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All Journal CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Journal of Electrical Technology Journal of Natural Science and Integration JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jatilima : Jurnal Multimedia Dan Teknologi Informasi INFOKUM MEANS (Media Informasi Analisa dan Sistem) INCODING: Journal of Informatics and Computer Science Engineering Instal : Jurnal Komputer Hello World Prosiding Seminar Nasional Teknik UISU (SEMNASTEK) Jurnal Pengabdian Mitra Masyarakat Pascal: Journal of Computer Science and Informatics
Satria Yudha Prayogi
Dosen Universitas Islam Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

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Application Of The Metaheuristic Algorithm To Optimize The K Value In K-NN In Grouping Factors Causing Stunting Antoni Antoni; Mbera Mehuli; Satria Yudha Prayogi
JET (Journal of Electrical Technology) Vol 11, No 2 (2026): : Edisi June
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i2.13792

Abstract

Stunting is a health problem that has a long-term impact on the quality of life of individuals and the development of a nation. Accurately identifying the factors that cause stunting is an important step in developing effective mitigation strategies. K-Nearest Neighbor (K-NN) is a machine learning algorithm that is widely used in data classification and grouping, but its performance is greatly influenced by the selection of optimal K value parameters. This research proposes the application of metaheuristic algorithms, such as genetic algorithms (GA) and Particle Swarm Optimization (PSO), to optimize the K value in K-NN in grouping factors that cause stunting. This method integrates the power of exploration and exploitation of metaheuristic algorithms to find K parameters that produce optimal accuracy. Based on the results of applying the metaheuristic algorithm, it was found that without optimization, K-NN only produces an accuracy of 63%, which shows the importance of choosing the right K value. The use of GA in K-NN optimization provides a substantial increase in accuracy, reaching 73%, thanks to its ability to explore the solution space effectively. Meanwhile, PSO also increases accuracy by up to 74%. It is hoped that the findings of this research will be a significant contribution in the development of a more accurate grouping model for analyzing factors causing stunting, so that it can support data-based decision making in an effort to address the stunting problem holistically.
STACKING ENSEMBLE MODEL MACHINE LEARNING DETEKSI DINI RISIKO KESEHATAN MENTAL DI LINGKUNGAN PENDIDIKAN Lia Umbari Putri; Rolly Yesputra; Satria Yudha Prayogi; Nasrun Marpaung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4147

Abstract

Abstract: Mental health issues such as depression are prevalent among students and significantly impact both academic performance and psychological well-being. While machine learning techniques have been widely employed to predict mental health conditions, single-model approaches often suffer from limited generalizability and interpretability. This study proposes a Stacked Ensemble Learning framework that integrates three heterogeneous base classifiers—Logistic Regression (LR), Support Vector Machine (SVM) with RBF kernel, and Random Forest (RF)—combined with a meta-learner to enhance the accuracy and robustness of depression prediction among students. Experiments were conducted on a large-scale student mental health dataset comprising 27,901 records, with preprocessing steps including feature standardization, class balancing using SMOTE, and stratified cross-validation. Performance evaluation utilized Confusion Matrix, F1-Score, Recall, Precision, and the Area Under the ROC Curve (AUC-ROC). The proposed ensemble model achieved a classification accuracy of 84%, an AUC of 0.911, and an average precision of 0.89, consistently outperforming individual baseline classifiers. These results validate that combining margin-based, non-linear, and tree-based models can yield more reliable and interpretable predictions. The proposed architecture presents a promising and explainable tool for early detection of mental health issues within educational settings. Keywords: depression detection; student mental health; ensemble learning;machine learning. Abstrak: Permasalahan kesehatan mental seperti depresi kerap dialami oleh siswa dan berdampak pada performa akademik serta kesejahteraan psikologis mereka. Meskipun pendekatan pembelajaran mesin telah banyak digunakan untuk prediksi kondisi ini, model tunggal kerap menghadapi keterbatasan dalam hal generalisasi dan interpretabilitas. Studi ini mengusulkan kerangka kerja Stacking Ensemble Learning yang mengintegrasikan tiga model dasar—Logistic Regression (LR), Support Vector Machine (SVM) dengan kernel RBF, dan Random Forest (RF)—yang dikombinasikan dengan meta-learner untuk meningkatkan akurasi dan stabilitas prediksi depresi pada siswa. Eksperimen dilakukan pada dataset berskala besar yang mencakup 27.901 entri, dengan penerapan preprocessing, standardisasi, penyeimbangan kelas menggunakan SMOTE, dan validasi silang stratifikasi. Evaluasi performa menggunakan metrik Confusion Matrix, F1-Score, Recall, Precision, serta AUC-ROC Curve. Hasil menunjukkan bahwa model ansambel yang diusulkan mencapai akurasi 84%, AUC 0,911, dan rata-rata precision 0,905, yang secara konsisten melampaui performa model individual. Temuan ini menegaskan bahwa kombinasi antara model berbasis margin, non-linear, dan pohon keputusan mampu menghasilkan prediksi yang lebih andal dan dapat dijelaskan, sehingga potensial untuk diimplementasikan dalam sistem pemantauan kesehatan mental berbasis institusi pendidikan. Kata kunci: deteksi depresi, kesehatan mental siswa, stacking ensemble, machine learning.
Co-Authors Abdul Haris Nasution Ahmad Bakhori Ananta, Ardhan Antoni Antoni Ardhan Ananta AULIA, RAHMAT Bonar Harahap Darlina Tanjung Ferdian, Ridho Frans Ikorasaki Harizan, Md Haslina, Siti Kennedi Tampubolon Lia Umbari Putri Luthfi Parinduri Mahrani Arfah Marpaung, Nasrun Mbera Mehuli Muarif, Muarif Muksin R. Harahap Nana Mardiana Nasrun Marpaung Oris Krianto Sulaiman Pandi Barita Nauli Simangunsong Putri, Lia Umbari Riswan Limbong Rolly Yesputra S, Sukartika Siddik Karo-Karo Sinaga, Sony Bahagia Sony Bahagia Sinaga Sony Bahagia Sinaga Sony Bahagia Sinaga Tamba, RL. Harmady