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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JSAI (Journal Scientific and Applied Informatics) Jurnal Kridatama Sains dan Teknologi JUKI : Jurnal Komputer dan Informatika Jurnal Sains dan Teknologi Journal of Advanced Sciences and Mathematics Education Paradigma Abdibaraya: Jurnal Pengabdian Masyarakat
Fersellia, Fersellia
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Prediction Customer Loyalty Using Random Forest Algorithm on Shopee Reviews Saputra, Ferdi; Fersellia, Fersellia
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.7940

Abstract

This research develops a Shopee customer loyalty prediction model using Random Forest algorithm, utilizing customer reviews from Google Play Store. One of the key issues in e-commerce is maintaining customer loyalty amidst intense competition, so it is important to identify loyal customers and understand the factors that influence their commitment. This study involves data collection through web scraping, data cleaning, loyalty labeling, and Random Forest-based prediction model building and evaluation. The evaluation process was conducted using a confusion matrix to measure accuracy, precision, recall, and F1-score. The model classified customers into loyal, neutral, and disloyal categories, with an overall accuracy of 97%. The model showed precision, recall, and F1-score of 0.98 for loyal customers, and 0.99, 1.00, and 0.99 for disloyal customers. However, identification of neutral customers is still a challenge, with precision, recall, and F1-score of 0.92, 0.85, and 0.88, respectively. The results of this study provide strategic insights for Shopee in improving customer retention strategies and demonstrate the effectiveness of the Random Forest algorithm in analyzing review data.
Model Rekomendasi Musik Berbasis Representasi Semantik Lirik Lagu Menggunakan BERT zia, Dziaul Hululiah; Fersellia, Fersellia
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9919

Abstract

The rapid growth of digital music platforms has resulted in an information overload problem, making it difficult for users to discover songs that match their preferences. This study proposes a content-based music recommendation model through semantic analysis of song lyrics using a Natural Language Processing approach with Bidirectional Encoder Representations from Transformers. The research stages include Indonesian song lyric data collection, data cleaning, text preprocessing, contextual lyric embedding generation, and lyric similarity computation using cosine similarity. Model performance is evaluated using Mean Squared Error and accuracy. Experimental results show that the proposed model achieves an accuracy of 83.69% with a Mean Squared Error value of 1.4066, indicating that lyric representations generated by Bidirectional Encoder Representations from Transformers effectively capture semantic meaning and quantitatively improve the relevance of music recommendations. Therefore, the proposed approach enhances the accuracy and personalization of content-based music recommendation systems.
Co-Authors AA Sudharmawan, AA Anggoro, Tri Citra Wuladari Ema Utami Endang Wahyuningsih Endang Wahyuningsih Fachri, Fahmi Fadjeri, Akhmad Fahmi Fachri, Fahmi Fachri Fina Hanifa Hidayati Idris, Achmad Choerul Umam Nur Lutfiyani, Anisa Muflih, Ghufron Zaida Mustikaningsih, Ajar Nugroho, Mei Aldi Nurchayati, Anisa Dwi Rizki Yanuar Rokhayati, Rani Salikhan, Salikhan Saputra, Ferdi Sofiyah, Elfa Sulma Safinatus Taufik Hidayat zia, Dziaul Hululiah