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All Journal G-Tech : Jurnal Teknologi Terapan
Zainal Abidin
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Impact of Preprocessing on Indonesian Extractive Summarization Using LexRank, TextRank, DivRank, and Cosine Similarity Andri Setiawan; Zainal Abidin; Mochamad Imamudin
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8306

Abstract

Extractive text summarization is a fundamental approach to tackle information overload, yet its quality is highly dependent on the pre-processing stage. Despite its crucial role, there is no consensus on the most optimal pre-processing scenario for the Indonesian language, which has a complex morphological structure. This study aims to fill this research gap by systematically analyzing the impact of seven pre-processing scenarios on four summarization methods: three graph-based methods (LexRank, TextRank, DivRank) and one topic-relevance method (Cosine Similarity against the title). Using a corpus of 3,000 Indonesian news articles and ROUGE evaluation metrics, the results show two key findings. First, the Cosine Similarity method significantly outperforms all graph-based methods, achieving the highest F1-Measure scores on ROUGE-1 (0.5073), ROUGE-2 (0.4018), and ROUGE-L (0.4574), which emphasizes the important role of the title in news texts. Second, a comprehensive pre-processing scenario involving Case Folding, Punctuation Removal, Tokenization, Normalization, Negation Handling, Stopword Removal and Stemming proves to be the most effective in improving the performance of all algorithms. These findings provide empirical evidence and practical recommendations that the combination of a title-relevancy approach with proper text normalization is the most effective strategy for optimizing extractive text summarization for the Indonesian language.
Integrating Boolean Logic-Based Feature Representation and Artificial Neural Networks for Digital Divide Level Classification in Online Learning Imalatul Hidayah; Ririen Kusumawati; Mochamad Imamudin; Zainal Abidin
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.v10i3.10060

Abstract

Digital divide remains a major challenge in online learning, affecting students’ ability to access and benefit from digital education. While previous studies have primarily focused on first-level digital divide factors or employed machine learning models using raw input variables, limited research has explored the integration of logical feature engineering and artificial intelligence to model the multidimensional nature of digital divide. This study proposes a hybrid classification framework that combines Boolean Logic-based feature engineering and Artificial Neural Networks (ANN) for digital divide classification in online learning. The proposed framework utilizes four variables, namely Primary Device, Internet Stability, Equity Score, and Accessibility Score. Boolean Logic operations (AND, OR, and XOR) were applied to generate additional logical representations of digital access conditions before the ANN classification process. Data were collected from 324 student respondents participating in online learning. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that the optimized ANN model achieved 93% accuracy, 93% weighted precision, 93% weighted recall, and 93% weighted F1-score. Comparative analysis revealed that Boolean Logic features contributed additional logical representations of digital access patterns, although their impact on predictive performance was not consistently significant. The findings highlight that digital divide should be viewed as a multidimensional phenomenon and demonstrate the potential of ANN based predictive modeling to support data-driven digital inclusion strategies in education.
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 Phishing Website Detection Using Artificial Neural Network with Logic Gate-Based Feature Interaction Modeling M. Halvi Rahman; Zainal Abidin; M. Amin Hariyadi
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.v10i3.10280

Abstract

Despite advances in machine learning-based phishing detection, existing Artificial Neural Network (ANN) models operate as black boxes with no interpretable explanation of feature interactions—a critical limitation for security analysts. Furthermore, most approaches deploy large feature sets without investigating whether a minimal subset achieves equivalent performance. This study develops a phishing detection system combining ANN with Logic Gate-Based Feature Interaction Modeling (LGFIM), a novel framework that characterizes ANN decisions through AND, OR, and XOR Boolean operations, addressing both accuracy and interpretability gaps. Using the PhiUSIIL dataset (235,795 instances), Pearson correlation identified URLSimilarityIndex (r=0.8604) and HasSocialNet (r=0.7843) as the two most discriminative features. An ANN (2-64-32-16-1, ReLU, Adam) trained on an 80/20 split achieved 99.63% accuracy, 100% recall, 99.68% F1-score, and 99.91% AUC-ROC with zero false negatives. The LGFIM analysis reveals the classification boundary follows a predominantly AND-type Boolean structure: the AND gate achieves 99.67% accuracy against true labels, while ANN predictions align with AND for 42.48% of samples and XOR for 57.52%, together accounting for 100% of all predictions. This is the first study to comprehensively characterize ANN phishing decisions through logic gate interaction patterns, providing a zero-cost interpretability layer for cybersecurity operations.
Comparison of Boolean OR, AND, and OR–AND Models for Monthly Rainfall Classification in Bawean Island Rudi Kasianto; Zainal Abidin; Totok Chamidy; Mochamad Imamudin
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.v10i3.10298

Abstract

Rainfall classification plays an important role in climate monitoring, water resource management, agricultural planning, and hydrometeorological disaster mitigation. While machine learning techniques have been widely used for rainfall classification, they often require substantial computational resources and complex training processes. This study proposes a simple, interpretable, and computationally efficient Boolean-based framework for monthly rainfall classification on Bawean Island, East Java, Indonesia. Monthly climatological data from 1972–2023, including rainfall, rainy days, mean temperature, and minimum temperature, were analyzed, yielding 624 observations. Rainfall was classified into three categories: low (<100 mm), moderate (100–299 mm), and high (≥300 mm). Rainy days were converted into ordinal scores, while mean and minimum temperatures were transformed into binary scores. Three Boolean-based rainfall classification models were developed and evaluated using confusion matrices, accuracy, precision, recall, and F1-score. Correlation analysis showed that rainy days had the strongest relationship with rainfall (r = 0.858), followed by minimum temperature (r = −0.592) and mean temperature (r = −0.463). The hybrid OR–AND model achieved the best overall performance, with 66% accuracy, 71% precision, 62% recall, and 61% F1-score, outperforming both the OR and AND models. These results demonstrate that the proposed Boolean-based framework provides an effective, transparent, and computationally efficient approach for monthly rainfall classification.
Co-Authors Andri Setiawan Imalatul Hidayah M. Amin Hariyadi M. Halvi Rahman Mochamad Imamudin Ririen Kusumawati Rudi Kasianto Selina Ayuningtyas Totok Chamidy Yunifa Miftachul Arif