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Journal : Journal of Applied Data Sciences

Clustering Cadet Training Performance Using K-Means and Ward's Method Evidence from OTMon Maritime Monitoring System Wahdiana, Dian; Ekohariadi, Ekohariadi; Suhartini, Ratna
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.958

Abstract

This study investigates cadet performance segmentation during on-board maritime training using clustering analysis of data from the On Training Monitoring (OTMon) system. Grounded in the competency-based education framework and experiential learning theory, the research aims to identify behavioral patterns and competency levels among 80 maritime cadets over a twelve-month sea-based training program. The OTMon application continuously recorded task completion rates, feedback interactions, sign-on consistency, and report submissions. K-Means clustering and Principal Component Analysis (PCA) revealed three distinct cadet profiles: Cluster 1 (high-performing) with average task completion of 92.4% and feedback frequency of 15.2 times/month; Cluster 2 (administratively consistent) with 88.1% completion but only 6.3 feedback interactions/month; and Cluster 3 (at-risk) with 67.5% completion and 3.8 feedback interactions/month. Linear Discriminant Analysis (LDA) validated the clusters with 98.8% resubstitution accuracy and 97.6% cross-validation accuracy, supported by generalized squared distances above 9.5 between all cluster pairs, indicating strong separation. These findings demonstrate that unsupervised clustering can reliably distinguish high-performing cadets from those needing targeted intervention, enabling data-informed mentoring and adaptive learning strategies in maritime education. The contribution of this study lies in integrating digital monitoring data with both unsupervised and supervised machine learning methods to enhance competency assessment. The novelty is in applying maritime-specific learning analytics for real-time performance segmentation, offering a scalable diagnostic framework for improving supervision quality and supporting individualized cadet development in vocational training contexts.
Co-Authors ADILLA HANNY, NITA Aidah, Luki Nur Ainur Rosidah Ainur Rosyida ALFIYA NUR ASNI, HAJAR Alhabsyi, Chodijah Ali Hasbi Ramadani Annisa', Nurin Arie Wardhono Arpila, Regigs ASRINING PURI, SEKAR ATHUR ROFIAH, LULUK Aurelia Yunita Permatasari AVANDRI, AHMAD Ayuni, Icha Riga Azaroh, Binti BASMALAZUWARA HASBI NABIL, EMHA Damayanti, Putri dharmayanti, ni made lia Dinda Ayu Dwi Puspita EKO HARIADI Ekohariadi Febriani, Ditha Ananda HARYUNINDA DANARDEWI, ALDIA Heru Arizal Hilmi, Mohamad Alfin I Gusti Putu Asto Buditjahjanto Ilmiyah, Mazidatul INDAH PERMATASARI, LINTANG Indarti Indarti Inty Nahari Irma Russanti ISTIGHFARI, FIRDITA Izzalinta Tika Gunawan Kartika Sari, Almassda Rizky Kholifah Tasyakuranti, Mauliddina Kurniarum, Santi Lilik Anifah Lutfiyah Hidayati Marniati Marniati MEIN KHARNOLIS Mein Kharnolis Muhammad Yusron Maulana El-Yunusi Nabilah, Fiqrotun NAILATUS SAADAH, LAILITA Nidia, Chanifathin Norma Hamidah Nugroho, Yuli Sutoto Nur Hidayati, Imania NUR SHOLIHAH, UMI Nurhayati, Indiyah Peppy Mayasari, Peppy PUJI LESTARI Puput Wanarti Rusimamto PurnamaSari, Surya Indah Putri Malinda Putri, Jane Fitria Ananda Rachmawardani, Rizmaningrum Rahayu, Imami Arum Tri Ramadhani, Bima Yatna Naugerah Raya Sulistyowati Renaissance Pasa, Revsi Rijanti, Tri Rintowati, Ariani Roezanda, Ananda Febiela Rohmawati, Fira Zulia Rosa Maharani Saadah, Ayu Ummatus Sintya Riska Dewi Soeryanto Soeryanto Suciati Suciati Suparji Suparji Supartini, Titin Syafitriani, Ellyza Nuzul Tri Rijanto TRIASARI, PIPIT Urip Wahyuningsih Urip Wahyuningsih Wahdiana, Dian WAHONO, AGUNG Wulansari, Sih Yuhri Inang Prihatina Yulistiana Yulistiana Yuniati, Mita