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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CAUCHY: Jurnal Matematika Murni dan Aplikasi
Ilyas, Muhaimin
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Computer Science & IT Engineering Mathematics

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Diabetes mellitus diagnosis method based random forest with bat algorithm Anam, Syaiful; Deny Tisna Amijaya, Fidia; Hadi Wijoyo, Satrio; Eka Ratnawati, Dian; Ayu Dwi Lestari, Cynthia; Ilyas, Muhaimin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1140-1149

Abstract

Diabetes mellitus (DM) is a very dangerous disease and can cause various problems. Early diagnosis of DM is essential to avoid severe effects and complications. An affordable DM diagnosis method can be developed by applying machine learning. Random forest (RF) is a machine learning technique that is applied to develop a DM diagnosis method. However, the optimization of RF hyperparameters determines the performance of RF approach. Swarm intelligence (SI) could be used to solve the hyperparameter optimization problem on RF. It is robust and simple to be applied and doesn’t require derivatives. Bat algorithm (BA) is one of SI techniques that gives a balance between exploration and exploitation to find a global optimal solution. This article proposes developing an RF-BA-based technique for diagnosing DM. The results of the experiment demonstrate that RF-BA can diagnose DM more accurately than conventional RF. RF-BA has higher performance compared to RF-particle swarm optimization (PSO) in terms of computational time. The RF-BA also are able to solve the overfitting problem in the conventional RF. In the future, the proposed method has a high chance of being implemented for helping people with early DM diagnosis with high accuracy, low cost, and high-speed process.
KFCM-PSOTD : An Imputation Technique for Missing Values in Incomplete Data Classification Ilyas, Muhaimin; Anam, Syaiful; Trisilowati, Trisilowati
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.25138

Abstract

Data mining is a very important process for finding out the data interpretation. Data preprocessing is the crucial data mining steps. The existence of missing values in the data is one of the primary issues with data preprocessing. Generally, this can be overcome with mean or median imputation because they are easy to implement. However, the use of these techniques is not recommended because they ignore the data variance. This research develops the Kernel Fuzzy C-Means Optimized by the Particle Swarm Optimizer with Two Differential Mutations (KFCM-PSOTD).  KFCM imputation is applied to obtain better estimation values due to its proven ability to recognize patterns in the data. In addition, the PSOTD algorithm is used as an optimization tool to boost the KFCM's performance. PSOTD is adopted because it has more balanced exploration and exploitation capabilities compared to classical PSO. Datasets that have been imputed on KFCM-PSOTD are classified using the Decision Tree algorithm. The results are evaluated using accuracy, precision, recall, and f1 score to determine the quality of the imputed values. The outcomes demonstrate that the KFCM-PSOTD algorithm has a better performance; even the difference in evaluation scores obtained reaches 10% better than other imputation techniques. 
Co-Authors Ayu Dwi Lestari, Cynthia Deny Tisna Amijaya, Fidia Dian Eka Ratnawati Hadi Wijoyo, Satrio Syaiful Anam Trisilowati Trisilowati, Trisilowati