Identification of Power Quality Disturbances Using S-Transform and Multi-Class Support Vector Machine

Abstract: An essential issue in power quality disturbances is identifying and classifying power quality disturbances from anywhere and at any time. This article proposed a new approach to identify and classify power quality disturbances over the web using S-transform, Multi-Class Support vector machine (SVM), and Matlab framework. S-Transform is used as an extraction feature to obtain the temporal frequency characteristics of power quality events. The development of the multi-class SVM classifier, in which the system classifies various power quality disturbances. Finally, the Matlab framework integrated the graphical and computational processes with remote access via the web. The test result indicated the suggested method's effectiveness and robustness for identifying and classifying power quality disturbances through the web.Abstrak: Masalah penting dalam gangguan kualitas daya adalah mengidentifikasi dan mengklasifikasikan gangguan kualitas daya dari mana saja dan kapan saja. Artikel ini mengusulkan pendekatan baru untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web menggunakan S-transform, Multi-Class Support vector machine (SVM), dan Matlab. S-Transform digunakan sebagai fitur ekstraksi untuk mendapatkan karakteristik frekuensi temporal dari peristiwa kualitas daya. Multi class SVM classifier dikembangkan dimana sistem mengklasifikasikan berbagai gangguan kualitas daya. Akhirnya, Matlab framework mengintegrasikan proses grafis dan komputasi sehingga dapat diakses jarak jauh melalui web. Hasil pengujian menunjukkan efektivitas dan robustnes metode yang usulkan untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web

Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease

Sistem Pemantauan Kelayakan Pelumas Oli pada Kendaraan Sepeda Motor dengan Memanfaatkan Teknologi Internet of Things

Sepeda motor memerlukan perawatan yang oprtimal terutama are mesin yaitu dengan melakukan pergantian oli yang berfungsi sebagai pelumas mesin. Oli mesin membantu menghindari gesekan langsung antara logam di mesin, sehingga mengurangi tingkat kerusakan mesin. Penelitian ini akan dirancang suatu sistem pemantauan pergantian oli sepeda motor berdasarkan jarak tempuh dan waktu pemakaian oli yang berbasis IoT. Dimana penggunaan sepeda motor digunakan untuk menyimulasikan perputaran roda, sensor proximity sebagai pendeteksi putaran roda, serta mikrokontroller ESP32 sebagai pengendali utama, buzzer untuk memperingati pengguna, relay digunakan untuk mematikan mesin sepeda motor, dan GSM sebagai pengirim data. Pada penelitian ini diperoleh hasil yang diharapkan, ketika jarak tempuh mencapai jarak 2000km maka sistem akan memperingati pengguna sepeda motor dan ketika pengguna mengabaikan peringatan tersebut dan jarak mencapai 2100km maka sistem mematikan kelistrikan sepeda motor. Namun jika jarak tidak tercapai tetapi pemakaian oli telah mencapai 60 hari maka sistem memperingati pengguna sepeda motor

SMART EARLY WARNING SYSTEM UNTUK KEAMANAN SEPEDA MOTOR BERBASIS PROSESOR XTENSA LX6

The motorcycle security system is still low resulting in a high crime rate of motorcycle theft. Data from BPS in 2018 there were 27,731 cases of motorcycle theft with various operational modes. There are several factors which are the background to this crime such us ranging from break-ins, negligence of vehicle owners to theft by force or destruction. In this study the author uses a qualitative approach related to the security system on a motorcycle. Therefore, an additional system on motorcycles is needed that can alert users by providing the latest location coordinates and can control electricity on motorcycles. The system is expected to increase security on motorcycles where users can track locations and be able turn off the Engine from long-range based on the Internet. This system is equipped by modul SIM800L to connect to the internet as interface between user and motorcycle. Through this research, the transmission time between devices is 4 to 6 seconds with parallel processing. With this system, security on motorcycles will increase so that it can reduce crime on motorcycle