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All Journal Jurnal Teknik Elektro ELKHA : Jurnal Teknik Elektro Semantik Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro JURNAL NASIONAL TEKNIK ELEKTRO Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Techne : Jurnal Ilmiah Elektroteknika Jurnal Informatika dan Rekayasa Elektronik Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Rekayasa elektrika
Fransiscus Dalu Setiaji
Universitas Kristen Satya Wacana
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Other

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Analisis dan Pengukuran Rangkaian Pengisi Baterai pada Beberapa Produk Lampu Baca Fransiscus Dalu Setiaji
Techné : Jurnal Ilmiah Elektroteknika Vol. 18 No. 1 (2019)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1089.19 KB) | DOI: 10.31358/techne.v18i01.186

Abstract

Produk lampu baca yang dilengkapi dengan baterai yang bisa diisi ulang dapat membantu penggunanya untuk membaca pada saat listrik padam. Terdapat banyak jenis produk lampu baca yang beredar, termasuk di antaranya yang belum memiliki tanda SNI (Standar Nasional Indonesia), seperti empat jenis produk yang diteliti pada makalah ini. Penelitian difokuskan pada bagian pengisi baterai (charger) yang merupakan bagian utama produk tersebut. Hasil penelitian menunjukkan bahwa metode pengisian yang digunakan adalah float charging, karena baterai terus diisi dalam kondisi penuh. Namun arus pengisian yang digunakan terlalu besar masih dan memiliki riak (ripple) yang signifikan. Padahal kedua faktor tersebut akan memperpendek umur baterai, seperti yang dialami empat dari 12 baterai yang diteliti. Baterai rusak tersebut tidak bisa diisi lagi, atau bersifat hubung buka, setelah digunakan selama tiga bulan. Kerusakan baterai ternyata membuat komponen lain pada charger menjadi rusak yaitu kapasitor perata arus, akibat mendapatkan tegangan berlebih dan indikator pengisian baterai, karena mendapat arus berlebih.
Energi Kinetik Alat Kebugaran Lat Pull Down untuk Lampu LED dan Pemandu Deddy Susilo; F. Dalu Setiaji; Martino Suherman
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 4 No 2: Mei 2015
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.809 KB)

Abstract

This research builds a fitness equipment Lat Pull Down which is modified to produce alternative energy. This tool works by pulling a lever connected to a gearbox. Rotor and generator are connected to the gearbox and will rotate with angular velocity at about 22 rad/s, thus, can generate electromotive force of about 202 V. This AC voltage will be converted by a full wave rectifier using Schottky diodes into 19,8 VDC. An electronic switch, then, will automatically connect the DC voltage, which is still fluctuative, to either an LM2577 or LM2576 chip that will increase or decrease the voltage, respectively, into a stable voltage of 13,8 V. The energy will be stored in the 12 V accumulator and can be used as an energy source for the 2,5 W LED and microcontroller-based guidance system. This system can generate energy up to 2,367 Wh each day.
Optimized PID-Like Neural Network Controller for Single-Objective Systems Gunawan Dewantoro; Johanes Nico Sukamto; Fransiscus Dalu Setiaji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25237

Abstract

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers.
Comparative Study of Computer Vision Based Line Followers Using Raspberry Pi and Jetson Nano Gunawan Dewantoro; Jamil Mansuri; Fransiscus Dalu Setiaji
Jurnal Rekayasa Elektrika Vol 17, No 4 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.749 KB) | DOI: 10.17529/jre.v17i4.21324

Abstract

The line follower robot is a mobile robot which can navigate and traverse to another place by following a trajectory which is generally in the form of black or white lines. This robot can also assist human in carrying out transportation and industrial automation. However, this robot also has several challenges with regard to the calibration issue, incompatibility on wavy surfaces, and also the light sensor placement due to the line width variation. Robot vision utilizes image processing and computer vision technology for recognizing objects and controlling the robot motion. This study discusses the implementation of vision based line follower robot using a camera as the only sensor used to capture objects. A comparison of robot performance employing different CPU controllers, namely Raspberry Pi and Jetson Nano, is made. The image processing uses an edge detection method which detect the border to discriminate two image areas and mark different parts. This method aims to enable the robot to control its motion based on the object captured by the webcam. The results show that the accuracies of the robot employing the Raspberry Pi and Jetson Nano are 96% and 98%, respectively.
Co-Authors Andika Sukma Deryawan Andreas Setiawan Budihardja Murtianta Chatarina Umbul Wahyuni Daniel Santoso Darmawan Utomo Deddy Susilo Diestha Djati Purnomo Eva Yovita Dwi Utami Gading Cassandra Gunawan Dewantoro Iwan Setyawan J. P. Tirtayasa Jamil Mansuri Johanes Nico Sukamto Kristian Visi Subekti Kuncoro Adi D. Lukas B. Setyawan Lukas Bambang Setyawan Martino Suherman Matias H. W. Budhiantho Matias H.W. Budhiantho Muhamad Asra Gupita Nicolas A.B. Oetama Nugraha, Benedictus Edwin Pebrianto, Daniel Prabelia, Chorintan Ramadhan, Muhammad Hisyamudin Roni Wijaya Rudy Santoso Lukito Simon Wedhatama Suryo Santoso Theodorus Leo Hartono Tio Pragustha, Tio Tri Wahyu Yulianingrum Yohanes Haryudanta D. Yonathan Berith Olam Yuyu Wahyu