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

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Sistem Pengenalan Peralatan Elektronik Dapur yang Terhubung pada Stop Kontak Menggunakan Metode K-Nearest Neighbor (K-NN) Rizka Ayudya Pratiwi; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The use of electronic devices that are used excessively and uncontrolled by the tenants in a boarding house will certainly have a negative impact on the owner side in terms of economy. Many of the board owners who provide rules to not use some additional electronic equipment such as electronic kitchen equipment and when used it will incur additional costs. But the regulation is also much violated by the tenant without the knowledge of the owner of the boarding. In this study designed a system to find out what kind of electronic kitchen equipment plugged into electric socket to facilitate the monitoring of electrical appliances. The system designed using the K-Nearest Neighbor (K-NN) method as its classification method, NodeMCU as the system's main controller and YHDC SCT-013-100 sensor as the current reader sensor. The system will classify the kitchen utensils of rice cooker, blender, juicer, heater and mixer based on the total current parameters out of the socket. The five equipments are classified on 3-hole so resulting in 10 classes in their classification. Furthermore, the current data obtained will be sent to NodeMCU to perform the classification process using K-Nearest Neighbor (K-NN) method. Results from the classification are then sent on Android smartphone. Based on the test results obtained percentage of 90.00% with a value of k = 1. The system can classify kitchen devices that are in use and require an average time of 10072.2 ms to perform data acquisition and require an average time of 12.4 ms for classification.
Co-Authors Dahnial Syauqy Fitriyah, Hurriyatul