms. In many public sector environments, employee attendance is still recorded manually, which often results in inefficiencies, delays in data processing, and a higher risk of recording errors. This study aims to develop and implement a web-based employee attendance system at the Legal Bureau of the Ministry of Education, Research, and Technology using Laravel 11 as the development framework and applying the System Development Life Cycle (SDLC) method as a structured development approach. In addition to system development, this research also evaluates the system’s functional performance through Black Box testing to ensure that each module operates according to the specified requirements and supports reliable administrative attendance management. The SDLC framework guides the development process through systematic stages, including planning, requirements analysis, system design, implementation, and testing, ensuring that the resulting system meets organizational needs effectively. The proposed system incorporates essential features such as secure user authentication, digital signature integration, and automated real-time attendance data recapitulation to enhance administrative accuracy and control. The improvement in administrative efficiency was evaluated by comparing the developed system with the previous manual attendance method, where employee data were recorded on paper-based forms, often causing delays in data collection and additional operational costs. The findings indicate that the developed system improves administrative efficiency, reduces manual data entry errors, enhances data accuracy, and accelerates reporting processes, thereby supporting administrative modernization and more effective digital governance.
Assessing Technology Acceptance for SIAKAD Usage among Scholarly Community
The deployment of academic information systems in higher education institutions necessitates both technological preparedness and user acceptance to guarantee sustained utilization. In practice, users frequently encounter challenges, including confusion when retrieving academic material, doubts regarding the accuracy of presented data, and dependence on manual verification despite the system's availability. Such circumstances diminish trust and render the system seen as a mere formal obligation rather than a beneficial academic resource. Differences in user perceptions of system quality, information quality, and behavioral aspects consequently affect the efficacy of system adoption. By integrating UTAUT 2 with the Delone & Mclean Model, this research aims to analyse the variables that influence user acceptance and the usage behaviour of the current academic information system. A quantitative explanatory approach was employed, utilizing survey data collected from 450 active users. PLS-SEM was employed to examine the data for the assessment of both measurement and structural models. The findings demonstrate that performance expectancy, effort, facilitating conditions, habit, system quality, and knowledge all have a substantial impact on behavioral intention and usage behavior. Conversely, social influence does not substantially impact behavioral intention, indicating that system utilization is primarily motivated by functional requirements rather than normative pressure. Practically, the results suggest that universities should prioritize system reliability, accurate and timely academic information, responsive user support, and routine integration of SIAKAD into core academic workflows to strengthen continued usage.