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.
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.
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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.
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.
Clarifying Organizational Requirements through Rapid Application Development: Evidence from a Performance Appraisal Information System
This study analyzes the iterative application of Rapid Application Development (RAD) in the engineering of an employee performance appraisal information system within a higher education context. The system was developed to replace a spreadsheet-based process that involved manual score aggregation and fragmented data storage. Using a software engineering case study approach, this research examines how system requirements evolved across development iterations. Design artifacts produced during successive RAD cycles were analyzed as evidence of requirement refinement. The findings show that early development focused on functional centralization, while later iterations introduced configurable modules for evaluation criteria, evaluator assignment, and score normalization. These interface and module revisions reflect progressive clarification of organizational needs and stabilization of business logic. The study demonstrates that RAD supports requirement refinement through iterative prototyping, especially in systems involving multiple roles and configurable structures. The results provide process-oriented insight into requirement evolution in internal organizational information systems.
Academic Information System At MA.Fathus Salafi Using The Scrum Method Based On The Laravel Framework
In today's digital era, academic data management in schools requires a system that is efficient, secure, and easily accessible. The development of a web-based Academic Information System was carried out to simplify the administration process, recording grades, attendance, lesson schedules, and managing student and teacher data. The development methodology uses a scrum approach that allows an iterative development process and is responsive to user needs. The technologies used include the Laravel version 11 framework and MySQL database, supporting fast, secure, and structured development. System testing was carried out using the black box method which showed that all the main features functioned according to specifications, and could be accessed easily by users. The implementation results show that the system is able to improve the efficiency of academic data management while making it easier for users to input, process data, and report in a real-time and organized manner. The developed system is proven to be able to increase transparency and accountability of administrative processes in schools. The conclusion of this research shows that the application of academic information system based on web technology and scrum methodology is able to produce a reliable, fast, and effective system. It is recommended that school managers continue to utilize, develop, and conduct regular training so that the system remains relevant and able to optimally support academic activities in the futurePrivacy Policy.
Drowsiness poses significant risks in safety-critical activities such as driving, industrial operations, and online learning. While advanced deep learning models (e.g., CNN-LSTM hybrids) achieve high accuracy in driver drowsiness detection, they often require substantial computational resources, limiting deployment on embedded or resource-constrained devices. This study addresses the research gap in lightweight, real-time, non-invasive drowsiness detection by developing an embeddable library using YOLOv12, an attention-centric single-stage detector known for balancing speed and accuracy. The model was trained on a custom dataset of 2312 video frame sequences (1011 "awake" and 1301 "drowsy" states, captured from varied angles under consistent lighting), augmented with standard techniques (e.g., brightness/contrast adjustments, flips, and rotations) to enhance generalization. It was evaluated through 80 real-time trials across multiple subjects. Performance metrics include accuracy of 93%, precision of 0.94, recall of 0.91, and F1-score of 0.93. The system detects drowsiness via facial bounding boxes followed by state classification (integrating eye/mouth aspect ratios) in real time. The main contribution is a proof-of-concept YOLOv12-based approach for non-invasive drowsiness monitoring, offering faster inference suitable for embedded applications (e.g., vehicle systems, meeting tools, or industrial safety) compared to heavier hybrid models. Limitations include some remaining sensitivity to extreme lighting/angles and dataset scale; future work will expand datasets, incorporate multi-modal cues, and further test robustness in diverse real-world conditions.
Evaluation of the Online Media Advertising Cost Calculation System Using the Cost Per Click and Cost Per Mille Methods
Online advertising platforms commonly employ Cost-Per-Click (CPC) and Cost-Per-Thousand-Impressions (CPM) models to determine advertiser fees based on user engagement and exposure metrics. While these pricing arrangements are mathematically simple, ensuring calculation accuracy and the scalability of the costing system is crucial for maintaining financial transparency and operational security. This study examines an online media advertising costing system using CPC and CPM methods through a structured system validation approach. The evaluation includes calculation verification, consistency testing against manual calculations, performance benchmarking, scalability assessment, and sensitivity analysis. A simulated dataset of 100 ad records was used to assess the system's accuracy and behavioral performance. The results demonstrate no calculation deviation between manual and system outputs, predictable linear scalability, and logistic revenue sensitivity under rate variations. These findings indicate that the evaluated system exhibits reliable calculation performance under controlled conditions. This study contributes to the evaluation of applied information systems by providing a structured methodology for validating ad collection systems.
Self-Supervised Customer Representation Learning for Segmentation and Next-Purchase Prediction on UCI Online Retail
Customer analytics in financial retail, payments, and bank marketing frequently relies on segmentation and propensity prediction, but transactional logs are sparse, high-dimensional, and only weakly labeled. This paper presents a fast and reproducible self-supervised learning pipeline that converts raw e-commerce transactions into customer representations and evaluates them on two downstream tasks: customer segmentation and next-purchase prediction. We conduct full experimental evaluation on the UCI Online Retail dataset (541,909 invoice-line transactions from 2010-12-01 to 2011-12-09). After deterministic cleaning (removing cancellations and non-positive prices/quantities), 397,884 valid line items remain, spanning 4,338 customers, 18,532 invoices, 3,665 products, and 37 countries. For each customer we construct an ordered invoice sequence and define a canonical item per invoice (the item with the largest aggregated quantity). For each invoice transition we build a dual-view customer state vector that concatenates a lifetime purchase count view and a recent-window view (30 days), then learn embeddings via TF-IDF reweighting and truncated SVD. To increase robustness we introduce a denoising ridge projection (DRP) objective: a linear denoising model trained to map corrupted TF-IDF state vectors back to clean SVD embeddings without using labels, which yields denoised customer embeddings for downstream models. Our main contribution is an applied, computationally light integration of TF-IDF+SVD embeddings with a denoising linear projection for reuse across segmentation and next-purchase prediction, rather than a fundamentally new learning paradigm. In next-purchase prediction restricted to the 200 most frequent target items, a multinomial logistic model trained on DualDRP embeddings achieves Hit@20=0.587, outperforming MostPopular (Hit@20=0.327) and Markov (Hit@20=0.291). In segmentation we apply k-means clustering and analyze cluster-level RFM statistics and dominant products, showing that the learned embeddings recover actionable segments such as high-value frequent buyers and low-activity long-tail customers. All results, tables, and figures are generated with fixed random seeds and are reproducible in this environment
Homepage J-Intech Volume 13 No 2 Desember 2025
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Optimizing Medical Equipment Inventory Management through Web-Based System Implementation for Real-Time Monitoring and Alerts
Manual inventory processes at PT Borneo Sejahtera Medika resulted in an 18.2% stock discrepancy rate, frequent expired items, and delayed procurement decisions. This study develops and empirically evaluates a web-based inventory management system integrating real-time monitoring, automated expiration alerts, and demand forecasting. A six-month before–after analysis was conducted to measure system impact using discrepancy rate, operational performance indicators, and forecasting accuracy metrics. The results show that the discrepancy rate decreased from 18.2% to 13.6%, representing a relative improvement of 25.27%. Operational performance improved significantly, with stock checking time reduced by 52%, expired items reduced by 57%, and emergency procurement reduced by 31%. The forecasting module achieved a Mean Absolute Percentage Error (MAPE) of 5.00%, indicating acceptable short-term prediction accuracy. These findings demonstrate that the implemented system provides measurable improvements in data accuracy, operational efficiency, and inventory control within a healthcare distribution context.
Implementation of Web Scraping and Data Mining for Performance Evaluation of PT Ceria Multimedia Services
This research aims to implement web scraping techniques to collect testimonial data from the CeriaMultimedia website and perform sentiment analysis to evaluate service quality. The collected data consists of a limited number of testimonials, which are then processed through text preprocessing stages including case folding, tokenizing, filtering, and stemming. The sentiment classification process is conducted using machine learning methods based on TF-IDF weighting and classification algorithms. Due to the limited dataset, the analysis results are used primarily to demonstrate the implementation process rather than to draw generalized conclusions. The results show that the sentiment categories obtained include positive, negative, and neutral sentiments, although not all categories consistently appear in the testing phase. This research highlights the effectiveness of web scraping and text processing techniques while also indicating the need for a larger dataset to improve evaluation accuracy in future studies.
Design and Implementation of an Android-Based Scholarship Search Application Using Web Scraping Techniques
Education is a vital need for humans; however, high education costs often become a barrier for students, particularly those from underprivileged backgrounds. Although scholarships offer an alternative solution, scholarship information is commonly scattered across multiple platforms and difficult to access efficiently. This study aims to design and implement an Android-based scholarship search application using web scraping techniques to integrate and centralize scholarship information from various reliable sources. The main contribution of this research lies in the application of automated web scraping using Google Cloud Function to support a mobile-based scholarship information system, enabling periodic data collection and real-time access to updated scholarship data. The scraping process runs every 24 hours and stores structured data in a Firestore database, which is then presented through an Android application developed using Kotlin and Jetpack Compose. Analytical results from usability testing indicate that 90% of users rated the application as easy or very easy to use, while system performance evaluation shows that all core features functioned successfully with fast data retrieval and display. Black-box testing confirms that searching, filtering, sorting, and favorite management features operate as expected. This application provides an effective solution for students to efficiently search and manage scholarship information through an integrated platform. Future development may focus on improving scraping performance, enhancing the user interface, and adding notification features for newly available scholarships
An Integrative Conceptual Review of Federated Learning Ontology: Reconceptualizing the Global Model as Distributed Intelligence
Federated Learning (FL) is widely recognized as a privacy-preserving architecture, yet the ontological status of the "global model" within this distributed system remains under-theorized. This study challenges the purely technical view by reinterpreting FL as an ontological regime of distributed intelligence, where the model exists as a causally effective informational entity. By synthesizing Informational Realism and Actor-Network Theory into a unified Ontological Mediation Framework (OMF), we argue that the global model acquires reality through continuous mediation among algorithms, local data, and institutional actors. To contextualize this framework, the study examines the Federated Tumor Segmentation (FeTS) Initiative, illustrating how a non-local model exerts tangible causal influence within a real-world medical consortium. Furthermore, the paper proposes two normative instruments, specifically the Ontological Impact Assessment (OIA) and the Ontological Audit Framework (OAF), to translate these philosophical insights into practical governance mechanisms for transparency and accountability. This research contributes to the foundations of Responsible AI by positioning Federated Learning not merely as a computational tool, but as a socio-technical process where existence, knowledge, and ethics are intrinsically linked.
Designing a Web-Based Dropship Management System for PT Xarana Djaya Motor
Indonesia’s online retail is expanding alongside the national digital economy: 56.1% of global internet users shop on weekends, with Indonesia ranking ninth at 59.3%. The digital economy is projected to reach IDR 5,800 trillion by 2030 (USD 360 billion), and in 2023 Indonesia accounted for 40% of ASEAN’s e-commerce market. In this context, PT Xarana Djaya Motor, a multi-brand motorcycle spare-parts distributor, runs a dropshipping model with multiple online sellers. Yet orders and shipment confirmations are still coordinated via chat and reentered manually, causing duplicate entries, processing delays, and limited real-time stock visibility. This study designs and implements a web-based Dropship Management Application that centralizes catalog, order, shipment, and return workflows. Analysis uses UML/ERD; implementation employs Laravel (PHP) and MySQL. Alpha/black-box testing validates key rules (one shipment per order) with multiple items and partial returns at the order-item level. Results indicate reduced manual handling, improved data accuracy, and clearer status visibility as supported by centralized workflows and functional (black-box) acceptance tests. The work contributes a reusable reference architecture for SMEs, with future extensions to marketplace API integration, automated label generation, analytics dashboards, and mobile access.
Development of the “Scentify” Application as a Web and Mobile-Based Perfume Recommendation System Using Content-Based Filtering Based on User Preferences
This study presents the development of Scentify, a web and mobile-based perfume recommendation system employing the Content-Based Filtering (CBF) algorithm. The research aims to assist users in identifying perfumes aligned with their personal preferences such as fragrance type, concentration, gender, age, and price range. The development methodology includes literature review, user preference data collection via questionnaires, system design, and implementation using Flutter for mobile and Flask for backend API. User preference data were integrated with curated perfume datasets obtained from reliable online sources to form the recommendation base. The results demonstrate that Scentify can produce personalized recommendations with relevant accuracy. It features login, registration, perfume questionnaire, favorites, and admin dashboard modules. The system was validated using black-box testing, proving its reliability and user-friendliness. This work confirms that Content-Based Filtering is effective in building personalized digital recommendation systems and contributes to digital innovation in the perfume and e-commerce industries.
VibeScreen: A Mood-Based Movie and Music Recommendation Mobile Application
The advancement of digital technology has driven the emergence of various innovations in delivering personalized entertainment content. One promising approach is a mood-based recommendation system, which enables users to receive suggestions for movies or music that match their emotional state. This study designed and developed VibeScreen, a prototype application for recommending movies and music based on user mood using sentiment analysis of text inputs. The system applies Natural Language Processing (NLP) techniques to classify user sentiment, which is then used to generate relevant entertainment recommendations. The application was developed using Flutter for the mobile interface and Flask for the backend services, with Firebase supporting user authentication and data storage. The dataset was collected through online questionnaires and secondary sources such as IMDb and Spotify. Testing results show that the system can provide mood-relevant recommendations with an interactive and responsive interface. This research contributes by integrating movies and music recommendations in a single platform, offering a more adaptive and emotionally relevant entertainment experience.
Application of Web and Mobile-Based Information System for Diabetes Patient Management at Bandar Lor Clinic
Diabetes mellitus is a chronic disease that requires continuous monitoring and management. Klinik Pratama Bandar Lor Kediri faces challenges in managing information for diabetic patients, including recording medical history, monitoring blood glucose, facilitating patient–doctor communication, and providing continuous education. Limited access to complete and up-to-date patient data hinders service delivery and complicates clinical decision-making, while patients often struggle to record examination results, adhere to treatment schedules, and obtain optimal consultations. To address these challenges, this study developed DiaCare, a web and mobile based information system built using the prototyping method with Flutter and Firebase. The system integrates key features such as health record management, medication reminders, online consultations, activity tracking with calorie estimation, digital delivery of laboratory results, dietary monitoring, and diabetes-friendly menu recommendations. Functional testing using the black-box method achieved a 100% success rate, validating that all features performed as expected. By enabling patients to self-monitor their condition while maintaining real-time communication with doctors, DiaCare improves efficiency, data accuracy, and patient engagement. The findings suggest that DiaCare has the potential to enhance diabetes management and optimize healthcare services for patients requiring regular monitoring at Klinik Pratama Bandar Lor Kediri.
Design and Implementation of FitSphere as an Android Application for Gym Membership Management at Chain Gym
The advancement of information technology in the digital era has driven transformation across various sectors, including the fitness industry. Chain Gym, a fitness center located in Klaten, faces challenges in managing data and membership services, which are still handled manually and separately. The lack of an integrated system has led to inefficiencies in processes such as class registration, fitness progress monitoring, and member information management, thereby affecting service quality and member satisfaction.This study designs and develops a mobile-based Android application named FitSphere, built using the Kotlin programming language with Firebase as the backend, to serve as an integrated platform supporting membership management at Chain Gym. The development process adopts the Rapid Application Development (RAD) methodology, which includes several stages: requirements planning through observation and interviews, system design using UML models and table relationship diagrams, construction involving coding and system integration, and the cutover phase that encompasses testing and deployment of the application.The application is equipped with key features such as membership registration, gym-related news, membership subscription, consultation, workout history, class registration, and Body Mass Index (BMI) calculation. The use of the RAD approach enables a faster and more adaptive development process that accommodates user needs and feedback.The results show that the features in FitSphere function as intended and have the potential to improve operational efficiency, making it easier for members to access services. Black Box Testing conducted on both member and admin features showed a 100% success rate, confirming that all functionalities operate correctly. The application also provides strategic benefits for management by offering deeper insights into user needs. Overall, the implementation of this application delivers an effective digital solution that enhances Chain Gym’s competitiveness in the rapidly growing fitness industry.
Classification of Stinging Nettle Plants Based on Leaf Images Using the CNN Method (Case Study: Biru-Biru Village)
The high number of skin irritation cases among residents in Desa Biru-Biru due to direct contact with stinging nettle plants highlights the need for an automatic identification system to distinguish plant types. This study aims to develop a leaf image classification model for stinging nettle plants using the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture. The image dataset was collected directly from the study area and classified into four categories: Jelatang Ayam, Jelatang Gajah, Jelatang Niru, and Non-Nettle plants. The research stages include data collection and analysis, pre-processing (resizing, normalization, augmentation), data splitting (70:10:20), model training, performance evaluation (accuracy, precision, recall, and F1-score), and web-based system implementation. The test results show that the model achieved an accuracy of 98%, with the highest precision score of 0.98, recall score of 0.98, and F1-score of 0.98. The system has also been successfully implemented as an interactive web application that allows users to identify nettle plant types quickly and accurately. This research contributes to risk mitigation efforts related to harmful plants in rural environments through the application of digital image processing technology.
Blockchain Adoption Challenges for SMEs: A Systematic Literature Review
Blockchain technology has emerged as a transformative force in various industries, with increasing interest from Small and Medium-sized Enterprises (SMEs). However, the adoption of blockchain by SMEs faces unique challenges that warrant comprehensive examination. This systematic literature review analyzes research on blockchain adoption challenges for SMEs published between 2019 and 2024. We reviewed 48 papers from major databases, including Scopus, Web of Science, and IEEE Xplore. Our analysis reveals three primary challenges: limited financial resources, lack of technical expertise, and regulatory uncertainty. The review also highlights a shift from conceptual to empirical studies over the period, indicating a maturing field of research. Geographically, 60% of studies focus on developing countries, particularly in Asia. Industry-wise, the supply chain sector dominates blockchain adoption research in SMEs (40%), followed by finance (25%) and manufacturing (20%). Despite challenges, significant benefits are identified, including enhanced supply chain transparency, improved operational efficiency, and better access to financing. This review contributes to the understanding of blockchain adoption in SMEs by synthesizing current knowledge, identifying research gaps, and proposing future research directions. Our findings provide valuable insights for researchers, practitioners, and policymakers involved infacilitating blockchain adoption among SMEs.
Small and Medium-sized Enterprises (SMEs)
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Development of an Indonesian Hoax Detection System Using Logistic Regression Based on TF-IDF
The massive spread of fake news (hoaxes) on digital platforms has become a serious challenge in Indonesia, with the potential to disrupt social stability and undermine public trust. This background drives the urgency of developing an automated system to combat disinformation. Unlike previous works relying on deep learning with high computational cost, this study demonstrates that a lightweight approach remains highly effective for Indonesian hoax detection. This study aims to develop and evaluate a lightweight and effective automatic classification system to detect Indonesian-language hoaxes using a machine learning approach. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to represent text content numerically, which is then classified using the Logistic Regression algorithm. This approach was chosen for its computational efficiency and ease of interpretation. The study utilizes a dataset collected from verified sources, consisting of 7,075 Indonesian-language news articles, which were divided into 80% training data and 20% test data. The evaluation results on the test data show excellent model performance, achieving an accuracy of 94.98%, a precision of 0.95, and an average F1-Score of 0.95. Specifically, the model demonstrated a strong ability to identify hoaxes with a recall value of 98% for the hoax class. This study concludes that the combination of TF-IDF and Logistic Regression is an efficient and accurate approach for Indonesian hoax detection, offering a practical solution that can be further developed to combat disinformation.
Application of K-Nearest Neighbor Algorithm for Estimating Fishery Product Quality at BPPMHKP Pontianak
Microbiological quality is a key parameter in ensuring the safety and overall integrity of fishery products. One of the primary indicators in microbiological testing is the Total Plate Count (TPC), locally known in Indonesia as Angka Lempeng Total (ALT), which measures the concentration of aerobic microorganisms in a sample. This study aims to develop a predictive system for ALT values in fishery products using the K-Nearest Neighbor (KNN) algorithm as the main classification approach. The dataset was derived from microbiological test results conducted at the BPPMHKP Laboratory in Pontianak between 2020 and 2024. Data preprocessing included converting ALT values from scientific text format to numeric values, applying Min-Max normalization, splitting the dataset into training and testing subsets, and implementing the KNN algorithm with K = 3. Predictions were generated by calculating the Euclidean distance between each test sample and the training set, selecting the three nearest neighbors, and averaging their ALT values. The proposed system achieved a prediction accuracy of 98.66% compared to actual ALT measurements. Based on the microbiological threshold of 5.0 × 10âµ colony-forming units per gram (CFU/g) used by BPPMHKP, the system effectively estimated product quality according to safety standards. These findings indicate the potential of the system to be developed into an application-based decision-support tool for government laboratories and quality control agencies. Keywords: Total Plate Count, K-Nearest Neighbor, Prediction, Fishery Products, Accuracy
Improving Software Defect Prediction Performance Using C4.5 Based Ensemble Learning with AdaBoost and Bagging Techniques
Software defect prediction (SDP) plays a crucial role in improving software quality by enabling the early detection of faulty modules during the development phase. However, class imbalance within software defect datasets remains a significant challenge that adversely impacts prediction accuracy. This study aims to address this issue by implementing ensemble learning methods—specifically Bagging and AdaBoost—combined with the C4.5 decision tree algorithm to enhance classification performance. The research utilized five well-known datasets from the NASA MDP Repository (CM1, JM1, KC1, KC2, and PC1), each containing comprehensive software metrics and defect labels. The methodology involved several stages: data preprocessing (normalization and discretization), model training using 10-fold cross-validation, and performance evaluation through metrics such as accuracy and Area Under the Curve (AUC). Results indicate that both ensemble methods outperformed the standalone C4.5 algorithm across all datasets. Notably, the AdaBoost + C4.5 model yielded the highest accuracy in most scenarios, with the PC1 dataset reaching 97.20% accuracy. In comparison, C4.5 alone and C4.5 with Bagging recorded lower values, demonstrating the significant impact of adaptive weighting in AdaBoost. These findings affirm that ensemble learning, particularly AdaBoost, effectively mitigates the impact of class imbalance and improves prediction performance in SDP tasks.
Identification of Palm Oil Fresh Fruit Bunches Worth Selling with K-Nearest Neighbors Algorithm
Indonesia is the world's largest palm oil producer, with annual production reaching more than 45 million tons. The quality of oil palm fresh fruit bunches (FFB) determines the quality of the oil produced. The quality of FFBs can be seen through their maturity and health. Fruit that is not ripe, overripe, or contaminated with mold can reduce oil quality due to high levels of free fatty acids (FFA). This research aims to build a classification model of FFB marketability using the K-Nearest Neighbors (K-NN) algorithm with RGB and GLCM features. Image data was collected from the plantation, then processed through the stages of preprocessing, feature extraction, and normalization. The model was tested in three approaches, namely using RGB-GLCM combination features, RGB only, and GLCM only, with various data sharing scenarios, namely 70:30, 80:20, and 90:10, as well as varying k values, namely k = 3, 5, 7, 9. The evaluation results show that the RGB-GLCM feature combination model in the 80:20 data sharing scenario and k = 5 value is the most optimal model, with accuracy reaching 88%. In addition to providing high accuracy, this model also shows good stability compared to the RGB and GLCM models alone. This proves that the use of a combination of features is more effective and reliable in identifying the marketability of oil palm FFB compared to the use of a single feature.