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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Konstelasi: Konvergensi Teknologi dan Sistem Informasi Indonesian Journal of Environment and Disaster
Sentana Putra, I Gusti Ngurah
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Computer Science & IT Earth & Planetary Sciences Environmental Science

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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 | DOI: 10.30871/jaic.v9i5.10600

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.
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.
MODELING CLIMATE CHANGE IN THE ASEAN REGION USING CO₂ TRADE NETWORK-BASED SPATIAL-TEMPORAL GRAPH NEURAL NETWORK (ST-GNN) AND IMPACT ANALYSIS ON INDONESIA Sentana Putra, I Gusti Ngurah
Indonesian Journal of Environment and Disaster Vol. 5 No. 1 (2026): Articles in Press
Publisher : Disaster Research Center, Universitas Sebelas Maret, Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijed.v5i1.2669

Abstract

Climate change in Southeast Asia is intensifying due to rapid economic growth, industrial expansion, and cross-border carbon transfers. This study employs a Spatial-Temporal Graph Neural Network (ST-GNN) to model CO₂ emissions embedded in ASEAN’s trade network, with Indonesia as the focal point due to its dominant role in regional emissions. Using Our World in Data (OWID) datasets (1990–2023), the ST-GNN framework captures interdependencies between trade-linked emissions and temperature change, outperforming traditional models with an RMSE of 0.011 when optimized (90% top features). Key findings reveal that Vietnam and Australia exert the strongest influence on Indonesia’s emission-driven temperature rise, while Singapore acts as a high-centrality hub in the carbon network. Permutation importance analysis identifies land-use change (CO₂_including_luc), energy consumption per capita, and coal-based emissions as the top predictors of warming trends. The temporal attention mechanism highlights critical periods, such as the 1998 financial crisis and post-2008 recovery, where economic shocks amplified emission impacts. Policy recommendations emphasize regional carbon accounting frameworks, deforestation control, coal phase-out strategies, and ASEAN-wide climate collaboration to mitigate transboundary emissions. This study demonstrates that ST-GNNs enhance climate modeling by quantifying spatial-temporal emission dynamics, offering actionable insights for decarbonizing trade-dependent economies.
Co-Authors Bagus Sartono Dito, Gerry Alfa Muhammad Yusran Sari, Jefita Resti Suhaeni, Cici