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All Journal Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Komputer dan Agri-Informatika TEKMAPRO Journal of Industrial Engineering and Management SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Informatik : Jurnal Ilmu Komputer SEINASI-KESI Jurnal Teknologi Informasi dan Pendidikan SULUH: Jurnal Abdimas Jurnal Ilmu Teknik dan Komputer JISA (Jurnal Informatika dan Sains) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Abmas Negeri (JAGRI) Jurnal Pengabdian Mandiri ABDIKOM
Hananto, Bayu
Universitas Pembangunan Nasional Veteran Jakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Performance comparison of algorithms in the classification of fresh fruit types based on MQ array sensor data Hananto, Bayu; Raafi'udin, Ridwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9070

Abstract

Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring quality control, minimizing waste, and enhancing food safety across the supply chain. This study evaluates the performance of four machine learning algorithms—artificial neural network (ANN), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF)—in classifying fruit freshness based on data obtained from electronic noses equipped with MQ array sensors. Experiments were conducted using a comprehensive dataset comprising various fruit combinations, and model performance was assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that the RF algorithm achieved the highest accuracy (100%) and precision (1.00), demonstrating superior performance in both classification accuracy and computational efficiency. ANN and KNN also performed well, with accuracies of 96.80% and 97.10%, respectively, while LR yielded a lower but still effective accuracy of 91.16%. Statistical analysis confirms that RF's superior performance is statistically significant when compared to the other algorithms. These findings suggest that RF is the most effective algorithm for fruit freshness classification using electronic nose data, offering fast and reliable results that are well-suited for integration into real-time monitoring systems in agricultural and food retail applications.
Co-Authors Anelia, Syifa Sabrina Aniek Irawati Anita Muliawati Azhari, Fikri Bambang Saras Yulistiawan Bambang Triwahyono Catur Nugrahaeni Puspita Dewi Desta Sandya Prasvita Didit Widiyanto DODY KURNIAWAN Erly Krisnanik Ernawati, Iin Handayani, Lusia I Wayan Widi Pradnyana Jayanta Jayanta Jayanta Jayanta Maherza, Syania Aulia Matondang, Nur Hafifah Matondang, Nurhafifah Muammar Fadhlurrohman Mulyantini, Sri Nainggolan, Hollywrith Travaganz Nugrahaeni Puspita Dewi, Catur Pangaribuan, A B PD, Catur Nugrahaeni Priono, Hendro Putranto, Dimas Perdana Putri, Dwi Amanda Rahmawati, Arindiah Ratih Nindyasari Ridwan Raafi'udin Rio Wirawan Ruth Mariana Bunga Wadu Sandya Prasvita, Desta Sarika Afrizal, Sarika Sarika Sarika Satria Yudhia Wijaya Sri Mulyantini Tri Rahayu Tri Rahayu Uljanatunissa Uljanatunissa Wahyono, Bambang Tri Widiastiwi, Yuni Wirawan, Rio Yani Hendrayani Zaidiah, Ati