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All Journal ILKOM Jurnal Ilmiah Journal Computer Science and Informatic Systems : J-Cosys
Ridlan, Ahmad
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Computer Science & IT

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Studi Sentimen Masyarakat Terhadap Layanan FLIX Cinemas di Twitter dengan Pendekatan Naïve Bayes dan SVM Ridlan, Ahmad; Latumaerissa, Daniel Eliazar; Hasanudin, Muhaimin; Derisma, Derisma; Fadli, Muhamad
Journal Computer Science and Information Systems : J-Cosys Vol 5, No 1 (2025): Maret
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v5i1.631

Abstract

Penelitian ini melakukan analisis sentimen opini publik terhadap FLIX Cinemas dengan platform Twitter menggunakan metode Naïve Bayes dan Support Vector Machine (SVM). Tujuan utama dari penelitian adalah klasifikasi sentimen yang akurat - baik positif maupun negatif terkait layanan FLIX untuk memperoleh denyut respons publik terhadap layanan. Metodologi yang digunakan adalah pengumpulan twitter, praproses, pelabelan manual dan otomatis, pelabelan yang dibobot dengan TF-IDF. Klasifikasi menggunakan Naïve Bayes dan SVM dengan tiga skenario berbagi data pada 90:10, 80:20, dan 70:30. Hasilnya menyatakan bahwa SVM memiliki akurasi maksimum sebesar 92% untuk pelabelan otomatis dan Naïve Bayes sebesar 87% dalam pelabelan manual. Dari hasil penelitian ini menyatakan bahwa SVM lebih efektif untuk dataset besar sedangkan Naïve Bayes berlaku untuk data kecil sehingga dapat menjadi salah satu acuan bagi FLIX dalam upayanya meningkatkan pelayanan sesuai dengan hasil analisis sentimen
Enhanced Violence Detection in CCTV Using LSTM Hasanudin, Muhaimin; Santoso, Hadi; Wahab, Abdi; Indrianto, Indrianto; Kuswardani, Dwina; Ridlan, Ahmad
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2318.196-202

Abstract

Violence detection in CCTV footage remains a critical challenge for public safety, necessitating automated solutions to overcome human monitoring limitations. This study proposes an LSTM-based framework to improve detection accuracy by analyzing temporal patterns in surveillance videos. Using a dataset of 2,000 videos (1,000 violent/1,000 non-violent), the model extracts spatial-temporal features via optical flow and achieves 93% training accuracy and 91% test accuracy, with a precision of 92% and AUC of 0.94. Results demonstrate significant improvements over traditional methods, particularly in dynamic scenarios, though performance dips for occluded actions or weapon-related violence. The discussion highlights the model’s real-time applicability, computational efficiency (120 ms latency per segment), and alignment with smart city surveillance needs. Limitations include dataset diversity and environmental variability, suggesting future directions in multi-modal data fusion and edge computing. This research advances AI-powered security systems, offering a robust tool for proactive threat detection while underscoring the need for scalable, context-aware solutions.
Co-Authors Abdi Wahab Dwina Kuswardani, Dwina Hadi Santoso Indrianto Indrianto Latumaerissa, Daniel Eliazar Muhamad Fadli