Article Per Year (5 Year)
p-Index From 2021 - 2026
5.965
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA 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 Indonesian Journal of Data and Science International Journal of Engineering, Science and Information Technology Best : Journal of Applied Electrical, Science and Technology Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Masyarakat dan Riset Pendidikan Academia Open
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 Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 56 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 56 Documents
Search

 2   3   4   5   6 
The Development of Train Artificial Intelligence (AI) Model for Bagapit Chess (Catur Bagapit) Engine using Random Forest Regressor Algorithm : a Traditional Game from Kalimantan, Indonesia Hastuti, Dwi; Rosyid, Harits Ar; Arifin, M. Zainal
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/eab74t67

Abstract

Bagapit Chess (Catur Bagapit) is a traditional strategy board game originating from the Kalimantan region of Indonesia. Despite its rich cultural heritage and strategic depth comparable to international Chess, Bagapit Chess remains largely unstudied from a computational intelligence perspective. This paper presents the development of an Artificial Intelligence (AI) model for the Bagapit Chess engine using the Random Forest Regressor (RFR) algorithm. The AI model is trained to evaluate board positions and generate competitive move decisions through a heuristic evaluation function augmented by machine learning. A dataset of 15,000 annotated game positions was constructed from expert gameplay, encoding board features including piece Material Advantage, Chess Movement, Defense Stance, mobility, and Attack Coverage across the 8×8 Bagapit board. The Random Forest Regressor model was integrated with a Negamax search tree enhanced by Alpha-Beta Pruning to achieve efficient and intelligent move selection. The trained model achieved an R² score of 0.9134, a Mean Absolute Error (MAE) of 0.0872, and a Root Mean Squared Error (RMSE) of 0.1104 on the test set. In engine evaluation against a rule-based baseline, the AI model won 84.2% of games under standard time control. This study contributes to the digitalization and preservation of Indonesian traditional games and demonstrates the applicability of ensemble machine learning to non-standard board game engines.
Comparative Analysis of Speech-to-Text APIs for Supporting Communication of the Deaf Community Anik Nur Handayani; Hariyono Hariyono; Ahmad Munjin Nasih; Rochmawati Rochmawati; Imanuel Hitipeuw; Harits Ar Rosyid; Jevri Tri Ardiansah; Rafli Indar Praja; Ahmad Nurdiansyah; Desi Fatkhi Azizah
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.327

Abstract

Hearing impairment can have a profound impact on the mental and emotional state of sufferers, as well as hinder communication and delay in accessing information directly that relies on interpreters. Advances in assistive technology, especially speech recognition systems that are able to convert spoken language into written text (speech-to-text). However, its implementation faces various challenges related to the level of accuracy of each speech-to-text Application Programming Interface (API), thus requiring an appropriate deep learning model. This study serves to analyze and compare the performance of speech-to-text API services (Deepgram API, Google API and Whisper AI) based on Word Error Rate (WER) and Words Per Minute (WPM), to determine the most optimal API in a web-based real-time transcription system using the JavaScript programming language and Glitch.com. The three API services were tested by calculating their error rates and transcription speeds, then evaluated to see how low the error accuracy rate was and how high the transcription speed was. On average, Whisper AI had a WER of 0% across all word categories, but its speed was lower than the other two APIs. Deepgram API displayed the best balance between accuracy and speed, with an average WER of 13.78% and 67 WPM. Google API performed stably, but its WER value was slightly higher than Deepgram API. In conclusion, based on the results, Deepgram API was deemed the most optimal for live transcription, as it is capable of producing fast and error-free transcriptions, significantly increasing the accessibility of information for the deaf community.
Strategi untuk Menguatkan Pemahaman Siswa Kelas 8G dengan Pendekatan Tarl pada Materi Analisis Data: Penelitian Nancy Nindyana Putri Nur’aini; Harits Ar Rosyid; Suparman
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 Tahun 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.3109

Abstract

Penelitian ini bertujuan untuk menguatkan pemahaman siswa kelas 8G SMPN 19 Malang pada materi analisis data melalui penerapan pendekatan Teaching at The Right Level ( TaRL ). Pendekatan ini dirancang untuk menyesuaikan proses pembelajaran dengan Tingkat kemampuan aktual siswa, bukan berdasarkan Tingkat kelas. Penelitian dilaksanakan dengan metode Penelitian Tindakan Kelas (PTK) model spiral Kemmis dan Taggart selama dua siklus. Instrumen yang digunakan meliputi asesmen diagnostic, lembar observasi, tes formatif, dan catatan lapangan. Hasil penelitian menunjukkan adanya peningkatan signifikan dalam keterampilan siswa mengolah dan menyajikan data menggunakan Microsoft Excel. Pada siklus I, ketuntasan belajar siswa tercatat 39,4 % dengan rata-rata nilai 67,2. Setelah perbaikan strategi pembelajaran, siklus II menunjukkan peningkatan ketuntuasan menjadi 78,8 % dengan rata-rata nilai 82,6. Pendekatan TaRL terbukti membantu siswa belajar sesuai edngan kemampuan masing-masing, mingkatkan keterlibatan, serta menjadikan pembelajaran lebih kontekstual dan menyenangkan. Hasil ini mendukung penerapan TaRL sebagai Solusi pembelajaran berdiferensiasi, khususnya dalam mata Pelajaran berbasis teknologi dan data.
Robustness Evaluation of Gaming Performance Against Input Rate Variations in Procedurally Generated Roguelike on Godot Engine: Evaluasi Robustness Performa Game Roguelike terhadap Variasi Input Rate Menggunakan Monkey Testing pada Godot Engine Santoso, Rizky Aji; Rosyid, Harits Ar
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.14129

Abstract

General Background: System resilience under high interaction frequency is essential for maintaining real-time game stability. Specific Background: This study evaluates the Dodge and Deflect roguelike game on Godot Engine 4.4.1 using Monkey Testing with controlled input rate variation in a low-end virtual environment. Knowledge Gap: Quantitative analysis of input intensity on engine stability remains limited compared to playability-focused studies. Aims: This study aims to identify performance degradation patterns and critical stability thresholds driven by input rate escalation. Results: A strong negative correlation (r = -0.93) is found between input intensity and system stability, with performance declining from Level 11 to Level 4 under extreme conditions, accompanied by FPS drops and increased freeze events, while memory usage remains stable. Novelty: The study applies controlled Monkey Testing to isolate input rate as the main stress factor in a procedurally generated roguelike setting. Implications: The findings provide an empirical basis for optimizing event handling and defining minimum system requirements. Highlights• Inverse pattern between interaction density and system endurance• Failure threshold identified through reduced progression capability• Processing constraints emerge as dominant limitation under stress KeywordsMonkey Testing; Godot Engine; Input Rate; Performance Degradation; Game Robustness
Comparing K-Prototypes and K-Medoids with Catboost for Health Profile Clustering of Pesantren Students Moch. Aghisna Hadzikunnuha; Harits Ar Rosyid; Arifin, M. Zainal
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.49369

Abstract

Health screening in pesantren is challenging due to communal living conditions, limited health facilities, and the need for early identification of vulnerable student groups. This study compares the performance of K-Prototypes and K-Medoids clustering for grouping student health profiles and evaluates the use of cluster labels as additional features in a CatBoost classification model. The dataset consists of 1,464 new students from Queen Al Falah Islamic Boarding School in the 2025/2026 academic year, collected through the admission system and analyzed after preprocessing. Clustering is performed using K-Prototypes and K-Medoids with three clusters to support interpretability of nutritional and health profiles. Although two clusters yield higher silhouette values, three clusters provide more meaningful distinctions for practical screening. Classification experiments use CatBoost with an 80:20 stratified train-test split, comparing baseline models and hybrid models that integrate cross-algorithm cluster features. The results show an asymmetric pattern. Adding K-Prototypes features improves K-Medoids target accuracy from 99.66 percent to 100 percent, while adding K-Medoids features slightly decreases K-Prototypes target accuracy from 98.98 percent to 98.63 percent. McNemar test results indicate that these differences are not statistically significant. Overall, the proposed framework supports reliable and interpretable health profile clustering for pesantren student monitoring.
Accuracy Evaluation of 2D MediaPipe-Based Pose Estimation for Archery Posture Detection Using N-MPJPE Prasetya, Muhammad Andhika Bayu; Harits Ar Rosyid; M. Zainal Arifin
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.49778

Abstract

Archery requires high consistency and precise body posture, where small deviations can affect stability and accuracy. Recently, 2D human pose estimation has become an effective approach for analyzing sports techniques through automatic joint detection. This study proposes a 2D pose estimation system based on the MediaPipe framework to detect eight fundamental phases of archery technique and evaluate accuracy using the Normalized Mean Per Joint Position Error (N-MPJPE) metric. The dataset consists of annotated images representing the eight phases, which serve as ground-truth references. Accuracy is measured by calculating the normalized Euclidean distance between predicted joint positions and ground-truth coordinates across all phases. Experimental results show an average N-MPJPE of 0.71, indicating low joint-position deviation after scale normalization. Compared with prior studies reporting N-MPJPE values between 0.6 and 1.2, the proposed system demonstrates competitive accuracy for real-time 2D pose estimation. These results indicate that the system can reliably capture posture variations across archery phases and provide quantitative feedback on body alignment, making it a practical tool to support athletes and coaches in improving training quality and shooting performance.
Co-Authors Abdullah, Dzulkifli Achmad Iffad Adhilaga, Hanif Aditya Galih Sulaksono, Aditya Galih Agung Bella Putra Utama Agusta Rakhmat Taufani Ahmad Adi Prasetyo Ahmad Munjin Nasih Ahmad Nurdiansyah 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 Desi Fatkhi Azizah Diederik Rousseau Dwi Hastuti 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 Hariyono Hariyono Hartarto Junaedi Hendrawan Armanto Herman Thuan To Saurik Heru Wahyu Herwanto Imanuel Hitipeuw Jevri Tri Ardiansah Joumil Aidil Saifuddin Khoiruddin Asfanie Khurin Nabila Kumalasari, Ira Kusuma Refa Haratama Liang, Yeoh Wen Lucyta Qutsyaning Rosydah M Baharuddin Yusuf M. Zainal Arifin M. Zainal Arifin Moch. Aghisna Hadzikunnuha 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 Nancy Nindyana Putri Nur’aini Nastiti Susetyo Fanany Putri Novian Dwi syahrizal Hilmi Nur A’yuni Ramadhani Nur Hidayatullah Nur Sa’ida Kismurdiani Prasetya, Muhammad Andhika Bayu Prasetyo, Ahmad Adi Prawidya, Della Murbarani Rafli Indar Praja Rahadyan Fannani Arif Rochmawati Rochmawati Santoso, Rizky Aji Sari, Tenty Luay Setumin , Samsul Shah Nazir Siti Sendari Suparman 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