Klasifikasi kualitas buah jeruk menggunakan computer vision dengan arsitektur YOLO V8
DOI:
https://doi.org/10.31571/saintek.v13i2.8346Keywords:
Yolov8, Klasifikasi mutu jeruk, Penyortiran otomatis, Kecerdasan buatanAbstract
Pengembangan sistem klasifikasi mutu jeruk berbasis kecerdasan buatan yang memanfaatkan fitur tekstur, warna, dan bentuk untuk meningkatkan efisiensi dan akurasi penyortiran jeruk siam Pontianak dari Kabupaten Sambas, Kalimantan Barat. Jeruk siam dikenal sebagai komoditas bernilai tinggi dengan permintaan nasional dan internasional yang besar, tetapi penyortiran manual masih menjadi tantangan bagi petani. Dataset penelitian terdiri dari 500 gambar jeruk dalam tiga kategori mutu antara lain matang, belum matang, dan busuk, yang diambil menggunakan kamera Sony Alpha a5100 beresolusi tinggi dan dianotasi melalui LabelImg. Model YOLOv8 digunakan untuk deteksi dan klasifikasi, mencapai akurasi pada data uji rata-rata sebesar 96,89 % untuk kategori matang, belum matang, dan busuk, dengan nilai mAP@0.5 sebesar 0,993, precision 0.98, recall 0.97, dan F1-score 0.99. Visualisasi hasil deteksi menunjukkan kemampuan model dalam mengidentifikasi area penting pada jeruk berdasarkan tekstur, warna, dan bentuk, serta memberikan hasil klasifikasi yang optimal. Implementasi sistem ini diharapkan dapat membantu petani meningkatkan efisiensi penyortiran jeruk, mengurangi beban kerja, dan memperluas daya saing produk di pasar.
Downloads
References
Akhter, N., & Idrees, M. (2021). Shape and texture-based classification of citrus using principal component analysis. International Journal of Agricultural Extension, 02. https://doi.org/10.33687/ijae.009.02.3525
Akyas Hifdzi Rahman, R., Adi Sunarto, A., & Asriyanik, A. (2024). Penerapan you only look once (yolo) v8 untuk deteksi tingkat kematangan buah manggis. JATI (Jurnal Mahasiswa Teknik Informatika), 8(5), 10566–10571. https://doi.org/10.36040/jati.v8i5.10979
Alfiano, O., & Rahayu, S. (2024). Implementasi algoritma deep learning yolo (you only look once) untuk deteksi kualitas kentang segar dan busuk secara real time. JORAPI : Journal of Research and Publication Innovation, 2(3), 2470–2478. https://jurnal.portalpublikasi.id/index.php/JORAPI/index
Ali, U., Ismail, M. A., Ariyaluran Habeeb, R. A., & Ali Shah, S. R. (2024). Performance evaluation of yolo models in plant disease detection. Journal of Informatics and Web Engineering, 3(2), 199–211. https://doi.org/10.33093/jiwe.2024.3.2.15
Barkah, M. F. (2020). Klasifikasi rasa buah jeruk pontianak berdasarkan warna kulit buah jeruk menggunakan metode k-nearest neighbor. Coding Jurnal Komputer dan Aplikasi, 8(1). https://doi.org/10.26418/coding.v8i1.39193
Hawibowo, M. S., & Muhimmmah, I. (2024). aplikasi pendeteksi tingkat kematangan pepaya menggunakan metode convolutional neural network (cnn) berbasis android. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 10(1), 162. https://doi.org/10.26418/jp.v10i1.77819
Hidayat, R., Firmando, A., Lestari, N. S., Hermawaty, & Fadriani, H. (2024). Klasifikasi jenis buah berdasarkan citra menggunakan metode ekstraksi ciri. Jurnal Teknologi Dan Sains Modern, 1(1), 36-48. https://doi.org/10.69930/jtsm.v1i1.62
Honestya, G., Sajida, M., & Ramadhanu, A. (2024). Klasifikasi jenis daun herbal menggunakan metode logistic regression dan decision tree classifier berdasarkan fitur (warna dan bentuk). Journal of Information System and Education Development, 2(1), 52–55. https://doi.org/10.62386/jised.v2i1.59
Ifmalinda, I., Andasuryani, A., & Shaufana, L. (2022). identifikasi bentuk buah alpukat (persea americana mill.) dengan analisis citra digital. Jurnal Teknologi Pertanian, 23(3), 215–226. https://doi.org/10.21776/ub.jtp.2022.023.03.5
Isa, I. G. T., & Junedi, B. (2022). Hyperparameter tuning epoch dalam meningkatkan akurasi data latih dan data validasi pada citra pengendara. Prosiding Sains Nasional Dan Teknologi, 12(1), 231. https://doi.org/10.36499/psnst.v12i1.6697
Jana, S., & Parekh, R. (2017). Shape-based Fruit Recognition and Classification. In J. K. Mandal, P. Dutta, & S. Mukhopadhyay (Eds.), Computational Intelligence, Communications, and Business Analytics (Vol. 776, pp. 184–196). Springer Singapore. https://doi.org/10.1007/978-981-10-6430-2_15
Kaya, E., & Saritas, İ. (2019). Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Computers and Electronics in Agriculture, 166, 105016. https://doi.org/10.1016/j.compag.2019.105016
Koklu, M., & Cinar, I. (2019). Classification of rice varieties using artificial intelligence methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188–194. https://doi.org/10.18201/ijisae.2019355381
Kristiandi, K., & Sitompul, N. (2020). Retensi vitamin c pada olahan limbah jeruk. Prosiding Seminar Nasional Riset Teknologi Terapan. https://jurnal.untidar.ac.id/index.php/senaster/article/view/2573
Lestari, D. P., Madenda, S., Octavia, L., Susetianingtias, D. T., Wibowo, H. T., Ligar, W., Kusuma, T. M., Suro, S., Rumambi, T., Prasetyo, E., Siswono, H., Yakti, B. K., Prayitno, R. H., Sudiro, S. A., Raharja, W. K., & Baaits, A. H. A. (2024). Komputer vision, kecerdasan artifisial, dan sistem tertanam. Gunadarma. https://penerbit.gunadarma.ac.id/wp-content/uploads/2024/07/KomputerVision-juli2024.pdf
Maneno, R., Baso, B., Manek, P. G., & Fallo, K. (2023). Deteksi tingkat kematangan buah pinang menggunakan metode support vector machine berdasarkan warna dan tekstur. Journal of Information and Technology, 3(2), 60–66. https://doi.org/10.32938/jitu.v3i2.5323
Manihuruk, J., Suswatiningsih, T. E., & Martini, R. (2018). Pemasaran buah jeruk di kecamatan silimakuta kabupaten simalungun. JURNAL MASEPI, 3(8). https://journal.instiperjogja.ac.id/index.php/JMI/article/download/517/491
Maulana, A. (2024). Literatur review implementasi artificiai intellegence dalam pertanian. 2. https://conference.upgris.ac.id/index.php/infest/article/view/6479
Maulana, I., Rahaningsih, N., & Suprapti, T. (2023). Analisis penggunaan model yolov8 (you only look once) terhadap deteksi citra senjata berbahaya. 7(6). https://ejournal.itn.ac.id/index.php/jati/article/view/8271
Naik, S., Patel, B., & Pandey, R. (2015). Shape, size and maturity features extraction with fuzzy classifier for non-destructive mango (Mangifera Indica L., cv. Kesar) grading. 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 1–7. https://doi.org/10.1109/TIAR.2015.7358522
Pawening, R. E., Shudiq, W. J., & Wahyuni, W. (2020). Klasifikasi kualitas jeruk lokal berdasarkan tekstur dan bentuk menggunakan metode k-Nearest Neighbor (k-NN). COREAI: Jurnal Kecerdasan Buatan, Komputasi Dan Teknologi Informasi, 1(1), 10–17. https://doi.org/10.33650/coreai.v1i1.1640
Prasetya, S. A. (2024). Building of Informatics, Technology and Science (BITS). 6(1). https://doi.org/ 10.47065/bits.v6i1.5396
Putra, A. S. J., Subroto, I. M. I., & Poetro, B. S. W. (2023). Identifikasi kematangan buah jeruk medan menggunakan K-Nearest Neighbor berbasis Metrik RGB. 5(3). http://jurnal.unissula.ac.id/online/index.php/EI
Rahmadewi, R., Sari, G. L., & Firmansyah, H. (2019). Pendeteksian kematangan buah jeruk dengan fitur citra kulit buah menggunakan transformasi ruang warna HSV. JTEV (Jurnal Teknik Elektro Dan Vokasional), 5(1.1), 166. https://doi.org/10.24036/jtev.v5i1.1.107560
Riastana, I. K., Astiari, N. K. A., & Sulistiawati, N. P. A. (2019). Kualitas buah jeruk siam (Citrus nobillis var microcarva L) selama penyimpanan pada berbagai tingkat kematangan buah. 1, 22-28. https://doi.org/10.22225/ga.24.1.1696.22-28
Saputra, D. H., Imran, B., & Juhartini. (2023). Object detection untuk mendeteksi citra buah-buahan menggunakan metode yolo. Jurnal Kecerdasan Buatan dan Teknologi Informasi, 2(2), 70–80. https://doi.org/10.69916/jkbti.v2i2.18
Shermila P, Josephin, Victor, A., Manoj, S. O., Sri Krishna College of Engineering and Technology, Devi, E. A., & Sathyabama Institute of Science and Technology. (2024). Automatic detection and classification of disease in citrus fruit and leaves using a customized CNN based model. Boletin Latinoamericano y Del Caribe de Plantas Medicinales y Aromaticas, 23(2), 180–198. https://doi.org/10.37360/blacpma.24.23.2.13
Siagian, S., Ibnutama, K., & Mahyuni, R. (2022). Implementasi metode ekstraksi ciri warna untuk mendeteksi kematangan buah jeruk. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 1(6), 898. https://doi.org/10.53513/jursi.v1i6.5861
Singh, S., & Malik, K. (2022). Feature selection and classification improvement of Kinnow using SVM classifier. Measurement: Sensors, 24, 100518. https://doi.org/10.1016/j.measen.2022.100518
Siswanto, I., Utami, E., & Raharjo, S. (2020). Klasifikasi Tingkat kematangan buah berdasarkan warna dan tekstur menggunakan metode K-Nearest Neighbor dan Nearest Mena Classifier. Inspiration: Jurnal Teknologi Informasi Dan Komunikasi, 10(1), 93. https://doi.org/10.35585/inspir.v10i1.2559
Tasya, S. E., & Novitasari, H. (2020). analisis kelayakan pada agroindustri jeruk siam di Kecamatan Sumbersari Kabupaten Jember. Jurnal Ilmiah Mahasiswa Agroinfo Galuh, 7(2), 455. https://doi.org/10.25157/jimag.v7i2.3491
Tuswan, T., Veda, K. A. B., Chrismianto, D., Mursid, O., Adam, S., Prabowo, A. R., & Nubli, H. (2024). Artificial intelligence-based ship hull plate corrosion monitoring using Convolutional Neural Network (CNN): Comparison of YOLOv8 and Detectron2 architecture models. Ship Technology Research, 1–11. https://doi.org/10.1080/09377255.2024.2397608
Udjulawa, D. (2023). Klasifikasi tanaman hias berdasarkan tekstur daun menggunakan metode svm dan fitur glcM. Klik - Jurnal Ilmu Komputer, 3(2), 121–127. https://doi.org/10.56869/klik.v3i2.418
Wijoyo, A., Saputra, A. Y., Ristanti, S., Sya’Ban, S. R., Amalia, M., & Febriansyah, R. (2024). Pembelajaran machine learning. 3(2). https://journal.mediapublikasi.id/index.php/oktal
Xu, L., Wang, Y., Shi, X., Tang, Z., Chen, X., Wang, Y., Zou, Z., Huang, P., Liu, B., Yang, N., Lu, Z., He, Y., & Zhao, Y. (2023). Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4. Computers and Electronics in Agriculture, 205, 107590. https://doi.org/10.1016/j.compag.2022.107590
Yazid Fauzan Nur Ashfani, Yovi Litanianda, & Rizqy Amalia Putri. (2024). Klasifikasi Jenis buah jeruk menggunakan metode convolutional neural network: Deep learning studi. Uranus : Jurnal Ilmiah Teknik Elektro, Sains Dan Informatika, 2(2), 70–79. https://doi.org/10.61132/uranus.v2i2.129
Yoghaswara, P., Anshory, I., & Ayuni, S. D. (2023). Rancang bangun alat deteksi kematangan buah pepaya berdasarkan warna pada kulit buah berbasis arduino. 6 (02), 32-37. https://doi.org/10.25124/jett.v10i1.6209
Downloads
View: 144
Download: 103
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Irwan Adhi Prasetya, Fadli Sukandiarsyah, Novi Aryani Fitri, Safri Adam
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read Jurnal Pendidikan Informatika dan Sains Posting Your Article Policy at http://journal.ikippgriptk.ac.id/index.php/saintek/about/submissions#onlineSubmissions
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Jurnal Pendidikan Informatika dan Sains agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
