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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Journal of ICT Research and Applications Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Indonesian Journal of Information System Teknika ABDIMASTEK
Joko Siswantoro
Departement Of Informatics Engineering, Faculty Of Engineering, Universitas Surabaya, Jalan Raya Kali Rungkut, Surabaya, 60293
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Journal of ICT Research and Applications

 1 
Volume Measurement Algorithm for Food Product with Irregular Shape using Computer Vision based on Monte Carlo Method Joko Siswantoro; Anton Satria Prabuwono; Azizi Abdullah
Journal of ICT Research and Applications Vol. 8 No. 1 (2014)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2014.8.1.1

Abstract

Volume is one of important issues in the production and processing of food product. Traditionally, volume measurement can be performed using water displacement method based on Archimedes' principle. Water displacement method is inaccurate and considered as destructive method. Computer vision offers an accurate and nondestructive method in measuring volume of food product. This paper proposes algorithm for volume measurement of irregular shape food product using computer vision based on Monte Carlo method. Five images of object were acquired from five different views and then processed to obtain the silhouettes of object. From the silhouettes of object, Monte Carlo method was performed to approximate the volume of object. The simulation result shows that the algorithm produced high accuracy and precision for volume measurement.
Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision Joko Siswantoro; Anton Satria Prabuwono; Azizi Abdullah; Bahari Indrus
Journal of ICT Research and Applications Vol. 11 No. 2 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.2.5

Abstract

Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.
Co-Authors Anasthasya Averina Anton Satria Prabuwono Anton Satria Prabuwono Arwoko, Heru Asmawti, Endah Azizi Abdullah Azizi Abdullah Bahari Indrus Edwin Indarto Helen Hadi Januar Rahmadiarto Jessica Millenia Mohammad Farid Naufal Mohammad Farid Naufal Mohammad Farid Naufal, Mohammad Farid Monica Widiasri, Monica Muhammad Alifian Fajar Pratama Muhammad Ghifari Kusuma Wicaksono Soebroto, Juan Timothy Sofia Ariyani Vincentius Riandaru Prasetyo