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All Journal Indonesian Journal of Data and Science JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Journal of Artificial Intelligence and Digital Business International Journal of Artificial Intelligence in Medical Issues
Naswin, Ahmad
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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Journal : Indonesian Journal of Data and Science

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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
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Co-Authors Admojo, Fadhila Tangguh Agus Aan Jiwa Permana Aris Wahyu Murdiyanto Batubulan, Kadek Suarjuna Hamada Zein I Gusti Ngurah Wikranta Arsa, I Gusti Ngurah I Putu Adi Pratama, I Putu Adi Purnamasari, Dwi Amalia Raden Mohamad Herdian Bhakti Radhitya, Made Leo Rismayanti, Nurul Sitti Mawaddah Umar Sumiyatun Sumiyatun Umar Zaky