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Journal : TEKNIK INFORMATIKA

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