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All Journal JUITA : Jurnal Informatika JUSIFO : Jurnal Sistem Informasi JOIN (Jurnal Online Informatika) Journal of Economic, Bussines and Accounting (COSTING) INTECOMS: Journal of Information Technology and Computer Science INTEK: Informatika dan Teknologi Informasi International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Data and Science G-Couns: Jurnal Bimbingan dan Konseling Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Artificial Intelligence in Medical Issues
Umar Zaky
University Technology Of Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Physics Public Health Other

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Journal : Indonesian Journal of Data and Science

 1 
Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset Zaky, Umar; Naswin, Ahmad; Sumiyatun, Sumiyatun; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.113

Abstract

Ensuring water potability is paramount for public health and safety. This research aimed to assess the efficacy of the Decision Tree classification algorithm in predicting water potability using the Water Quality and Potability dataset. Employing a 5-fold cross-validation technique, the model showcased a moderate performance with an average accuracy of approximately 54.33%. While the Decision Tree provides a baseline and interpretable mechanism for classification, the results emphasize the need for further exploration using more intricate models or ensemble methods. This study contributes to the broader effort of leveraging machine learning techniques for water quality assessment and provides insights into the potential and limitations of such models in predicting water safety
Assessing the Predictive Power of Logistic Regression on Liver Disease Prevalence in the Indian Context Alwiah, Izmy; Zaky, Umar; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.121

Abstract

This study delves into the application of Logistic Regression through a Voting Classifier to predict liver disease prevalence within the Indian demographic, specifically analyzing data from the NorthEast of Andhra Pradesh. Employing a dataset encompassing 584 patient records, the research utilizes a 5-fold cross-validation approach to evaluate the model's performance across accuracy, precision, recall, and F1-Score metrics. The findings reveal accuracy rates ranging from 69.23% to 74.14%, with variable precision and recall, indicating a promising yet improvable predictive capability of the model. The study significantly contributes to the existing body of knowledge by demonstrating the potential of Logistic Regression in medical diagnostics, especially in the context of liver disease, and highlighting the critical role of machine learning models in enhancing diagnostic processes. Through a detailed discussion, the research aligns with previous studies on the efficacy of machine learning in healthcare, advocating for the integration of more comprehensive data and suggesting further exploration into the model's applicability across diverse populations. The study's implications extend to healthcare professionals and policymakers, underscoring the necessity for advanced diagnostic tools in the early detection of liver diseases.
Co-Authors Afwan Anggara Alwiah, Izmy Ari Prasetyoaji Aris Wahyu Murdiyanto Diansah, Febri Nur Habibi, Farid Afif Hanggraini Dea Astuti Ilham Fathullah Ilham Fathullah Iman Yanuar Kholis, Muhammad Yunus Kusumojakti, Mochamad Aqsha Pasalewa Laksana, Danu Dwiki Muhammad Zakariyah Naswin, Ahmad Norfajriyah, Norfajriyah Prasetyo, Sushilo Pratama, Satria Yoga Purnamasari, Dwi Amalia Rabbani, Muhammad Riefky Raden Andy Kurniawan Ramadhan, Idham Rismayanti, Nurul Rizka Amanah Sumiyatun Tati Indriani