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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri International Journal of Advances in Intelligent Informatics AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Abdi: Media Pengabdian Kepada Masyarakat Abimanyu: Journal of Community Engagement MATHunesa: Jurnal Ilmiah Matematika
Maulana, Dimas Avian
Universitas Negeri Surabaya
Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : International Journal of Advances in Intelligent Informatics

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Enhanced diabetes and hypertension prediction using bat-optimized k-means and comparative machine learning models Sofro, A'yunin; Ariyanto, Danang; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu; Maharani, Asri; Oktaviarina, Affi; Kurniawan, Ibnu Febry; Khikmah, Khusnia Nurul; Al Akbar, Muhammad Mahdy
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1816

Abstract

This research aims to develop an analytical approach in classification statistics. The proposed approach is the use of machine learning combined with optimization effects. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework by combining the clustering results of random forest from the k-means method with the bat algorithm optimization to enhance performance prediction in the case of athlete prediction. The proposed method aims to explore data by comparing the quality of classification results in random forest machine learning, extremely randomized trees, and support vector classification methods. We conducted a case study on primary data with 200 respondents from Surabaya State University and the East Java National Sports Committee. The accuracy found in this study indicates that the best approach based on the performance evaluation metric of the proposed approach is the random forest clustering results from the k-means method with bat algorithm optimization, which provides the best accuracy value compared to other machine learning approaches at 81.25%. This research offers a novel machine-learning–optimization framework that significantly improves athlete performance prediction by integrating k-means clustering, random forest, and bat algorithm optimization. The approach provides higher accuracy than conventional classifiers, enabling more data-driven decision-making for talent identification and sports analytics in Indonesia.
Co-Authors AFFIATI OKTAVIARINA AGUNG LUKITO Al Akbar, Muhammad Mahdy Amelia Permata Indah Andriananda, Salsabila Reza Asri Maharani Aulia Nurita Sari Auliya, Elok Rizqi Danang Ariyanto Dwi Juniati Erlix Rakhmad Purnama Hanifah, A'idah Nur Hanifah, A’idah Nur Hunaifi, Muhammad Arif I Ketut Budayasa Ibnu Febry Kurniawan Ika Kurniasari Ika Kurniasari Jihan Fadhilah Khikmah, Khusnia Nurul Mirwa Adiprahara Anggarani Oktaviarina, Affi Prihanto, Junaidi Budi Purnama, Erlix Rakhmad Purnama, Mohammad Dian Ramadani, Ardian Nugraha Ramadhan, Rafi Rachmad Rana Hisanah Azizah Rasyidah, Salma Azmi Raya Sulistyowati Retno Wulansari Ricky Eka Putra Ricky Eka Putra Romadhonia, Riska Wahyu RUDIANTO ARTIONO, RUDIANTO Sofro, A'yunin Tri Sudarwanto Yohanes Billy Surya Wijaya Yuliani Puji Astuti YUSUF FUAD Yusuf Fuad