Pengenalan Kualitas Tempe Berbasis YOLOv8 untuk Deteksi Dini Kegagalan Fermentasi

Isa Mahfudi; Mila Kusumawardania; Moechammad Moechammad Sarosa; Chandrasena Setiadi; Galih Putra Riatma; Farida Arinie Soelistianto; Nabila Izzatul Muslimah; Nadia Yumni Izati

doi:10.26905/jasiek.v7i2.16091

Authors

Isa Mahfudi Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia https://orcid.org/0000-0001-9259-7665
Mila Kusumawardania Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Moechammad Moechammad Sarosa Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Chandrasena Setiadi Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Galih Putra Riatma Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Farida Arinie Soelistianto Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Nabila Izzatul Muslimah Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia
Nadia Yumni Izati Politeknik Negeri Malang, Jl. Soekarno Hatta No.9, Jatimulyo, Kec. Lowokwaru, Kota Malang, Indonesia

DOI:

https://doi.org/10.26905/jasiek.v7i2.16091

Keywords:

Deep Learning, Fermentation, Quality Detection, Tempe, YOLOv8

Abstract

Tempe is a traditional Indonesian food whose fermentation process is highly influenced by temperature, humidity, and soybean quality. Inadequate environmental conditions can lead to fermentation failure, reducing product quality and causing economic losses. Traditionally, quality assessment of tempe has been carried out manually by artisans, which is subjective and inconsistent. This study aims to develop an automatic tempe quality recognition system based on YOLOv8, implemented on a Raspberry Pi 4B with a Logitech C270 camera, monitoring webserver, and buzzer as an early warning system. The YOLOv8 model was trained to detect two main categories, namely good tempe and failed tempe, through real-time visual analysis. Experimental results show system performance with an average accuracy of 93.1%, precision of 93.5%, recall of 91.2%, and mAP@50 of 94.7%. Confidence score analysis indicates that the model is more certain in detecting failed tempe (0.94–0.95) compared to good tempe (0.80–0.86), due to clearer visual differences.

Downloads

Download data is not yet available.

References

H. Hendrarini, T. Soedarto, A. Putri, S. Rusdianto, and R. F. Setiawan, “Inventory of Raw Materials and Business Feasibility in Tempe Agroindustry in Sedenganmijen Village , Sidoarjo District,” Himal. J. Agric., pp. 1–8, 2021.

N. R. Reddy, “Tempe,” in Tempe: Persembahan Indonesia untuk Dunia, 2019, pp. 95–40.

Y. Asbur and K. Khairunnisyah, “Tempe sebagai sumber antioksidan: Sebuah Telaah Pustaka,” Agril. J. Ilmu Pertan., vol. 9, no. 3, pp. 2021–183, 2021.

C. S. Wulandari, M. Murhadi, S. Rizal, and M. E. Kustyawati, “Pengaruh Perbandingan Rhizopus oligosporusdan Saccharomyces cerevisiaeTerhadap Sifat Sensori Tempe Selama Fermentas,” Semin. Nas. LPPM UMMAT, vol. 22, pp. 442–451, 2024.

R. S. Sari, N. Nuryanto, and A. Widiyanto, “Temperature and Humidity Control System for Tempe Gembus Fermentation Process Based on Internet of Things,” Urecol Journal. Part G Multidiscip. Res., vol. 1, no. 1, pp. 39–45, 2021.

A. A. Prasetyo et al., “Analisis Tingkat Keberhasilan Dan Kegagalan Dalam Pembuatan Tempe Dengan Menerapkan Distribusi Binomial,” Bull. Appl. Ind. Eng. Theory, vol. 2, no. 2, pp. 91–93, 2021.

G. M. Aji, A. F. Pratiwi, and S. W. Utami, “Rancang Bangun Inkubator Tempe Untuk Mempercepat Waktu Fermentasi,” Agroteknika, vol. 7, no. 4, pp. 488–497, 2024.

P. Damayanti, “Sistem Monitoring Suhu Dan Kelembaban Pada Inkubator Fermentasi Tempe Menggunakan Thinger.Io,” J. Elektro Kontrol, vol. 3, no. 2, pp. 1–10, 2023.

Rosidin, R. Novianti, K. P. Ningsih, D. Haryadi, G. Chrisnawati, and N. Anripa, “Peran Kecerdasan Buatan Dalam Pengembangan Sistem Otomatisasi Proses Bisnis,” J. Rev. Pendidik. dan Pengajaran, vol. 7, pp. 9320–9329, 2024.

M. Trigka and E. Dritsas, “A Comprehensive Survey of Machine Learning Techniques for Brain Tumor Detection,” Sensors, vol. 796, pp. 309–319, 2024.

T. Taufiqurrahman, A. P. Hadi, and R. E. Siregar, “Evaluasi Performa Yolov8 Dalam Deteksi Objek Di Depan Kendaraan Dengan Variasi Kondisi Lingkungan,” J. Minfo Polgan, vol. 13, no. 2, pp. 1755–1773, 2024.

R. Hidayattullah, N. Suarna, I. Ali, and D. I. Efendi, “Deep Learning Algoritma Yolov8 Untuk Meningkatkan Analisis Kepadatan Lalu Lintas,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 2, pp. 1–8, 2025.

I. Istiadi, A. Y. Rahman, and A. D. R. Wisnu, “Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 1, pp. 286–294, 2023.

Y. Hendrawan et al., “Classification of soybean tempe quality using deep learning,” IOP Conf. Ser. Earth Environ. Sci., vol. 924, no. 1, 2021.

A. H. A. Ash’shobir, K. G. P. Harli, A. P. P. Rudi, I. G. S. Putro, and O. D. P. Cahyono, “Sistem Deteksi Kualitas Cabai Rawit Menggunakan Metode YOLO,” Modem J. Inform. dan Sains Teknol., vol. 3, no. 1, pp. 114–132, 2025.

N. M. Yasen, S. Rifka, R. Vitria, and Y. Yulindon, “Pemanfaatan Yolo Untuk Deteksi Hama Dan Penyakit Pada Daun Cabai Menggunakan Metode Deep Learning,” Elektron J. Ilm., vol. 15, pp. 63–71, 2023.

R. Padilla, S. L. Netto, and E. A. B. Da Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” Int. Conf. Syst. Signals, Image Process., vol. 2020-July, no. July, pp. 237–242, 2020.

H. Zhang and X. Yang, “A Survey on Algorithms and Performance Metrics for Object Detection,” Int. Core J. Eng., vol. 9, no. 9, pp. 133–145, 2023.