Klasifikasi Kematangan Tanaman Hidroponik Pakcoy Menggunakan Metode SVM
Pakcoy Hydroponic Maturity Classification Using SVM Method
Abstract
Pakcoy is a type of vegetable plant belonging to the Brassica family. Pakcoy plants can be cultivated using hydroponic techniques, namely plant cultivation techniques without soil media. The advantage of cultivating Pakcoy plants using hydroponic techniques is that it does not require a large area of land, so it is easy to apply in the yard. However, cultivation with hydroponic techniques has drawbacks such as farmers need to make regular observations to determine the harvest readiness of each plant. This causes a lack of effectiveness of farmers in cultivating Pakcoy plants. With the development of Machine Learning technology, a model can classify the maturity of Pakcoy plants based on digital image data. By applying the Support Vector Machine (SVM) Algorithm, the Machine Learning model can learn to classify a digital image of Pakcoy plants with the category "Small" to represent immature Pakcoy plants and "Large" to represent mature Pakcoy plants which results in an accuracy level of above 79%. It can be concluded that Machine Learning can be implemented in Pakcoy cultivation activities to support hydroponic farmers.
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References
E. Mutryarny and S. Lidar, “RESPON TANAMAN PAKCOY (Brassica rapa L) AKIBAT PEMBERIAN ZAT PENGATUR TUMBUH HORMONIK,” J. Ilm. Pertan., vol. 14, no. 2, pp. 29–34, 2018, doi: 10.31849/jip.v14i2.258.
M. Mardilla and A. Pratiwi, “Budidaya Tanaman Pakcoy (Brassica Rapa Subsp. Chinensis) Dengan Teknik Vertikultur Pada Lahan Sempit Di Kelurahan Penaraga Kecamatan Raba Kota Bima,” J. Pengabdi. Magister Pendidik. IPA, vol. 4, no. 1, 2021, doi: 10.29303/jpmpi.v4i1.537.
L. Sarido and Junia, “Uji Pertumbuhan dan Hasil Tanaman Pakcoy (Brassica rapa L.) dengan Pemberian Pupuk Organik Cair pada Sistem Hidroponik,” J. AGRIFOR, vol. 16, no. 1, pp. 65–74, 2017, doi: https://doi.org/10.31293/af.v16i1.2591.
R. Purwasih, “Pemanfaatan Lahan Pekarangan untuk Budi Daya Sayuran Secara Hidroponik di Kecamatan Sungailiat, Kabupaten Bangka, Provinsi Kepulauan Bangka Belitung,” Agrokreatif J. Ilm. Pengabdi. Kpd. Masy., vol. 5, no. 3, pp. 195–201, 2019, doi: 10.29244/agrokreatif.5.3.195-201.
L. E. Rahmadhani, L. I. Widuri, and P. Dewanti, “Kualitas Mutu Sayur Kasepak (Kangkung, Selada, Dan Pakcoy) Dengan Sistem Budidaya Akuaponik Dan Hidroponik,” J. Agroteknologi, vol. 14, no. 01, p. 33, 2020, doi: 10.19184/j-agt.v14i01.15481.
I. W. A. Suranata and I. G. H. Prathama, “Arsitektur Moisture Meter dengan Capacitive Sensing dan Serverless IoT untuk Hidroponik Fertigasi,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 1–3, 2021, doi: https://doi.org/10.29207/resti.v5i2.2993.
R. Uchiyama, S. Yamaguchi, and Y. Takahashi, “Solar Power Generation for Compact Hydroponic Plant Cultivation System,” in 2019 19th International Conference on Control, Automation and Systems (ICCAS), 2019, pp. 827–832, doi: 10.23919/ICCAS47443.2019.8971554.
M. A. Afandi, I. Hikmah, and C. Agustinah, “Jurnal Nasional Teknik Elektro Microcontroller-based Artificial Lighting to Help Growth the Seedling Pakcoy,” vol. 3, 2021, doi: https://doi.org/10.25077/jnte.v10n3.943.2021.
C. J. G. Aliac and E. Maravillas, “IOT hydroponics management system,” 2018 IEEE 10th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. HNICEM 2018, pp. 1–5, 2019, doi: 10.1109/HNICEM.2018.8666372.
V. D. K and M. Syaryadhi, “Monitoring Suhu Dan Kelembaban Menggunakan Mikrokontroler Atmega328 Pada Proses Dekomposisi Pupuk Kompos,” J. Karya Ilm. Tek. Elektro, vol. 2, no. 3, pp. 91–98, 2017.
M. Khotib and S. Sutikno, “Prototipe Sistem Kontrol Parameter Fisik ( Suhu - Kadar Air Tanah - Kelembaban Udara ) Pada Green House Untuk Budidaya Tanaman Cabai,” J. Tek. Elektro dan Komputasi, vol. 1, no. 2, pp. 86–92, 2019, doi: 10.32528/elkom.v1i2.3087.
A. Safrimawan, “Sistem Kontrol Pemberian Nutrisi pada Budi Daya Tanaman Aeroponik Berbasis Fuzzy Logic,” J. Appl. Electr. Eng., vol. 3, no. 1, pp. 19–23, 2019, doi: 10.30871/jaee.v3i1.1397.
M. Irfan, B. A. Ardi Sumbodo, and I. Candradewi, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 7, no. 2, p. 139, 2017, doi: 10.22146/ijeis.18260.
P. Rianto and A. Harjoko, “Penentuan Kematangan Buah Salak Pondoh Di Pohon Berbasis Pengolahan Citra Digital,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 2, p. 143, 2017, doi: 10.22146/ijccs.17416.
N. Yelliy N, “Pengolahan Citra Digital Perbandingan Metode Histogram Equalization Dan Spesification Pada Citra Abu-Abu,” J-Icon, vol. 7, no. 1, pp. 87–95, 2019, doi: https://doi.org/10.35508/jicon.v7i1.889.
A. R. CHAIDIR, K. ANAM, and G. A. RAHARDI, “Lane Tracking pada Robot Beroda Holonomic menggunakan Pengolahan Citra,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 8, no. 1, p. 69, 2020, doi: 10.26760/elkomika.v8i1.69.
I. Supiyani, J. Haerul, and T. N. Padilah, “Nusantara ( Jurnal Ilmu Pengetahuan Sosial ),” Nusant. J. Ilmu Pengetah. Sos., vol. 8, no. 2, pp. 2204–2212, 2021, doi: 10.31604/jips.v8i7.2021.2204-2212.
H. Muchtar and F. Said, “Sistem Identifikasi Plat Nomor Kendaraan Menggunakan Metode Robert Filter dan Framing Image Berbasis Pengolahan Citra Digital,” Resist. (elektRonika kEndali Telekomun. tenaga List. kOmputeR), vol. 2, no. 2, p. 105, 2019, doi: 10.24853/resistor.2.2.105-112.
A. Fadjeri, A. Setyanto, and M. P. Kurniawan, “Pengolahan Citra Digital Untuk Menghitung Ekstrasi Ciri Greenbean Kopi Robusta Dan Arabika (Studi Kasus: Kopi Temanggung),” J. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 8–13, 2020, doi: 10.30646/tikomsin.v8i1.462.
M. Orisa and T. Hidayat, “Analisis Teknik Segmentasi Pada Pengolahan Citra,” J. Mnemon., vol. 2, no. 2, pp. 9–13, 2019, doi: 10.36040/mnemonic.v2i2.84.
S. Naseera, G. K. Rajini, B. Venkateswarlu, and M. Jasmin Pemeena Priyadarisini, “A review on image processing applications in medical field,” Res. J. Pharm. Technol., vol. 10, no. 10, pp. 3456–3460, 2017, doi: 10.5958/0974-360X.2017.00644.8.
M. A. Afandi, H. Kusuma, and T. A. Sardjono, “Carotid Artery Plaque Image Recognition Using Gabor Wavelet and Principal Component Analysis,” Proceeding - 2018 Int. Semin. Intell. Technol. Its Appl. ISITIA 2018, pp. 461–464, 2018, doi: 10.1109/ISITIA.2018.8710967.
L. Jiang, Y. Fan, Q. Sheng, X. Feng, and W. Wang, “Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area,” Eurasip J. Image Video Process., vol. 2018, no. 1, 2018, doi: 10.1186/s13640-018-0384-5.
D. B. Pratama, “Pengendalian Mobile Robot Berdasarkan Objek Traffic Light Menggunakan Pengolahan Citra,” J. Arus Elektro Indones., vol. 7, no. 1, pp. 17–23, 2021, doi: https://jurnal.unej.ac.id/index.php/E-JAEI/article/view/23040.
A. R. T. H. Ririd, A. W. Kurniawati, and Y. Yunhasnawa, “Implementasi Metode Support Vector Machine Untuk Indentifikasi Penyakit Daun Tanaman Kubis,” J. Inform. Polinema, vol. 4, no. 3, pp. 181–189, 2018, doi: 10.33795/jip.v4i3.204.
L. K. S. Tolentino et al., “Yield evaluation of brassica rapa, Lactuca Sativa, and brassica Integrifolia using image processing in an IoT-based Aquaponics with temperature-controlled greenhouse,” Agrivita, vol. 42, no. 3, pp. 393–410, 2020, doi: 10.17503/agrivita.v42i3.2600.
Andri Heru Saputra and Dhomas Hatta Fudholi, “Realtime Object Detection Masa Siap Panen Tanaman Sayuran Berbasis Mobile Android Dengan Deep Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 647–655, 2021, doi: 10.29207/resti.v5i4.3190.
N. Gobalakrishnan, K. Pradeep, C. J. Raman, L. J. Ali, and M. P. Gopinath, “A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases,” Proc. 2020 IEEE Int. Conf. Commun. Signal Process. ICCSP 2020, pp. 465–468, 2020, doi: 10.1109/ICCSP48568.2020.9182046.
D. E. Saputra, D. Rahmawati, and A. F. Ibadillah, “Pengolahan Citra Digital Dalam Penentuan Panen Jamur Tiram,” J. Tek. Elektro dan Komput. TRIAC, vol. 6, no. 1, pp. 2–6, 2019, doi: 10.21107/triac.v6i1.4356.
M. K. Tripathi and D. D. Maktedar, “A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey,” Inf. Process. Agric., vol. 7, no. 2, pp. 183–203, 2020, doi: 10.1016/j.inpa.2019.07.003.
J. W. G. Putra, “Pengenalan konsep pembelajaran mesin dan deep learning,” Comput. Linguist. Nat. Lang. Process. Lab., vol. 4, pp. 1–235, 2020, [Online]. Available: https://www.researchgate.net/publication/323700644.
A. Ahmad Hania, “Mengenal Artificial Intelligence, Machine Learning, & Deep Learning,” J. Teknol. Indones., no. June, 2017, [Online]. Available: https://amt-it.com/mengenal-perbedaan-artificial-inteligence-machine-learning-deep-learning/.
F. S. Jumeilah, “Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 19–25, 2017, doi: 10.29207/resti.v1i1.11.
I. C. R. Drajana, “Metode Support Vector Machine Dan Forward Selection Prediksi Pembayaran Pembelian Bahan Baku Kopra,” Ilk. J. Ilm., vol. 9, no. 2, pp. 116–123, 2017, doi: 10.33096/ilkom.v9i2.134.116-123.
A. Z. Fuadi, I. N. Haq, and E. Leksono, “Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory,” RESTI J., vol. 5, no. 3, pp. 466–473, 2021, doi: https://doi.org/10.29207/resti.v5i3.2947.
Z. Y. Lamasigi, M. Hasan, and Y. Lasena, “Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor,” Ilk. J. Ilm., vol. 12, no. 3, pp. 208–218, 2020, doi: 10.33096/ilkom.v12i3.667.208-218.
Z. Karimi, “Confusion Matrix,” Encycl. Mach. Learn. Data Min., no. October, pp. 260–260, 2017, doi: 10.1007/978-1-4899-7687-1_50.
A. Luque, A. Carrasco, A. Martín, and A. de las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognit., vol. 91, pp. 216–231, 2019, doi: https://doi.org/10.1016/j.patcog.2019.02.023.
Adi Nugroho, Agustinus Bimo Gumelar, Adri Gabriel Sooai, Dyana Sarvasti, and Paul L Tahalele, “Perbandingan Performansi Kinerja Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 998–1006, 2020, doi: 10.29207/resti.v4i5.2316.
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