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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Data and Science Teknomatika (Jurnal Teknologi dan Informatika) Jurnal Abdimas Bina Bangsa Jurnal Pengabdian Masyarakat Bangsa International Journal of Artificial Intelligence in Medical Issues
Rezania Agramanisti Azdy, Rezania Agramanisti
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Indonesian Journal of Data and Science

 1 
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.
Development of a Decision Tree Classifier for Breast Cancer Diagnosis Using Fine Needle Aspirate Data Halid, Agus; Wikranta Arsa, I Gusti Ngurah; Azdy, Rezania Agramanisti; Jiwa Permana, Agus Aan
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.v5i3.202

Abstract

Breast cancer is one of the leading causes of mortality among women globally, necessitating early and accurate detection to improve survival rates. This study leverages machine learning to develop a decision tree classifier for distinguishing between benign and malignant breast masses using the Kaggle Breast Cancer FNA dataset. The dataset underwent rigorous pre-processing, including the removal of irrelevant columns, data cleaning, label encoding, and feature scaling. The model was evaluated using 5-fold cross-validation, achieving an average accuracy of 84.0%, with a test set accuracy of 83.72%. Performance metrics such as precision, recall, and F1-score further validated the model's robustness, with an overall accuracy of 90.24% on the test set. The decision tree classifier demonstrated high interpretability, making it a practical tool for aiding clinical decision-making. While the results are promising, the study highlights opportunities for improvement, including the use of ensemble methods and larger datasets to enhance generalizability. This research contributes to the growing body of evidence supporting machine learning applications in medical diagnostics, particularly in breast cancer detection.
Co-Authors Admojo, Fadhila Tangguh Agus Aan Jiwa Permana Agus Halid, Agus Edi Faizal Fahmi Ajismanto Febrianty, Febrianty Febriyanti Darnis Firamon Syakti, Firamon Guntoro Barovih, Guntoro Halim D., R.M. Nasrul Hannah, Merrieayu Puspita Hanzani, Hanzani I Gede Iwan Sudipa I Gusti Ngurah Wikranta Arsa, I Gusti Ngurah Ika Arfiani Misinem, Misinem Nicodemus Mardanus Setiohardjo Nirwana, Nirwana Nita Rahmawati, Nita Putra, M. Soekarno Rini, Arsia Saputri, Nurul Adha Oktarini Sumiyatun Sya'ban, Gema Awal Syam, Rahmat Fuadi Wuryanti, Lestari