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All Journal INSTALL: Information System and Technology Journal
M. Riko Anshori Prasetya
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : INSTALL: Information System and Technology Journal

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A Naïve Bayes Approach to Understanding Students’ Pychological Well-Being : Pendekatan Naïve Bayes untuk Memahami Kesejahteraan Psikologis Siswa Febria Sera Darnefi; M. Riko Anshori Prasetya
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.547

Abstract

High levels of stress and depression among Indonesian students are significant issues that demand special attention. According to data from the National Crime Information Center (Pusiknas) of the Indonesian National Police (Polri), there were 971 cases of suicide related to the mental health of students from January to October 18, 2023. This study utilizes an open dataset from Kaggle, which includes information on students' mental health conditions, and applies the Naive Bayes algorithm to determine accuracy, precision, and recall values. The Naive Bayes approach is employed to classify the mental health status of students and identify those who require special assistance. The results indicate that the Naive Bayes algorithm is effective in identifying students needing special help, with a high accuracy rate. Testing with RapidMiner yielded an accuracy of 90.00%, a recall rate of 89.66% for the 'No' class and 100.00% for the 'Yes' class. Precision for the 'No' label was 100.00%, while for the 'Yes' label, it was 25.00%. This approach can aid campuses, families, and professionals in identifying students who need special attention.
A Exploratory Data Analysis (EDA) of Social Aid Recipients in Murung Raya District Central Kalimantan Province with Machine Learning Approach: Analisis Data Eksplorasi (EDA) Penerima Bantuan Sosial di Kabupaten Murung Raya Provinsi Kalimantan Tengah dengan Pendekatan Machine Learning Regina; M. Riko Anshori Prasetya; Ahmad Hidayat
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.543

Abstract

Various kinds of problems often occur in programs implemented by the government, one of which is the problem that often arises is the uneven distribution of social assistance carried out to people with low economic levels, poverty is the main problem being faced by all countries in the world, including Indonesia. The purpose of implementing the dataset of social assistance recipients using EDA. To find the correlation of 4 variables, namely Number of Population, Number of Recipients, Occupation, and Age in the dataset of social assistance recipients in Murung Raya District, Central Kalimantan Province, with a machine learning approach. Then the research method used here is quantitative methods and experiments with machine learning approaches. So by using the EDA method on data on social assistance recipients in Murung Raya Regency, Central Kalimantan Province, it can be seen that the variable Number of population has.
Co-Authors Ahmad Hidayat Febria Sera Darnefi Regina