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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknik Informatika Unika Santo Thomas (JTIUST)
Ginabila
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Computer Science & IT Decision Sciences, Operations Research & Management

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Komparasi Algoritma Logistic Regression dan Random Forest pada Prediksi Cacat Software Prasetyo, Rizal; Nawawi, Imam; Fauzi, Ahmad; Ginabila
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v6i2.1522

Abstract

Testing becomes the standard in producing quality software, testing can be assessed through certain measures and methods, one of the benchmarks for software quality is ISO, which was made by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) , In the prediction of software defects software defect prediction error is a very bad thing, the prediction results can have an effect on the software itself. This study compares the results of the Logistic Regression Algorithm and Random Forest before and after the resampling method is applied, the test results show that Random Forest with resampling produces a higher level of accuracy. From the test results above, it can be concluded that the Random Forest with resampling method is more effective in predicting software defects
Klasifikasi Human Stress Menggunakan Adagrad Optimization untuk Arsitektur Deep Neural Network Azis, Mochammad Abdul; Fauzi, Ahmad; Ginabila; Nawawi, Imam
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 7 No. 1 : Tahun 2022
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v7i1.1916

Abstract

According to the World Health Organization, stress is a type of mental illness that affects human health and there is no one in this world who does not suffer from stress or depression. Stress is a term that is often used synonymously with negative life experiences or life events. . Analysis of data that has an unbalanced class results in inaccuracies in predicting human stress. This study shows that using the Deep Neural Network (DNN) Architecture model by optimizing several parameters, namely the optimizer, Learning rate and epoch. The best DNN Architect results are obtained with 4 Hidden Layers, Adagard Optimization, Learning rate 0.01 and the number of epochs 100. Accuracy, precision, recall and f-measure scores get 98.25%, 83.00%, 98.25%, 91.00%, respectively.
Komparasi Algoritma Logistic Regression dan Random Forest pada Prediksi Cacat Software Prasetyo, Rizal; Nawawi, Imam; Fauzi, Ahmad; Ginabila
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (665.169 KB) | DOI: 10.54367/jtiust.v6i2.1522

Abstract

Testing becomes the standard in producing quality software, testing can be assessed through certain measures and methods, one of the benchmarks for software quality is ISO, which was made by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) , In the prediction of software defects software defect prediction error is a very bad thing, the prediction results can have an effect on the software itself. This study compares the results of the Logistic Regression Algorithm and Random Forest before and after the resampling method is applied, the test results show that Random Forest with resampling produces a higher level of accuracy. From the test results above, it can be concluded that the Random Forest with resampling method is more effective in predicting software defects
Klasifikasi Human Stress Menggunakan Adagrad Optimization untuk Arsitektur Deep Neural Network Azis, Mochammad Abdul; Fauzi, Ahmad; Ginabila; Nawawi, Imam
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 7 No. 1 : Tahun 2022
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v7i1.1916

Abstract

According to the World Health Organization, stress is a type of mental illness that affects human health and there is no one in this world who does not suffer from stress or depression. Stress is a term that is often used synonymously with negative life experiences or life events. . Analysis of data that has an unbalanced class results in inaccuracies in predicting human stress. This study shows that using the Deep Neural Network (DNN) Architecture model by optimizing several parameters, namely the optimizer, Learning rate and epoch. The best DNN Architect results are obtained with 4 Hidden Layers, Adagard Optimization, Learning rate 0.01 and the number of epochs 100. Accuracy, precision, recall and f-measure scores get 98.25%, 83.00%, 98.25%, 91.00%, respectively.
Co-Authors Azis, Mochammad Abdul Fauzi Ahmad Muda Imam Nawawi, Imam Prasetyo, Rizal