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All Journal JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Ermawan, Bagas Restya
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Computer Science & IT

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OPTIMASI METODE KLASIFIKASI MENGGUNAKAN FASTTEXT DAN GRID SEARCH PADA ANALISIS SENTIMEN ULASAN APLIKASI SEABANK Ermawan, Bagas Restya; Cahyono, Nuri
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1523

Abstract

Perkembangan pesat teknologi digital telah memberikan dampak signifikan pada sektor perbankan, termasuk aplikasi perbankan digital seperti SeaBank. Namun, pengguna melaporkan masalah kritis seperti hilangnya saldo dan keterlambatan transfer, yang memerlukan analisis sentimen pengguna berdasarkan ulasan di Google Play Store. Penelitian ini menganalisis ulasan pengguna SeaBank menggunakan metode klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN), yang dioptimalkan melalui Grid Search dan ekstraksi fitur FastText. Sebanyak 15.000 ulasan dari Juli hingga September 2024 diproses, dilabeli secara manual (positif, negatif, netral), dan diseimbangkan menggunakan SMOTE. Hasil penelitian menunjukkan bahwa optimasi Grid Search secara signifikan meningkatkan kinerja model, dengan SVM mencapai akurasi 92% dan KNN meningkat menjadi 91%. SVM yang dioptimalkan (C: 100, kernel: 'rbf') mencapai metrik tertinggi (presisi 0,93, recall 0,92, F1-score 0,92).
Optimizing Sentiment Classification Models for TikTok Comments using Emotion-Based Preprocessing and Grid Search Ermawan, Bagas Restya; Prayoga, Mahendra Bayu; Fadhillah, Akmal Rafi; Utami, Ema
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11742

Abstract

TikTok has become one of the social media platforms with a significant influence on public opinion formation in Indonesia. However, the linguistic characteristics of user comments which are expressive, concise, and feature emotional forms like emojis, emoticons, and excessive capitalization pose challenges for sentiment analysis. This research aims to optimize a sentiment classification model for TikTok comments using emotion-based preprocessing and hyperparameter optimization via Grid Search. The dataset comprises 4,500 comments from three different time periods discussing the Minister of Finance, Purbaya Yudhi Sadewa. Three testing scenarios were conducted: common preprocessing, emotion-based preprocessing, and a combination of emotion-based preprocessing with Grid Search. The results indicate that emotion-based preprocessing improved model accuracy by 4–5%, while Grid Search optimization provided an additional increase of up to 3%, achieving a peak F1-score of 0.92 with the LightGBM model. Analysis based on sentiment time-periods reveals that across the three different periods, sentiments remained predominantly positive. The integration of emotion-based processing and parameter tuning proved effective in enhancing the model's ability to understand emotional variations in text and to map periodic changes in public sentiment on Indonesian-language social media.
Co-Authors Ema Utami Fadhillah, Akmal Rafi Nuri Cahyono Prayoga, Mahendra Bayu