MODEL HYBRID CNN-SVM UNTUK KLASIFIKASI KESEGARAN BUAH PARE BERDASARKAN CITRA
DOI:
https://doi.org/10.31000/jika.v9i4.14773Abstrak
Kualitas dan kesegaran buah sangat memengaruhi nilai jual serta keamanan konsumsi masyarakat. Pare (Momordica charantia) merupakan salah satu buah yang rentan mengalami penurunan mutu akibat suhu dan kelembapan yang tidak stabil. Oleh karena itu, diperlukan sistem yang mampu mengklasifikasikan kondisi kesegaran pare secara otomatis dan akurat. Penelitian ini bertujuan untuk membangun model klasifikasi citra digital pare segar dan busuk menggunakan pendekatan hybrid Convolutional Neural Network (CNN) dan Support Vector Machine (SVM). CNN digunakan untuk mengekstraksi fitur visual dari gambar, sedangkan SVM digunakan sebagai algoritma klasifikasi akhir. Dataset citra diproses melalui tahapan pre-processing seperti resize, normalisasi, dan augmentasi hingga mencapai data seimbang. Model CNN terdiri dari beberapa lapisan konvolusi, pooling, dan flattening, lalu dihubungkan ke SVM. Hasil pengujian menunjukkan bahwa model hybrid CNN-SVM mampu mengklasifikasikan kondisi kesegaran pare dengan akurasi 94,97%, presisi 92,68%, recall 97,44%, dan F1-score 95,00%. Penelitian ini membuktikan bahwa pendekatan hybrid CNN-SVM efektif untuk klasifikasi mutu buah dari citra digital.Referensi
Agung Mujiono, A., Kartini, K., & Yulia Puspaningrum, E. (2024). IMPLEMENTASI MODEL HYBRID CNN-SVM PADA KLASIFIKASI KONDISI KESEGARAN DAGING AYAM. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 756–763. https://doi.org/10.36040/jati.v8i1.8855
Bastian, A., Priyadi, D., Zaliluddin, D., Mardiana, A., Wahid, A., Rifki, M., & Aziz, M. F. (2025). Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Kualitas Beras sebagai Strategi Peningkatan Keamanan Pangan di Indonesia. TEMATIK, 12(1). https://doi.org/https://doi.org/10.38204/tematik.v12i1.2332
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with SKLerni, Keras and TensorFlow. In Hands-On Machine Learning with R. “ O’Reilly Media, Inc.â€
Deng, L., & Yu, D. (2013). Deep learning: Methods and applications. In Foundations and Trends in Signal Processing (Vol. 7, Issues 3–4, pp. 197–387). Now Publishers, Inc. https://doi.org/10.1561/2000000039
Dheny Arina Hartawaty, Marosimy Millaty, & Hermiza Aulia. (2025). STRATEGI PEMASARAN SAYUR KEMAS SIAP MASAK (VEGETABLE MIX) DI UMKM PACK INSTAN. Jurnal Pertanian Agros, 27(2), 199–206. https://doi.org/10.37159/jpa.v27i2.62
Hilir, A. (2021). Teknologi Pendidikan di Abad Digital (M. P. Singgih Subiyantoro (ed.)). Penerbit Lakeisha.
Khairunnisa, D. A., & Syariah, M. E. (2024). MANAJEMEN RISIKO DALAM RANTAI PASOK HALAL KOMODITAS SAYURAN : STUDI KASUS DESA TANI.
Peryanto, A., Hakim, L., & Nugrahantoro, A. (2025). Klasifikasi Citra Bekicot Menggunakan Algoritma Support Vector Machine. 6(2), 59–64. https://ejournal.uhb.ac.id/index.php/IKOMTI/article/view/1790
Rabbani, S., Safitri, D., Rahmadhani, N., Sani, A. A. F., & Anam, M. K. (2023). Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 153–160. https://journal.irpi.or.id/index.php/malcom/article/view/897
Santoso, D., & Egra, S. (2022). Teknologi Penanganan Pascapanen. Syiah Kuala University Press. https://books.google.co.id/books?id=PuBkEAAAQBAJ
Santoso, J. T. (2021). Kecerdasan Buatan & Jaringan Syaraf Buatan. In M. K. Muhammad Sholikan, M. M. T. Dr. Mars Caroline Wibowo. S.T., & M. K. Irdha Yunianto, S.Ds. (Eds.), Penerbit Yayasan Prima
Agus Teknik (Vol. 7, Issues 1 SE-Judul Buku). Yayasan Prima Agus Teknik Bekerja sama dengan Universitas Sains & Teknologi Komputer (Universitas STEKOM). https://penerbit.stekom.ac.id/index.php/yayasanpat/article/view/177
Santoso, J. T. (2023). Kecerdasan Buatan (Artiï¬cial Intelligence). Penerbit Yayasan Prima Agus Teknik, 1–227.
Sudirwo, Abdul Hadi, Loso Judijanto, Nuraini Purwandari, & Neni N. L. Ersela Zain. (2025). Artificial Intelligence: Teori, Konsep, dan Implementasi di Berbagai Bidang. PT. Sonpedia Publishing Indonesia.
Syahputra, R. A., & Hanifah, M. R. (2024). Metode Analisis Kesehatan Dengan Mengguakan Machine Learning dan Data Mining: Literature Review. Jurnal Industri Dan Inovasi (INVASI), 1(2).
Tahir, M. M. (2023). Penanganan Pasca Panen Dan Produk Olahan Sayuran. In Hukum Perumahan. Nas Media Pustaka. https://books.google.co.id/books?id=MIG3EAAAQBAJ
Agung Mujiono, A., Kartini, K., & Yulia Puspaningrum, E. (2024). IMPLEMENTASI MODEL HYBRID CNN-SVM PADA KLASIFIKASI KONDISI KESEGARAN DAGING AYAM. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 756–763. https://doi.org/10.36040/jati.v8i1.8855
Bastian, A., Priyadi, D., Zaliluddin, D., Mardiana, A., Wahid, A., Rifki, M., & Aziz, M. F. (2025). Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Kualitas Beras sebagai Strategi Peningkatan Keamanan Pangan di Indonesia. TEMATIK, 12(1). https://doi.org/https://doi.org/10.38204/tematik.v12i1.2332
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with SKLerni, Keras and TensorFlow. In Hands-On Machine Learning with R. “ O’Reilly Media, Inc.â€
Deng, L., & Yu, D. (2013). Deep learning: Methods and applications. In Foundations and Trends in Signal Processing (Vol. 7, Issues 3–4, pp. 197–387). Now Publishers, Inc. https://doi.org/10.1561/2000000039
Dheny Arina Hartawaty, Marosimy Millaty, & Hermiza Aulia. (2025). STRATEGI PEMASARAN SAYUR KEMAS SIAP MASAK (VEGETABLE MIX) DI UMKM PACK INSTAN. Jurnal Pertanian Agros, 27(2), 199–206. https://doi.org/10.37159/jpa.v27i2.62
Hilir, A. (2021). Teknologi Pendidikan di Abad Digital (M. P. Singgih Subiyantoro (ed.)). Penerbit Lakeisha.
Khairunnisa, D. A., & Syariah, M. E. (2024). MANAJEMEN RISIKO DALAM RANTAI PASOK HALAL KOMODITAS SAYURAN : STUDI KASUS DESA TANI.
Peryanto, A., Hakim, L., & Nugrahantoro, A. (2025). Klasifikasi Citra Bekicot Menggunakan Algoritma Support Vector Machine. 6(2), 59–64. https://ejournal.uhb.ac.id/index.php/IKOMTI/article/view/1790
Rabbani, S., Safitri, D., Rahmadhani, N., Sani, A. A. F., & Anam, M. K. (2023). Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 153–160. https://journal.irpi.or.id/index.php/malcom/article/view/897
Santoso, D., & Egra, S. (2022). Teknologi Penanganan Pascapanen. Syiah Kuala University Press. https://books.google.co.id/books?id=PuBkEAAAQBAJ
Santoso, J. T. (2021). Kecerdasan Buatan & Jaringan Syaraf Buatan. In M. K. Muhammad Sholikan, M. M. T. Dr. Mars Caroline Wibowo. S.T., & M. K. Irdha Yunianto, S.Ds. (Eds.), Penerbit Yayasan Prima
Agus Teknik (Vol. 7, Issues 1 SE-Judul Buku). Yayasan Prima Agus Teknik Bekerja sama dengan Universitas Sains & Teknologi Komputer (Universitas STEKOM). https://penerbit.stekom.ac.id/index.php/yayasanpat/article/view/177
Santoso, J. T. (2023). Kecerdasan Buatan (Artiï¬cial Intelligence). Penerbit Yayasan Prima Agus Teknik, 1–227.
Sudirwo, Abdul Hadi, Loso Judijanto, Nuraini Purwandari, & Neni N. L. Ersela Zain. (2025). Artificial Intelligence: Teori, Konsep, dan Implementasi di Berbagai Bidang. PT. Sonpedia Publishing Indonesia.
Syahputra, R. A., & Hanifah, M. R. (2024). Metode Analisis Kesehatan Dengan Mengguakan Machine Learning dan Data Mining: Literature Review. Jurnal Industri Dan Inovasi (INVASI), 1(2).
Tahir, M. M. (2023). Penanganan Pasca Panen Dan Produk Olahan Sayuran. In Hukum Perumahan. Nas Media Pustaka. https://books.google.co.id/books?id=MIG3EAAAQBAJ
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