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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi JTET (Jurnal Teknik Elektro Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Walisongo Journal of Information Technology Jurnal Informatika Teknologi dan Sains (Jinteks)
Akhmad Khanif Zyen, Akhmad Khanif
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Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Evaluation of Telecommunication Customer Churn Classification with SMOTE Using Random Forest and XGBoost Algorithms Wakhidah, Lisa Nusrotul; Zyen, Akhmad Khanif; Wahono, Buang Budi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8740

Abstract

Competition in the telecommunications industry, particularly among Internet Service Providers (ISPs), significantly influences customer churn, which negatively impacts revenue, profitability, and business sustainability. An effective approach to mitigate churn involves identifying potential churners early, enabling companies to implement strategic retention measures. However, predicting churn can be challenging due to the limited data available on churned customers. This study aims to predict customers likely to terminate or discontinue their subscriptions, focusing on addressing data imbalance using the Synthetic Minority Over-Sampling Technique (SMOTE). The dataset, sourced from Kaggle, comprises 21 attributes and 7,034 entries. The pre-processing phase includes data cleaning, feature encoding, and the implementation of Random Forest and XGBoost algorithms after data balancing with SMOTE. The findings reveal that the XGBoost algorithm achieves a prediction accuracy of 82%, outperforming Random Forest with 81%. Key factors influencing churn include Contract, TotalCharges, and tenure. The study concludes by emphasizing the significance of contract flexibility and the need to prioritize customers with high total costs or extended subscription periods to reduce churn rates. Future research is encouraged to investigate alternative methods for handling data imbalance and to explore advanced machine learning algorithms to further enhance prediction accuracy and the effectiveness of customer retention strategies.
Optimizing Decision Tree and Random Forest with Grid Search and SMOTE for Malware Classification on IoT Network Traffic Siroj, Muhammad Nurus; Zyen, Akhmad Khanif; Wibowo, Gentur Wahyu Nyipto
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.v9i5.10542

Abstract

The rapid growth of the Internet of Things (IoT) has increased the risk of malware attacks, posing serious threats especially to micro, small, and medium enterprises (MSMEs) that often lack sufficient cybersecurity resources. This study aims to optimize Decision Tree (DT) and Random Forest (RF) classifiers using Grid Search, while addressing the class imbalance problem through the Synthetic Minority Oversampling Technique (SMOTE). The Security Attacks Malware IoT Networks dataset with five classes (Benign, Malware, DDoS, Brute Force, Scanning) was used and divided into training and testing sets with stratified 80:20 split. Experimental results show that DT achieved 67.3% accuracy with a macro F1-score of 42.9%, while RF achieved 70.7% accuracy but a very low macro F1-score of 21.4%, indicating bias toward the majority class despite balancing. Boosting methods provided stronger baselines, with XGBoost reaching 87.0% accuracy and 66.7% F1-score, while LightGBM achieved 85.6% accuracy and 64.4% F1-score. ROC curves and confusion matrices confirmed that boosting methods were more balanced in recognizing minority classes. In terms of efficiency, DT required the shortest training time (8 seconds), while LightGBM provided the best trade-off between accuracy and computational cost (26 seconds). Paired t-tests further confirmed that performance differences between DT and RF were not significant, while boosting methods significantly outperformed RF. Overall, optimizing DT and RF with Grid Search and SMOTE enhances their performance, but boosting methods remain more robust for malware detection in IoT traffic. These findings provide practical insights for MSMEs in balancing accuracy and efficiency when deploying intrusion detection systems.
Comparison of Support Vector Machine (SVM) and Random Forest Algorithms in the Analysis of SOcial Media X User Sentiment Towards the TNI Bill Rochmawati, Nur; Zyen, Akhmad Khanif; Widiastuti, Nur Aeni
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.v9i5.10883

Abstract

The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to compare the effectiveness of two popular sentiment analysis algorithms, Support Vector Machine (SVM) and Random Forest (RF). A total of 525 relevant tweets collected between February and May 2025 were analyzed and classified into three sentiment categories: positive, negative, and neutral. The results reveal that neutral opinions dominate at 81.4%, followed by negative sentiments at 11.1% and positive sentiments at 7.4%. The performance comparison shows that SVM achieved an accuracy of 92%, outperforming RF which obtained 91%. These findings highlight that strategic defense issues tend to generate predominantly informative public opinions, while critical voices show an increasing trend as the discourse evolves. The novelty of this study lies in the application of three-class sentiment classification and the comparative evaluation of SVM and RF within the domain of defense policy. This research contributes to the academic discourse by extending sentiment analysis beyond electoral and marketing topics, while also providing practical insights for policymakers in understanding and responding to public aspirations more effectively.
Co-Authors A.A. Ketut Agung Cahyawan W Agus Subhan Akbar, Agus Subhan Buang Budi Wahono, Buang Budi Dina, Maula Hashina Husen, Muhamad Nur Aeni Widiastuti Prasetyo, Muhammad A'an R Hadapiningradja Kusumodestoni Rochmawati, Nur Siroj, Muhammad Nurus Teguh Tamrin Wakhidah, Lisa Nusrotul Wibowo, Gentur Wahyu Nyipto