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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Indonesian Community Journal Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : Scientific Journal of Informatics

 1 
Optimization of Random Forest Algorithm with SMOTE Method to Improve the Accuracy of Early Diabetes Prediction Nisa, Siti Khoirun; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.22986

Abstract

Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.
Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction Ilmiyah, Miftakhul; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22886

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination. Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate. Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%. Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.
Co-Authors Abdul Aziz Amalia, Salsabila Dani Andiyani, Putri Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Dina, Intan Rachma Dwi Irnawati Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Edi Noersasongko Efendi, Ervina Putri Fina Indri Silfana Hidayah, Alvinatul Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Jauhar Vikri, Muhammad Laili, Elok Fathiyatul Levia, Zachdyna Aurelya Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Munir, Ach Sirojul Muzakka, Moch. Arifuddin Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Pratiwi, Fannisa Salsabila Purwanto Purwanto Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Sahri Sahri Sahri Santi, Nirma Ceisa Saputra, Agus Bima Silfana, Fina Indri Sinta Ningrum Usman Nurhasan Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi