Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network

Deny Haryadi, Sasmi Hidayatul Yulianing Tyas, Adi Kuncoro, Fiqry Firdhan Pratama Putra, Andri Ariyanto


The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification.


Crude cooking oil; Identification; Image Processing; Quality; Color

Full Text:



D. Haryadi and D. Adidrana, “Implementation of K-Medoids Clustering Algorithm for Grouping Palm Oil Exports by Destination Country,” in 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS, Jakarta, Indonesia, Oct. 2021, pp. 129–134. doi: 10.1109/ICIMCIS53775.2021.9699176.

D. Haryadi, “Penerapan Algoritma K-Means Clustering Pada Produksi Perkebunan Kelapa Sawit Menurut Provinsi,” p. 15, 2021.

R. Jawahir Gustav, “Konsumsi Minyak Goreng Sawit di Indonesia,” Kompas, 2022.

D. Haryadi and R. Mandala, “Prediksi Harga Minyak Kelapa Sawit Dalam Investasi Dengan Membandingkan Algoritma Naïve Bayes, Support Vector Machine dan K-Nearest Neighbor,” IT Soc., vol. 4, no. 1, Mar. 2019, doi: 10.33021/itfs.v4i1.1181.

Minyak goreng sawit. BSN, 2019.

M. Y. Ramadan, “Implementasi metode klasifikasi Support Vector Machine (SVM) terhadap pemakaian minyak goreng,” 2018.

T. Majchrzak, W. Wojnowski, A. Głowacz-Różyńska, and A. Wasik, “On-line assessment of oil quality during deep frying using an electronic nose and proton transfer reaction mass spectrometry,” Food Control, vol. 121, p. 107659, Mar. 2021, doi: 10.1016/j.foodcont.2020.107659.

T. Majchrzak, W. Wojnowski, T. Dymerski, J. Gębicki, and J. Namieśnik, “Electronic noses in classification and quality control of edible oils: A review,” Food Chem., vol. 246, pp. 192–201, Apr. 2018, doi: 10.1016/j.foodchem.2017.11.013.

S. Baskara, D. Lelono, and T. W. Widodo, “Pengembangan Hidung Elektronik untuk Klasifikasi Mutu Minyak Goreng dengan Metode Principal Component Analysis,” IJEIS Indones. J. Electron. Instrum. Syst., vol. 6, no. 2, p. 221, Oct. 2016, doi: 10.22146/ijeis.15347.

P. N. Raj, M. Prakash, and K. K. Bhat, “QUALITY ASSESSMENT OF OIL BLENDS BY ELECTRONIC NOSE TECHNIQUE AND SENSORY METHODS,” J. Sens. Stud., vol. 21, no. 3, pp. 322–332, Jun. 2006, doi: 10.1111/j.1745-459X.2006.00068.x.

N. Khan, M. A. Kamaruddin, U. U. Sheikh, Y. Yusup, and M. P. Bakht, “Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps,” Agriculture, vol. 11, no. 9, p. 832, Aug. 2021, doi: 10.3390/agriculture11090832.

C. A. Jaramillo-Acevedo, W. E. Choque-Valderrama, G. E. Guerrero-Álvarez, and C. A. Meneses-Escobar, “Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods,” Int. J. Food Eng., vol. 0, no. 0, Sep. 2020, doi: 10.1515/ijfe-2019-0161.

Z. Ibrahim, N. Sabri, and D. Isa, “Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network,” vol. 10, no. 3, p. 5.

J. Naranjo-Torres, M. Mora, R. Hernández-García, R. J. Barrientos, C. Fredes, and A. Valenzuela, “A Review of Convolutional Neural Network Applied to Fruit Image Processing,” Appl. Sci., vol. 10, no. 10, p. 3443, May 2020, doi: 10.3390/app10103443.

A. Y. Saleh and E. Liansitim, “Palm oil classification using deep learning,” Sci. Inf. Technol. Lett., vol. 1, no. 1, pp. 1–8, Apr. 2020, doi: 10.31763/sitech.v1i1.1.

N. Saranya, K. Srinivasan, and S. K. P. Kumar, “Banana ripeness stage identification: a deep learning approach,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 8, pp. 4033–4039, Aug. 2022, doi: 10.1007/s12652-021-03267-w.

A. Syaifuddin, L. N. A. Mualifah, L. Hidayat, and A. M. Abadi, “Detection of palm fruit maturity level in the grading process through image recognition and fuzzy inference system to improve quality and productivity of crude palm oil (CPO),” J. Phys. Conf. Ser., vol. 1581, no. 1, p. 012003, Jul. 2020, doi: 10.1088/1742-6596/1581/1/012003.

W. S. Pambudi and A. N. Tompunu, “Aplikasi Sensor Vision untuk Deteksi MultiFace dan Menghitung Jumlah Orang,” p. 8, 2012.

R. F. Rachmadi and I. K. E. Purnama, “Vehicle Color Recognition using Convolutional Neural Network.” arXiv, Aug. 15, 2018. Accessed: Aug. 18, 2022. [Online]. Available:

M. R. Mahajan, “Electrical and Electronic Engineering,” p. 55.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization.” arXiv, Jan. 29, 2017. Accessed: Nov. 18, 2022. [Online]. Available:


Article Metrics

Abstract view : 0 times
PDF - 0 times


  • There are currently no refbacks.

View My Stats


Creative Commons License

Jurnal Rekayasa Elektrika (JRE) is published under license of Creative Commons Attribution-ShareAlike 4.0 International License.