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All Journal Sainteks Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO IJID (International Journal on Informatics for Development) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Edsence: Jurnal Pendidikan Multimedia ScientiCO : Computer Science and Informatics Journal Building of Informatics, Technology and Science International Journal of Advances in Data and Information Systems Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence in Medical Issues ABDIMASTEK
Ika Arfiani
Universitas Ahmad Dahlan
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology 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.
Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors Iwan Sudipa, I Gede; Habibi, Muhammad; Jullev Atmadji, Ery Setiyawan; Arfiani, Ika
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.v5i3.201

Abstract

Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
Co-Authors Abdullah Sayfei Nurdin Agus Aan Jiwa Permana Alfian, Mukhsin Apriatna, Sendy Arfiani Nur Khusna Bambang Sugiantoro Dwi Normawati, Dwi Erma Susanti Herman Yuliansyah Herman Yuliansyah, Herman I Gede Iwan Sudipa Isnan Arif Cahyadi Jullev Atmadji, Ery Setiyawan Lisna Zahrotun Muhamad Fadli Muhammad Dzikrullah Suratin Muhammad Dzikrullah Suratin Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Habibi Mukhsin Alfian Murien Nugraheni Murien Nugraheni Nicodemus Mardanus Setiohardjo Nur Rochmah Dyah Pujiastuti Nur Rochmah Dyah Pujiastuti, Nur Rochmah Dyah P utra Rizqa Yasira Probo Prasetyo Ramadhan, Imam Rezania Agramanisti Azdy, Rezania Agramanisti Rochmah Dyah Pujiastuti, Nur Siami, M. Ikbal Sulistyono, Danang Sumiyatun Sunia Raharja, I Made Supriyanto Tarigan, Thomas Edyson Tia Purwantias Zain Ahmad Taufik