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All Journal Prosidia Widya Saintek JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Silviana Silviana
Universitas Widya Gama Malang
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation

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The Student Mental Health Pattern Using Clustering and Classification Approaches Audrey Suitela; Silviana Silviana; Fahmi Bahaluan; Maurecia Tima; Indah Dewi Nurhayati; Zaenuddin Zaenuddin
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Students mental health is a key factor in their academic and social development. However, the patterns and factors that influence mental health in college students are still not fully understood. This study utilizes machine learning-based clustering and classification techniques to identify hidden patterns in college students’ mental health data, focusing on social and demographic factors. Using the K-Means algorithm for clustering and Random Forest for classification, we group college students based on their mental health conditions and analyze the associations between variables such as age, marital status, anxiety, and medical history. The process begins with data exploration, followed by data cleaning and feature transformation to ensure optimal input quality. In the clustering stage, we find three main groups of college students with different mental health patterns, which are then used as the basis for a classification model. A Random Forest model is built to predict potential mental disorders, such as depression and anxiety, by identifying the features that have the most influence on the prediction results. The model evaluation shows significant performance with adequate accuracy, where the importance of social factors such as marital status and history of visits to medical professionals is clearly revealed. The results of this study not only offer important insights into students’ mental health patterns, but also provide recommendations for university policies in creating an environment that supports students’ mental well-being. This combined approach of clustering and classification opens up new opportunities in the application of machine learning for more precise and data-driven mental health analysis.
ANALISIS POSTUR KERJA MENGGUNAKAN RULA REBA PADA PEKERJA PACKING PT.C UNTUK MENGURANGI RISIKO MUSCOLOSKELETAL DISORDERS Muhammad Affif Nugroho; Arie Restu Wardhani; Silviana Silviana
Prosidia Widya Saintek Vol. 5 No. 2 (2026)
Publisher : Universitas Widyagama Malang

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Musculoskeletal Disorders (MSDs) merupakan gangguan pada sistem otot dan rangka akibat postur kerja yang tidak ergonomis, aktivitas repetitif, dan beban kerja yang tinggi. Aktivitas pada area packing PT. C Pasuruan masih dilakukan secara manual sehingga berpotensi meningkatkan risiko ergonomi. Penelitian ini bertujuan menganalisis tingkat risiko ergonomi menggunakan metode Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Ovako Working Posture Analysis System (OWAS), serta mengidentifikasi keluhan muskuloskeletal menggunakan Nordic Body Map (NBM). Penelitian menggunakan metode deskriptif kuantitatif terhadap delapan pekerja bagian packing. Data diperoleh melalui observasi, pengukuran sudut postur, dan penyebaran kuesioner NBM. Hasil penelitian menunjukkan bahwa aktivitas weighing memiliki risiko ergonomi tinggi akibat posisi lengan atas mencapai sudut 120°–140°, sedangkan aktivitas adjustment bag berisiko tinggi hingga sangat tinggi karena postur membungkuk 20°–40°. Skor NBM berkisar 70–81, menunjukkan seluruh pekerja mengalami keluhan muskuloskeletal, terutama pada bahu, lengan, pergelangan tangan, dan punggung. Hasil penilaian RULA, REBA, dan OWAS menunjukkan sebagian besar aktivitas memerlukan tindakan perbaikan segera. Usulan perbaikan meliputi pembaruan SOP, penerapan micro-breaks, program peregangan, pembatasan lembur, penggunaan operator cadangan, serta penyesuaian tinggi mesin timbangan dan konveyor untuk menurunkan risiko MSDs dan meningkatkan keselamatan serta produktivitas kerja
Co-Authors Arie Restu Wardhani Audrey Suitela Fahmi Bahaluan Indah Dewi Nurhayati Maurecia Tima Muhammad Affif Nugroho Zaenuddin Zaenuddin