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Deny Zainal Arifin
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Jurusan Teknik Informatika Fakultas Sains dan Teknologi Universitas Islam Negeri Maulana Malik Ibrahim Malang Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144
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MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : 1978161X     EISSN : 24772550     DOI : https://doi.org/10.18860/mat
Core Subject : Science,
Computer Science & IT
MATICS is a scientific publication for widespread research and criticism topics in Computer Science and Information Technology. The journal is published twice a year, in March and September by Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia. The journal publishes two regular issues per year in the following areas : Algorithms and Complexity; Architecture and Organization; Computational Science; Discrete Structures; Graphics and Visualization; Human-Computer Interaction; Information Assurance and Security; Information Management; Intelligent Systems; Networking and Communication; Operating Systems; Platform-Based Development; Parallel and Distributed Computing; Programming Languages; Software Development Fundamentals; Software Engineering; Systems Fundamentals; Social Issues and Professional Practice.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 11 Documents
 1   2 
Search results for , issue "vol 18, no 1 (2026): matics" : 11 Documents clear
Predicting Budget Absorption Categories Using Random Forest and Support Vector Machine Methods Novardy, Novardy; Kusumawati, Ririen; Hariyadi, Muhammad Amin; Harini, Sri; Imamudin, Muhammad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37223

Abstract

Budget classification plays a crucial role in planning, management, and budgeting, from implementation to accountability. We create budgets by considering various types of expenditures and funding sources. Each type of expenditure, such as employee salaries, goods, capital, grants, social assistance, subsidies, interest, and non-tax revenue (PNBP) or public service agencies (BLU), has its own set of rules and methods for tracking money. This study aims to demonstrate how budget classification, based on expenditure types and funding sources, is applied in the implementation of the Revenue Budget. This study aims to assess the classification performance of two models, namely the Random Forest Classifier (RFC) and Support Vector Machine (SVM), based on historical data and evaluate the performance of each model. Tests show that the Random Forest model consistently outperforms the SVM model for each data proportion, with a ratio of 90:10 to 60:40. The Random Forest model achieved its best performance at the 80:20 data split, with an accuracy score of 94 percent, a precision score of 94 percent, a recall score of 94 percent, and an F1 score of 87 percent. The average accuracy score of the SVM test results was 80 percent.
A Robust Framework for Dissolved Oxygen Forecasting in Precision Aquaculture: A LightGBM Approach with Advanced Feature Engineering Prasetya, Nyoman Wira; Harianto, Richard Wijaya
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37617

Abstract

Accurate prediction of necessary water quality parameters such as Dissolved Oxygen (DO) is very critical in precision aquaculture and is essential for performance-based decision-making. This thesis fills the gap between reactive monitoring and predictive intelligence through the construction of a solid machine learning infrastructure. We convert high frequency multivariate time series data into a supervised learning problem by an advanced feature engineering process that generates temporal predictions including lag features and rolling window statistics. A Light Gradient Boosting machine (LightGBM) algorithm trained using the above-mentioned engineered dataset has an extreme predictive power. Results of single-variable interpretation analysis showed that short term data, especially the 5-minute rolling statistics of DO and turbidity variability, are the main driving factors for the model prediction. This research confirms that a feature-engineered LightGBM approach is a computationally efficient, but highly accurate approach to supporting the development of early warning systems in modern aquaculture as a computationally scalable approach.
Evaluating Website Performance Using EdgeOne as an Automated Web Assessment Tool Putri, Mayang Anglingsari; Aprijani, Dwi Astuti; Trihapningsari, Denisha; Putri Martinasari, Made Diyah; Junianto, Mochamad Bagoes Satria
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37752

Abstract

A university website functions not only as an information portal but also as a reflection of institutional credibility and academic reputation. Therefore, maintaining website quality—particularly web performance—is essential and must be evaluated continuously. The website of the Information Systems Study Program at Universitas Terbuka was selected as the focus of this study due to its role in delivering academic information and supporting communication with stakeholders. This research evaluates the website’s web performance using EdgeOne, an automated assessment tool that measures key performance indicators such as Time to First Byte (TTFB), First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, Total Blocking Time (TBT), and Page Load Time. A descriptive quantitative approach is applied to interpret the performance metrics and identify areas requiring optimization. The results show that although the website maintains stable structural functionality, several performance indicators—particularly loading speed—remain below the recommended threshold for modern web standards. These findings highlight the importance of continuous web performance monitoring and technical optimization to improve user experience and ensure the reliability of institutional digital platforms.
An AI-Based Mobile Application for Personalized Learning in Secondary Education Kusuma, Ellysha Dwiyanthi; Kusuma, Lianny Wydiastuty; Wijaya, Hartana; Yuardi, Arya Bodhi; Dharma, Michael Sidharta
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.39498

Abstract

The development of Artificial Intelligence (AI) provides opportunities to support personalized learning, particularly at the secondary school level, where students have diverse learning needs. This study aims to develop an AI-assisted mobile application prototype to support personalized learning and assist teachers in monitoring student progress. The research adopts a Research and Development (RD) approach, which includes stages of user needs analysis, system design, prototype development, and user evaluation. The application was developed in two versions: a student application and a teacher application, both implemented on Android devices. The student application provides features such as a learning dashboard, adaptive quizzes, learning analytics, feedback, and rule-based learning recommendations derived from student performance, while the teacher application offers class monitoring, grade input, and learning material upload functionalities. The evaluation involved 43 students and 3 teachers using usability questionnaires. Results indicate that more than 85% of students found the application easy to use and beneficial in supporting their learning, while teachers reported that the system supports monitoring and instructional activities. These findings suggest that the proposed application is feasible and well-accepted as a personalized learning support tool. However, further studies are required to evaluate its impact on learning outcomes using more rigorous experimental methods.
Analysis of Inhibiting Factors for QRIS Adoption in the Digitalization of MSMEs in East Jakarta Agustin, Alviandi; Irnawati, Oky
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.36972

Abstract

The Quick Response Code Indonesian Standard (QRIS) was introduced by Bank Indonesia to unify QR-based payments and accelerate the digitalization of Micro, Small, and Medium Enterprises (MSMEs). However, its adoption among MSMEs in East Jakarta remains suboptimal. This study aims to analyze the inhibiting factors affecting QRIS adoption by applying the Unified Theory of Acceptance and Use of Technology (UTAUT), extended with the perceived risk construct. A quantitative approach was employed using purposive sampling of 100 MSME respondents. Data were collected through a structured questionnaire consisting of 13 items and analyzed using descriptive statistics and Spearman’s rank correlation. The results show that social influence is the most dominant inhibiting factor, while perceived risk also contributes but to a lesser extent. Conversely, facilitating conditions, effort expectancy, and performance expectancy are relatively favorable. Correlation analysis indicates that effort expectancy plays a central role, as it is strongly associated with other constructs. These findings suggest that improving digital literacy, strengthening awareness campaigns, and enhancing trust in digital transactions are necessary to accelerate QRIS adoption among MSMEs in East Jakarta. Future research may include moderating variables such as trust and policy support to provide deeper insights into long-term adoption behavior.
Decision Support System for Identifying Student Learning Styles in Elementary School using Naïve Bayes Algorithm khakim, ikhsanul; Mujianto, Ahmad Heru; Vitadiar, Tanhella Zein; Mashuri, Chamdan
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.39725

Abstract

Identifying student learning styles is essential for teachers to design effective and adaptive teaching strategies. At SDN Rejoagung 3, this process is currently conducted manually through observation and interviews, which are prone to subjective bias. This research develops a web-based decision support system to classify student learning styles—Visual, Auditory, and Kinesthetic—using the Naïve Bayes algorithm. The system was built using data collected via questionnaires from students in grades 1 to 6. Testing was conducted using a confusion matrix to evaluate the model's performance. The results show that the Naïve Bayes algorithm successfully classified learning styles with an accuracy of 94.12%. This system provides a more objective and systematic tool for teachers to identify students' preferences, enabling more personalized instructional delivery in an elementary school context
Carprice Intelligence: Prediction Price of Second Car using Machine Learning Sulaiman, Rahmat; Isnanto, Burham
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37602

Abstract

Determining a fair market price for a used vehicle is a significant challenge for both sellers and buyers due to a lack of data transparency and the variety of influencing factors such as brand, production year, and mileage. This research aims to address this issue by developing a market data-based used vehicle price prediction system using a machine learning approach. The methodology adapts the CRISP-DM framework. Data is collected through web scraping from leading online marketplaces and processed through cleaning, normalization, and encoding before being used for modeling. Various regression algorithms were implemented, and the Linear Regression model was chosen for its optimal performance. The model was evaluated using the R Squared metric, yielding a score of 74% on the training data and 76% on the test data, demonstrating good accuracy and adaptability to new data. The best model was then implemented into a simple user interface based on Streamlit, allowing users to get a more objective recommendation for buying and selling prices. Overall, this system has great potential to facilitate more efficient and transparent transactions in the used automotive market, helping users make smarter and more profitable decisions.
EVALUATING ACADEMIC SUPPORT SERVICE QUALITY IN DISTANCE LEARNING: A FUZZY-SERVQUAL APPROACH TO STUDENT SATISFACTION Junianto, Mochammad Bagoes Satria
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.38578

Abstract

The quality of academic support services is pivotal to student success and retention in distance education. This study evaluates the service quality of the Distance Learning Skills Training (PKBJJ) at Universitas Terbuka using the Fuzzy-SERVQUAL approach. A parallel 22-item SERVQUAL instrument was administered to 100 students to capture Expectations (E) and Perceptions (P) across five dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Linguistic judgments on a 1–5 Likert scale were mapped to triangular fuzzy numbers (TFN), aggregated, and defuzzified (centroid) to obtain crisp scores for E and P; gaps were computed as P−E at both item and dimension levels. Findings show negative gaps across all dimensions, indicating services do not fully meet student expectations. Priority deficits were observed in Reliability (gap ≈ −0.038) and Responsiveness (≈ −0.0127), followed by Tangibles (≈ −0.0092), while Assurance (≈ −0.0060) and Empathy (≈ −0.0010) performed comparatively better but still below expectations. The results provide an actionable improvement map emphasizing process consistency, timely responses, and incremental enhancement of facilities, while maintaining strengths in assurance and empathy. Methodologically, the integration of fuzzy logic with SERVQUAL demonstrates better sensitivity to subjective and ambiguous appraisals common in distance learning contexts, yielding more representative and decision-ready metrics for quality improvement. Keywords: Service Quality; Fuzzy-SERVQUAL; Student Satisfaction; Distance Learning; Gap Analysis; Universitas Terbuka
Talent Development Center Recommendation System Using Content-Based Filtering Putri, Maysha Permata; Daniawan, Benny
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.32816

Abstract

The development of digital technology has led to increased gadget usage among children, often resulting in a decline in interest in productive physical and social activities. The Indonesian Child Protection Commission reported that over 71.3% of school-age children using gadgets daily. This condition highlights the need for efforts to redirect children's attention toward more beneficial activities, one of which is talent development. Early talent development is crucial for supporting personal potential and future career paths. However, limited information often becomes an obstacle in choosing the right place for talent development that suits an individual's needs and interests. This study aims to design a system that can provide talent development center recommendations for seekers. By implementing the Content-Based Filtering (CBF) method, the system matches user preferences—such as interests, skills, and preferred types of activities expressed in keywords—with the descriptions of available talent development center. Weighting is carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to enhance the relevance of the recommendations by calculating the similarity level between talent development center descriptions based on keyword weights. This approach allows the system to provide more personalized recommendations without relying on other users' data. The testing conducted in this study, using 7 sample talent development places, resulted in 5 recommendations with the top recommendation being Chic’s Musik, which had the highest TF-IDF value of 1.9029.Index Terms— Content-Based Filtering, Recommendation System, TF-IDF, Talent Development
EVENT-DRIVEN ARCHITECTURE FOR REAL-TIME NOTIFICATION SYSTEMS IN STUDENT ENVIRONMENTS: A PERFORMANCE AND SCALABILITY STUDY USING THE WHATSAPP API Belluano, Poetri Lestari Lokapitasari; Majid, Abd.; Fuad, Muhammad Nur; Saputra Scania, Muhammad Rifky
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37878

Abstract

Event notification systems play a crucial role in supporting academic and student organizational activities. However, many institutions still rely on monolithic architectures with synchronous processing that are unable to handle spikes in communication loads, resulting in high latency and reduced delivery reliability. This study proposes a notification system based on Event-Driven Architecture (EDA) integrated with a microservices environment to improve the efficiency, scalability, and reliability of information dissemination, particularly through the WhatsApp Business API as the primary communication channel. The proposed system leverages asynchronous event processing, distributed message brokers, and isolated gateway services to enable parallel message delivery while addressing external service constraints such as rate limiting. The system is evaluated within a single controlled experimental setup using 1,011 notification messages under consistent workload conditions. For comparison purposes, a simulated synchronous baseline is used to represent the characteristics of traditional sequential processing systems. The results show that the EDA-based system achieved a 100% delivery success rate with an average latency of 3,222 ms and a stable throughput of 17 messages per second, while the simulated baseline exhibits limitations in maintaining performance under the same conditions. These findings indicate that the proposed architecture improves system performance within the evaluated experimental context and demonstrates strong potential for scalable real-time communication in controlled deployment environments.

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