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All Journal JURTEKSI Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara
Fazli Nugraha Tambunan
STIKOM Tunas Bangsa
Computer Science & IT Education Mathematics

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SELENIUM–INDOBERT PIPELINE FOR PSEUDO-LABELING SENTIMENT ANALYSIS OF INDONESIAN YOUTUBE COMMENTS Fazli Nugraha Tambunan; Heru Satria Tambunan; Doughlas Pardede
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4415

Abstract

YouTube has become a major platform for public discourse in Indonesia, yet large-scale sentiment analysis of its comments remains challenging due to dynamic content, informal language, and limited labeled data. This study proposes a Selenium–IndoBERT pipeline for sentiment analysis of Indonesian YouTube comments using a pseudo-labeling approach. Data were collected from ten YouTube videos discussing the One Piece flag phenomenon, yielding 10,842 comments after preprocessing. Selenium was employed to extract comments from dynamic pages, while IndoBERT was fine-tuned on a small manually labeled dataset and used to generate pseudo-labels for unlabeled data. Model performance was evaluated using probabilistic metrics, including Coverage, Expected Calibration Error (ECE), and Brier Score. At a confidence threshold of 0.75, 78.5% of comments received pseudo-labels, with an ECE of 0.095 and a Brier Score of 0.174. Manual validation showed substantial agreement with human annotations (Fleiss’ kappa = 0.72). The results indicate that the proposed pipeline enables scalable and reliable sentiment analysis with minimal manual annotation.
Aplikasi SPK Evaluasi Kualitas Pengajaran Dosen Menggunakan Metode SAW Fadillah Azzahra; Mhd.Hanafi; Mhd.Iwang Wandana; Fazli Nugraha Tambunan
Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara Vol. 4 No. 1 (2026): Volume 4 No 1 Tahun 2026
Publisher : PT Siantar Codes Academy Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.260396/gqs31e21

Abstract

Penilaian terhadap kualitas kinerja dosen merupakan elemen fundamental dalam upaya penjaminan mutu pendidikan tinggi karena berimplikasi langsung terhadap capaian akademik mahasiswa. Namun, mekanisme evaluasi yang masih mengandalkan proses manual sering kali menghadapi berbagai permasalahan, seperti tingginya potensi bias penilaian, lambatnya pengolahan data, serta inkonsistensi hasil evaluasi antarresponden. Oleh karena itu, penelitian ini diarahkan pada pengembangan dan penerapan Sistem Pendukung Keputusan (SPK) berbasis metode Simple Additive Weighting (SAW) sebagai sarana penilaian kinerja dosen yang lebih sistematis dan objektif. Pendekatan penelitian dilakukan secara terpadu melalui metode kualitatif dan kuantitatif dengan teknik pengumpulan data berupa observasi, wawancara, serta analisis data evaluasi dari mahasiswa. Sistem dikembangkan dalam bentuk aplikasi berbasis Android yang terintegrasi dengan basis data untuk mengelola proses normalisasi, pembobotan kriteria, serta perhitungan nilai preferensi secara otomatis. Hasil penelitian menunjukkan bahwa sistem yang diimplementasikan mampu meningkatkan ketepatan hasil penilaian, mempercepat proses evaluasi, serta memperkuat pengambilan keputusan akademik yang berbasis data dan transparan.
Co-Authors Doughlas Pardede Fadillah Azzahra Heru Satria Tambunan Mhd.Hanafi Mhd.Iwang Wandana