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All Journal INOVTEK Polbeng - Seri Informatika
Pradema Sanjaya, Ucta
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Computer Science & IT

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Journal : INOVTEK Polbeng - Seri Informatika

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Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Jauhar Vikri, Muhammad; Wisma Dwi Prastya, Ifnu; Pradema Sanjaya, Ucta; Agung Barata, Mula
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
From Data Imbalance to Precision: SMOTE-Driven Machine Learning for Early Detection of Kidney Disease Adi Bhirawa, Aldani; Pradema Sanjaya, Ucta
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7jgjmg64

Abstract

Chronic Kidney Disease (CKD) has become a significant global health issue, with its prevalence rising sharply, particularly in developing countries like Indonesia. According to the Kementrian Kesehatan (KEMENKES), the Synthetic Minority Over-sampling Technique (SMOTE) has been widely adopted to address this. SMOTE generates synthetic samples for the minority class, enhancing the model’s ability to identify high-risk patients. Studies demonstrate SMOTE’s effectiveness, particularly when combined with ensemble learning algorithms like Random Forest and Gradient Boosting. The data collection focused on relevant medical parameters critical for the study, encompassing laboratory test results, diagnostic reports, and clinical observations related to kidney function. This dataset in kidney disease is used to predict whether someone has chronic kidney disease or not with a total sample of 400 data obtained from the Ungaran Regional Hospital and several clinics that can detect kidney disease. Recent research highlights that SMOTE significantly improves model accuracy, with Random Forest achieving 99.30% accuracy. These findings emphasise the importance of data balancing in enhancing diagnostic precision, offering promising avenues for early CKD detection and improved patient outcomes.
Co-Authors Adi Bhirawa, Aldani Agung Barata, Mula Jauhar Vikri, Muhammad Wisma Dwi Prastya, Ifnu