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All Journal Indonesian Journal of Electronics and Instrumentation Systems Transmisi: Jurnal Ilmiah Teknik Elektro INDONESIAN JOURNAL OF APPLIED PHYSICS Jurnal Teknik Elektro dan Komputasi (ELKOM) Al-Khidmah Jurnal Pengabdian Masyarakat Elektriese: Jurnal Sains dan Teknologi Elektro Jurnal Teknik Terapan (J-TETA) International Journal of Health and Information System (IJHIS) International Journal of Technology, Food and Agriculture SEJAGAT : Jurnal Pengabdian Masyarakat
Afianah, Nuzula
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Machine Learning-Based Cow Milk Quality Classification using Recursive Feature Elimination Cross-Validation Wicaksono, Damar; Mareta, Affix; Erdiyanto, Ardy; Afianah, Nuzula; Ramadhani, Rafly
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 2 (2024): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i2.85064

Abstract

Milk quality is of paramount importance as it directly impacts consumer health and well-being. High-quality milk is rich in essential nutrients such as calcium, protein, and vitamins, contributing to overall nutrition. Moreover, ensuring milk quality is vital for preventing the transmission of diseases and contaminants through dairy products. Therefore, research in this field is essential to guaranteeing the safety and nutritional value of milk consumed by individuals of all ages. In this paper, the design of machine learning-based grade measuring devices with recursive feature elimination with cross-validation (RFECV) is carried out as a guide in the design of a milk grade detection system. The milk is rated as low, medium, or high based on these criteria. The sensors will gather this information from the milk with the aid of the microcontroller. The algorithms utilized in this study and the results obtained from K-Nearest Neighbors (KNN) combined with the RFECV algorithm have a higher accuracy value: 17,20% better than the support vector machine (SVM) model, 25.37% better than the single K-Nearest Neighbors (KNN), and 26.37% better than the random forest (RF) model trained without RFECV. Using seven input features (pH, temperature, taste, odor, fat, turbidity, and color), the proposed model produces 96.27% accuracy.
Co-Authors Aditya Wahyu Pratama Afriansyah, Faisal Lutfi Alam, Ahmad Mutafakkir Alfiansyah, Faisal Luthfi Anggita, Silvia Nur Damar Wicaksono Dicky Adi Tyagita Dwianto, Sihmaulana Dwinanda, Angga Erdiyanto, Ardy Fanani, Nurul Zainal Febriani, Siti Diah Ayu Hakim, Irham Maulana Kautsar, Syamsiar Kindi, Muhammad Koesacep, HAP Mareta, Affix Nafsiyah, Haniatun Nari, Mochamad Irwan Nari, Mochammad Irwan Nari, Muhammad Irwan Purnomo, Fendik Eko Putri, Salsabila Liandra Qanitah, Qanitah Ramadhani, Rafly Rofi'i, Ahmad Rofi’i, Ahmad Rohman, Ardianto Syaifur Tunjung Genarsih, Tunjung Wahyu Pratama, Aditya Winadi, Aditya Wahyu Winardi, Aditya Wahyu Yuana Susmiati