Fruit Ripeness Classification System Using Convolutional Neural Network (CNN) Method
Abstract
The increasing consumer demand in the fruit industry has also demanded that various sectors of the fruit processing industry be able to adapt to this situation. The demand for good quality and fresh fruit requires technological advances and supporting systems that can be used in the fruit processing industry to produce the best quality fruit. Referring to this, this study aims to detect the type and maturity of fruit using machine learning with the CNN (Convolutional Neural Network) method using the function of a camera that is integrated with the program algorithm. This research is a refinement of previous research that has been made at the university by increasing the ability to read objects based on color with different methods. In this programming language, Python also requires several additional libraries to carry out the object detection process, namely by using the cvzone library as the main library. This study shows that the detection of fruit and ripeness using the CNN method was successful in detecting the type and maturity of the fruit. In the design and trial of this research, it can run well according to the algorithm created by the researcher. The success rate and accuracy of the detection of the type and maturity of this fruit reach 90%.
Keywords
Full Text:
PDFReferences
Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., et al. (2020). Teachable machine: Approachable web-based tool for exploring machine learning classification. Conference on Human Factors in Computing Systems - Proceedings.
Hussain, M., Bird, J. J., & Faria, D. R. (2019). A study on CNN transfer learning for image classification. Advances in Intelligent Systems and Computing, 840(June), 191–202.
Khairunnas, K., Yuniarno, E. M., & Zaini, A. (2021). Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot. Jurnal Teknik ITS, 10(1).
Komarayanti, S. (2017). Ensiklopedia Buah-buahan Lokal Berbasis Encyclopedia of Local Fruits Based On Natural ENSIKLOPEDIA BUAH-BUAHAN. Journal of Biology and Biology Learning, 2(1), 61–75.
Limin, N. S., Sari, J. Y., & Purnama, I. P. N. (2019). Identifikasi Tingkat Kematangan Buah Pisang Menggunakan Metode Ektraksi Ciri Statistik Pada Warna Kulit Buah. Ultimatics, 10(2), 98–102.
Maulana, F. F., & Rochmawati, N. (2020). Klasifikasi Citra Buah Menggunakan Convolutional Neural Network. Journal of Informatics and Computer Science (JINACS), 1(02), 104–108.
Nafiah, N. (2019). Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN. Jurnal Elektronika Listrik dan Teknologi Informasi Terapan, 1(2), 1–4. Retrieved from https://ojs.politeknikjambi.ac.id/elti
Najmurrokhman, A., Nugraha, A., Kusnandar, U. K., & ... (2017). Perancangan dan Realisasi Sistem Pendeteksi Objek menggunakan Perangkat Lunak Python 2.7. Lppm.Unjani.Ac.Id, 125–130. Retrieved from http://lppm.unjani.ac.id/wp-content/uploads/2018/10/125-130-Asep-SNIJA-2017.pdf
Naranjo-Torres, J., Mora, M., Hernández-GarcÃa, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020). A review of convolutional neural network applied to fruit image processing. Applied Sciences (Switzerland), 10(10).
Prabowo, D. A., & Abdullah, D. (2018). Deteksi dan Perhitungan Objek Berdasarkan Warna Menggunakan Color Object Tracking. Pseudocode, 5(2), 85–91.
Prasetya, D. A., & Nurviyanto, I. (2012). Deteksi wajah metode viola jones pada opencv menggunakan pemrograman python. Simposium Nasional RAPI XI FT UMS, 18–23.
Prayoga, A., Tawakal, H. A., & Aldiansyah, R. (2018). Pengembangan Metode Deteksi Tingkat Kematangan Buah Melon Berdasarkan Tekstur Kulit Buah Dengan Menggunakan Metode Ekstraksi Ciri Statistik Dan Support Vector Machine (Svm). Jurnal Teknologi Terpadu, 4(1), 24–30.
Rizki, D., Muhammad, R. ;, Fadillah, R., Igwahyudi, Q., & Dewanto, S. (2012). Alat Penyortir Dan Pengecekan Kematangan Buah Menggunakan Sensor Warna. Jurnal Teknik Komputer, 20(2), 88–92.
Setiawan Ghanie, C. E., & Setiawan, F. B. (2020). Penerapan Sistem Pan-Tilt Camera untuk Deteksi Objek berdasarkan Warna menggunakan Raspberry Pi. Prosiding Seminar Nasional Teknik Elektro, 5(2020), 92–96.
DOI: https://doi.org/10.33387/protk.v10i1.5549
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
| Journal Policies | Submissions | People | Information |
Editorial Office :
Address: Jusuf Abdulrahman 53 Gambesi, Ternate City, Indonesia.
Email: protek@unkhair.ac.id, WhatsApp: +6282292852552