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All Journal International Journal of Electrical and Computer Engineering Semesta Teknika Journal of Electrical Technology UMY Emerging Information Science and Technology
Karisma Trinanda Putra
Universitas Muhammadiyah Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Journal : Journal of Electrical Technology UMY

 1 
University Course Timetabling with Genetic Algorithm: A Case Study Toha Ardi Nugraha; Karisma Trinanda Putra; Nur Hayati
Journal of Electrical Technology UMY Vol 1, No 2 (2017)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.1213

Abstract

University Course Timetabling Problems is a scheduling problem to allocate some lectures with some constraint, such as the availability of lecturers, number of classrooms and time slot in each day. The schedule of courses is one of important factors before start the semester in order to manage the study process. Generally, the university course scheduling in some universities are usually created manually through administration office. It needs to synchronize for all schedules from all departments in faculty of the university. In addition, the limitations of classroom and timeslot can make collision of the courses, lecturers and also incompatibility between the room capacity and the number of students whom take the course in the class. This paper proposes the university course time tabling systems. Based on some simulations with 93 courses, 18 lecturers and up to six classrooms, the result is that the system will get the best violation if the system adds more number of iteration. This situation also happens in the result of the scheduling lectures, the system will get the best percentage when the number of iteration sets as maximum.
Design and Development of Fan Speed and Light Speed Control Systems with Android-Based Voice Commands Anna Nur Nazilah Chamim; Rama Okta Wiyagi; Karisma Trinanda Putra; Faruliyan Arya Ferisnanda; Yessi Jusman
Journal of Electrical Technology UMY Vol 2, No 2 (2018)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.2236

Abstract

At this time controlling electrical equipment in the form of fans and lights are still mostly using conventional switches. By using an android voice command device with a bluetooth connection, controlling the equipment can be easier. Using a Bluetooth connection will reduce the use of cables and speed up the process of activating or deactivating electronic devices because they do not have to get close to reach the switch. With input in the form of voice commands is very easy in controlling electronic devices such as fans and lights. With the adjustment of the fan speed and brightness of the lights with voice commands can save electricity and more easily in its settings
Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation Febrian Dhimas Syahfitra; Ramadoni Syahputra; Karisma Trinanda Putra
Journal of Electrical Technology UMY Vol 1, No 3 (2017)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.1316

Abstract

The National Electricity Company (PT PLN) should have an estimated peak load of the substation transformer in the future. This is useful to be able to achieve transformer capability and can be used as a first step to anticipate the possibility of replacement of a new transformer. This research presents a peak load forecasting system transformer1 and transformer2 in Bumiayu substation using Backpropagation Artificial Neural Network (ANN). This study includes the procedures for establishing a network model and manufacture forecasting system based GUI (Graphic User Interface) using MATLAB 2015a. The formation of the network model refers to input variables consisting of GRDP data, population data and historical data of peak load of transformer. In this research, a multilayer network model, which consists of 1 input layer, 2 hidden layers and 1 output layer, is used. The peak load forecasting of transformer1 produces 5.7593e-08 for training MSE and 5.3784e-04 for testing MSE. Meanwhile, forecasting the peak load transformer2 generated 3.3433e-08 for training MSE and 9,4710e-04 for testing MSE.
Co-Authors Ahmad Zaki Arrayyan Anna Nur Nazilah Chamim Apriani, Yosi Cahya Darmarjati Faaris Mujaahid Faruliyan Arya Ferisnanda Febrian Dhimas Syahfitra Iswanto Iswanto Jusman, Yessi Maemonah, Maemonah Mosiur Rahaman Muhammad Fahrul Al Farisi Noor Pratama Apriyanto Prayitno Ramadoni Syahputra Ramadoni Zaki Syahputra Sunardi, Sunardi Suripto, Slamet Toha Ardi Nugraha Widodo, Agung Mulyo Wiyagi, Rama Okta Yoga Agil Pamungkas