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Synchronous Magnet Generator (PMSG) is one of the main components of wind power generation. This generator has excellent low speed applied especially for less strong winds. Electromotive Force (EMF) output is expected to be enhanced for higher PMSG efficiency. This research aims to analyze the influence of thick and magnetic types on EMF Back PMSG 12S8P. Thickness and type of magnets vary to get the highest EMF output. The thickness is variated at a value of 3 mm and 6 mm, while the magnetic material used Neodymium Iron Boron (NdFeB) and PM12 Br 1.2 Mur 1. Linkage flux value is generated with Finite ElemenMethode (FEM) using Magnet 7,5 Software for each variation. From analyze results and compared to other variations, the highest EMF value obtained from the variation in magnetic thickness 6 mm with material type PM12 Br 1.2 Myrrh 1 with EMF value reaching 21.13122 Volt.

AbstrakSepeda 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.Kata kunci: Gsm, Mikrokontroler, Otomasi, Sensor, Sepeda MotorAbstractMotorcycles require optimal maintenance, especially the engine, namely by changing the oil, which functions as an engine lubricant. Engine oil helps avoid direct friction between the metals in the engine, thereby reducing engine damage. This research will design a motorcycle oil change monitoring system based on mileage and time of oil usage based on IoT. Where the use of a motorcycle is used to simulate wheel rotation, a proximity sensor is used to detect wheel rotation, as well as the ESP32 microcontroller as the main controller, a buzzer to warn the user, a relay is used to turn off the motorcycle engine, and GSM as a data sender. In this study, the expected results are obtained, where when the distance reaches a distance of 2000km, the system will warn the motorcycle user and when the user ignores the warning and the distance reaches 2100km, the system will turn off the motorcycle electricity. However, if the distance is not reached but the oil consumption has reached 60 days, the system will warn motorcycle users.Keywords: Gsm, Microcontroller, Automation, Sensors, Motorcyle.

The high rate of motorcycle theft was reportedly caused by the negligence of the owner of the motorcycle itself, including forgetting to double lock and leave the key on the motorcycle. This research offers a new approach to prevent motorcycle theft by designing an automatic double lock system through ignition keys, buzzer reminders for left keys, and coordinator points using GPS to track the position of a motorcycle. Automatic dual locks are designed using a solenoid that locks the front and rear disc brakes automatically when the ignition is in the lock position. The buzzer is activated by using a crossbeam sensor consisting of infrared and photodiode mounted on the ignition key slot and seat lock slot which will sound when the user leaves the key in one of the key slots. The Ublox Neo-6m GPS module is used to determine the coordinates of the motorcycle's position if the motorcycle has been stolen. The test results show that the system can work well. User satisfaction testing shows that respondents agree that the system that has been designed has ease of use, is simple and very useful, seen from the average score of usability factor 4.18, simplicity 4.32 and interactivity 4.38 from a scale of 5.

Artikel ini meneliti penerapan sebuah pendekatan pembelajaran mesin bernama Support Vector Machine (SVM) untuk pengenalan bentuk manusia dan bukan manusia. Banyak penelitian telah menunjukkan bahwa kontur siluet sebuah bentuk mengandung informasi bentuk yang esensial. Oleh karena itu, sebuah skema yang cocok, yang kami namai sebagai profil pusat, dikembangkan yang merepresentasikan jarak euclidean antara pusat bentuk dan piksel batas bentuknya. Profil pusat 100 bentuk manusia dan non manusia pada interval 10° diekstraksi dan menghasilkan 36 profil fitur pusat. Profil fitur yang terekstraksi ini kemudian dianalisa dan dikenakan ke proses seleksi fitur untuk mengoptimalkan jumlah profil fitur yang digunakan. Ada dua metode yang digunakan dalam penelitian ini yaitu metode statistik analisis varian (ANOVA) dan metode forward feature selection. Fitur terpilih diatur dari kedua metode tersebut dan digunakan sebagai input untuk SVM. Secara keseluruhan, kemampuan klasifikasi SVM ditemukan tidak berpengaruh lintas tiga fungsi inti yaitu linear, polynomial, dan basis radial gaussian. Metode ANOVA terbukti unggul dibandingkan metode forward feature selection. Setelah dipertimbangkan semuanya, efektifitas SVM sebagai pengklasifikasi terconfirmasi. Kinerja yang sempurna bisa dicapai ketika SVM diberikan sekumpulan fitur yang teroptimasi sebagai input. Temuan dalam penelitian ini membuktikan potensi yang besar dari penerapan SVM pada pendeteksian manusia untuk berbagai penerapan.

The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.