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All Journal JURNAL BISNIS STRATEGI TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Inovasi Teknologi Pendidikan Jurnal Edukasi dan Penelitian Informatika (JEPIN) POROS TEKNIK Journal of Animation & Games Studies Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal Jurnal ULTIMATICS CYBERNETICS Jurnal Keperawatan dan Fisioterapi (JKF) Jurnal Repositor Jurnal Perempuan & Anak Joutica : Journal of Informatic Unisla JURNAL INOVASI DAN PENGABDIAN MASYARAKAT INDONESIA Makara Journal of Technology JGGAG (Journal of Games, Game Art, and Gamification)
Hardianto Wibowo
Universitas Muhammadiyah Malang
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
3D Reconstruction using convolution smooth method Sofyan Arifianto; Hardianto Wibowo; Wildan Suharso; Raditya Novidianto; Dani Harmanto
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.1991

Abstract

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.
Classification of batik patterns using K-Nearest neighbor and support vector machine Agus Eko Minarno; Fauzi Dwi Setiawan Sumadi; Hardianto Wibowo; Yuda Munarko
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.555 KB) | DOI: 10.11591/eei.v9i3.1971

Abstract

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and support vector machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include energy, entropy, contras, correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
Co-Authors Abims Fardiansa Achmadi, Taufan Reza Agus Eko Minarno Ahmad Faiz, Ahmad Akbi, Denar Regata Ali Akbar Montazeri Andriyani, Vivi Aris Widayati Bayu Fajar Pratama BAYU PRASTOWO Chandra Sebastian Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Dani Harmanto Dani Harmanto Daroe Iswatiningsih Dewantoro, Muhammad Bagas Diah Ayu Fitriani Didih Rizki Chandranegara Djamarullah, Ahmad Raihan Eko Budi Cahyono Eko Mulyanto Yuniarno Fachrunnisa Firdausi Firdausi, Fachrunnisa Firmansyah, Tino Fitriani, Diah Ayu Hapzi Ali Harmanto, Dani Hazmi Rizky Heni Pujiastuti Ibnu Jahsy Karyati, Rina L. Yasril Imam Lailatul Huasniah Lailatul Husniah Lusianti, Aaliyah Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mochammad Yusuf Fachroni Moh Ridwan Multohadi Hamzaturrazak Naser Jawas, Naser Norhabibah, Siti Novidianto, Raditya Nuryasin, Ilyas Raditya Novidianto Raffi Ainul Afif Rahayu Nurul Khasanah Rina Karyati Sabrina Nurul Ubay Safun Rahmanto Sari, Zamah Siti Norhabibah Sofyan Arifianto Sofyan Arifianto Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Taufan Reza Achmadi Tino Firmansyah Ubay, Sabrina Nurul Vivi Andriyani Vizza Dwi Vitanti Wahyu Andhyka Kusuma Wicaksono, Galih Wasis Wildan Suharso Wildan Suharso Wildan Suharso Yuda Munarko Yufis Azhar Zamah Sari Zidni Lubis