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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Fundamental Mathematics and Applications (JFMA)
Priyo Sidik Sasongko
The Informatics Departement, Faculty Of Mathemathics And Sciences, Diponegoro University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences

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Journal : telkomnika telecommunication computing electronics and control

 1 
Detection of Ship Using Image Processing and Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.7357

Abstract

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.
Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network Sutikno Sutikno; Indra Waspada; Nurdin Bahtiar; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3486

Abstract

One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.
Solid waste classification using pyramid scene parsing network segmentation and combined features Khadijah Khadijah; Sukmawati Nur Endah; Retno Kusumaningrum; Rismiyati Rismiyati; Priyo Sidik Sasongko; Iffa Zainan Nisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.18402

Abstract

Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed  to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
Co-Authors Eko Adi Sarwoko Fajar Agung Nugroho Helmie Arif Wibawa Iffa Zainan Nisa Indra Waspada Indriyati Indriyati Jason Al Hilal Sabda Dewa Khadijah Khadijah Khadijah, Khadijah Mahendra, Ida Bagus Satya Muhamad, Nida Nisa, Iffa Zainan Nurdin Bahtiar Okky Widya Arditya Prasetyo, Syalwa Dea Putri Rachmat Gernowo Rachmawati Soewono, Rachmawati Ragil Saputra Redemtus Heru Tjahjana Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Satriyo Adhy Satya Sandika Putra Sirait, Dheo Ronaldo Sukmawati Nur Endah Sukmawati Nur Endah Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno, Sutikno Sutimin Sutimin Tatik Widiharih Triatmaja, Yudha Kusuma Vebriana, Melanie Safira Widowati, Widowati Wildan Azka Adzani