Article Sidebar

Submitted
March 22, 2025
Published
September 30, 2024
Download
pdf
Statistic
Read Counter : 64 Download : 68

Main Article Content

Abstract

Chili is one of the main agricultural commodities in Indonesia with significant economic value. Various types of chili, such as large chili, bird's eye chili, and green chili, are often difficult to distinguish manually due to their physical similarities. To support advancements in the agricultural sector, this study utilizes artificial intelligence technology, specifically the Convolutional Neural Network (CNN) with VGG-16 architecture, to automatically identify chili types through image analysis based on color and shape. This study aims to measure the accuracy, sensitivity, and specificity of the model in classifying chili types. The results show that the VGG-16 architecture achieved 100% accuracy in training data testing, indicating the model’s ability to detect and classify chili types optimally. In the model evaluation (fold 5), the accuracy was 91.8%, sensitivity was 88%, and specificity was 93.8%. This study confirms that CNN with VGG-16 is effective for image classification, especially when test data shares similar characteristics with training data. This system offers significant potential for application in the agricultural sector, particularly in improving the efficiency and accuracy of identifying other agricultural commodities.

Keywords

Convolutional Neural Network (CNN) Image Classification Chili Type

Article Details

References

  1. Anggraeni, T.N. and Fadlil, A. (2013) ‘SISTEM IDENTIFIKASI CITRA JENIS CABAI (Capsicum Annum L.) MENGGUNAKAN METODE KLASIFIKASI CITY BLOCK DISTANCE’, Jurnal Sarjana Teknik Informatika, 1(2), pp. 409–418.
  2. Betris Tosi, R., Mbura, H.D. and Kaesmetan, Y.R. (2024) ‘Implementasi CNN Dalam Mengidentifikasi Kematangan Cabai Berdasarkan Warna’, INDOTECH Indonesian Journal of Education And Computer Science, 2(1).
  3. Bismi, W., Novianti, D. and Qomaruddin, M. (no date) ‘Jurnal Informatika dan Rekayasa Perangkat Lunak Analisis Perbandingan Klasifikasi Citra Genus Panthera dengan Pendekatan Deep learning Model MobileNet’.
  4. Chunjing, Y. et al. (2017) ‘Application of convolutional neural network in classification of high resolution agricultural remote sensing images’, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing, pp. 989–992. Available at: https://doi.org/10.5194/isprs-archives-XLII-2-W7-989-2017.
  5. Dogan, Y., Ozdemir, C. and Kaya, Y. (2024) ‘Enhancing CNN model classification performance through RGB angle rotation method’, Neural Computing and Applications [Preprint]. Available at: https://doi.org/10.1007/s00521-024-10232-z.
  6. Dwi Ayu Lestari, R., Setya Rintyarna, B. and Dasuki, M. (2022) Application Of N-Gram On K-Nearest Neighbor Algorithm To Sentiment Analysis Of TikTok Shop Shopping Features, Jurnal Mantik. Online.
  7. Fauzi, L., Dasuki, Moh. and Muharom, L.A. (2024) ‘Penerapan Algoritma Support Vector Machine untuk Klasifikasi Sentimen Vaksin Booster pada Twitter’, JUSTIFY : Jurnal Sistem Informasi Ibrahimy, 2(2), pp. 135–143. Available at: https://doi.org/10.35316/justify.v2i2.4005.
  8. Hasan, Moh.A., Riyanto, Y. and Riana, D. (2021) ‘Grape leaf image disease classification using CNN-VGG16 model’, Jurnal Teknologi dan Sistem Komputer, 9(4), pp. 218–223. Available at: https://doi.org/10.14710/jtsiskom.2021.14013.
  9. Kamilaris, A. and Prenafeta-Boldú, F.X. (2018) ‘A review of the use of convolutional neural networks in agriculture’, Journal of Agricultural Science. Cambridge University Press, pp. 312–322. Available at: https://doi.org/10.1017/S0021859618000436.
  10. Laksono, F.B. (2024) ‘Deteksi penyakit tanaman dengan convolution neural network: Kombinasi arsitektur VGG16 dan ResNet34 untuk klasifikasi daun’, JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI, 2(2). Available at: https://doi.org/10.26714/jkti.v2i2.13932.
  11. Maulana, R. et al. (2024) IDENTIFIKASI JENIS REMPAH-REMPAH INDONESIA DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN ARSITEKTUR VGG16, Jurnal Mahasiswa Teknik Informatika.
  12. Munir, M.M., Kasih, P. and Sanjaya, A. (2024) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) 1626, Agustus. Online.
  13. Noprisson, H. (2022) ‘Fine-Tuning Model Transfer Learning VGG16 Untuk Klasifikasi Citra Penyakit Tanaman Padi’, JSAI: Journal Scientific and Applied Informatics, 5(3). Available at: https://doi.org/10.36085.
  14. Pebrianto, R. and Haryono, T. (2023) ‘Optimasi Sistem Klasifikasi Biji Tanaman Cabai Menggunakan CNN: Pendekatan Inovatif dalam Agribisnis’, IJCIT (Indonesian Journal on Computer and Information Technology), 8(2), pp. 121–129.
  15. Perlindungan, I. (2020) PENGENALAN TANAMAN CABAI DENGAN TEKNIK KLASIFIKASI MENGGUNAKAN METODE CNN.
  16. Sanusi Mashuri, A. and Sunyoto, A. (2024) ‘Klasifikasi Penyakit Pada Daun Cabai Menggunakan Arsitektur VGG16’, Journal homepage: Journal of Electrical Engineering and Computer (JEECOM), 6(2). Available at: https://doi.org/10.33650/jeecom.v4i2.
  17. Setia Budi, E. et al. (2024) ‘RESOLUSI : Rekayasa Teknik Informatika dan Informasi Optimasi Model Machine Learning untuk Klasifikasi dan Prediksi Citra Menggunakan Algoritma Convolutional Neural Network’, Media Online, 4(5), p. 509. Available at: https://djournals.com/resolusi.
  18. Setiono, M. (2024) ‘Klasifikasi Penyakit Antraknosa Citra Cabai Rawit Dengan Metode Convolutional Neural Network (CNN)’, Hal; Jl. Jembatan Merah No.84 C Gejayan Yogyakarta, 11(2).
  19. Setyadi, R.A. et al. (2024) ‘IMPLEMENTASI TRANSFER LEARNING UNTUK KLASIFIKASI PENYAKIT PADA DAUN CABAI MENGGUNAKAN CNN’, Jurnal Teknologi Informasi, 5(2). Available at: https://doi.org/10.46576/djtechno.
  20. Tri, B.N. et al. (no date) Program Studi Teknik Informatika, Universitas Nusantara PGRI Kediri.
  21. Tsany, A. and Dzaky, R. (no date) Deteksi Penyakit Tanaman Cabai Menggunakan Metode Convolutional Neural Network.
  22. Yu, F. et al. (2023) ‘Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles’, Remote Sensing. MDPI. Available at: https://doi.org/10.3390/rs15122988.
  23. Yulestiono, A.Y. et al. (2024) Perbandingan Kinerja Metode Convolutional Neural Network (CNN) dan VGG-16 dalam Klasifikasi Rambu Lalu Lintas, JAMASTIKA.