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All Journal Applied Information System and Management G-Tech : Jurnal Teknologi Terapan
Yunifa Miftachul Arif
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Languange, Linguistic, Communication & Media

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Rainfall Classification in Malang Regency Using Artificial Neural Networks with Boolean Logic-Based Feature Engineering Selina Ayuningtyas; Zainal Abidin; Yunifa Miftachul Arif
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10263

Abstract

This study classifies monthly rainfall in Malang Regency using an Artificial Neural Network (ANN) with the Backpropagation algorithm and Boolean logic approaches (AND, OR, and AND-OR). The dataset consists of 144 monthly climatological records (2012–2023) obtained from the East Java Climatology Station, with three input variables: rainfall, minimum temperature, and relative humidity. Rainfall was grouped into three categories: Low (0–100 mm; 39.58%), Moderate (101–300 mm; 37.50%), and High (301–500 mm; 22.92%). Boolean logic features were generated using the mean values of relative humidity (77.34%) and minimum temperature (17.89°C). The ANN model was tested with three hidden-layer configurations containing 5, 8, and 10 neurons. Data were divided into 70% training and 30% testing sets using a random state of 42. The results show that the 10-neuron configuration achieved the best performance, with 75.00% accuracy, 75.97% precision, 75.00% recall, and 75.00% F1-score. In comparison, the 5-neuron and 8-neuron models achieved accuracies of 68.18% and 65.91%, respectively. The AND-OR Boolean logic approach provided more stable feature representation than the AND or OR approaches alone by combining multiple atmospheric conditions. These findings indicate that ANN with an appropriate architecture can effectively classify rainfall patterns in Malang Regency.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Siska Farizah Mauludiah; Cahyo Crysdian; Yunifa Miftachul Arif
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): 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.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Co-Authors Cahyo Crysdian Selina Ayuningtyas Siska Farizah Mauludiah Zainal Abidin