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All Journal DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Applied Information Technology and Computer Science (AICOMS)
Siti Fatimah Az Zahrah
Universitas Multi Data Palembang
Computer Science & IT

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Analisis Sentimen Fenomena “Brewek” Kartu Pokémon Pada Platform Reddit Menggunakan Arsitektur RoBERTa Siti Fatimah Az Zahrah; Klaudius Audie Irsansaputra; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/t3a91d20

Abstract

Social media platforms such as Reddit have long served as major discussion forums for various communities. This study aims to analyze public sentiment in order to understand community trends and perceptions toward Pokémon TCG. The research applies the RoBERTa (Robustly Optimized BERT Approach) Deep Learning architecture using the pre-trained model “cardiffnlp/twitter-roberta-base-sentiment” to perform sentiment analysis. The text data were cleaned, tokenized with a maximum limit of 512 tokens, and classified into positive, neutral, and negative sentiments, followed by word length distribution analysis and Top-N Words extraction. The model successfully classified sentiments objectively. The visualization results reveal the characteristics of word distribution after outlier handling and identify the top ten keywords representing the main discussion focus within each sentiment label. The findings indicate that the community sentiment is predominantly negative, providing a clear overview of the opinion dynamics within the Pokémon community on Reddit.
Optimasi Strategi Repeat Buyer pada E-commerce Indonesia Melalui Pendekatan Dynamic Programming untuk Bundling Product Multi-Kategori Siti Fatimah Az Zahrah; Yeremia Agung Chandra; Yohannes Yohannes
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 7, No 1: JUNI 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v7i1.8956

Abstract

Penelitian ini bertujuan untuk mengoptimalkan strategi peningkatan repeat buyer pada e-commerce di Indonesia melalui penyusunan rekomendasi bundling product multi-kategori berbasis pendekatan komputasional. Pendekatan yang digunakan adalah Dynamic Programming melalui model optimasi Knapsack yang dikombinasikan dengan analisis Threshold Standard Deviation untuk menyaring kategori produk berdasarkan kedekatan demografis pelanggan. Proses penelitian meliputi tahap preprocessing data, pemodelan parameter bobot dan profit, optimasi kombinatorial, serta penentuan prioritas rekomendasi berbasis customer profiling. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan rekomendasi bundling yang relevan dan terpersonalisasi berdasarkan usia dan riwayat transaksi pelanggan. Dynamic Programming menunjukkan performa yang lebih stabil dan efisien pada kompleksitas data yang lebih tinggi, meskipun pada dataset kecil Brute Force memiliki waktu eksekusi lebih cepat. Secara keseluruhan, pendekatan yang diusulkan dinilai mampu meningkatkan akurasi rekomendasi serta mendukung strategi pemasaran untuk mendorong loyalitas pelanggan.
Co-Authors Klaudius Audie Irsansaputra Muhammad Rizky Pribadi Yeremia Agung Chandra Yohannes, Yohannes