Pemodelan Kepribadian Mahasiswa Santri Berbasis Data Science dengan Multi-View Clustering untuk Pembinaan Karakter

Eko Prasetio Widhi; Aziz Musthafa; Triana Harmini; Miftakhurrokhmat .

doi:10.30872/jurti.v10i3.23603

Pemodelan Kepribadian Mahasiswa Santri Berbasis Data Science dengan Multi-View Clustering untuk Pembinaan Karakter

Eko Prasetio Widhi, Aziz Musthafa, Triana Harmini, Miftakhurrokhmat .

Abstract

Pesantren merupakan lembaga pendidikan karakter tertua di Indonesia, namun proses pembinaan kepribadian mahasiswa santri masih didominasi oleh penilaian subjektif dan belum didukung pendekatan analitik berbasis data. Penelitian ini bertujuan mengembangkan model pemetaan kepribadian mahasiswa santri menggunakan pendekatan Multi-View Clustering untuk mendukung pembinaan karakter yang lebih objektif dan personal. Penelitian menerapkan metodologi CRISP-DM pada dataset yang terdiri atas 558 responden dengan 21 atribut, meliputi 6 atribut demografis dan 15 dimensi kepribadian. Untuk memperoleh representasi data yang lebih komprehensif, dilakukan Exploratory Factor Analysis (EFA) sehingga diperoleh empat faktor laten yang diintegrasikan dengan atribut asli dalam proses Multi-View Clustering. Hasil penelitian menunjukkan bahwa Gaussian Mixture Model Multi-View (GMM-MV) dengan dua klaster memberikan performa terbaik berdasarkan Silhouette Score, Calinski–Harabasz Index, dan Davies–Bouldin Index, serta menghasilkan dua profil kepribadian utama, yaitu Dominant–Analytic dan Empathic–Adaptive, sebagai dasar rekomendasi pembinaan karakter. Evaluasi lebih lanjut menunjukkan bahwa model memenuhi aspek fairness dengan tidak terpengaruh oleh lokasi kampus dan memiliki kemampuan generalisasi yang memadai berdasarkan pengujian Leave-One-Campus-Out (LOCO). Model yang dihasilkan selanjutnya diintegrasikan ke dalam Decision Support System (DSS) untuk menghasilkan rekomendasi pembinaan karakter yang lebih objektif, adaptif, dan berbasis data.

Keywords

Pesantren; Kepribadian; Multi-View Clustering; Data Science; Pembinaan Karakter.

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DOI: http://dx.doi.org/10.30872/jurti.v10i3.23603

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