IMPLEMENTASI HYBRID INTELLIGENCE SYSTEM UNTUK KLASIFIKASI BIJI-BIJIAN DENGAN ALGORITMA PCA DAN KNN
DOI:
https://doi.org/10.33480/inti.v19i2.6397Keywords:
artificial intelligence, K-Nearest Neighbor, principal component analysis, seed classificationAbstract
Food security has become a pressing global issue with the increasing population and food consumption needs. Red kidney beans, peanuts, and sunflower seeds play a crucial role in meeting the nutritional needs of society and serving as raw materials for various industries. This study aims to develop a seed classification system based on the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms. The system is designed to recognize three types of seeds—red kidney beans, peanuts, and sunflower seeds—to improve the efficiency and accuracy of the classification process compared to manual methods. The dataset consists of 58 seed image samples, divided into training data (48 samples) and test data (10 samples). The research stages include image preprocessing (cropping, background removal, and thresholding segmentation), feature extraction using PCA to reduce data dimensionality, and classification with KNN based on Euclidean distance. A value of K=3 is used in the KNN algorithm to determine the proximity between data points. The test results show a classification accuracy of 90%, with 9 out of 10 test data correctly classified. PCA successfully simplified high-dimensional data into two main components without significant information loss, while KNN demonstrated strong capability in distinguishing the three types of seeds. This research contributes to the development of an AI-based automatic classification system for the food industry, with broader potential applications in high-dimensional data processing across various fields.
Downloads
References
Akbar, J. M., Sabirin, M., Nugraha, G. S., & Alamsyah, N. (2022). Penerapan Metode Principal Component Analysis (Pca) Dan K-Nearest Neighbors (Knn) Untuk Klasifikasi Data Kanker Paru-Paru. Jurnal Teknologi Informasi, Komputer Dan Aplikasinya (JTIKA), 4(2), 144–151. http://jtika.if.unram.ac.id/index.php/JTIKA/
Chan, F. R., Annas, F., Yuspita, Y. E., & Darmawati, G. (2024). Implementation of Convolutional Neural Networks ( CNN ) in An Emotion Detection System for Measuring Learning Concentration Levels. Knowbase: International Journal of Knowledge in Database, 04(01), 49–61.
Dinanti, A., & Purwadi, J. (2023). Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis. Jambura Journal of Mathematics, 5(1), 155–165. https://doi.org/10.34312/jjom.v5i1.17098
Fitri, E. N., Ariansyah, M. H., Winarno, S., Budiman, F., (2023). Performance of K-Means Clustering and Knn Classifier in Fish Feed Seller Determination Models. J. Tek. Inform. https://www.academia.edu/download/119655849/308.pdf
Hasan, M. A. (2023). Classification of Apple Types Using Principal Component Analysis and K-Nearest Neighbor. International Journal of Information System Technology and Data Science, 1(1), 15–22. http://ejournal.lotusaruna.id/index.php/ijistdas/article/view/11
Hidayat, S., Ramadhan, H. M. T., & ... (2023). Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification. Indonesian Journal https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/70
Krismawan, A. D., & Rachmawanto, E. H. (2022). Principal Component Analysis (PCA) dan K-Nearest Neighbor (KNN) dalam Deteksi Masker pada Wajah. Prosiding Sains Nasional Dan Teknologi, 12(1), 382. https://doi.org/10.36499/psnst.v12i1.7066
Marisa, F., Maukar, A. L., Farhan, A., Widodo, E. A., Sa, I., & Dasilva, R. T. L. (2022). Pengukuran Tingkat Kematangan Kopi Arabika Menggunakan Algoritma K-Nearest Neighbour. JIMP : Jurnal Informatika Merdeka Pasuruan, 6(3), 4–8.
Mujadin, A., Rifaldi, S. P., Samijayani, O. N., & Suyono, H. (2024). Pengujian Kemurnian Minyak Kayu Putih Berbasis Electronic Nose Menggunakan Metode PCA Dan Neural Network. BULLET : Jurnal Multidisiplin Ilmu, 3(01), 40–47.
Nugraha, R. A., Hidayat, E. W., & Shofa, R. N. (2023). Klasifikasi Jenis Buah Jambu Biji Menggunakan Algoritma Principal Component Analysis dan K-Nearest Neighbor. Gener. J. https://www.academia.edu/download/110957018/3143.pdf
Nuraini, R. (2022). Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis. Journal of Computer System and Informatics (JoSYC), 3(3), 133–140. http://ejurnal.seminar-id.com/index.php/josyc/article/view/1694
Nurfitri, K., Pradana, A. D., & Widaningrum, I. (2021). PENERAPAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN K-NEAREST NEIGHBORS (KNN) PADA KLASIFIKASI. Jurnal Rekayasa Teknologi Dan Komputasi, 1(1), 1–19.
Putra, A., Abdullah, D., & Daud, M. (2024). Klasterisasi Kualitas Biji Kopi Berdasarkan Taraf Penyusutan Menggunakan Metode K-Harmonic Means dengan Validasi Silhouette Index dan C-index. Jurnal Janitra Informatika Dan Sistem https://janitra.org/index.php/home/article/view/203
Rasyid, N., Novella, T. J., & Nuraminah, A. (2022). IMPLEMENTASI METODE K-NEAREST NEIGHBOUR DALAM MEMPREDIKSI CURAH HUJAN DI KOTA BOGOR. JURNAL RISET RUMPUN ILMU TEKNIK, 1(1). https://prin.or.id/index.php/JURRITEK/article/view/592
Raysyah, S. R., Veri Arinal, & Dadang Iskandar Mulyana. (2021). Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode Knn Dan Pca. JSiI (Jurnal Sistem Informasi), 8(2), 88–95. https://doi.org/10.30656/jsii.v8i2.3638
Rosiani, U. D., Mentari, M., & Prastya, A. N. (2019). Klasifikasi Kualitas Biji Jagung Berdasarkan Deteksi Warna dan Bentuk Menggunakan Metode K-Nearest Neighbor. Seminar Informatika Aplikatif 2019. http://jurnalti.polinema.ac.id/index.php/SIAP/article/view/641/222
Rulaningtyas, R., & Winarno, W. (2023). Classification of cervical cancer cells using the K-nearest neighbor (KNN) method based on geometric feature extraction. AIP Conference Proceedings, 1. https://pubs.aip.org/aip/acp/article-abstract/2858/1/030003/2906771
Sabry, F. (2023). K Nearest Neighbor Algorithm: Fundamentals and Applications. One Billion Knowledgeable.
Setiawan, A. (2022). Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski dalam Klasifikasi Menggunakan Metode KNN pada Data Iris. Jurnal Sains Dan Edukasi Sains. https://ejournal.uksw.edu/juses/article/view/6097
Simanullang, G., Ngadeni, A., & Hartyana, T. (2021). FORMULASI SEDIAAN SABUN PELEMBAB TRANSPARAN YANG MENGANDUNG MINYAK BIJI BUNGA MATAHARI (Sunflowerseed Oil). Pharmacoscript, 4(1), 10–31. https://doi.org/10.36423/pharmacoscript.v4i1.604
Siregar, S. D., Ginting, Y. U. R., Sintami, N., Butar-Butar, H. S., (2023). Implementation of KNN algorithm in classifying diabetic ulcers in patients with diabetes mellitus. Jurnal Mantik. http://www.iocscience.org/ejournal/index.php/mantik/article/view/3928
Sriani, S., Lubis, A. H., & Nasution, R. R. (2024). PENERAPAN DATA MINING UNTUK PREDIKSI PENJUALAN PRODUK ELEKTRONIK TERLARIS MENGGUNAKAN METODE K-NEAREST NEIGHBOR. JOURNAL OF SCIENCE https://jurnal.goretanpena.com/index.php/JSSR/article/view/2153
Sugiharto, A., Wirawan, P. W., Nugroho, F. A., & ... (2022). Comparison of SVM, Random Forest and KNN Classification By Using HOG on Traffic Sign Detection. 2022 6th International Conference on Informatics and Computational Sciences (ICICoS). https://ieeexplore.ieee.org/abstract/document/9930588/
Tarigan, E., Hutauruk, H., Siahaan, I. S., & ... (2024). Analisis Reduksi Dimensi Produktivitas Jagung di Provinsi-Provinsi Indonesia Menggunakan PCA. Interdisciplinary Explorations in Research Journal, 2(3), 1967–1976. https://shariajournal.com/index.php/IERJ/article/download/778/410
Unihehu, A. L., & Suharjo, I. (2021). The Klasifikasi Jenis Ikan Berbasis Jaringan Saraf Tiruan Menggunakan Algoritma Principal Component Analysis (PCA). Jurnal Ilmiah Ilmu Komputer, 7(2), 27–32. https://doi.org/10.35329/jiik.v7i2.200
Yanti, R., Chan, F. R., & Ramadhanu, A. (2024). Penerapan Image Processing untuk Identifikasi Jenis Pisang Emas dan Pisang Kapas Menggunakan Metode K-Means Clustering. Journal of Education Research, 0738(4), 4938–4943.
Zanah, N., & Sriani, S. (2024). Classification of Types of Dental Disease Using Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Methods. JINAV: Journal of Information and Visualization. https://jinav.org/index.php/jinav/article/view/2778
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Fajri Rinaldi Chan, Agung Ramadhanu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Penulis yang menerbitkan jurnal ini menyetujui ketentuan berikut:
1. Penulis memegang hak cipta dan memberikan hak jurnal mengenai publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution 4.0 International License. yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas karya penulis dan publikasi awal pada jurnal.
2. Penulis dapat memasukkan pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi jurnal yang diterbitkan (misalnya, mengirimkannya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan atas publikasi awalnya pada Jurnal.
3. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, dalam penyimpanan institusional atau di situs web mereka) sebelum dan selama proses pengiriman, karena hal itu dapat menghasilkan pertukaran yang produktif, serta kutipan dari karya yang diterbitkan sebelumnya.
