Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Authors

  • Istiadi Widyagama Unversity of Malang, Indonesia
  • Aviv Yuniar Rahman Widyagama Unversity of Malang, Indonesia
  • Alif Dio Raka Wisnu Widyagama Unversity of Malang, Indonesia

DOI:

10.33395/sinkron.v8i1.12006

Abstract

Tempe is one of the traditional foods in Indonesia which has nutritional content and benefits that are very much favored by all Indonesian people. To determine the maturity of tempe, it is generally done by fermenting it into tempeh using a certain temperature and usually tempe entrepreneurs are done traditionally. But in this way, tempe producers do not know what temperature and humidity are right for tempeh maturity. In this study, researchers used the MATLAB R2018a application with a total data set of 137 raw data, 137 ripe data and 136 rotten data, totaling 410 tempe image data. The purpose of this research is to produce a system that can detect the ripeness of tempe using the KNN (K-Nearest Neighbor) method which is equipped with GLCM texture feature extraction, with extraction of 8 color features, using the PCA (Principal Component Analysis) selection feature. And compare the results with the same method, namely KNN (K-Nearest Neighbor) without using the PCA (Principal Component Analysis) selection feature with the required running time between the two. KNN with PCA selection feature gets an average accuracy value of 80.63% and takes 1.06 seconds. Compared with the same method, namely KNN without using the selection feature, it gets an average accuracy value of 81.67% with a time of 1.18 seconds.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Adenugraha, S. P., Arinal, V., & Mulyana, D. I. (2022). Klasifikasi Kematangan Buah Pisang Ambon Menggunakan Metode KNN dan PCA Berdasarkan Citra RGB dan HSV. JURNAL MEDIA INFORMATIKA BUDIDARMA, 6(1), 9-17.

Alamsyah, D., & Pratama, D. (2019). Segmentasi Warna Citra Bunga Daisy dengan Algoritma K-Means pada Ruang Warna Lab. Jurnal Buana Informatika, 10(2), 153-163.

Arun, C. H., & Durairaj, D. C. (2017). Identifying medicinal plant leaves using textures and optimal colour spaces channel. Jurnal Ilmu Komputer dan Informasi, 10(1), 19-28.

Aryanta, I. W. R. (2020). Manfaat tempe untuk kesehatan. Widya Kesehatan, 2(1), 44-50.

Taningrum, D. R., Hidayat, B., & Hariyani, Y. S. (2016). Sistem Pengidentifikasian Plat Nomor Kendaraan Mobil Menggunakan Principal Component Analysis Dan Klasifikasi KNN. E-Proceeding Eng, 3(2), 1868-1876.

Edha, H., Sitorus, S. H., & Ristian, U. (2020). Penerapan metode transformasi ruang warna hue saturation intensity (HSI) untuk mendeteksi kematangan buah mangga harum manis. Coding Jurnal Komputer dan Aplikasi, 8(1).

Eriksson, I., & Tabachnikova, N. (2022). “Learning models”: Utilising young students’ algebraic thinking about equations. LUMAT: Luonnontieteiden, matematiikan ja teknologian opetuksen tutkimus ja käytäntö, 10(2).

Fadjeri, A., Saputra, B. A., Ariyanto, D. K. A., & Kurniatin, L. (2022). Karakteristik Morfologi Tanaman Selada Menggunakan Pengolahan Citra Digital. Jurnal Ilmiah Sinus (JIS) Vol, 20(2).

Galib, S. L., Tahir, F. S., & Abdulrahman, A. A. (2021). Detection Face parts in image using Neural Network Based on MATLAB. Engineering and Technology Journal, 39(1B), 159-164.

Gunawan, B., & Sukardi, S. (2020). Rancang Bangun Pengontrolan Suhu dan Kelembaban pada Proses Fermentasi Tempe Berbasis Internet of Things. JTEIN: Jurnal Teknik Elektro Indonesia, 1(2), 168-173.

Hasan, M. A., & Liliana, D. Y. (2020). Pengenalan Motif Songket Palembang Menggunakan Deteksi Tepi Canny, PCA dan KNN. vol, 6, 1-7.

Ilhamsyah, I., Rahman, A. Y., & Istiadi, I. (2021). Klasifikasi Kualitas Biji Kopi Menggunakan MultilayerPerceptron Berbasis Fitur Warna LCH. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1008-1017.

Junianto, E., & Zuhdi, M. Z. (2018). Penerapan Metode Palette untuk Menentukan Warna Dominan dari Sebuah Gambar Berbasis Android. Jurnal Informatika, 5(1), 61-72.

Nabella, F. Y., Sari, Y. A., & Wihandika, R. C. (2019). Seleksi Fitur Information Gain Pada Klasifikasi Citra Makanan Menggunakan Hue Saturation Value dan Gray Level Co-Occurrence Matrix. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X.

Rambe, A., Tanjung, J. P., & Muhathir, M. (2022). Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K–Nearest Neighbor. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 5(2), 414-422.

Raysyah, S., Arinal, V., & Mulyana, D. I. (2021). Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode Knn Dan Pca. JSiI (Jurnal Sistem Informasi), 88-95.

Sinlae, A. A. J., Alamsyah, D., Suhery, L., & Fatmayati, F. (2022). Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA). Sinkron: jurnal dan penelitian teknik informatika, 7(1), 93-100.

Yana, Y. E., & Nafi’iyah, N. (2021). Klasifikasi Jenis Pisang Berdasarkan Fitur Warna, Tekstur, Bentuk Citra Menggunakan SVM dan KNN. Journal of Computer, Information System & Technology Management, 4(1), 5.

Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems, 29(5), 1774-1785.

Downloads


Crossmark Updates

How to Cite

Istiadi, I., Rahman, A. Y. ., & Wisnu, A. D. R. . (2023). Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(1), 286-294. https://doi.org/10.33395/sinkron.v8i1.12006