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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) PENDIPA Journal of Science Education Journal on Education COMPTON: Jurnal Ilmiah Pendidikan Fisika Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Innovative Science Education
Yustina Yesisanita Yeyen
Universitas Nusa Nipa
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Economics, Econometrics & Finance Education Health Professions Materials Science & Nanotechnology Mathematics Physics Public Health

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Deteksi Adulterasi VCO (Virgin Coconat Oil) Berbasis Arduino dengan Alogritma Machine Learning melalui Analisis Sifat Dielektrik Yustina Yeyen; Maria Yunita; Yohanes Eudes Debrito; Lusitania Floribunda Mele; Yohanes Maria Vianney
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.13481

Abstract

The mixing of pure coconut oil (VCO) with cheaper vegetable oils has negative impacts on both consumers and producers. This study aims to develop a method for detecting VCO adulteration using an ESP32-based dielectric sensor combined with a Random Forest classification algorithm. The research employed an experimental design using 225 samples, including pure VCO, canola oil, corn oil, and various mixture ratios, each measured with five repetitions. The results show that pure VCO exhibits the highest capacitance values (58.4–62.4 pF), followed by canola oil (44.8–47.8 pF) and corn oil (43.2–46.6 pF), indicating clear differences in dielectric properties related to fatty acid composition. ANOVA analysis confirmed a significant difference between pure VCO and adulterated oils (p < 0.05). The Random Forest model achieved an accuracy of 53–58% for 15-class classification, while binary classification (pure vs adulterated oil) reached more than 90% accuracy. This finding is discussed in terms of the effectiveness of dielectric sensing combined with machine learning for distinguishing oil authenticity. In conclusion, the proposed system provides a fast, low-cost, mobile, and user-friendly solution for VCO quality monitoring, with potential applications in supply chain control and consumer protection.
Co-Authors Agustina Elisabeth Agustina Elizabeth Alfonsus Wajong Bertolomeus Haryanto Agung Desak Made Anggraeni Early, Indira Elfrida Loliyanti Bura Lusitania Floribunda Mele Maria Bernadetha Dua Riong Maria Bernadetha Riong Maria Fransiska Olyvia Maria Paulina Witin Maria Yunita Maria Yunita Mole, Paulina Nelce Nathalia Wea, Kristiana Nicolaus Hilary Kokan Uran Suban , Maria Enjelina Wajong, Alfonsus Yohanes Eudes Debrito Yohanes Maria Vianney Yustina Tuti Lein