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Taşpınar, Yavuz Selim
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Computer Science & IT

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Analysis of Different Sensor Data Using Machine Learning Methods for the Purpose of Determining Milk Quality Sevinç, Sinan; Taşpınar, Yavuz Selim
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5367

Abstract

Milk is a product with high nutritional value, but its quality may vary depending on factors from production to consumption. Milk is a food that can spoil over time and carries a disease risk due to microorganism growth. Therefore, continuous monitoring of milk quality is important. Quality loss can cause changes in milk components such as protein, fat, and lactose. In recent years, sensors have been used to evaluate milk quality by quickly measuring parameters such as chemical components, pH value, temperature, and fat content. These sensor data provide information not only about milk quality but also about the productivity and health of cows. This enables more efficient production processes and early detection of potential diseases. Sensor measurements help determine both milk quality and cow care needs. In this study, quality classification was performed using data from 1059 different milk samples. The dataset consists of 7 features and 1 class feature, and milk quality was classified into three classes: “high”, “medium”, and “low”. kNN (k-Nearest Neighbor), ANN (Artificial Neural Network), DT (Decision Tree), and RF (Random Forest) methods were used for classification. Model performance was evaluated using confusion matrix, accuracy, precision, recall, and F1 score, and detailed analysis was performed using the ROC curve. The kNN model achieved 99.8% accuracy, the ANN model 99.9%, the DT model 99.4%, and the RF model 100%. The RF model showed the highest success. Overall, the classification performances of all models were close to each other, and all can be used to determine milk quality.
Classification of Liquid Aroma Profiles Using Electronic Nose and Classical Machine Learning Methods Saycan, Binnur; Taspinar, Yavuz Selim
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5344

Abstract

The identification of aroma and quality profiles in liquids such as milk, coffee, tea, and vinegar is crucial for improving product quality. Since traditional methods are time-consuming and costly, the rapid detection of volatile organic compounds (VOCs) in such liquids using sensors has gained importance in recent times. Therefore, the AI Nose Dataset 250 data set obtained from the Electronic Nose (E-Nose) system was used in this study. This dataset contains 7 features consisting of 6 chemical and environmental sensors and 5 different classes: Perfume, Air, Coffee, Tea, and Vinegar. The Naive Bayes (NB) algorithm was used along with Random Forest (RF), k-Nearest Neighbor (kNN), AdaBoost, and Decision Tree (DT) methods to classify these data. To analyze the classification performance of the models, the Confusion Matrix was used along with the metrics Accuracy, Precision, Recall, and F1 Score. The ROC Curve was used for a detailed analysis of the classification performance of the models. As a result of the training and testing of the models, classification performance close to 100% was achieved with the RF and kNN models. The highest classification performance was achieved with the RF model. When the results were examined, it was seen that the classification performance of all Machine Learning models
Co-Authors Saycan, Binnur Sevinç, Sinan