Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Konstelasi: Konvergensi Teknologi dan Sistem Informasi
Sentana Putra, I Gusti Ngurah
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Integrasi RFM, K-Means, dan XGBoost untuk Optimalisasi Retensi Pelanggan pada Online Retail Sentana Putra, I Gusti Ngurah
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): Juni 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v5i1.11409

Abstract

Retensi pelanggan merupakan aspek kritis dalam bisnis, terutama di industri retail dan perbankan. Penelitian ini mengintegrasikan analisis RFM (Recency, Frequency, Monetary), K-Means Clustering, dan XGBoost untuk mengoptimalkan strategi retensi pelanggan. Analisis RFM digunakan untuk mengelompokkan pelanggan berdasarkan perilaku pembelian, sementara K-Means Clustering memungkinkan segmentasi yang lebih mendalam. XGBoost digunakan untuk memprediksi churn dengan akurasi tinggi. Hasil penelitian menunjukkan bahwa Frequency merupakan faktor dominan dalam menentukan churn, dengan akurasi prediksi mencapai 99,77%. Studi kasus pada dataset Online Retail mengidentifikasi empat cluster pelanggan dengan karakteristik dan tingkat churn yang berbeda. Rekomendasi strategis difokuskan pada peningkatan frekuensi transaksi dan personalisasi layanan. Penelitian ini memberikan kontribusi dalam pengembangan strategi retensi yang lebih efektif dan terarah.
Evaluating Fasttext and Glove Embeddings for Sentiment Analysis of AI-Generated Ghibli-Style Images Sentana Putra, I Gusti Ngurah; Yusran, Muhammad; Sari, Jefita Resti; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of text-to-image generation technology based on artificial intelligence has triggered mixed public reactions, especially when applied to iconic visual styles such as Studio Ghibli. This research aims to evaluate public sentiment towards the phenomenon of Ghibli-style AI images by comparing two static word embedding methods, namely FastText and GloVe, on three classification algorithms: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN). Data in the form of Indonesian tweets were collected from Twitter using hashtags such as #ghibli, #ghiblistyle, and #hayaomiyazaki during the period 25 March to 25 April 2025. Each tweet was manually labelled with positive or negative sentiment, then preprocessed and represented using pre-trained FastText and GloVe embeddings. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, both macro and weighted. Results showed that FastText consistently performed the best on most models, especially in terms of precision and overall accuracy, thanks to its ability to handle sub-word information and spelling variations in social media texts. The combination of CNN with FastText yielded the highest performance with a macro F1-score of 76.56% and accuracy of 84.69%. However, GloVe still showed competitive performance in recall on the Logistic Regression model, making it relevant for contexts that prioritise sentiment detection coverage. This study emphasizes the importance of selecting embeddings and models that are appropriate to the characteristics of the data and the purpose of the analysis in informal social media-based sentiment classification.
Co-Authors Bagus Sartono Dito, Gerry Alfa Muhammad Yusran Sari, Jefita Resti Suhaeni, Cici