Journal : Jurnal Ilmiah Informatika dan Komputer Frequency : June and December DOI : 10.69533 (Crossref) E-ISSN : 3047-4752 Editor In Chief : Rizal Furqan Ramadhan, S.Kom., M.T Publisher : Rumah Jurnal Rizania Media Pratama Indexing : SINTA 4 | Google Scholar | Crossref | Garuda | Dimension Jurnal Ilmiah Informatika dan Komputer (E-ISSN : 3047-4752) is a national journal with open access that publishes research articles in the fields of Informatics Engineering and Computer Science. The scope of the journal includes Artificial Intelligence, Information Systems, Robotics, Computer Networks, Image Processing, Mobile Applications, Data Mining and other fields of informatics. The Jurnal Ilmiah Informatika dan Komputer journal is managed and published by Rumah Jurnal RIZANIA MEDIA PRATAMA. This journal is published as a means and forum for lecturers, scientists, researchers and experts in the fields of Informatics Engineering and Computer Science to publish their research results to support the Tri Dharma of Higher Education Tasks and Programs in general. The Jurnal Ilmiah Informatika dan Komputer journal is published twice a year in June and December. Jurnal Ilmiah Informatika dan Komputer (E-ISSN: 3047-4752) has been officially accredited as SINTA Rank 4 (SINTA 4) pursuant to the Keputusan Direktur Jenderal Sains Dan Teknologi Kementerian Pendidikan Tinggi, Sains, Dan Teknologi Republik Indonesia NomoR 355/DST/D.D1/HM.01.01/2026. This accreditation reflects the journal's commitment to maintaining high standards of scholarly publishing through a rigorous peer-review process, ethical publication practices, and continuous quality improvement. The journal is dedicated to publishing original, high-quality research that contributes to the advancement of Informatics and Computer Science while fostering collaboration among researchers, academics, practitioners, and industry professionals at both national and international levels. The journal is managed by Rumah Jurnal Rizania Media Pratama under the auspices of CV.RIZANIA MEDIA PRATAMA, legally proven by the decree of the Ministry of Law and Human Rights with the number AHU-0012457-AH.01.14 of 2024.
Optimizing iPhone Spare Parts Inventory Using K-Medoid Clustering
MR. GADGET store in Bengkulu faces significant challenges in managing iPhone spare parts inventory due to a manual recording system. This study proposes a data science-based solution using the K-Medoid Clustering algorithm to group data based on characteristic similarity. Utilizing a dataset of 471 products, this study compares K-Medoid with conventional partitioning methods (like K-Means), demonstrating its superior robustness against outliers by using actual data points as cluster centers. The clustering quality is evaluated using the Silhouette Score and Davies-Bouldin Index (DBI), yielding best-performing results with a Silhouette Score of 0.681 and a DBI of 0.798. The algorithm generates three main clusters: Fast-Moving, Medium-Moving, and Slow-Moving. The system's functionality is validated through Black Box Testing. The results indicate that this approach provides more accurate procurement recommendations, optimizes inventory turnover, and reduces the risk of inventory imbalance, offering a practical data-driven framework for local retail businesses.
Copyright (c) 2026 Naurah Atikah Nurpadhilah, Aliya Dwi Ardiyanti, M. Aufa Rafiqi, Nur Ayu Siti Hardianti, Yusa Virginiawan Guntara (Author) https://creativecommons.org/licenses/by-sa/4.0
Developing a Content-Based Book Recommendation System for School E-Libraries Using TF-IDF and Cosine Similarity
The rapid growth of e-libraries has increased the availability of digital book collections, making it difficult for users to identify relevant reading materials. This study develops an automated book recommendation system for the SMKN 1 Semen e-library using Content-Based Filtering with TF-IDF for term weighting and Cosine Similarity for similarity measurement. The dataset consisted of 35 book records containing metadata and book descriptions collected from the SMKN 1 Semen e-library. Text data were processed through case folding, tokenization, stopword removal, and stemming before recommendation generation. The system was developed using the Laravel framework and a MySQL database following the Research and Development (R&D) method with the Waterfall model. Recommendation performance was evaluated using a Top-5 recommendation scenario, where the recommended books were compared with manually identified relevant books based on content similarity using Precision and Recall metrics, while system functionality was verified through Black Box Testing. The experimental results achieved a Precision of 80% and a Recall of 57.14%, indicating that the proposed approach effectively generates relevant book recommendations. This study contributes by demonstrating that the integration of Content-Based Filtering, TF-IDF, and Cosine Similarity provides an effective recommendation approach for school e-libraries with limited user interaction data, enabling personalized book recommendations and improving the efficiency of book discovery in small-scale digital library environments.
Copyright (c) 2026 Muhammad Fatkul Roziq, Riska Nurtantyo Sarbini, Halimahtus Mukminna, Moh. Syaiful Anam (Author) https://creativecommons.org/licenses/by-sa/4.0
Apriori and FP-Growth Comparative Analysis of MPL Indonesia Season 13 Hero Drafts
Hero combination selection or drafting is a crucial factor in determining victory in Mobile Legends: Bang Bang (MLBB) games at the professional level such as MPL Indonesia Season 13. However, counter-pick strategies are often based solely on the subjective intuition of players or coaches. This study aims to provide an objective basis for determining winning hero combination patterns by applying the Association Rule Mining (ARM) technique. Two main algorithms, namely Apriori and Frequent Pattern Growth (FP-Growth), are compared to evaluate the performance efficiency and accuracy of the resulting rules. The research data includes 183 winning transactions during the regular season of MPL ID Season 13, with parameters of minimum support 0.05 (5%), minimum confidence 0.40 (40%), and minimum lift 1.2. The results show that the strongest association rules are found in the combinations {Lapu-lapu} → {Fredrinn} (confidence 0.71) and {Cici} → {Fredrinn} (confidence 0.59). In terms of technical performance, the Apriori algorithm recorded a faster execution time than FP-Growth on this dataset. This study concluded that both algorithms produce identical association rule outputs, while Apriori demonstrated faster execution on this small-scale dataset, a finding attributed to the limited transaction volume rather than a universal superiority of Apriori over FP-Growth. The resulting rules can serve as a data-driven strategic recommendation system for professional esports teams in the pick and ban phase.
Copyright (c) 2026 Andre Immanuel Porayou, Jacob Alfanicolls Rahayaan, Ade Yusupa, Yaulie Deo Y. Rindengan (Author) https://creativecommons.org/licenses/by-sa/4.0
AI-Driven Learning Personalization in LMS Platforms:A Systematic Review Of Mechanisms, Effectiveness, And Computational Challenges
Background: The widespread adoption of Learning Management System (LMS) platforms in higher education has yet to overcome the fundamental limitation of uniform content delivery, which fails to accommodate individual differences in prior knowledge, learning pace, and cognitive style. Artificial Intelligence (AI) offers a transformative pathway to address this gap through data-driven personalization. Objective: This Systematic Literature Review (SLR) synthesises empirical evidence on the computational mechanisms, implementations, effectiveness outcomes, and technical-ethical challenges of AI-driven learning personalization in LMS environments within higher education, with an explicit focus on informatics and computational perspectives. Method: Adhering to PRISMA 2020 guidelines, 38 articles were selected from 312 candidates retrieved from Google Scholar, ScienceDirect, IEEE Xplore, and DOAJ (2021–2026), following three-stage screening and quality appraisal using the Mixed Methods Appraisal Tool (MMAT; minimum score 3/5). Results: Five dominant computational mechanism clusters were identified: (1) behavioral log analytics using sequence mining, clustering, and NLP; (2) academic failure prediction with Random Forest and Gradient Boosting (AUC up to 0.91, accuracy 78–89%); (3) hybrid recommender systems combining collaborative filtering, content-based filtering, and Knowledge Graph-GNN approaches (Precision@K gains of 14.3%); (4) adaptive assessment via Bayesian Knowledge Tracing combined with Item Response Theory; and (5) emerging applications of Large Language Models, Retrieval-Augmented Generation (RAG), Federated Learning, and Explainable AI (XAI/SHAP). A meta-analytic synthesis across 47 experimental studies yields a pooled effect size of d = 0.52 (medium-to-large) on academic performance. Significant challenges persist in data privacy compliance (UU PDP No. 27/2022), algorithmic fairness for 3T-region students, instructor AI literacy, and infrastructure disparity. Novelty: This review introduces a computational taxonomy of AI mechanisms in LMS, differentiating it from prior SLRs that focus predominantly on pedagogical or descriptive dimensions. Six priority research gaps are identified, including XAI adoption, culturally-fair algorithm design, and federated architectures for decentralised Indonesian institutions.
Copyright (c) 2026 Jacob Alfanicolls Rahayaan, Andre Immanuel Porayou, Arpen Patanduk, Ade Yusupa (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of Naive Bayes and Support Vector Machine for Sentiment Classification of Indonesian-Language Mobile Application Reviews on Google Play Store
This study conducted a comparative performance evaluation of Multinomial Naive Bayes and Support Vector Machine (SVM) with a linear kernel in classifying the sentiment of Indonesian-language mobile application reviews collected from the Google Play Store. A total of 2,847 reviews targeting the GoPay digital wallet application were gathered via web scraping using the google-play-scraper library. After preprocessing, including case folding, cleansing, tokenization, stopword removal, and stemming using the Sastrawi library, the final dataset comprised 2,634 usable reviews. Sentiment labeling was conducted automatically based on star ratings: ratings of 4 and 5 were assigned as positive (1,841 reviews, 69.9%), while ratings of 1 and 2 were assigned as negative (793 reviews, 30.1%). Feature extraction used TF-IDF with a vocabulary size of 8,432 unique terms. Model training used an 80:20 train-test split with stratified sampling. SVM parameters were set to kernel=linear and C=1.0; Naive Bayes used alpha=1.0 (Laplace smoothing). Experimental results show that SVM achieved an accuracy of 88.3%, precision of 0.89, recall of 0.88, and F1-score of 0.88, while Naive Bayes obtained an accuracy of 82.1%, precision of 0.84, recall of 0.82, and F1-score of 0.83. SVM demonstrated superior performance across all four evaluation metrics, with the largest gap observed in the F1-score for the negative class (SVM: 0.71 vs. Naive Bayes: 0.56). These findings confirm that SVM is more robust against class imbalance in informal Indonesian-language review data.
Copyright (c) 2026 Norris Elden Salassa, Arpen Patanduk, Ade Yusupa, Yaulie Deo Y. Rindengan (Author) https://creativecommons.org/licenses/by-sa/4.0
Smart Trafo: A Random Forest and LLM-Based Decision Support System for Power Transformer Fault Diagnosis via Dissolved Gas Analysis
Manual management of Dissolved Gas Analysis (DGA) data for power transformers at PT. PLN (Persero) UPT Manado has been identified as a critical operational bottleneck, with existing spreadsheet-based workflows susceptible to human error, poor historical traceability, and limited scalability. Prior studies on DGA-based transformer diagnosis have been predominantly confined to standalone classification models without integration into operational management systems, leaving a significant gap in practical field deployment. This research contributes a novel integrated Decision Support System named Smart Trafo, which is the first to combine a Random Forest classification model, Duval Pentagon visualization, historical trending analysis, and an LLM-based conversational assistant (Volty AI) within a unified full-stack web platform. The Random Forest model was trained on 375 DGA samples across six fault classes using five gas parameters conforming to IEEE C57.104, achieving an overall accuracy of 84% and a macro-average F1-score of 0.83. Feature importance analysis revealed Hydrogen (H₂) as the dominant diagnostic indicator at 26.2%. The system successfully automates DGA fault classification, eliminates manual calculation errors, and provides real-time technical recommendations, thereby enabling more efficient and data-driven preventive maintenance decisions at PT. PLN (Persero) UPT Manado.
Copyright (c) 2026 Jeremia David Anthony Paduli, Jonathan Gabrillio Kaligis, Ade Yusupa, Yaulie Rindengan (Author) https://creativecommons.org/licenses/by-sa/4.0
Queue-Based Batch Processing Architecture for Scalable Educational Assessment Systems Using MERN Stack
The increasing volume of student assessment data requires educational information systems that remain responsive during concurrent access and large-scale data import. This study proposes and evaluates a queue-based batch processing architecture for a web-based educational assessment system developed using the MERN stack. The main contribution is the integration of a RESTful API layer, FIFO-oriented asynchronous job queue, and batch segmentation strategy to decouple bulk Excel import from foreground user requests. The system was developed using Extreme Programming to support iterative requirement refinement and continuous testing. Evaluation was designed through functional testing using Cypress, API testing using HTTPie/Thunder Client, load testing using Apache JMeter, and frontend quality assessment using Google PageSpeed Insights. The scalability benchmark compares direct synchronous insertion as a baseline against the proposed asynchronous queue-based batch architecture under multiple concurrency levels, dataset sizes, and batch configurations. The verified PageSpeed results indicate excellent web quality scores, namely Performance 93, Accessibility 96, Best Practices 96, and SEO 100. The JMeter-based metrics, including average response time, 95th percentile response time, throughput, error rate, CPU usage, memory usage, queue waiting time, and job completion time, should be inserted from the exported test logs before final submission. The proposed architecture is expected to improve responsiveness, prevent server overload during bulk import, and provide a more reliable foundation for scalable educational assessment management.
Copyright (c) 2026 April Firman Daru, Aria Hendrawan, Toti Kresna Wardana (Author) https://creativecommons.org/licenses/by-sa/4.0
Design and Development of a WhatsApp Chatbot-Based Expert System Using the Depth-First Search Algorithm for Thesis Topic Recommendation
The rapid development of digital technology has encouraged the creation of various intelligent systems that can support decision-making processes, one of which is an expert system. In the academic environment, students often encounter difficulties in determining appropriate thesis topics that align with their areas of interest. Therefore, this study aims to develop an expert system integrated with a WhatsApp chatbot to assist students in identifying suitable thesis themes based on their academic interests. The system employs the Depth-First Search (DFS) algorithm as a problem-solving approach due to its flexibility in exploring decision paths within the knowledge base. The expert system is implemented through a WhatsApp chatbot interface to ensure ease of use, as the messaging interface is already widely familiar among university students. This approach allows users to interact with the system naturally without the need to learn a new interface. The results of system testing indicate that the developed expert system effectively assists students in determining thesis topics. Evaluation results show an accuracy level of 95.4%, demonstrating that the proposed system provides reliable recommendations. Furthermore, the use of a chatbot-based interface enhances user accessibility and usability. In conclusion, the integration of an expert system with the DFS algorithm and a WhatsApp chatbot interface provides an effective and user-friendly solution for supporting students in selecting appropriate thesis topics based on their interests.
Copyright (c) 2025 Galuh Indah Zatadini (Author) https://creativecommons.org/licenses/by-sa/4.0
Implementation of Edge Detection Using the Sobel Operator on Papaya Leaf Images
Recent advances in digital image processing and computer vision have enhanced feature extraction techniques for plant identification based on leaf morphology. Edge detection is a fundamental operation that highlights intensity discontinuities corresponding to object boundaries. This study implements the Sobel operator to perform edge detection on tropical leaf images using an experimental–computational approach. The workflow involves grayscale conversion, horizontal and vertical Sobel filtering, and gradient magnitude computation implemented in Python using the OpenCV library. Experimental evaluation demonstrates that the Sobel operator effectively delineates primary leaf contours and preserves morphological consistency, despite reduced performance under non-uniform illumination and noisy conditions. These results confirm that the Sobel operator remains a reliable preprocessing technique for leaf-based feature extraction and classification, offering a computationally efficient baseline for future integration with machine learning-based plant recognition systems.
Copyright (c) 2025 Yuda Apriansyah, Nouval Khairi, Haikal Habibi Siregar, Supiyandi, Aidil Halim Lubis (Author) https://creativecommons.org/licenses/by-sa/4.0
Classification of Organic and Non-Organic Waste Using Convolutional Neural Network (CNN)
The increase in waste volume in Indonesia, which reached emergency levels in 2024, requires technological solutions that can assist in the sorting process quickly and accurately. Previous research on CNN-based waste classification generally focused on recyclable waste categories with many classes and used structured datasets, which did not adequately represent real-world waste conditions, especially organic waste, which has more varied shapes and conditions. Based on this gap, this study proposes a Convolutional Neural Network (CNN) model for classifying two main categories—organic and inorganic—using 25,077 images and direct testing on field samples. The model was trained using the Adam optimizer and categorical crossentropy loss. The results show high accuracy for inorganic waste (96%), but lower accuracy for organic waste (62%) due to the complexity of texture and natural damage. This study contributes to the field of informatics through the application of more applicable and realistic deep learning for automatic waste sorting systems, as well as opening up opportunities for the development of model architectures that are more adaptive to waste conditions in the actual environment.
Copyright (c) 2025 Muhammad Farhan, Mhd Farhan Aditiya, Dafa Ikhwanu Shafa, Supiyandi, Aidil Halim Lubis (Author) https://creativecommons.org/licenses/by-sa/4.0
Real-Time Driver Microsleep Detection Using Lightweight MobileViT and Haar Cascade
Microsleep is a brief and involuntary loss of awareness that increases the risk of driving accidents. This study proposes a real-time microsleep detection system using Haar Cascade for eye localization and the lightweight MobileViT-XXS model for eye-state classification. The model was trained on a public dataset and achieved a training accuracy of 99.49%, a test accuracy of 98%, and a real-time accuracy of 90–94%. A microsleep event is detected when the eyes remain closed for ≥ 2 seconds. While the method performs well under controlled conditions, real-time testing revealed technical limitations such as sensitivity to lighting variation, non-frontal head pose, and motion, which affect detection stability and represent common robust-vision challenges. Despite these limitations, the system runs efficiently on CPU-only hardware and demonstrates strong potential as a lightweight early-warning system to support driving safety. Future research may explore expanding dataset diversity, improving environmental adaptation, and deploying the system on embedded or mobile platforms to enhance robustness and scalability.
Copyright (c) 2025 Syahada Mawarda Hutagalung, Muhammad Alfariz Rasyid, Supiyandi, Aidil Halim Lubis (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimization of Employee Task Assignments Using the Hungarian Method and POM-QM at Serupa Cafe
This study aims to optimize employee task assignments at Serupa Café, which faces issues of workload imbalance and inefficient task completion times. The research gap lies in the limited application of algorithm-based optimization methods within human resource management in the food and beverage service sector. To address this, the study applies the Hungarian Method, an algorithmic approach with a time complexity of O(n³), to determine the most efficient pairing between employees and tasks. The computation and validation processes were carried out using the POM-QM for Windows software as a quantitative analysis tool. The results indicate that the optimal assignment configuration achieved a total completion time of 85 minutes, with complete consistency between manual and software-generated results. System testing demonstrated high computational efficiency and no logical errors during data processing. Therefore, this study concludes that integrating the Hungarian Method with POM-QM significantly enhances operational performance and contributes to the application of informatics techniques for optimizing human resource management within the service industry.
Copyright (c) 2025 Tirsa Ninia Lina, Matheus Supriyanto Rumetna, Jeni Karay, Charliany Hetharia, Jalmijn Tindage (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimization of the Internal Quality Audit Process Through the Development of an Integrated Information System
The Internal Quality Audit (IQA) process is a crucial element for maintaining the quality of higher education. However, its implementation at STIMIK Tunas Bangsa Banjarnegara faces challenges, particularly with manual documentation. The use of paper-based forms, decentralized recording of findings, and separate reports often cause delays, data duplication, and difficulty in tracking follow-up actions. This research aims to develop an integrated information system to optimize the IQA process and improve the ease of documentation up to the findings report stage. This study uses a qualitative approach with a case study design. Data was collected through observation to understand the manual workflow and interviews with relevant parties to gather system requirements. The system was developed using the Prototype method and built with the PHP programming language and a MySQL database. The research resulted in a system named SIMATIKA (Sistem Audit Mutu Internal Kampus), all of whose features function well based on BlackBox Testing. These features include audit period management, indicator management, user configuration, and audit assignment. The system also received a score of 86% from a user questionnaire, indicating very good feasibility and acceptance. Thus, SIMATIKA successfully optimizes the IQA process at the campus.
Copyright (c) 2025 Febri Sarif Hidayat, Khalimaturofi’ah, Heni Rahmawati (Author) https://creativecommons.org/licenses/by-sa/4.0
Implementation Of A Website-Based Recording And Reporting System For Mosque Construction Donations
Managing donation funds requires transparency and accountability to maintain trust and ensure proper use of funds. This study aims to implement a web-based fund recording and reporting information system to assist in the efficient and transparent financial management of the Al-Ikhlas Mosque. The problems faced by the Al-Ikhlas Mosque management include manual recording that is prone to loss, miscalculations, and a lack of transparency to residents. To address these issues, a "Website-Based Recording and Reporting System for Development Donations at the Al-Ikhlas Mosque" was developed. The system development method used is the Waterfall method, with stages of analysis, design, implementation, testing, and maintenance. Based on the results of the system trial using Black Box Testing and Likert Scale calculations, the manager received a score of 81.31%, while the community test yielded a score of 81.63%. This information system facilitates management in managing donation funds and facilitates public access to financial reports. Therefore, this information system can be implemented to accelerate and improve the efficiency of the donation fund management process at the Al-Ikhlas Wirasari Mosque.
Copyright (c) 2025 Rurianto, Yuniar Laeli Nur Faizah, Purwanto (Author) https://creativecommons.org/licenses/by-sa/4.0
Web-Based Student Learning Activities and Development Information System Using Pieces Analysis at OSI Tutoring Center
Tutoring (BIMBEL) is a form of non-formal education that plays a role in improving students' academic achievements. Oemah Sinau Indonesia (OSI) in Banjarnegara still uses Excel and physical reports to record learning activities and student development, making the process of searching and distributing data less efficient. This study aims to build a web-based information system for learning activities and student development using the PIECES analysis as a system evaluation method at OSI tutoring. The system development uses the Waterfall method with PHP programming language, Laravel framework, and data storage based on Firebase Realtime Database. System testing is conducted using Black-box testing to ensure the system functions according to user needs, and all 10 test scenarios were successfully executed with a "successful" status. System feasibility evaluation uses PIECES analysis (Performance, Information, Economy, Control, Efficiency, Service) was conducted through the distribution of questionnaires to 11 respondents consisting of admins, mentors, and student guardians. The evaluation results showed an average feasibility score of 95%, categorized as very feasible. Specifically, the Performance and Efficiency aspects received the highest scores of 97%, indicating an improvement in the speed and ease of administrative processes as well as real-time access to information. Based on the testing and analysis results, this system is considered effective in replacing manual record-keeping, improving data accuracy, strengthening access control, and facilitating communication between the tutoring center and student guardians. Thus, this information system is proven feasible to be used as a solution for digitalizing learning administration at OSI.
Copyright (c) 2025 Anugrah Septiani, Heni Rahmawati, Luthfi Nur Azizah (Author) https://creativecommons.org/licenses/by-sa/4.0
Application of the Hungarian Algorithm for Workforce Task Optimization in Mobile Device Repair Operations
An imbalance in task assignments within a business unit can decrease efficiency and extend job completion time. To address this issue, an optimization approach is required to allocate tasks effectively and minimize total working time. This study analyzes the application of the Hungarian Method to optimize employee assignments at Konter Bintang Sorong, aiming to achieve the most efficient mobile phone repair time. A quantitative approach was employed using the Hungarian algorithm implemented through POM-QM for Windows version 5. Data were collected through observation, interviews, and documentation related to repair durations for each task type. The findings show that the Hungarian Method produced the most efficient task allocation: employee Willi repaired connectors in 25 minutes, Aksa handled software issues in 60 minutes, and Fadli repaired LCD components in 10 minutes. These results indicate that the Hungarian Method effectively reduces average repair time and increases the number of devices serviced within a given period. Thus, it serves as an effective optimization tool for improving task distribution, operational efficiency, and customer satisfaction.
Copyright (c) 2025 Matheus Supriyanto Rumetna, Tirsa Ninia Lina, Jeni Karay, Agustinus Budi Santoso, Wiesje Ferdinandus (Author) https://creativecommons.org/licenses/by-sa/4.0
Android Based Activity Information Application in Surabaya
Surabaya is the capital of East Java which also became the second metropolitan city after Jakarta. As one of the largest cities in Indonesia, Surabaya often holds major events. During the past year counted already dozens or even hundreds of activities held in Surabaya. Usually events are held in order to commemorate the anniversary of the city and other celebrate. The usual events include cultural fairs, culinary festivals, and job fair. Android is an open source operating system that allows software to be freely modified and distributed by device makers and app developers. Therefore the author has the idea to utilize android technology as one of the media information by creating an application. This application is made to publish an events to be held in Surabaya, which is titled "Application Information of Events in Surabaya Based Android".
Copyright (c) 2025 Tommy Nuril Huda, Pamudi, David Hermansyah (Author) https://creativecommons.org/licenses/by-sa/4.0
Property Product Recommendation System as a Marketing Strategy at PT Java Abadi Sejahtera
This study addresses the challenges faced by PT. Java Abadi Sejahtera in matching customer needs with available property products and the frequent data loss caused by manual recording processes. A web-based recommendation system was developed using the Profile Matching method, where criteria such as property type, location, size, price, payment method, and facilities are weighted according to their importance and processed using GAP calculations to determine the suitability between customer requirements and product data. Black box and white box testing results indicate that the system functions correctly and is able to process product data, customer member data, and follow-up data accurately. The system generates ranked product recommendations based on suitability calculations. The findings demonstrate that the recommendation system effectively supports marketing decision-making, improves the efficiency of the matching process, and enhances the overall quality of marketing strategies implemented by the company.
Copyright (c) 2025 Siti Munawaroh, Achmad Choiron, Ratna Nur Tiara Shanty (Author) https://creativecommons.org/licenses/by-sa/4.0
Decision Support System For Scholarship Candidate Selection
System used by the university. Soetomo surabaya for the selection of prospective students who are eligible to receive a scholarship is still using the manual way. Given the enormous number of submissions by scholarship recipients will make it difficult for universities in selecting students who are eligible to receive scholarships. Therefore it is necessary that there is a system that can be used as supporting decision-making to determine the students who are eligible to get a scholarship at the University Dr Soetomo Surabaya in order to facilitate the University. The problem that occurs is the absence of a system used for the selection process of scholarship recipients at Dr. University. Soetomo Surabaya. This selection is done using Analytical Hierarcy Process (AHP) method, it will simplify the way the system to perform the selection, the work concept of Analytical Hierarcy Process (AHP) is able to determine the best alternative choice in determining a goal. Analytical Hierarcy Process (AHP) will determine the right student to get a scholarship viewed from several attributes that exist. With this decision support system, it will facilitate the University's work to determine the students who are eligible for a scholarship in terms of some existing attributes. The result of this decision support system is the obtaining of scholarship recommendation data in the form of value with percent form where this value which will be the management consideration to determine the scholarship recipient.
Copyright (c) 2025 Emilyati Lailatul Qodriyah, Dwi Cahyono, Ratna Nur Tiara Shanty (Author) https://creativecommons.org/licenses/by-sa/4.0
Adaptive Ensemble Learning for Real-Time Anomaly Detection in 5G Networks
5G networks enable ultra-high speed, low latency, and massive connectivity for critical applications such as IoT, autonomous vehicles, and digital healthcare. However, the complexity and high traffic volume in 5G architectures increase the risk of anomalies that threaten service quality and security. This study addresses the problem by proposing a real-time anomaly detection framework based on streaming data and ensemble learning algorithms. Network traffic is processed through a stream processing platform, while ensemble models such as Random Forest, Gradient Boosting, and Voting Classifier are applied to improve detection accuracy. Experimental results show that the proposed system achieves high accuracy and low latency in detecting anomalies, including Distributed Denial of Service (DDoS) attacks and technical failures. This research contributes a scalable and effective solution to enhance 5G network security and reliability, advancing the field of cybersecurity and network analytics.
Copyright (c) 2025 Agus Dendi Rachmatsyah, Benny Wijaya, Syafrul Irawadi, Ari Amir Al-Kodri, lili Indah Sari (Author) https://creativecommons.org/licenses/by-sa/4.0
Generating Unimodular Matrix in Python for Solving Systems of Linear Equations
Linear equation systems with integer solutions are widely used in modern computing fields such as cryptography and optimization, but conventional methods often produce inaccurate decimal solutions. To address this issue, this research developed a Python program based on NumPy that can efficiently generate unimodular matrices. The method involves three main stages: initializing an upper triangular matrix with diagonal elements of ±1, filling non-diagonal elements with random integers, and transforming the matrix through elementary row operations. Test results show that the program successfully generates unimodular matrices of sizes 4×4 to 9×9 with perfect accuracy (determinant exactly ±1), an average computation time of 0.5 seconds for a 4×4 matrix, and efficient memory usage (under 20 MB). The solutions to the linear equations are always exact integers, meeting the requirements for high-precision computation. This implementation not only provides a practical solution for integer linear equation systems but also opens up opportunities for applications in cryptographic algorithm development and optimization techniques that require absolute precision. The findings of this research confirm that numerical computation approaches can produce both accurate and efficient mathematical solutions.
Copyright (c) 2025 Muhammad Irfan Mustakim, Diny Syarifah Sany (Author) https://creativecommons.org/licenses/by-sa/4.0
Matrix-Based Computation in Informatics: A Conceptual Review of Linear Algebra Applications
Linear algebra and matrices play a critical role in informatics, especially in understanding multidimensional data and computational processes. This study aims to explore the fundamental concepts and practical applications of linear algebra and matrices using a descriptive qualitative method with a literature review approach. Data sources include textbooks, international journals, and recent academic articles. The findings reveal that concepts such as vector spaces, determinants, eigenvalues, and matrix transformations are widely applied in machine learning, image processing, and modern data analysis. Techniques like Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are proven effective for dimensionality reduction and feature extraction. The study concludes that linear algebra acts as a bridge between mathematical theory and informatics applications, serving as a foundation for developing intelligent systems and algorithms. This research is expected to serve as a conceptual reference for students and scholars in the field of information technology.
Copyright (c) 2025 Muhammad Nazar Aufa Nazar, Diny Syarifah Sany Diny (Author) https://creativecommons.org/licenses/by-sa/4.0
Matrix in Linear Algebra for Modern Computational Solutions
This study aims to examine the basic concepts of matrix in linear algebra through conceptual and computational approaches. Matrix are understood not only as computational tools, but also as representations of linear transformations and relationships in vector spaces. The method used is a combination of theoretical analysis and numerical experiments using NumPy and SymPy. A case study was conducted by constructing a distance matrix between 20 districts in Cianjur in the form of a 20x20 symmetric matrix. The results show that the determinant of the matrix reflects the stability of the spatial system, while the inverse is useful for solving systems of linear equations. Eigenvalue and eigenvector analysis identified the most strategically located districts within the network. Additionally, the farthest distance between districts was successfully determined and can be utilized for more efficient transportation route planning. In conclusion, a conceptual and computational understanding of matrix structure is crucial, not only in linear algebra theory but also in practical applications such as regional planning and transportation network management.
Copyright (c) 2025 Miral Setiawan, Diny Syarifah Sany (Author) https://creativecommons.org/licenses/by-sa/4.0
Development of an Integrated Information System for Monitoring and Validation of Health Workers Practice Licenses (STR) in Healthcare Facilities
The management of Health Workers ' Registration Certificates (STR) in Indonesia faces challenges such as manual processing, data fragmentation, and the risk of expired or fake STR, which can endanger patient safety and hinder supervision by health facilities and authorities. This study developed a web-based information system using the Waterfall approach, through the stages of needs analysis, design, implementation, testing (Black Box and User Acceptance Testing), and deployment on Anwar Medika University servers, with centralized database features, automatic notifications, QR code verification, and compliance reports. As a result, the system improved efficiency, accuracy, and transparency, with 87% of respondents rating the intuitive interface and service satisfactory, 89% felt the time efficiency, and 95% considered the system easy to use. The system is recommended to be widely implemented, integrated with the national e-Health, with regular maintenance and training of users to support the management of accountable and qualified health personnel.
Copyright (c) 2025 Rony Kriswibowo, Agung Budi Setyawan, Rusina Widha Febriana (Author) https://creativecommons.org/licenses/by-sa/4.0
Matrix: Basic Concepts And Practical Applications In Daily Life
Matrix, as a fundamental mathematical concept, have crucial applications in computer science. This study analyzes: the basic structure of matrices (definition, types, addition-multiplication-inverse operations), and their implementation in computational fields, including: digital image processing (pixel representation, image transformation), intelligent systems (matrix-based neural networks), data security (matrix encryption), and network optimization (routing algorithms). A combination of literature review and case analysis reveals that understanding matrices is the backbone of modern computing, particularly in the development of machine learning and computer vision. Findings indicate a gap between conventional matrix theory and the demands of large-scale computing in industry. This study recommends integrating examples of computer science applications into matrix education to prepare digital talent.
Copyright (c) 2025 Muhamad Dimas Aprijal, Diny Syarifah (Author) https://creativecommons.org/licenses/by-sa/4.0
Linear Algebra for Modern Statistics: Efficiency and Interpretability Challenges in Regression and PCA
The development of modern statistics faces the challenge of high-dimensional data complexity, which requires an efficient yet interpretable approach. Linear algebra offers a solution through matrix representation, but its limitations in non-linear contexts and high-dimensional interpretation need to be examined in greater depth. This study analyzes algebraic methods through case studies of linear regression (normal equation solutions) and PCA (eigen decomposition), tested on synthetic datasets and MNIST. The results show: (1) a 40% computational acceleration in matrix-based regression, (2) PCA successfully reduces the MNIST dimension to 3 main components (retaining 85% of the variance), but a survey reveals that 73% of practitioners have difficulty interpreting high-dimensional components. Despite its efficiency advantages, algebraic methods require further development through hybrid approaches (kernel PCA) and interpretable techniques to address limitations in linearity and high-dimensional complexity.
Copyright (c) 2025 Khoerul Rahman, Diny Syarifah Sany (Author) https://creativecommons.org/licenses/by-sa/4.0
Linear Algebra in Geometric Transformations
This study aims to deeply examine the role of Linear Algebra in geometric transformation through a combined qualitative-quantitative approach. By combining systematic literature studies, content analysis, GeoGebra-based virtual laboratory experiments, and field case studies, this study found that transformation matrices, vector spaces, and orthogonal projections are the main foundations in representing and manipulating geometric objects precisely. The results show that a deep understanding of Linear Algebra improves students' spatial visualization abilities by 34% and the efficiency of Augmented Reality (AR) application design by up to 27%.Startup NextVision AR Lab (Jl. Gegerkalong Hilir, Bandung) – cloud-based AR specialist for manufacturing & education. Problem Rendering high-detail 3D objects (>100k vertices) on a Snapdragon XR2 headset frequently drops frames. Hypothesis Optimizing the matrix transformation algorithms (model-view-projection, normal, pose) can reduce GPU load by up to 30% without compromising visual fidelity.
Copyright (c) 2025 Raihan Faisal Dzikra, Diny Syarifah (Author) https://creativecommons.org/licenses/by-sa/4.0
The Utilization of Exam Browser Application as Assessment Media at Madrasah Tsanawiyah Islamiyah Trimoharjo
This study aims to measure the level of effectiveness of the Exam Browser application as a tool in the learning evaluation process in Islamiyah Trimoharjo. This research was conducted using a quantitative descriptive method. The subjects in this study are students of MTs Islamiyah Trmoharjo. The data in this study is in the form of answers from questionnaires. The results of the study show that most of the students from MTs Islamiyah Trimoharjo consider this application to provide convenience in the implementation of the exam. They see that the application can increase focus and help improve discipline and honesty in working on problems. While most students can overcome difficulties in accessing the app, technical constraints such as errors, lag, and performance are still concerns. The exambro application used for exams is accepted by both teachers and students because it facilitates the implementation of exams with user-friendly features, question randomization and real-time monitoring, obstacles that occur during the use of this application by teachers and students only on technical issues such as unstable internet connections and device compatibility, and positive perceptions from teachers and students regarding the use of the application.
Copyright (c) 2025 Dwi Prayitno, Femas Adi Prakasa, Nirma, Thoha Firdaus (Author) https://creativecommons.org/licenses/by-sa/4.0
Al-Istiqomah Islamic Boarding School Academic Information System Based on the Waterfall Model
This article discusses the development of a web-based Academic Information System (AIS) for Istiqomah Islamic Boarding School using the Laravel framework. The main objective of this study is to create an integrated and efficient system to manage student academic data, including grades, report cards, attendance, and information about both instructors and students at Al-Istiqomah Islamic Boarding School. The research method used is Research and Development (R&D) with a waterfall approach, starting from requirements analysis, system design, implementation, and testing. Data collection was carried out through interviews with pesantren administrative staff and observation of ongoing academic processes. The findings of the study show that the AIS developed successfully facilitates the digital management of academic data, reducing reliance on manual note-taking, which is prone to errors and slow. The system provides essential modules such as student data management, grade input, attendance recapitulation, student financial reports, and class schedules. System testing using the black box testing method shows that all functions are operating well and meeting the needs of the user. Thus, this system is expected to increase efficiency and accuracy in the management of academic information, as well as support better decision-making at the Istiqomah Islamic Boarding School.
Copyright (c) 2025 Thoha Firdaus, Muhammad Iksannudin, Muhammad Naufal Jamil Al Basyari Sutoyo, Deri Feriyansah (Author) https://creativecommons.org/licenses/by-sa/4.0
Development Digital Payment Application Using Next.js and Xendit with Prototyping-Agile Approach
This research presents the development of a comprehensive digital payment application for PT. Fortuna Sada Nioga using the Next.js framework and Xendit payment gateway. The primary objective was to develop an automated digital system that supports multiple payment methods and provides real-time transaction monitoring to replace manual payment processes. The research employed a hybrid methodology that combines a Prototyping approach for concept validation with Agile Scrum methodology for systematic development. The main outcomes demonstrate the successful implementation of AES-256-GCM encryption, rate limiting mechanisms, input validation, and webhook integration with comprehensive security testing validation. Performance testing showed optimal response times with an average load time of less than 2 seconds for website browsing and the capability to handle up to 100 concurrent users without performance degradation. The system includes customer order management, multi-payment gateway integration, an admin dashboard with role-based access control, and real-time order tracking features. The conclusions indicate that combining Next.js with Xendit provides a robust, secure, and scalable foundation for digital payment systems, while the hybrid Prototyping-Agile methodology proves effective for complex system development within constrained timeframes.
Copyright (c) 2025 Angga Rakhmansyah, Abdul Jabbar, Muhammad Ramzy, Wasis Haryono (Author) https://creativecommons.org/licenses/by-sa/4.0