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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Bakti Akademisi
Lubis, Fahdi Saidi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Digital Transformation Education for Identifying Future Business Opportunities Halim, Hendra; Lubis, Muharman; Lubis, Fahdi Saidi; Dawood, Taufiq Carnegie; Majid, M. Shabri Abd.; Amri, Amri; Zulkifli, Zulkifli; Purnami, Ni Made; Nurlina, Eka; Silvia, Vivi
Jurnal Pengabdian Bakti Akademisi Vol 2, No 2 (2025): Jurnal Pengabdian Bakti Akademisi
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jpba.v2i2.45681

Abstract

The rapid advancement of digital technology has created a pressing need for digital transformation in various sectors, including entrepreneurship. However, many students and academics still lack adequate understanding and skills to identify future business opportunities through digital approaches. This community service activity, titled Digital Transformation Education for Identifying Future Business Opportunities, was conducted to address this issue. The program involved students and lecturers from Telkom University's Faculty of Industrial Engineering and Syiah Kuala University. Using the Service Learning (SL) approach, the activity was carried out through an online seminar that combined theoretical explanations and interactive discussions with digital business practitioners. Two experts in Islamic economics and information technology served as the resource persons. The activity aimed to increase digital literacy and entrepreneurial awareness among participants. Assessment through pre-test and post-test evaluations revealed a significant improvement in understanding, with the average score increasing from 63.5 to 85.8. The results demonstrate that structured digital transformation education can effectively enhance the capacity of the academic community in identifying and developing digital-based business opportunities. This activity also contributes to the achievement of Sustainable Development Goals (SDGs), particularly in promoting quality education, decent work and economic growth, and fostering innovation.
A COMPARATIVE STUDY OF PIPELINE-VALIDATED MACHINE LEARNING CLASSIFIERS FOR PERMISSION-BASED ANDROID MALWARE DETECTION Lubis, Arif Ridho; Wulandari, Dewi; Adha, Lilis Tiara; Ariyani, Tika; Lase, Yuyun; Lubis, Fahdi Saidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1675-1692

Abstract

The growing prevalence of Android malware distributed through third-party APK sideloading poses a significant security threat to users and developers. This study aims to evaluate the effectiveness of three machine learning algorithms—Logistic Regression (LR), Random Forests (RF), and Gradient Boosting Machine (GBM)—for static Android malware detection based on permission features. The experiment employs the publicly available Android Malware Prediction Dataset (Kaggle, accessed 2025), containing 4,464 application samples with 328 binary permission attributes. A leakage-free CRISP-DM workflow was implemented, integrating data cleaning, automated feature selection via SelectKBest (Mutual Information), and hyperparameter optimisation using GridSearchCV with stratified 5-fold cross-validation. Results on the unseen hold-out test set show that GBM achieved the best performance, with 96.05% accuracy and 0.9924 ROC-AUC, outperforming LR and RF. In addition, GBM exhibited superior probability calibration (Brier Score = 0.0344) and interpretability, as confirmed through SHAP analysis. The ablation study further validated that optimal model performance saturates at 30–40 selected features. This research contributes a reproducible and pipeline-validated comparative framework for static Android malware detection, addressing prior studies’ limitations regarding feature selection bias and data leakage. Nevertheless, the study is limited by its reliance on static permission features and the absence of dynamic behavioural data, which may restrict generalisation to evolving malware families.
Co-Authors Adha, Lilis Tiara Amri Amri Ariyani, Tika Dewi Wulandari Halim, Hendra Lase, Yuyun Lubis, Arif Ridho M. Shabri Abd. Majid Muharman Lubis Ni Made Purnami Nurlina, Eka Silvia, Vivi Taufiq Carnegie Dawood Zulkifli Zulkifli