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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pekommas Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Pendas : Jurnah Ilmiah Pendidikan Dasar at-tamkin: Jurnal Pengabdian kepada Masyarakat Mobile and Forensics TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Belantika Pendidikan International Journal of Engineering, Science and Information Technology Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application Jurnal Informatika: Jurnal Pengembangan IT
Harits Ar Rosyid
Universitas Negeri Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Digitalization of information systems and educational laboratory management in higher education institutions Fauzi, Rochmad; Ar Rosyid, Harits; Herwanto , Heru Wahyu
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.740

Abstract

This study aims to develop an Information and Educational Laboratory Management System application based on SIONLAP. SIONLAP is designed and developed following educational institution elements' duties, needs, and functions. The system is developed to convert manual procedures, forms, and workflows into digital formats. Workflow processes can be optimized and automated through the implementation of SIONLAP. Documents and records generated by SIONLAP will be in digital data form, which can facilitate data processing and strategic analysis for planning, organizing, implementing, documenting, monitoring, reporting, evaluating, and developing educational laboratories, thereby improving management and continuous services in support of the implementation of the Tri Dharma of Higher Education. The research method refers to the waterfall method, with testing using the black box method. The results of the SIONLAP 2.0 application research show that it 1) provides more user-friendly user access management capabilities to facilitate users in higher education institutions with multi-role functions; 2) simplifies the data management and information workflow of equipment inventory; and 3) offers a laboratory asset rental feature as a means for higher education institutions to generate revenue from their laboratory assets
Design and development of face recognition-based security system using expression game as liveness detection Yusmanto, Yunan; Ar Rosyid, Harits; Prasetya Wibawa, Aji
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.756

Abstract

Face recognition as a security system has undergone significant developments, but challenges in live detection are still a major issue in preventing fraud. Liveness detection is a method that helps face recognition security more resistant to fraud. This research aims to address this issue by developing an innovative security system that integrates face recognition with a facial expression game, enhancing live detection and user engagement. The primary objectives are to ensure seamless integration, maintain a fun and challenging user experience, and demonstrate practical applicability. We applied a Waterfall method in our research to ensure a straightforward approach. We successfully applied this system for the door lock-unlock mechanism, simulating a restricted area. YuNet, a face detection model runs in the web interface and controls the NodeMCU to either lock or unlock the door.  The study concluded 95% success rate from the participants in making facial expressions: Smile, Normal, and Sad. However, expressing Sadness within the 3-second timeframe posed some difficulties. The average duration for completing the mini-game was approximately 16.31 seconds from the start. The integration of a facial expression game as a liveness detection required careful design to balance security and user engagement that is fun to experience. This research underscores the significance of addressing current challenges in biometric security by integrating an interactive element into the live detection process. The developed system contributes to the field by enhancing the robustness and user experience of face recognition security systems, demonstrating potential for broader application in restricted access scenarios.
Optimizing Cleaning Path for Coal Dust Removal Using Dual Stage Tracking Method Kumalasari, Ira; Ar Rosyid, Harits; Sendari , Siti; Mokhtar , Norrima Binti; Setumin , Samsul
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.29806

Abstract

Manual disaster mitigation at the Java Bali power plant, particularly related to fire risks from coal dust during electricity production, often requires halting operations, leading to significant revenue loss and power outages. This study aims to address this issue by proposing an automated solution to clean coal dust without interrupting production, utilizing a dual-stage tracking method for robot-assisted coal dust cleaning. The research contributes by developing a dual-stage A* algorithm that optimizes robot movements for cleaning tasks in power plant environments, outperforming single-stage BFS and single-stage A* algorithms. The research is divided into two phases: object detection and robot motion path selection. The dual-stage A* algorithm is compared against single-stage BFS and single-stage A* methods through a series of experiments evaluating their efficiency and effectiveness. The dual-stage A* method demonstrates superior performance in terms of path optimization, reducing cleaning time, and improving operational safety. Specifically, the dual-stage A* algorithm reduces energy consumption by 169 units and grid traversal by 84 units compared to single-stage methods, ensuring thorough dust removal while minimizing fire hazards. The dual-stage A* algorithm proves to be the optimal solution for coal dust cleaning in power plants, allowing for safe, continuous operation without the need for production halts. Future work should focus on addressing implementation costs and technical constraints to enhance real-world applicability.
Constructing Qur’an Recitation Classification using Alexnet Algorithm Rosyid, Harits Ar; Abdullah, Dzulkifli; Alqahtani, Mohammed S.
Knowledge Engineering and Data Science Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i22024p152-163

Abstract

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel-Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC and AlexNet yields promising accuracy in classifying Surah Al-Ikhlas recitations, suggesting its potential application for automatic reading correction. This approach significantly improves over traditional methods, contributing to more effective tools for Qur'an memorization assistance. Future work could explore its application in other significant improvement contexts and address potential challenges related to varying audio quality.
Kuntilanak as a Runtime Entity: Technical Integration of Javanese Folklore Using Manga Matrix in a 2D Horror Game Saurik, Herman Thuan To; Rosyid, Harits Ar; Wibawa, Aji Prasetya; Setiawan, Esther Irawati
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.961

Abstract

In this work, Kuntilanak, a mythological creature from Javanese mythology, is used as a dynamic element in a 2D horror game to provide a technical framework for integrating culturally infused folklore into interactive gaming. The design process breaks down the character's appearance, attire, and personality into workable technical specifications using the Manga Matrix framework as a guide. With C# scripted behaviours like unexpected appearances, animation state changes (controlled by Unity's Animator Controller), audio triggers (laughing, crying), and interactive reactions to in-game objects like yellow Bamboo (for hiding) and scissors (for repelling), Kuntilanak was created as a sprite-based runtime entity inside the Unity game engine. The character can be dynamically instantiated thanks to this technical approach, which supports procedural horror encounters and is consistent with traditional narratives. The effectiveness of the suggested technological integration was validated by a quantitative assessment using a Likert scale (N=50), which showed 82.2% agreement on cultural authenticity and 79.5% on emotional impact. The findings support the methodology's capacity to turn folklore characters into functional game entities and offer a replicable model for serious games that consider cultural sensitivity. The findings support the methodology's capacity to turn folklore characters into functional game entities and provide a replicable model for serious games that consider cultural sensitivity, with direct implications for designing engaging educational experiences that promote cultural heritage preservation.
Mono Background and Multi Background Datasets Comparison Study for Indonesian Sign Language (SIBI) Letters Detection using YOLOv8 Andriyanto, Teguh; Handayani, Anik Nur; Ar Rosyid, Harits; Wiryawan, Muhammad Zaki; Azizah, Desi Fatkhi; Liang, Yeoh Wen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3462

Abstract

The research in this paper focuses on the detection of Indonesian Sign Language System (SIBI) letters using the YOLOv8 object detection model. The study compares two datasets, one with mono-background (a simple, uniform background) and another with multi-background (complex and varied backgrounds). The research aims to evaluate how the complexity of image backgrounds affects the performance of the YOLOv8 model in detecting SIBI letters This study uses a dataset consisting of 24 SIBI letters (excluding J and Z due to the complexity of their gestures), sourced from Mendeley. The dataset was processed with and without data augmentation (rotation, brightness adjustments, blur, and noise) to test the model under various conditions. Four models were trained and tested: one using mono-background images, another using augmented mono-background images, a third using multi-background images, and a final model trained on augmented multi-background images. The results showed that the YOLOv8 model performed best with the multi-background dataset, achieving a precision of 0.995, recall of 1.000, F1 score of 0.997, and mAP50 of 0.994Adding to the model made it better at generalizing, but it took longer to train. The study finds that multi-background datasets with augmentation make the model much better at finding SIBI letters in real-world settings. This makes it a promising tool for projects that aim to improve communication for deaf people in Indonesia. The study suggests that more research should be done on augmentation techniques and bigger datasets to make detection more accurate. 
Implementasi Algoritma Binary Space Partitioning Untuk Procedural Map Generation Dalam Gim Rosyid, Harits Ar; Prasetyo, Ahmad Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8629

Abstract

The popularity of games as an interactive entertainment medium continues to grow, with 2D maps playing a vital role in enhancing user experience. Manual map creation is time-intensive, particularly as game worlds become increasingly complex. Procedural Content Generation (PCG) offers a solution by automating map creation, improving replayability, and reducing designer workload. This research explores the use of the Binary Space Partitioning (BSP) algorithm for procedural dungeon map generation, incorporating random connections between rooms to create more exploratory and dynamic maps. The process includes three stages: developing a dungeon map generator, implementing BSP with random room connectors, and validating the generated maps to ensure navigability. Space Syntax analysis, including Visibility Graph Analysis (VGA) and Axial Line Analysis, is applied to evaluate the quality of the maps in terms of connectivity, visibility, and integration. Results show that BSP-generated maps with random connections offer dynamic layouts, while Space Syntax measures reveal that smaller minimum room sizes result in lower integration and connectivity but increase interaction hotspots. This study demonstrates the potential of BSP in generating varied game maps and the utility of Space Syntax for assessing their spatial properties.
Utilization of the Particle Swam Optimization Algorithm in Game Dota 2 Armanto, Hendrawan; Rosyid, Harits Ar; Muladi, Muladi; Gunawan, Gunawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.3503

Abstract

Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files.
PENERAPAN PENDEKATAN CULTURALLY RESPONSIVE TEACHING PADA MATERI ANALISIS DATA MENGGUNAKAN MICROSOFT EXCEL UNTUK MENINGKATKAN KREATIVITAS DAN HASIL BELAJAR SISWA Nur Sa’ida Kismurdiani; Harits Ar Rosyid; Suparman
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 Terbit
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i04.35952

Abstract

This study focuses on enhancing students' creativity and learning outcomes through the implementation of the Culturally Responsive Teaching (CRT) approach in the topic of data analysis using Microsoft Excel in class VIII-J of SMP Negeri 19 Malang. The main problem faced by students is the low understanding of Informatics, particularly data analysis, which is often perceived as abstract and difficult to grasp. This research employed Classroom Action Research (CAR) using the Kemmis and McTaggart model, which consists of two cycles. Data were collected through observation and learning outcome tests. The results showed an improvement in student creativity from the "fair" category (58.61%) in the pre-cycle to "excellent" (82.77%) in the second cycle. Furthermore, learning mastery increased from 31.25% in the pre-cycle to 90.63% in the second cycle. The implementation of CRT, which connects learning content with local culture such as traditional foods and regional products, was able to create meaningful and relevant learning experiences and increase students' active participation. This research demonstrates that CRT is an effective approach to improving the quality of Informatics learning, particularly in fostering student creativity and understanding of data-based material.
Towards Intelligent Performance Monitoring for Blockchain-Based Learning Systems: A Multi-Class Classification Approach Sulaksono, Aditya Galih; Patmanthara, Syaad; Rosyid, Harits Ar
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1138

Abstract

This study proposes a multi-class classification framework for monitoring blockchain system performance as a step toward integration within blockchain-based learning management systems (LMS). Reliable performance monitoring is essential because smart contracts in educational settings depend on timely and accurate system responses to ensure valid grading and credential issuance. A dataset of 3,081 transactional logs was generated from simulated blockchain testbed, capturing throughput, latency, block size, and send rate. Throughput values were discretized into seven qualitative categories ranging from “Very Poor” to “Very Good” using quantile-based binning. Preprocessing involved data cleaning, categorical encoding, Z-score normalization, and label encoding to ensure model compatibility. Five algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were trained and evaluated using stratified 80–20 partitioning and 5-fold cross-validation with grid search for hyperparameter tuning. Performance metrics included accuracy, macro precision, recall, and F1-score. Random Forest achieved the best results with 91.35% accuracy, 0.910 macro precision, 0.911 recall, and 0.910 F1-score, outperforming other models by handling complex feature interactions and reducing variance. Decision Tree offered strong interpretability (88.32% accuracy), while Logistic Regression (84.97%) and SVM (84.86%) provided stable performance. KNN showed balanced results (87.78%) but incurred high computational costs. The findings demonstrate that multi-class stratification provides more actionable insights than binary methods, supporting low-latency decision-making for smart contract execution in decentralized LMS ecosystems. The novelty of this research lies in applying multi-class classification instead of binary methods, enabling nuanced monitoring. Future work will validate the framework in real blockchain-LMS deployments.
Co-Authors Abdullah, Dzulkifli Achmad Iffad Adhilaga, Hanif Aditya Galih Sulaksono, Aditya Galih Agung Bella Putra Utama Agusta Rakhmat Taufani Ahmad Adi Prasetyo Aji Prasetya Wibawa Akmal Vrisna Alzuhdi Ali M. Mohammad Salah Alqahtani, Mohammed S. Amalia Amalia Anie Yulistyorini Anik Nur Handayani Ardi Anugerah Wicaksana Aripriharta - Asa Luki Setiawan Asfani, Khoirudin Ashar, Muhammad Aulia Yahya Harindra Putra Aya Sofia Mufti Azhar Ahmad Smaragdina Azizah, Desi Fatkhi Brillianta Zayyan Muhammad Danang Rahmat Bachtiar Denny Kurniawan Diederik Rousseau Dyah Lestari Edwin Meinardi Trianto Elfonda Daffa Risqullah Elmiyadi Novia Farma Esther Irawati Setiawan Fajariani, Erna Fatma Yuniardini Fauzi, Rochmad Febrianto Alqodri Felix Andika Dwiyanto Ferdinand, Miftakhul Anggita Bima Gunawan Gunawan Gunawan Hakkun Elmunsyah Hartarto Junaedi Hendrawan Armanto Herman Thuan To Saurik Heru Wahyu Herwanto Joumil Aidil Saifuddin Khoiruddin Asfanie Khurin Nabila Kumalasari, Ira Kusuma Refa Haratama Liang, Yeoh Wen Lucyta Qutsyaning Rosydah M Baharuddin Yusuf Mohammad Musthofa Al Ansyorie Mohammad Yasser Chuttur Mokhtar , Norrima Binti Muchamad Andis Setiawan Muhammad Akbar Muhammad Iqbal Akbar Muhammad Naufal Farras Muladi Mursyit, Mohammad Mutyara Whening Aniendya Nastiti Susetyo Fanany Putri Novian Dwi syahrizal Hilmi Nur A’yuni Ramadhani Nur Hidayatullah Nur Sa’ida Kismurdiani Prasetyo, Ahmad Adi Prawidya, Della Murbarani Rahadyan Fannani Arif Sari, Tenty Luay Setumin , Samsul Shah Nazir Siti Sendari Suparman Syaad Patmanthara Teguh Andriyanto, Teguh Theodora Monica Timothy John Pattiasina Tinesa Fara Prihandini Utomo Pujianto Wahyu Irianto Wako Uriu Wiryawan, Muhammad Zaki Yudhistira, Moch Rajendra Yusmanto, Yunan Zaeni, Ilham Ari Elbaith