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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Hurriyatul Firtiyah
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Pendeteksi Kualitas Tanah Tanaman Kedelai Menggunakan Metode K-Nearest Neighbor (K-NN) dengan Arduino Nano Andika Bhayangkara; Eko Setiawan; Hurriyatul Firtiyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soybean is a top priority crop in food and also includes a commodity that has a high protein in helping meet the nutritional needs of the community at a relatively cheap price when compared to animal protein commodities. Soybeans with a high enough protein must be able to be produced well so that people can get good protein with relatively cheap prices. With the target of increasing production that is able to meet the needs of the community, it is necessary to consider plant growing media in order to grow well. Soybean media media uses soil that has a good nutrient retreat so that it can grow well. So with this we need a system that is able to detect soil quality that will be used as a planting medium for soybean plants. This study will challenge the system that is able to improve the quality of soybean plants using the K-Nearest Neighbor (KNN) classification method by using soil pH parameters and process moisture through the soil supported by Arduino Nano boards as data processing. The classification process will be carried out by collecting data from several soil qualities, then the conversion of land is approved using a pH sensor and a Capacitive Soil sensor, the pH data of the humidity data obtained will use the K-Nearest Neighbor method. The results of the classification process will be continued through the LCD in the form of ph data and moisture results from the detected soil quality. In the process of testing the system using the K-Nearest Neighbor method with K = 3 has the highest verification that is equal to 86.6% compared to K initiating 5, 7 or 9.
Co-Authors Andika Bhayangkara Eko Setiawan