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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Quancom: Jurnal Quantum Komputer
Moningka, Nirwan
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

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KLASIFIKASI MENTAL MAHASISWA MENGGUNAKAN METODE MACHINE LEARNING Raynold
JURNAL QUANCOM: QUANTUM COMPUTER JURNAL Vol. 1 No. 2 (2023): Desember 2023
Publisher : LPPM-ITEBA

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Abstract

The high level of stress and depression among students is a serious problem that must be faced, there are 971 suicides in Indonesia in 2023. Classifying the mental health conditions of students can provide effective assistance for students who have mental health problems and can also help the campus and family in identifying the condition of the student. In this study, we used an open dataset from kaggle that includes the mental health conditions of students and college students. The methods used are K-Nearest Neighbor (KNN) and Naive Bayes algorithms to find accuracy, precision, recall, and f1-score values. First, students will fill out a questionnaire. Next, the data from the questionnaire will be processed to group students into several different clusters based on pattern similarity. In the dataset there are 100 data that are processed and get the results of KNN measurements of Depression 80%, Panic 70% and Anxiety 85%,the results of Naive Bayes measurements of Depression 70%, Panic 75% and Anxiety 75%.
Use of Data Mining Technology to Identify Narcotics Distribution Patterns Moningka, Nirwan; Kusrini, Kusrini
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 1 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i1.32064

Abstract

The abuse of narcotics has become one of the significant social and health problems in various countries worldwide. Conventional methods relying on manual analysis or traditional approaches may not be effective enough in addressing this challenge. Therefore, a more sophisticated and efficient approach is needed to tackle this issue. Data mining uses techniques from statistics, machine learning, and pattern recognition to extract valuable information from large data sets. This research employs data collection methods from the Narcotics Investigation Directorate of the Maluku Regional Police from 2021 to 2023. This data includes profiles of narcotics users, such as the age of the perpetrators, gender, last education level, occupation, location of arrest, and type of narcotic. The aim is to identify the patterns of narcotic distribution in the Maluku Province using data mining techniques, namely the Apriori algorithm, Naive Bayes, Random Forest, and Support Vector Machine (SVM). The exclusion of the age variable was a correct decision, as it resulted in an increase in accuracy. This increase is likely due to the high variation in the age variable. The accuracy improvement was more evident in the Random Forest algorithm compared to Naive Bayes and SVM. Random Forest achieved satisfactory results with an accuracy of 0.96. This indicates that Random Forest is a good algorithm for predicting narcotics user data. These results suggest that the pattern of narcotics distribution is closely associated with specific factors, including the male gender, the highest level of education being high school, a self-employed occupation, arrest locations on public roads, and the type of narcotic being Shabu.
Co-Authors Kusrini, Kusrini