Caisim Growth Model Using Smart Farm IoTBased Machine Learning
DOI:
https://doi.org/10.34818/eoe.v9i6.18931Abstrak
Abstract— Indonesia is a developing country where the majority of people still work as farmers. The caisim plant is one of the most extensively farmed plants. Caisim production increases year after year. Farmers, on the other hand, have been unable to satisfy the demands of a very high market demand because they frequently encounter crop failure due to caisim plants harmed by pests and diseases. The difficulties encountered are due to the scenario for remote plant monitoring and a lack of understanding about growth parameters in caisim plants. Based on these issues, the development of a website and growth prediction model will provide a solution for producing caisim plants with optimal growth. When measuring QoS (Quality of Service) for data transmission from the tool to the website, the average latency was 371,57 ms. During the QoS test, the average throughput for reading data from the device to the database was 3469,14 bit/s. Meanwhile, data for the plant growth prediction model was retrieved from the website and converted to a CSV file dataset. In this prediction model, the KNN approach was used. This algorithm will generate classification results in the form of optimal and nonoptimal values for each characteristic used. Keywords — smart farm, caisim, MySQL, machine learning, IoT.Unduhan
Referensi
[1] R. Rukmana, “Bertanam petsai dan sawi,” Yogyakarta: Kanisius, 1994.
[2] E. Margiyanto, “Budidaya tanaman sawi,” Bantul: Cahaya Tani, 2008.
[3] M. J. O’Grady and G. M. O’Hare, “Modelling the smart farm,” Information processing in agriculture, vol. 4, no. 3, pp. 179–187, 2017.
[4] “Ieee draft standard for framework of blockchain-based internet of things (iot) data management,” IEEE P2144.1/D3, August 2020, pp. 1–20,2020.
[5] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of things (iot): A vision, architectural elements, and future directions,” Future generation computer systems, vol. 29, no. 7, pp. 1645–1660, 2013.
[6] N. Islam, B. Ray, and F. Pasandideh, “Iot based smart farming: Are the lp- wan technologies suitable for remote communication?” in 2020 IEEE Inter- national Conference on Smart Internet of Things (SmartIoT), 2020, pp. 270– 276.
[7] B. Sarma, R. Baruah, and A. Borah, “Internet of things based smart farming,” in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 30–34.
[8] D. Negi, A. Kumar, P. Kadam, and B. N. Savant, “Smart harvest analysis using raspberry pi based on internet of things,” in 2018 Fourth International Con- ference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1–5.
[9] L. Kamelia, Y. S. Nugraha, M. R. Effendi, and T. Priatna, “The iot-based monitoring systems for humidity and soil acidity using wireless communica- tion,” in 2019 IEEE 5th International Conference on Wireless and Telematics (ICWT), 2019, pp. 1–4.
[10] M. Suresh, S. Ashok, S. Kumar, and P. Sairam, “Smart monitoring of agri- cultural field and controlling of water pump using internet of things,”in 2019 IEEE International Conference on System, Computation, Automation and Net- working (ICSCAN), 2019, pp. 1–5.
[11] U. Uyoata, J. Mwangama, and R. Adeogun, “Relaying in the internet of things (iot): A survey,” IEEE Access, pp. 1–1, 2021.
[12] S. Truitt, T. D. Gage, B. E. Vincent, and S. Chun, “Low-cost remote monitor- ing system for small-scale ups installations in developing countries,” in 2019 IEEE Global Humanitarian Technology Conference (GHTC), 2019, pp. 1–6.
[13] I. G. E. W. Putra, I. K. P. Suniantara, and I. N. S. Kumara, “Sunlight intensity measurement system with solar tracking system,” in 2018 2nd International Conference on Applied Electromagnetic Technology (AEMT), 2018, pp. 12– 15.
[14] Y. A. Ahmad, T. Surya Gunawan, H. Mansor, B. A. Hamida, A. Fikri Hishamudin, and F. Arifin, “On the evaluation of dht22 temperature sensor for iot application,” in 2021 8th International Conference on Computer and Communication Engineering (ICCCE), 2021, pp. 131–134.
[15] E. P. D. Winarsih and E. Saptiningsih, “Kadar serat dan kadar air serta penam- pakan fisik produk pascapanen daun caisim (brassica juncea l.),” in (IEEE): D. Winarsih, E. Prihastanti, and E. Saptiningsih, ”Kadar Serat dan Kadar Air serta Penampakan Fisik Produk Pascapanen Daun Caisim (Brassica juncea L.) yang Ditanam pada Media dengan Penambahan Pupuk Organik Hayati Cair dan Pupuk Anorganik,”, vol. 14, no. 1, 2012, pp. 25–32.
[16] N. Istiqosari, ““net income analysis and risk of caisim (brassica rapa) produc- tion farming in liang anggang district, banjarbaru city,” in IOSR Journal of Agriculture and Veterinary Science), 2020, pp. 42–50.
[17] W. . F. B. N. . N. D. Hindersah, R. ; Rachman, “Populasi mikrob di rizosfer dan pertumbuhan caisim (brassica juncea) di tanah dikontaminasi insektisida organoklorin setelah aplikasi konsorsia mikrob dan kompos.” Jurnal Natur Indonesia 2013, vol. 15, pp. 115–120, 2013.
[18] H. Xianzhe, “Room temperature and humidity monitoring and energy-saving system,” in 2011 6th International Conference on Computer Science Educa- tion (ICCSE), 2011, pp. 537–540.
[19] S. B. Khot and M. S. Gaikwad, “Development of cloud-based light inten- sity monitoring system for green house using raspberry pi,” in 2016 Inter- national Conference on Computing Communication Control and automation (ICCUBEA), 2016, pp. 1–4.
[20] N. Putjaika, S. Phusae, A. Chen-Im, P. Phunchongharn, and K. Akkarajitsakul, “A control system in an intelligent farming by using arduino technology,” in 2016 Fifth ICT International Student Project Conference (ICT-ISPC), 2016, pp. 53–56.
[21] J. Wei, Y. Zhao, K. Jiang, R. Xie, and Y. Jin, “Analysis farm: A cloud-based scalable aggregation and query platform for network log analysis,” in 2011 International Conference on Cloud and Service Computing, 2011, pp. 354– 359.
[22] P. Dedeepya, U. Srinija, M. Gowtham Krishna, G. Sindhusha, and T. Gnanesh, “Smart greenhouse farming based on iot,” in 2018 Second International Con- ference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1890–1893.
[23] R. Varghese and S. Sharma, “Affordable smart farming using iot and machine learning,” in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018, pp. 645–650.
[24] M. T. Shakoor, K. Rahman, S. N. Rayta, and A. Chakrabarty, “Agricultural production output prediction using supervised machine learning techniques,” in 2017 1st International Conference on Next Generation Computing Applica- tions (NextComp), 2017, pp. 182–187.
[25] B. Lotfi, M. Mourad, M. B. Najiba, and E. Mohamed, “Treatment methodol- ogy of erroneous and missing data in wind farm dataset,” in Eighth Interna- tional Multi-Conference on Systems, Signals Devices, 2011, pp. 1–6.
[26] T. Rismawan, A. W. Irawan, W. Prabowo, and S. Kusumadewi, “Sistem pen- dukung keputusan berbasis pocket pc sebagai penentu status gizi menggu- nakan metode knn (k-nearest neighbor),” Teknoin, vol. 13, no. 2, 2008.
[27] B. Santosa, “Data mining teknik pemanfaatan data untuk keperluan bisnis,”Yogyakarta: Graha Ilmu, vol. 978, no. 979, p. 756, 2007.
[28] F. R. J. L o´pez and A. J. Lo´pez, “Field variables monitoring in real time (gps, soil moisture, temperature) with precision farming applications,” in 2012 6th Euro American Conference on Telematics and Information Systems (EATIS), 2012, pp. 1–5.
[29] S. Wang, D. Xu, and S. Yan, “Analysis and application of wireshark in tcp/ip protocol teaching,” in 2010 International Conference on E-Health Networking Digital Ecosystems and Technologies (EDT), vol. 2, 2010, pp. 269–272.
[30] A. Montazerolghaem and M. H. Yaghmaee, “Load-balanced and qos-aware software-defined internet of things,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3323–3337, 2020.
[31] X. Li and S. Song, “Stabilization of delay systems: Delay-dependent impul- sive control,” IEEE Transactions on Automatic Control, vol. 62, no. 1, pp. 406–411, 2017.
[32] S. Kompella and A. Ephremides, 2014.
[33] D. J. Hand, “Principles of data mining,” Drug safety, vol. 30, no. 7, pp. 621– 622, 2007.
