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All Journal Jurnal Komputer Terapan Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence
Perdana Arifin, Satria
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Klasifikasi Pengaduan Kekerasan Berbasis Bidirectional Encoder Representations from Transformers (BERT) pada Kementrian Pemberdayaan Perempuan dan Perlindungan Anak Kementrian (PPPA) Jundi, Muhammad; Rahmaddeni; Utami, Putri; Perdana Arifin, Satria; Sinaga, Leonardo
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1109

Abstract

Kekerasan terhadap perempuan dan anak merupakan isu serius di Indonesia, dengan ribuan laporan masuk setiap tahun ke lembaga perlindungan. Sebagian besar laporan disampaikan dalam bentuk narasi bebas, yang menyulitkan proses identifikasi jenis kekerasan dan respon yang cepat. Penelitian ini mengembangkan model klasifikasi otomatis berbasis IndoBERT untuk mendeteksi jenis kekerasan dan sentimen pelapor secara simultan. Model dilatih menggunakan pendekatan multi-task learning dengan dua label keluaran: kategori kekerasan (multi-label) dan sentimen (biner). Dataset terdiri dari laporan berbahasa Indonesia yang telah dianotasi manual. Evaluasi dilakukan pada data uji (20%) menggunakan metrik F1-score, precision, dan recall. Hasil menunjukkan bahwa model mencapai F1-score sebesar 0,8528 untuk klasifikasi kekerasan dan 0,8375 untuk sentimen. Pengujian pada narasi fiktif juga membuktikan ketepatan model dalam menangkap konteks semantik dan ekspresi emosional pelapor. Model ini menunjukkan potensi signifikan untuk mendukung lembaga pemerintah dalam mempercepat analisis dan tindak lanjut laporan kekerasan secara digital.
ANALISIS FAKTOR DOMINAN KEBERHASILAN AKADEMIK MAHASISWA PENERIMA BEASISWA MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS Mufidah, Nur; Syarif Sihabudin Sahid, Dadang; Perdana Arifin, Satria; Ari Sandi, Wahyu
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6819

Abstract

The academic success of scholarship recipients is not only determined by their academic abilities but also by various non-academic factors that are often difficult to measure objectively. The large number of interrelated indicators presents a challenge in identifying the most dominant factors, especially when these variables exhibit high correlations and lead to data redundancy. This study aims to identify the dominant non-academic factors influencing the academic success of scholarship recipients using Principal Component Analysis (PCA). A total of 262 students completed a questionnaire consisting of 54 non-academic items that had previously undergone validity and reliability testing. PCA was employed to reduce data dimensionality and produced 38 principal components with a cumulative explained variance of 95.08%, indicating effective dimensionality reduction without significant loss of information. The loading matrix analysis revealed that psychological conditions, learning methods, major suitability, learning motivation, and financial conditions were the most dominant contributors to the principal components. These findings provide a more structured understanding of the non-academic factors that should be considered in the development and monitoring of scholarship programs.
Co-Authors Ari Sandi, Wahyu Jundi, Muhammad Mufidah, Nur Putri Utami, Putri Rahmaddeni Rahmaddeni Sinaga, Leonardo Syarif Sihabudin Sahid, Dadang