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All Journal Jurnal Simetris E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal DISPROTEK Jurnal Informatika dan Rekayasa Elektronik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Jurnal Scientia JINAV: Journal of Information and Visualization Journal of Dinda : Data Science, Information Technology, and Data Analytics SPEED - Sentra Penelitian Engineering dan Edukasi International Journal Software Engineering and Computer Science (IJSECS) Jurnal Minfo Polgan (JMP) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Formosa Journal of Applied Sciences (FJAS) Innovative: Journal Of Social Science Research Jurnal Pengabdian Harapan Bangsa Journal of Social Work and Empowerment Jurnal Indonesia : Manajemen Informatika dan Komunikasi TECHNOVATE
Wibowo, Gentur Wahyu Nyipto
Universitas Islam Nahdlatul Ulama Jepara
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Mental Health Classification Using Naïve Bayes and Random Forest Algorithms Faisti, Muhammad Jazum; Kusumodestoni, R. Hadapiningradja; Wibowo, Gentur Wahyu Nyipto
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10144

Abstract

Mental health is a crucial issue affecting individual and societal well-being. This study aims to investigate and compare the performance of Machine Learning algorithms, namely Naïve Bayes and Random Forest, for text-based mental health classification. The dataset used is the Mental Health Corpus from Kaggle, consisting of 27,977 English text messages from online forums, with binary labels (0: no indication of mental disorder, 1: indication of mental disorder) pre-annotated by the dataset creators. Text preprocessing involved lowercasing, negation handling, stopword removal, slang normalization, tokenization, and stemming. Data transformation was performed using TF-IDF. Model evaluation utilized accuracy, precision, recall, and F1-score metrics, along with 5-Fold Cross Validation. Evaluation results indicate high performance for both algorithms. Naïve Bayes achieved 88.7 % accuracy, 84.2 % precision, 95.2 % recall, and 89.3 % F1-score on the test data. Random Forest demonstrated more balanced performance with 89.3 % accuracy, 88.1 % precision, 90.5 % recall, and 89.3 % F1-score. The 5-Fold Cross Validation for Naïve Bayes yielded average scores of 88.8 % accuracy, 84.4 % precision, 94.9 % recall, and 89.3 % F1-score. In contrast, Random Forest showed averages of 89.2 % accuracy, 88.8 % precision, 89.5 % recall, and 89.3 % F1-score. While Naïve Bayes had higher recall, Random Forest exhibited the best overall performance, considering the combination of accuracy, precision, and stable generalization, making it more effective for mental health text classification.
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.
Co-Authors Abdilllah, Muh Sajid Ah Dani Fian Cahya Akhmad Khanif Zyen, Akhmad Khanif Anis Sofiyati Anwari, Hilal Beni Ernando Charles Firmansyah Decky Rochmanto, Decky Dewantara, Rizki Faisti, Muhammad Jazum Fathurrozi, Muhammad Gogor Christmass Setyawan Gun Sudiryanto Hanifah Nurul Muthmainah Haydar, Muhammad Rifqi Fajrul I Nengah Sandi Indriyani Indriyani Isyfa Fuhrotun Nadhifah Jeperson Hutahaean Joko Minardi, Joko Khasan Galuh Ramadhan Kraugusteeliana Kraugusteeliana Kusuma, Tahta Rias Tika C Laros Tuhuteru Maori, Nadia Annisa Mohammad, Gunawan Mulyo, Harminto Nadia Annisa Maori Nadia Annisa Maori Nafissatus Zahro Nanda, Meindita Andriano Nugroho, Muhammad Bayu Nur Ali Nurofi, Muhammad Adib R Hadapiningradja Kusumodestoni Rahma Dwi Ningsih Ricardus Anggi Pramunendar Rivaldo, Muchammad Dwi Safrizal Safrizal Saludin Saludin Sarwido Sarwido Sarwido Sa’adah, Puji Siroj, Muhammad Nurus Sitorus, Lamhot Susatyo Adhi Pramono, Susatyo Adhi Syamsul Ma'arif TEDDY SETIADY Teguh Tamrin Tobias Duha Wenda, Alex Yatusifa, Cecilia Zaenal Arifin