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All Journal SemanTIK : Teknik Informasi INTECOMS: Journal of Information Technology and Computer Science bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Infotek : Jurnal Informatika dan Teknologi Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Simpatik: Jurnal sistem Informasi dan Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Abdi Rakyat
Sancoko, Sulistyo Dwi
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences Other

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IMPLEMENTASI APLIKASI TRACKING KARYAWAN BERDASARKAN LOKASI MENGGUNAKAN GEOFENCING BERBASIS ANDROID Sari, Martina Diana; Sancoko, Sulistyo Dwi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6877

Abstract

Keselamatan dan pemantauan posisi pekerja di area industri berisiko tinggi seperti kilang minyak merupakan faktor penting dalam pemeliharaan efisiensi operasional dan keamanan. Tujuan penelitian ini adalah mengembangkan aplikasi mobile berbasis Android bernama WorkZone untuk memantau lokasi pekerja secara real time di Kilang Pertamina RU IV Cilacap. Metode penelitian ini menggunakan pendekatan Research and Development (R&D) mencakup analisis kebutuhan, perancangan sistem menggunakan Flutter, dan integrasi Firebase Realtime Database untuk pengelolaan data lokasi. Penerapan teknologi geofencing difokuskan untuk mendeteksi keberadaan pekerja di dalam atau di luar zona kerja yang telah ditetapkan. Hasil implementasi aplikasi menunjukkan bahwa WorkZone mampu menampilkan posisi pekerja secara akurat pada peta, memperbarui status zona secara dinamis, dan memberikan notifikasi otomatis ketika pekerja melintasi batas area. Sistem ini diharapkan dapat meningkatkan efektivitas pengawasan, memperkuat keselamatan kerja, dam mendukung manajemen operasional di lingkungan industri migas.
Sentiment Analysis of the Merah Putih Movie Using Naïve Bayes and Support Vector Machine Sancoko, Sulistyo Dwi; Nafiah, Ulfah; Manda, Yudit; Mukti, Novera Sari
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3823

Abstract

Public engagement on YouTube provides a valuable source for examining audience responses to film productions; however, sentiment classification of Indonesian-language comments remains methodologically challenging due to informal expressions, noisy text, and imbalanced class distributions. This study evaluates the robustness of a classical machine learning pipeline for sentiment classification of YouTube comments on the trailer of the Indonesian animated film Merah Putih: One for All. A total of 5,469 comments were collected using the YouTube Data API v3. After preprocessing and lexicon-based pseudo-labeling, 5,192 comments were retained, consisting of 4,006 negative and 1,186 positive instances. Text features were represented using TF-IDF, while SMOTE was applied only to the training set after a stratified 80:20 split to prevent data leakage. Two classifiers were compared under identical experimental conditions: Multinomial Naïve Bayes and linear Support Vector Machine. The SVM model achieved 81.59% accuracy, 83% precision, 82% recall, and 82% F1-score on the original held-out test set, outperforming Naïve Bayes, which obtained 76.82% accuracy. The findings suggest that margin-based classification is more suitable than probabilistic classification for sparse, high-dimensional Indonesian YouTube comments, particularly when feature independence assumptions are likely violated. The study contributes a leakage-controlled evaluation of classical sentiment classification under imbalanced social-media conditions and highlights the methodological implications of pseudo-labeling and synthetic oversampling in Indonesian film-related opinion mining.
Co-Authors Al Fatah, Iqbal Cahyo, Fajar Dwi Diansah, Febri Nur Diwandari, Saucha Fatahillah, Fahmi Febian, Michael Valensio Manda, Yudit Moh. Ali Romli Mukti, Novera Sari Mumtazah, Binta Bailina Nafiah, Ulfah Novanto, Bandrio Ilham Nurhadi, Muhammad Purnomo, Taufiq Dwi Putra Ridwan, Claodio Nova Putra, Farozi Rizky Putra, Handhira Bayu Pradhana Ridwan Halim Saifullah, Fatah Nur Sari, Martina Diana Sukur, Wahyu Muhammad Surya, Joshua Isaac Suryanto, William Kessler Taalungan, Anggita Wibowo, Nadhif Atha Satria Yuliana, Arief Dwi