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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 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

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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.
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 April - Juni
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
Towards Intelligent Performance Monitoring for Blockchain-Based Learning Systems: A Multi-Class Classification Approach Aditya Galih Sulaksono; Syaad Patmanthara; Harits Ar Rosyid
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.
Comparison of Naïve Bayes Algorithm and Decision Tree C4.5for Hospital Readmission Diabetes Patientsusing HbA1c Measurement Pujianto, Utomo; Setiawan, Asa Luki; Ar Rosyid, Harits; Salah, Ali M. Mohammad
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.
Comparison of Indonesian Imports Forecastingby Limited Period using SARIMA Method Ar Rosyid, Harits; Aniendya, Mutyara Whening; Herwanto, Heru Wahyu
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of Indonesia's imports fluctuate over years. Inability to anticipate such rapid changes can cause economic slump due to inappropriate policy. For instance, recent years imports in rice led to the extermination of rice reserves. The reason is to maintain the market price of rice in Indonesia. To overcome these changes, forecasting the amount of imports should assist the Government in determining the optimum policy. This can be done by utilizing an algorithm to forecast time series data, in this case the amount of imports in the next few months with a high degree of accuracy. This study uses data obtained from the official website of the Indonesian Ministry of Trade. Then, Seasonal Autoregressive Integrated Moving Average (SARIMA) method is applied to forecast the imports. This method is suitable for the interconnected dependent variables, as well as in forecasting seasonal data patterns. The results of the experiment showed that 6-period forecast is the most accurate results compared to forecasting by 16 and 24 periods. The research resulted in the best model, that is ARIMA (0, 1, 3)(0, 1, 1)12 produces forecasting with a MAPE value of 7.210 % or an accuracy rate of 92.790 %. By applying this imports forecast model, the government can have a forward strategic plans such as selectively imports products and carefully decide the amount of the incoming products to Indonesia. Hence, it could maintain or improve the economic condition where local businesses can grow confidently.
Optimizing Random Forest Algorithm to Classify Player's Memorisation via In-game Data Alzuhdi, Akmal Vrisna; Ar Rosyid, Harits; Chuttur, Mohammad Yasser; Nazir, Shah
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. Then, we trained and optimized three Random Forest (RF) classifiers to predict the player's Memorization. We chose RF because it can generalize well given the high-dimensional dataset. We used RF as the classifier, subject to optimization using its hyperparameter: n_estimators. We implemented a Grid Search Cross Validation (GSCV) method to identify the best value of n_estimators. We utilized the statistics of GSCV results to reduce the weight of n_estimators by observing the region of interest shown by the graphs of performances of the classifiers. Overall, the classifiers fitted using the BEST n_estimators (i.e., 89, 31, 89, and 196 trees) from GSCV performed well with around 80% accuracy. Moreover, we successfully identified the smaller number of n_estimators (OPTIMAL), at least halved the BEST n_estimators. All classifiers were retrained using the OPTIMAL n_estimators (37, 12, 37, and 41 trees). We found out that the performances of the classifiers were relatively steady at ~80%. This means that we successfully optimized the Random Forest in predicting a player's Memorization when playing the Chem Fight game. An automated technique presented in this paper can monitor student interactions and evaluate their abilities based on in-game data. As such, it can offer objective data about the skills used.
Co-Authors Abdullah, Dzulkifli Achmad Iffad Adhilaga, Hanif Aditya Galih Sulaksono 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. Alzuhdi, Akmal Vrisna Amalia Amalia Anie Yulistyorini Aniendya, Mutyara Whening 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 Chuttur, Mohammad Yasser Danang Rahmat Bachtiar Denny Kurniawan Desi Fatkhi Azizah Diederik Rousseau Dwi Hastuti Dwiyanto, Felix Andika 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 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 Nazir, Shah Novian Dwi syahrizal Hilmi Nur A’yuni Ramadhani Nur Hidayatullah Nur Sa’ida Kismurdiani Prasetyo, Ahmad Adi Prawidya, Della Murbarani Putra, Aulia Yahya Harindra Putri, Nastiti Susetyo Fanany Rafli Indar Praja Rahadyan Fannani Arif Rochmawati Rochmawati Salah, Ali M. Mohammad Santoso, Rizky Aji Sari, Tenty Luay Setiawan, Asa Luki Setumin , Samsul Shah Nazir Siti Sendari Suparman Suparman Syaad Patmanthara Teguh Andriyanto, Teguh Theodora Monica Timothy John Pattiasina Tinesa Fara Prihandini Uriu, Wako Utama, Agung Bella Putra Utomo Pujianto Wahyu Irianto Wako Uriu Wiryawan, Muhammad Zaki Yudhistira, Moch Rajendra Yusmanto, Yunan Zaeni, Ilham Ari Elbaith