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OPTIMASI KONTROL PADA MANEUVERING KAPAL MENGGUNAKAN NI SB-RIO Siwindarto, Ponco; Eritha, Fadila Norasarin; Aswin, M.
Wave: Jurnal Ilmiah Teknologi Maritim Vol 13, No 2 (2019)
Publisher : Badan Pengkajian dan Penerapan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (784.275 KB) | DOI: 10.29122/jurnalwave.v13i2.3883

Abstract

Maneuvering kapal merupakan karakteristik dari sebuah kapal. Uji manever kapal sangat penting dan salah satu pengujiannya adalah zigzag. Jurnal ini menyodorkan salah satu sistem kontrol pada maneuvering kapal menggunakan NI SB-RIO yang memiliki dimensi kecil dan terkategori ringan dibandingkan pendahulunya. Perancangan sistem autopilot ini divalidasi melalui simulasi uji zigzag model kapal dan dari hasil validasi ini didapatkan bahwa sistem kontrol ini berfungsi dan mampu melakukan lintasan zig-zag sesuai yang diharapkan.
Reduced Overshoot of The Electroforming Jewellery Process Using PID Utomo, Arie Cahyo; Siwindarto, Ponco; Setyawati, Onny
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 3, August 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i3.1059

Abstract

The electroforming jewellery is the electrodeposition process of coating metal on an insulator object to make a jewellery product. The problems are burnt and uneven results in their products, it happened because electrical currents while the process increased. So, too many metal particles attached to object. The problems of electroforming process can fix with a control system, where controller must makes constant electrical currents while the process. In this paper, the problems was changed to the equation by the polynomial regression method as a plant.  Secondly the characteristic of current sensor was  found by the linier regression method as a feedback system. The system used buck converter as the actuator, where it was written to the equation by the state space method. The controller was chosen by comparison 4 types controller, they are a conventional controller, proportional controller, proportional – integral (PI) controller, and proportional – integral – derivative (PID) controller. Xcos Scilab used to simulated the system and got the system with a proportional – integral – derivative controller is the best controller. The system with a proportional – integral – derivative controller have a Rise time 1.3687 Seconds and Overshoot 2.5420%. The result of research will be base to makes hardware system where it will help the advancement of the creative economy industry in Malang City.
Kinerja Pendekatan Convolutional Neural Network dan Dense Network dalam Klasifikasi Citra Malaria Dafid, Achmad; Siwindarto, Ponco; Siswojo, Bambang
Rekayasa Vol 14, No 2: Agustus 2021
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v14i2.10735

Abstract

Indonesia is an archipelago, which three of its five main island consists mainly, or dense tropical rainforest. This rainforest is main breeding ground for malaria disease that mostly affect regions near said forest. In an effort to treat malaria disease, a diagnostic process is performed to correctly identify the disease. Several image pattern recognition technique been developed and have potential to be utilized as malaria diagnostic tool. In this research, a method is described on designing neural network to detect a blood cell parasitized by malaria. The method consists of utilizing a dense network, and a convolutional neural network, to be trained using publicly available training dataset. Both models’ performance is then compared and analyzed. Before the data is used, a process of padding is performed to resize the input image into 200 x 200 pixels. The resized input data is then used to train both models. From the training and testing, it is found that the dense network achiever 64.78% accuracy. On the other hand, model based on convolutional neural network achiever 94.32%. From analysis, it is found that the size of the model being used is not big enough to achieve better performance. Hence, it is suggested for future research to increase the model size in terms of network width and depth.