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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.
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 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 Rafli Indar Praja Rahadyan Fannani Arif Rochmawati Rochmawati 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