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All Journal JAIS (Journal of Applied Intelligent System) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Applied Information System and Management Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains) Jurnal Sistem Informasi dan Sains Teknologi Jurnal Sains dan Edukasi Sains Jurnal Ilmiah Wahana Pendidikan Bulletin of Information Technology (BIT) Academic Journal of Computer Science Research (AJCSR) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Ilmu Terapan Universitas Jambi JUARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Pelita Pengabdian SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Gatot Tri Pranoto
Universitas Trilogi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Predicting Consumer Purchase Intention in Informal Retail Using Machine Learning and the Purchase Intention Probability Index (PIPI) Gatot Tri Pranoto; Yoga Religia; Dwi Pebrianti
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.49459

Abstract

Informal retail remains a growing and predominant form of shopping for many people. However, modern and well-organized supermarkets, using data-driven approaches to attract consumers, have increasingly challenged informal retailers in recent years. This phenomenon presents new challenges, particularly in predicting consumers' purchase intentions given limited, unstructured, and poorly documented data. Therefore, this study aims to develop and evaluate a predictive model for consumer purchase intention in informal retail using machine learning techniques and to introduce the Purchase Intention Probability Index (PIPI) as a probability-based aggregation approach to enhance predictive sensitivity. The study uses the Subsistence Retail Consumer Dataset from Mendeley Data, comprising 281 consumer records with 38 demographic, behavioral, and psychological attributes, with purchase intention as the binary target variable. Three widely used classification algorithms in consumer behavior research (decision tree, random forest, and support vector machine (SVM)) were employed to identify purchase-predictive patterns in the data. Based on these models, the PIPI was developed, which aggregates the highest probabilities from all three models to produce more robust predictions, particularly for small and heterogeneous datasets, and supports cross-model performance evaluation. The results show that the proposed PIPI method achieves the highest recall (1.00), outperforming individual classifiers in detecting purchase intention. This fact indicates that informal retailers can apply machine-learning-based analytics to improve marketing effectiveness and decision-making without requiring advanced technological infrastructure. 
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction Rudi Setiawan; Gatot Tri Pranoto; Zed Abdullah; Satria Abadi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1732

Abstract

Employee turnover prediction remains challenging in Human Resource (HR) analytics because class imbalance can reduce the ability of machine learning models to identify employees at genuine risk of leaving. This study develops and evaluates a comprehensive machine learning framework that balances minority-class detection and false-positive control. A publicly available HR dataset containing demographic, organizational, performance, and training-related attributes was analyzed using seven algorithms: Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, XGBoost, LightGBM, and CatBoost. Cost-sensitive learning and three resampling methods, SMOTEENN, ADASYN, and Tomek Links, were compared through stratified 10-fold cross-validation. Performance was evaluated using ROC-AUC, PR-AUC, Balanced Accuracy, Matthews Correlation Coefficient, G-Mean, Sensitivity, and Specificity, followed by threshold adjustment and SHAP analysis. Original LightGBM achieved the highest discrimination performance (ROC-AUC = 0.5975 ± 0.0546; PR-AUC = 0.2020 ± 0.0426), while cost-sensitive LightGBM produced the most balanced results (Balanced Accuracy = 0.5221 ± 0.0303; MCC = 0.0499 ± 0.0685). SHAP identified Department Type, Current Employee Rating, Training Cost, and Age as key predictors. Overall, integrating cost-sensitive learning, threshold optimization, and explainability improved model interpretability and practical utility for evidence-based HR decision-making processes in employee retention management and planning.
Optimasi penggunaan baterai menggunakan mesh simplification pada aplikasi Marker-Based Augmented Reality Elia Syalom; Gatot Tri Pranoto; Yaddarabullah
Jurnal Sains dan Edukasi Sains Vol. 9 No. 2 (2026): Jurnal Sains dan Edukasi Sains
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/juses.v9i2p87-95

Abstract

Teknologi Augmented Reality (AR) telah banyak dimanfaatkan sebagai media pembelajaran interaktif karena mampu menggabungkan objek virtual dengan lingkungan nyata secara real-time. Namun, aplikasi AR berbasis marker yang dikembangkan menggunakan Unity umumnya memiliki konsumsi daya baterai yang tinggi akibat proses rendering objek tiga dimensi yang kompleks. Penelitian ini bertujuan untuk mengoptimalkan penggunaan baterai pada aplikasi AR pembelajaran cerita penciptaan berbasis marker melalui penerapan teknik mesh simplification. Aplikasi dikembangkan menggunakan Unity dan Vuforia SDK dengan enam aset objek 3D yang merepresentasikan tahapan penciptaan. Proses optimasi dilakukan dengan mengurangi kompleksitas mesh melalui teknik mesh decimation sehingga jumlah vertices dan triangles pada setiap objek berkurang tanpa mengubah bentuk visual utama. Evaluasi dilakukan menggunakan metode eksperimen komparatif dengan membandingkan aplikasi sebelum dan sesudah optimasi berdasarkan parameter konsumsi baterai, suhu perangkat, dan frame rate (FPS). Hasil pengujian selama 30 menit menunjukkan bahwa aplikasi yang telah dioptimasi menurunkan konsumsi baterai dari 23% menjadi 15%, meningkatkan frame rate dari sekitar 9 FPS menjadi 27–30 FPS, serta menurunkan suhu maksimum perangkat dari 61°C menjadi 56°C. Hasil tersebut menunjukkan bahwa penerapan mesh simplification mampu meningkatkan efisiensi energi sekaligus menjaga performa aplikasi AR pada perangkat Android.
Co-Authors Almira Ayumi Prijanisa Amali Amali Annisa Maulana Majid Bajsair, Faik Baskara, Jaka Adi Budiarto, Eko Calvin Manayang Candra Naya Dewi Lestari Diana Mulhimah Donny Maulana Dwi Pebrianti Edy Widodo Elia Syalom Faizah Syihab Fajar Ariya Putra Fauzi Baisyir Henifa Henifa Henny Yulianti Intan Sari Rahayu Lestari Agusalim Majid, Annisa Maulana Maulana Majid, Annisa Maulana, Donny Muhamad Ilham Fauzan Muhamad Syafareno Muhammad Darwis Muhammad Ramadhan Natan Kharisma A Nawangsih, Ismasari Pebrianti, Dwi Pupung Purnamasari Putri, Wahyuningdiah Trisari Harsanti Qinara Azra Puja Kaspia Ramadhan, Raka Hikmah Rifai Ady Setiawan romanuddin, ahmad Rudi Setiawan Samidi Samidi, Samidi Santosa, Egar Dika Satria Abadi Surojudin, Nurhadi Suwancita, I Made Syafatul Fida Syafran Nurrahman Syifa Amalia Syihab, Faizah Taofik Safrudin Tchinda Eliza Piliang Tjahjanto, Tjahjanto Wahyu Hadikristanto Wicaksana, Yusuf Eka Yaddarabullah, Yaddarabullah Yoga Religia Yoga Religia Yusman, Roni Zed Abdullah Zy, Ahmad Turmudi