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All Journal Tech-E Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System PROFICIO: Jurnal Pengabdian Masyarakat Journal of Informatics and Communication Technology (JICT) eProceedings of Engineering
Dewi Marini Umi Atmaja
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Type 2 Diabetes Mellitus Diagnosis Model Using the C4.5 Algorithm Ruaida Susanti; Dewi Marini Umi Atmaja; Arif Rahman Hakim; Amat Basri
Tech-E Vol. 7 No. 2 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i2.2676

Abstract

Type 2 Diabetes Mellitus (DM) is a metabolic disorder characterized by elevated blood sugar resulting from decreased insulin secretion by pancreatic beta cells and/or impaired insulin function (insulin resistance). Over the last 50 years, there has been a rapid increase in the prevalence of diabetes, paralleling the rise in obesity rates. This study aims to develop a diagnostic model for type 2 DM using C4.5, incorporating feature selection and analyzing age and gender parameters of Type II DM patients. The research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM). Based on the dataset used, the C4.5 model demonstrated superior performance compared to SVM and Random Forest, achieving an AUC value of 72.5%, indicating a reasonably good classification level. The predominant gender among Type II DM patients is female, comprising 210 patients or 54.8% in the age range of 18-94 years, while 173 male patients or 45.2% fall within the age range of 23-80 years.
Performance Analysis of Classification and Regression Tree (CART) Algorithm in Classifying Male Fertility Levels with Mobile-Based Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Andri Ariyanto
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2110

Abstract

Fertility is the ability to produce offspring in a man or the ability of the reproductive organs to work optimally in fertilization. Fertility rates have declined drastically in the last fifty years. Machine Learning is a field devoted to understanding and building learning methods. This study will use machine learning algorithms to classify male fertility levels, namely the Classification and Regression Tree (CART) algorithm and the K-Fold Cross Validation validation method. The fertility dataset used in this study was obtained from the UCI Machine Learning website, with a total of 100 data and the variables used are Age, Childish diseases, Accident or serious trauma, Surgical intervention, High fevers in the last year, Frequency of alcohol consumption, Smoking habit, Number of hours spent sitting per day and Diagnosis. K-Fold Cross Validation can be used together with CART to measure the performance of the CART model on different data, so as to avoid overfitting or underfitting the CART model. Based on the calculation of the CART algorithm and the K-Fold Cross Validation validation method (K = 1 to K = 9), the average accuracy value for training data is 98.70% and the average accuracy value for testing data is 81.16%. The results of this study have proven that the CART algorithm can be used to classify the level of fertility in men well. In addition, the classification model formed can be implemented into a mobile application (android) so that it is easy to use and understand.
Co-Authors Amat Basri Andri Ariyanto Arif Rahman Hakim Arif Rahman Hakim Aufa Hafiy Andhika Deny Haryadi Dio Kurnia Sandi Ilham Roni Yansyah Ruaida Susanti Suwaryo, Niko