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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 248 Documents
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Development of a Prototype Room Security Monitoring System for Early Fire Detection Using a Prototyping Method Based on Sensors and IoT Alfonsius, Eric
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 1 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i1.29521

Abstract

Many public spaces have implemented monitoring systems to detect fires. Traditional fire monitoring systems typically involve manual supervision of each room, requiring personnel to physically visit locations daily, which is time-consuming and inefficient. This study aims to develop a prototype room security monitoring system designed for early fire detection. The system utilizes IoT technology and a web-based platform, allowing operators to monitor all rooms remotely. The prototype is equipped with fire detection sensors and an alarm system for real-time alerts. Each room is outfitted with a flame detector sensor operated by a microcontroller (Arduino Nano), which serves as the central control for all connected devices. To transmit data from the sensors to the web-based system, the prototype uses the ESP8266 Wi-Fi module, enabling seamless communication between the sensors and the monitoring platform. The system development was carried out using the prototyping method, which involved iterative design, construction, and testing. In addition, blackbox testing was conducted to evaluate the system's functionality without examining the internal code. The results indicate that the system successfully detects fires early and sends real-time notifications to the web platform with high accuracy. The system also allows for rapid operator response through the alarm system. Based on the blackbox testing results, all key features, such as fire detection, web notifications, and alarms, functioned as specified. Thus, this prototype is deemed effective in enhancing the efficiency of room security monitoring.
Classification Cyber Harassment on Twitter using Multinomial Naïve Bayes Karisma, Ria Dhea Layla Nur
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.29866

Abstract

Multinomial Naïve Bayes is a classification method in Naïve Bayes Classifier based on Bayes Theorem and multinomial distribution. This method works optimally in the multiclass classification of text data. Furthermore, it calculates the probability of occurrence of each word by multiplying the class prior probability by the likelihood value of the occurrence of each word in each class. The phenomenon of Cyber Harassment is defined as the behavior of utilizing technology to harm or humiliate people, which has four types of behavior, namely Physical Threats, Purposeful Embarrassment, Racist, and Sexual Harassment. The number of Cyber Harassment cases always increases every year even though the government has made policies to deal with Cyber Harassment cases. The study aims to classify results accurately regarding the types of Cyber Harassment on Twitter using the Multinomial Naïve Bayes method. The classification results obtained are 20 tweets classified as Physical Threats, 10 tweets classified as Purposeful Embarrassment, 25 tweets classified as Racist, and 22 tweets classified as Sexual Harassment. The accuracy of classification of types of Cyber Harassment on Twitter social media using Multinomial Naïve Bayes is 77%, and the results of the model performance test with K-fold cross-validation is 76.21%, showing that the Multinomial Naïve Bayes method can classify the types of Cyber Harassment on Twitter social media is well effective.
Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk Mola, Sebastianus Adi Santoso; Goru, Alfonsus Maria De Liguori; Lamapaha, Christian Jaquelino; Bekayo, Yoseph Kurubingan
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.30256

Abstract

Abstract—Baldness is a common condition affecting both men and women, primarily caused by age, hormones, and genetics. Predicting the risk of baldness is crucial for early diagnosis and prevention of further hair loss. This study aims to compare the performance of Linear Regression (LR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) in predicting baldness risk using data with variables such as age, gender, occupation, stress levels, and other lifestyle factors. A dataset of 5925 samples was processed through a series of steps, including normalization, parameter tuning, cross-validation, and residual analysis. The results show that Random Forest Regression outperformed other models with the lowest MSE (0.0979) and the highest R² (0.9056) on both training and testing data, followed by Decision Tree Regression and Linear Regression. Hyperparameter optimization using Grid Search significantly enhanced model performance. In conclusion, Random Forest Regression is the most suitable model for predicting baldness risk with complex datasets, while Linear Regression remains a viable alternative for simpler datasets.
Implementation of BERT in Sentiment Analysis of National Digital Samsat (SIGNAL) User Reviews Based on Machine Learning Savitri, Ratna; Rizki, Fido; Sobri, Ahmad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32059

Abstract

The SIGNAL application facilitates online vehicle tax payments for the public. The application's quality is frequently evaluated through user reviews on platforms like the Google Play Store. This study aims to analyze the sentiment of SIGNAL user reviews using a Machine Learning-based approach, specifically the BERT (Bidirectional Encoder Representations from Transformers) model. The dataset consists of 20,000 user reviews. After preprocessing, the remaining data comprises 17,287 reviews, categorized into 12,758 positive reviews, 2,160 neutral reviews, and 2,369 negative reviews. To address data imbalance, the Random Over Sampling (ROS) technique was applied. The evaluation was performed using metrics such as accuracy, precision, recall, and F1-score. The results of the study indicate that the IndoBERT model can classify sentiments with an accuracy of 99% and a validation accuracy of 98% after five epochs of training. Confusion matrix analysis shows that the model achieved an overall accuracy of 99.72% on training data and 98.68% on testing data. This study demonstrates that the IndoBERT model is highly effective in classifying sentiment and makes a significant contribution to understanding the user experience of SIGNAL, which can serve as a foundation for future improvements to the application.
Coral Reef Image Classification Using Multilayer Perceptron Fauzan, Abd. Charis; Sari, Hetty Elvina
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32134

Abstract

Coral reefs are one of the marine organisms that play many crucial roles for other organisms within them. Coral reefs are often referred to as tropical rainforests because they serve as shelters for small fish and produce food for other marine organisms. Over time, various threats have emerged that disrupt the stability of the marine ecosystem, one of which is coral reef degradation, such as bleaching or physical damage caused by multiple factors. These factors include climate change, chemicals resulting from fishing with explosives, and pollution. As a result, coral reefs become damaged and can no longer serve as a refuge for small species.  Therefore, this study aims to mitigate the impact of coral reef damage by developing a coral reef classification model using one of the deep learning algorithms and artificial neural networks, namely the Multilayer Perceptron (MLP), which employs multiple hidden layers in its modeling process. The classification results using this algorithm achieved an accuracy of 73%, indicating that the model performs well in classifying coral reefs in image form. Thus, it is hoped that deep learning innovations for coral reef classification can contribute significantly to coral reef conservation and marine resource management.
Sentiment Analysis on the Application of Tabungan Perumahan Rakyat (TAPERA) Policy in Indonesia Dzulfikar, Naufal
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32215

Abstract

Tapera is a program provided by the Indonesian government with the aim of providing poor people with decent housing, by charging all working classes with salaries above the Regional Minimum Wage of 3% for Tapera contributions. Even though it seems that this program aims to help poor people, this has given rise to a new polemic, which can be seen from some people who don't like this, as can be seen from several negative comments on social media, one of which is X or Twitter. The purpose of writing this paper is to find out whether the sentiment of the TAPERA policy is positive or negative for Indonesians citizens that affected by the policy, with the aim that the author can provide recommendations for government also encourage the usage of Social Media Analytics on government sector in Indonesia, even the result of the sentiment analysis is majority positive or majority negative. This research is conducted by collecting data from Twitter and then processed in such a way, and later will be checked for sentiment analysis with the naive bayes method.  From 742 datasets that got from Twitter (after getting sortied, previously, 1680 data was get), it shows that there are 651 tweets that have negative sentiments, and 91 tweets have positive sentiments, which shows how people are disappointed with the TAPERA policy.
Comparison Various Analytical Approaches to Find The Most Efficient and Effective Method for Peak Hour Identification Hapsani, Anggi Gustiningsih; Putri, Mayang Anglingsari
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.29193

Abstract

The Peak hour sales identification is essential to manage staff, inventory, and service capacity in coffee shop operations. This study compares an exploratory heatmap with two forecasting models, linear regression and Seasonal ARIMA (SARIMA) using six months of hourly transaction data from a coffee shop (1 March–17 August 2024) . The heatmap offers rapid visual recognition of high traffic periods but provides no predictive capability. For prediction, this study trained a linear regression and a SARIMA specification tuned by standard diagnostics; model performance was assessed on a held out set using MAE, RMSE, and MAPE. Linear regression yielded RMSE = 6.68, MAE = 5.40, and MAPE = 138.06%, indicating inadequate fit for intraday demand dynamics. In contrast, SARIMA achieved RMSE = 0.828, MAE = 0.557, and MAPE = 40.34%, substantially reducing error by explicitly modeling autocorrelation and recurrent seasonal cycles. The results show that seasonality aware time series modeling delivers actionable, interpretable forecasts for near term operational planning (such as staffing and product preparation). Overall, the proposed pipeline, heatmap for rapid situational awareness plus SARIMA for prediction, constitutes a practical baseline for peak hour identification in small scale retail.
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.

Page 24 of 25 | Total Record : 248

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