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All Journal Speed - Sentra Penelitian Engineering dan Edukasi JIKO (Jurnal Informatika dan Komputer) Respati International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues
Sumiyatun
Fakultas Teknologi Informasi, Jurusan Sistem Informasi, Universitas Teknologi Digital Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science 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
Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment Iwan Sudipa, I Gede; Azdy, Rezania Agramanisti; Arfiani, Ika; Setiohardjo, Nicodemus Mardanus; Sumiyatun
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.v5i1.125

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.
Co-Authors Aris Wahyu Murdiyanto Cuk Subiyantoro Cuk Subiyantoro, Cuk Edi Faizal Edi Iskandar Femi Dwi Astuti Hera Wasiati I Gede Iwan Sudipa Ika Arfiani Maulidinnawati, Andi Naswin, Ahmad Nicodemus Mardanus Setiohardjo Prayitna, Adiyuda Pulut Suryati Retyanto Wardoyo Rezania Agramanisti Azdy, Rezania Agramanisti Sudarmanto Sudarmanto, Sudarmanto Syam, Rahmat Fuadi Syamsu Windarti Tarigan, Thomas Edyson Thomas Edyson Tarigan Thomas Edyson Tarigan Totok Suprawoto Umar Zaky Yagus Cahyadi Yasinta Marto Enga