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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Tarwoto, T
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Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Kesehatan Mental Mahasiswa dengan Algoritma J48 dan Hubungan Atribut Demografis serta Akademis Cahyani, Evril Fadrekha; Aeni, Alfina Nur; Wahyuni, Irmawati Tri; Tarwoto, T
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.812

Abstract

The college years are a period of significant development in various aspects, including physical, psychological, and intellectual growth. Students often face mental health issues such as depression, anxiety, and panic attacks, which frequently go untreated and can persist into adulthood. This study aims to analyze the mental health of college students using the J48 algorithm to classify the target attribute "Did you seek any specialist for treatment?". The dataset used was sourced from a public repository, consisting of 101 instances. Through the stages of data collection, preprocessing, classification with the J48 algorithm, attribute correlation analysis, and comorbidity identification, the study found that 6% of students required specialist treatment, while 94% did not. Further analysis revealed that mental health issues are more prevalent among women and first-year students. Depression affects 82.86% of women, anxiety affects 70.59% of women, and panic attacks affect 75.76% of women. The study also identified comorbidities where some students experienced more than one mental health issue. The findings highlight the importance of social support and a conducive learning environment to improve students' mental well-being. These findings can be used to develop more effective interventions to support students' mental health and academic success.
Penerapan K-Means Clustering Untuk Mengelompokan Tingkat Kemiskinan Di Provinsi Kalimantan Barat Cahyo, Samsul Dwi; Wahyuni, Irmawati Tri; Maharani, Revalyna Octavia; Nurfaizi, Maulana; Saputro, Rujianto Eko; Tarwoto, T
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.855

Abstract

This study aims to use the K-means clustering algorithm to categorize poverty levels in the West Kalimantan province. The data used for clustering represents poverty levels across four districts: Melawi, Kapuas Hulu, Sekadau, and Kayong Utara. The K-means clustering method is employed to group these districts based on similarities in their poverty levels. The clustering results reveal four distinct categories of poverty levels: Cluster 0 represents areas with very high poverty rates; Cluster 1 shows Melawi with a high poverty rate; Cluster 2 includes Sambas, Kapuas Hulu, and Sintang, with relatively low poverty rates; and Cluster 3 includes Landak, Sanggau, and Ketapang, with high poverty rates. The analysis reveals interesting patterns in the distribution of poverty across West Kalimantan, which can assist local governments in designing more effective policies for poverty reduction. This study makes a significant contribution to understanding poverty dynamics in West Kalimantan and provides a basis for more efficient decision-making in poverty alleviation efforts.
Co-Authors Aeni, Alfina Nur Cahyani, Evril Fadrekha Cahyo, Samsul Dwi Maharani, Revalyna Octavia Nurfaizi, Maulana Rujianto Eko Saputro Wahyuni, Irmawati Tri