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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Defri Kurniawan
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Optimasi Algoritma Decision Tree, Logistic Regression dan SVM Menggunakan Soft Voting Yosiko Aditya Pratama; Fikri Budiman; Sri Winarno; Defri Kurniawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6856

Abstract

Agriculture constitutes a fundamental pillar of a nation's economy. One key to success in agriculture is the selection of suitable land. The prediction of whether land is fertile or not can be efficiently accomplished through a data mining approach. This is because data mining offers several algorithms for extracting crucial information from vast datasets through classification. However, classification algorithms in data mining often encounter the challenge of data imbalance, which can lead to low accuracy rates. Processing data with calculation models that have low accuracy rates can result in numerous erroneous predictions (fail predictions). To address this issue, this research conducts testing and comparative analysis of the confusion matrix results from four calculation models: the Decision Tree algorithm, Logistic Regression, SVM, and the combination of these three algorithms using the Soft Voting ensemble technique. The test results indicate that processing data using the Decision Tree, Logistic Regression, and SVM algorithms, along with the optimization of the Soft Voting ensemble model, achieves the highest accuracy rate of 91.53%. This accuracy rate is higher compared to the other three calculation models: the Decision Tree algorithm with a difference of 3.83%, Logistic Regression with a difference of 2.66%, and SVM with a difference of 1.36%. This research makes a significant contribution by identifying an efficient solution to improve the accuracy of identifying fertile agricultural land, which is a crucial step in supporting the success of the agricultural sector in the country's economy.
Optimasi Hyperparameter Random Forest untuk Klasifikasi Depresi Mahasiswa Menggunakan GridSearchCV dan RandomizedSearchCV Eka Wahyu Utami; Defri Kurniawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9366

Abstract

Student mental health is an important issue that requires a data-driven approach to support the classification process of student depression. This study aims to analyze the factors that cause depression and optimize the performance of the classification model by applying the Random Forest algorithm. The data used in this research is secondary data from the Student Depression Dataset obtained from the Kaggle platform, with a total of 27,901 data points. The research stages begin with data collection followed by Exploratory Data Analysis (EDA), which includes descriptive statistical analysis and correlation between variables using a heatmap. Data preprocessing involves removing irrelevant features, handling missing values, encoding categorical data, and splitting the data into training and testing sets. Model development is carried out through three scenarios: a baseline model, hyperparameter optimization using GridSearchCV, and RandomizedSearchCV. Model performance evaluation is measured using a Confusion Matrix to analyze accuracy, precision, recall, and F1-score. The results show that all models produce relatively stable accuracy in the range of 0.84–0.85. The model with GridSearchCV optimization provides the best performance with a recall value of 0.8869 and an F1-score of 0.8719. This increase in recall is important to minimize the risk of false negatives in identifying students experiencing depression. It is hoped that these findings can contribute as a decision support system for educational institutions in more accurately detecting and managing students' mental health.
Co-Authors Eka Wahyu Utami Fikri Budiman Sri Winarno Yosiko Aditya Pratama