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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JICTE (Journal of Information and Computer Technology Education) Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information System,Graphics, Hospitality and Technology Teknika Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Eduvest - Journal of Universal Studies Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application
Esther Irawati Setiawan
Institut Sains Dan Teknologi Terpadu Surabaya
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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Detection of Banana and Its Ripeness Using Residual Neural Network Erwin Dhaniswara; Yosi Kristian; Esther Irawati Setiawan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.4844

Abstract

Automatic fruit detection utilizing computer vision techniques has been carried out to help the agriculture and plantation industries. This study researches smart systems to detect bananas and ripeness classification utilizing residual neural networks. The method used to detect bananas is transfer learning from pretraned Model VGG-19. Whereas, in the bananas ripeness classification process, residual neural networks, which are trained from the start, are used. Sliding Windows is used to detect the position of bananas followed by Non-Max Suppression to summarize the results of several detected bananas. Previous studies were limited to the level of ripeness, but in this study, bananas are detected and followed by the level of bananas ripeness (raw, ripe, and overripe). This study’s data uses bananas which were mixed with other kinds of fruit. There two kinds of bananas detection architecture used in this study, VGG-19 and Restnet. After they were used to detect bananas, it was found that VGG-19 was more suitable. The results of this study are very satisfying as it is seen from the bananas detection testing percentage using VGG-19 architecture which shows 100% ripe bananas, 99 % raw bananas, and 100% overripe bananas.Keywords: Detection of banana, banana ripeness, Non-Max suppression, residual block.
Co-Authors Adi Sutanto Adriel Ferdianto Aidil Aji Prasetya Wibawa Albertus Josef Andika Arif Priyambodo Arya Tandy Hermawan Christian Nathaniel Purwanto Dhaniswara, Erwin Edwin Pramana Edwin Pramana Effendy Hadi Sutanto Eko Mulyanto Yuniarno Endang Setyati F.X. Ferdinandus Fachrul Kurniawan Ferdinandus, F. X. Fitria Fitria Fujisawa, Kimiya Gunawan Gunawan Gunawan Halim, Kevin Jonathan Hans Juwiantho Hans Keven Budi Prakoso Harianto, Reddy Alexandro Harits Ar Rosyid Hartono, Luluk Setiawati Hartono, Patrick Heppi Siswanto Herman Budianto Herman Thuan To Saurik Honoris Setiahadi I Ketut Eddy Purnama Ika Lestari Imron, Syaiful Joan Santoso Johanes, Sugiharto Kimiya Fujisawa Kristian, Yosi Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Lim, Ernest Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mohamad Fahmi Yusuf Muhammad Arief Rahman Mustaqin, Farhan Faisal Zainul Nagari, Widean Natalie, Valerynta Putra, Adi Surya Rachmatullah, Sholeh Rahmatullah, Dewangga Ramadhan Ramadhan Safitri, Lia Samuel Budi Wardhana Kusuma Santoso , Joan Saputra, Daniel Gamaliel Setya Ardhi Singh, Vrijraj Sugiono, Judi Prajetno Surya Sumpeno Syaiful Imron Syaiful Imron Titis Arwindarti Tjendika, Patrick Tjwanda Putera Gunawan Vu, Tong Nam Tuan Wardoyo, Nikko Riestian Putra Yosi Kristian Yuliana Yuliana YUNI YAMASARI Zaman, Lukman