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All Journal INTECOMS: Journal of Information Technology and Computer Science International Journal of Humanities Education and Social Sciences
Oktifar Tri Bandono
Universitas Pamulang
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Evaluasi Kinerja Database Relasional dan Layanan Cloud Storage Untuk Transmisi Data Media Dalam Jaringan Andhi Saputro; Sahrul Ramadhan; Rendinis Rendinis; Oktifar Tri Bandono; Ahmad Muhammad; Toyyibah T
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 3 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i3.10584

Abstract

Transmisi data media menjadi semakin penting dengan adanya pertumbuhan konten multimedia yang besar dan beragam. Database relasional, seperti MySQL, dan layanan cloud storage, seperti Amazon S3, adalah dua pilihan utama untuk penyimpanan dan transmisi data media. Studi ini mengusulkan metodologi untuk membandingkan kinerja kedua sistem berdasarkan kecepatan transmisi, reliabilitas, skalabilitas, keamanan, biaya, dan kemudahan integrasi dengan infrastruktur jaringan yang ada. Metodologi ini mencakup pengumpulan data empiris dari skenario pengujian yang dirancang untuk mensimulasikan kondisi nyata dalam penggunaan aplikasi multimedia. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja database relasional dan layanan cloud storage dalam konteks transmisi data media pada sistem jaringan. Hasil penelitian diharapkan dapat memberikan panduan yang bermanfaat bagi pengembang dan administrator jaringan dalam memilih solusi penyimpanan data yang optimal untuk aplikasi media mereka. Selain itu, penelitian ini diharapkan dapat memberikan kontribusi pada literatur akademik di bidang jaringan komputer dan manajemen data dengan menyediakan pemahaman yang lebih mendalam tentang perbandingan kinerja antara database relasional dan layanan cloud storage.
YouTube Sentiment Analysis on Felt Earthquake News Using LSTM and IndoBERT Oktifar Tri Bandono; Agung Budi Susanto; Makhsun Makhsun
International Journal Of Humanities Education and Social Sciences (IJHESS) Vol 6 No 1 (2026): IJHESS AUGUST 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhess.v6i1.2420

Abstract

This study analyzes public sentiment in YouTube comments on felt-earthquake news in Indonesia and compares a bidirectional Long Short-Term Memory implementation (LSTM) with IndoBERT. An experimental quantitative design was used. Comments were collected through the YouTube Data API v3 using official earthquake-event references from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) and keyword-based video searches covering January 2021 to August 2025. The acquisition stage produced 51,870 comments from 1,626 unique videos. Data were processed through duplicate removal, text cleaning, case folding, slang normalization, tokenization, and stopword removal, resulting in 49,041 clean comments. Positive, negative, and neutral labels were assigned by aggregating word-polarity scores from the Indonesian Sentiment Lexicon (InSet), which served as weak supervision. Stratified sampling divided the dataset into 80% training data and 20% testing data. The LSTM model used a 100-dimensional embedding, a 64-unit bidirectional LSTM layer, global max pooling, and early stopping; IndoBERT was fine-tuned from indobenchmark/indobert-base-p2 for four epochs. Performance was assessed with accuracy, macro precision, macro recall, macro F1-score, and confusion matrices. Positive sentiment accounted for 41.2% of the corpus, negative sentiment for 35.5%, and neutral sentiment for 23.2%. IndoBERT achieved 91.50% accuracy and a 91.03% macro F1-score, outperforming LSTM at 90.91% accuracy and a 90.34% macro F1-score. IndoBERT provided the strongest contextual classification, while LSTM remained a competitive and substantially lighter option for resource-constrained monitoring
Co-Authors Agung Budi Susanto Ahmad Muhammad Andhi Saputro Makhsun Makhsun Rendinis Rendinis Sahrul Ramadhan Toyyibah T