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All Journal Mimbar Sekolah Dasar Journal of Information Systems Engineering and Business Intelligence JIEET (Journal of Information Engineering and Educational Technology) Journal of Robotics and Control (JRC) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Abdimas: Papua Journal Of Community Service Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Ranah Research : Journal of Multidisciplinary Research and Development PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Education Technology and Information System
Wiyli Yustanti
Prodi Manajemen Informatika, Fakultas Teknik, Universitas Negeri Surabaya
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Optimizing UKT Prediction Based on Socio-Economic Features: A Multimodel Evaluation with Feature Selection Srategies Putri, Windy Chikita Cornia; Yustanti, Wiyli; Yohannes, Ervin
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31828

Abstract

Determining the tuition fee group (UKT) for new students in Indonesian public universities represents a complex challenge requiring an equitable, data-driven approach. This study introduces an integrative feature selection strategy that combines five popular techniques Chi-Square, Recursive Feature Elimination (RFE), LASSO Regression, Random Forest Importance, and Exploratory Factor Analysis (EFA) to extract the most relevant attributes from 53 socioeconomic variables of prospective students at Universitas Negeri Surabaya. As a novelty, the study identifies intersecting features consistently selected by all five methods and evaluates their impact on the performance of five classification algorithms: Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes. Experimental results demonstrate a significant improvement in accuracy, with SVM increasing from 0.7550 to 0.7810. These findings confirm that integrative feature selection can optimize model performance while reducing data complexity. This study provides a replicable methodological contribution for developing transparent and adaptive classification systems based on socioeconomic data in higher education contexts.
Two-Stage RFM and Macroeconomics Interaction Model for Accurate CLV Prediction in Direct Sales Istopo Hartanto, Unung; Putu Asto Buditjahjanto, I Gusti; Yustanti, Wiyli
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.642

Abstract

This study introduces a two-stage predictive model integrating Recency, Frequency, Monetary (RFM) metrics with macroeconomic indicators to estimate Customer Lifetime Value (CLV) in direct sales, addressing dynamic customer behavior in volatile markets. Data from the Halalmart Sales Integrated System (January 2023–July 2025, 29,893 transactions, ~431 unique customers monthly) were combined with Indonesian macroeconomic indicators (Consumer Confidence Index, Consumer Expectation Index) from Bank Indonesia and inflation data from the Central Bureau of Statistics (BPS). The first stage uses CatBoost classification, achieving 89.3% accuracy to identify active customers, followed by an ensemble regression (CatBoost, XGBoost, LightGBM, Ridge, RandomForest), yielding an R2 of 0.894 for CLV prediction. RFM features contribute 40.3% to classification and 16.2% to regression variance, while macroeconomic interactions dominate, contributing 59.7% and 83.8%, respectively. A key interaction, Monetary and Consumer Confidence Index, shows a 0.773 correlation with CLV. SHAP analysis enhances model interpretability. Despite a skewed dataset with approximately 65% zero CLV, the model supports targeted marketing strategies, offering valuable insights for strategic decision-making in direct sales environments
Co-Authors Alpiana, Intan Andi Iwan Nurhidayat ANITA QOIRIAH ARI KURNIAWAN Ariyanto, Savira Rahmania Putri Aulia Akbar, Rafy Aulia, Novi Rosidhatul Aviana, Anisah Nurul Ayuningtyas, Nimas Bayu Budi Prakoso choirullah, Sultan Dani, Andrea Dini Amalia, Dini Ervin Yohannes Farid Baskoro Fitriani, Erlina Eka Hartanto, Unung Istopo I Gusti Putu Asto Buditjahjanto I Nyoman Budiantara IRMA FEBRIYANTI Iskandar Java, Muhammad Istianah, Eva Istopo Hartanto, Unung Karputri, Diah Leni Kurniasari, Calycha Lumban Gaol, Gebryana Hotmida Lamtiar Maulidia, Ridhotul Meidyan, Martinus Ade meilita, Bunga Mohammad Akbar, Mohammad Muhammad Risalah Naufal Mutmainah Mutmainah Nabila Putri Listyanto Naim Rochmawati Nautika, Puji Septiyana Nuraini, Ulfa Siti Nurlyan, Reynisa Beta Prasetyo, Andhika Edo Priadana, Benny Widya Putra, Fachrian Bimantoro Putri, Windy Chikita Cornia Putu Asto Buditjahjanto, I Gusti Rachmaddhani, Gilang Raden Mohamad Herdian Bhakti Rahayu, Aulia Anisa Puji Rahman, Naufal Aditya Rahmawati, Naim Ricky Eka Putra Rina Harimurti Rizal, Mochammad Rochmawati, Naim Saputra, Andika Dermawan Sifriyani, Sifriyani Suroto Suroto Syandika, Novliyan Dimas Vebriani, Mutiara Widi Aribowo Wulandari, Rahmah Yuni Yamasari