Implementasi Deep Learning Menggunakan Cnn Untuk Klasifikasi Tingkat Kematangan Buah Jeruk Berbasis Android

Authors

  • Fajar Rahardika Bahari Putra Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Sorong, Papua Barat Daya, 98418
  • Muhammad Rizki Setyawan Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Sorong, Papua Barat Daya, 98418
  • Rendra Soekarta Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Sorong, Papua Barat Daya, 98418
  • Nabila Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Sorong, Papua Barat Daya, 98418
  • La Jupriadi Fakhri Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Sorong, Papua Barat Daya, 98418

Keywords:

Classification, Citrus, Convolutional Neural Network, VGG 16

Abstract

Indonesia is a country that heavily relies on the agricultural sector, including various types of horticultural commodities, especially fruits. One example is oranges, which have many benefits and a sweet, refreshing taste. To obtain the best flavor and freshness, fully ripe oranges are the preferred choice. However, the process of recognizing the ripeness of oranges still faces many challenges. With advances in computer technology, particularly through smartphones, many human tasks can now be performed more efficiently and practically. One useful technology is computer vision, which can be used to automatically identify and determine the ripeness of oranges. This research aims to implement Convolutional Neural Networks (CNN) to measure the model's performance and ensure its capability in classifying the ripeness of oranges. The results of the research show that classification using CNN with the VGG-16 architecture achieved a training accuracy of 96% and a validation accuracy of 97%.

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References

Y. T. L. Calvin Bill Roring, Dadang Iskandar Mulyana, “Klasifikasi Tingkat Kematangan Buah Jambu Bol Berdasarkan Warna Kulit Menggunakkan Metode Naïve Bayes,” J. Pendidik. Tambusai, vol. 6, no. 1, pp. 2938–2948, 2022.

J. C. Lapendy, A. A. C. Resky, H. Makmur, A. B. Kaswar, D. D. Andayani, and F. Adiba, “Klasifikasi Rasa Jeruk Siam Berdasarkan Warna Dan Tekstur Berbasis Pengolahan Citra Digital,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 2, pp. 756–767, 2024, doi: dx.doi.org/10.26418/positron.v12i2.64594.

A. M. Simarmata, A. Zizwan Putra, and A. Mahmud Husein, “Penerapan Metode Computer Vision Dalam Klasifikasi Buah Jeruk Menggunakan Teknik Image Pre-Processing,” Data Sci. Indones., vol. 3, no. 2, pp. 110–116, 2023, doi: 10.47709/dsi.v3i2.4010.

N. M. Dwicahyo, R. Wulanningrum, Aswi, and R. Ramadhani, “Penyortiran Buah Jeruk Dengan Ekraksi Ciri Rgb To Hsv Menggunakan Naïve Bayes,” INOTEK, vol. 8, pp. 894–902, 2024, doi: doi.org/10.29407/inotek.v8i2.5018.

R. Soekarta, N. Nurdjan, and A. Syah, “Klasifikasi Penyakit Tanaman Tomat Menggunakan Metode Convolutional Neural Network (CNN),” Insect (Informatics Secur. J. Tek. Inform., vol. 8, no. 2, pp. 143–151, 2023, doi: doi.org/10.33506/insect.v8i2.2356.

A. S. J. Putra, I. M. I. Subroto, and B. S. W. Poetro, “Identifikasi Kematangan Buah Jeruk Medan Menggunakan K-Nearest Neighbor berbasis Metrik RGB,” J. Transistor Elektro dan Inform. (TRANSISTOR EI), vol. 5, no. 3, p. 50112, 2023, doi: dx.doi.org/10.30659/ei.5.3.155-160.

Y. Sutanto, C. P. H., Nurhasanah, D. Wahyuni, Y. Arman, and Hasanuddin, “Implementasi Convolutional Neural Network dalam Menentukan Tingkat Kematangan Jeruk Siam Pontianak Berdasarkan Citra,” Positron, vol. 12, no. 2, pp. 163–170, 2022, doi: dx.doi.org/10.26418/positron.v12i2.64594.

S. Sanjaya, “Aplikasi Pengenalan Tingkat Kematangan Buah Tomat Menggunakan Fitur Warna Hsv Berbasis Android,” J. Teknoinfo, vol. 16, no. 1, p. 26, 2022, doi: doi.org/10.33365/jti.v16i1.1489.

Yazid Aufar, Muhammad Helmy Abdillah, and Jiki Romadoni, “Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 1, pp. 71–79, 2023, doi: doi.org/10.29207/resti.v7i1.4622.

A. R. I. Fauzy and Erwin Budi Setiawan, “Detecting Fake News on Social Media Combined with the CNN Methods,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 271–277, 2023, doi: doi.org/10.29207/resti.v7i2.4889.

W. N. Waluyo, R. Rizal Isnanto, and Adian Fatchur Rochim, “Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 1, pp. 80–87, 2023, doi: doi.org/10.29207/resti.v7i1.4626.

Jalu Nusantoro, Faldo Fajri Afrinanto, Wana Salam Labibah, Zamah Sari, and Yufis Azhar, “Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and Transfer Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 430–441, 2022, doi: 10.29207/resti.v6i3.4118.

E. G. Sihombing, E. Aristawati, and L. S. Dewi, “Application of Learning Rate in Artificial Neural Networks to Increase Prediction Accuracy on Rubber Tree Maintenance Costs Universitas Bina Sarana Informatika , Indonesia Universitas Nusa Mandiri , Jakarta , Indonesia,” KESATRIA J. Penerapan Sist. Inf. (Komputer Manajemen), vol. 5, no. 4, pp. 1461–1470, 2024, doi: doi.org/10.30645/kesatria.v5i4.467.g462.

H. Nurrani, Andi Kurniawan Nugroho, and Sri Heranurweni, “Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 1, pp. 168–178, 2023, doi: doi.org/10.29207/resti.v7i1.4715.

Mayanda Mega Santoni, Nurul Chamidah, Desta Sandya Prasvita, Helena Nurramdhani Irmanda, Ria Astriratma, and Reza Amarta Prayoga, “Penerapan Convolutional Neural Networks untuk Mesin Penerjemah Bahasa Daerah Minangkabau Berbasis Gambar,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1153–1160, 2021, doi: doi.org/10.29207/resti.v5i6.3614.

Rima Dias Ramadhani, A. Nur Aziz Thohari, C. Kartiko, A. Junaidi, T. Ginanjar Laksana, and N. Alim Setya Nugraha, “Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 312–318, 2021, doi: doi.org/10.29207/resti.v5i2.2754.

R. Sistem et al., “Analisis Perbandingan Algoritma Klasifikasi MLP dan CNN pada Dataset American Sign Language,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 489–495, 2021, doi: doi.org/10.29207/resti.v5i3.3009.

F. R. B. Putra, A. Fadlil, and R. Umar, “Application of Forward Chaining Method , Certainty Factor , and Bayes Theorem for Cattle Disease,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 14, no. 1, pp. 365–374, 2024, doi: https://doi.org/10.18517/ijaseit.14.1.18912.

I. Salamah, S. Humairoh, and S. Soim, “Implementasi Convolutional Neural Network Pada Alat Klasifikasi Kematangan dan Ukuran Buah Nanas Berbasis Android,” INOVTEK Polbeng - Seri Inform., vol. 8, no. 2, p. 243, 2023, doi: doi.org/10.35314/isi.v8i2.3413.

J. Christian and S. I. Al Idrus, “Introduction to Citrus Fruit Ripens Using the Deep Learning Convolutional Neural Network (CNN) Learning Method,” Asian J. Appl. Educ., vol. 2, no. 3, pp. 459–470, 2023, doi: doi.org/10.55927/ajae.v2i3.5003.

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Published

2024-12-30

How to Cite

Rahardika Bahari Putra, F., Rizki Setyawan, M., Soekarta, R., Nabila, & Fakhri , L. J. (2024). Implementasi Deep Learning Menggunakan Cnn Untuk Klasifikasi Tingkat Kematangan Buah Jeruk Berbasis Android. JURNAL MAHAJANA INFORMASI, 9(2), 85–94. Retrieved from https://e-journal.sari-mutiara.ac.id/index.php/7/article/view/5462

Issue

Vol. 9 No. 2 (2024): JURNAL MAHAJANA INFORMASI

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Artikel

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Copyright (c) 2024 Fajar Rahardika Bahari Putra, Muhammad Rizki Setyawan, Rendra Soekarta, Nabila, La Jupriadi Fakhri

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