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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) COPING (Community of Publishing in Nursing) JUTI: Jurnal Ilmiah Teknologi Informasi Creative Information Technology Journal SISFOTENIKA Jurnal Sains Dan Teknologi (SAINTEKBU) Jurnal Komputer Terapan CogITo Smart Journal Jurnal Eksplora Informatika specta: Journal of Photography, Arts, and Media JUTEKIN (Jurnal Manajemen Informatika) Jurnal Ilmiah Sinus CCIT (Creative Communication and Innovative Technology) Journal METIK JURNAL Technologia: Jurnal Ilmiah G-Tech : Jurnal Teknologi Terapan Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat CAHAYA tech Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA DEVICE Jurnal Pengabdian Seni Jurnal Ekonomi dan Teknik Informatika EXPLORE Techno Jurnal Permata Indonesia Panggung JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Bisnis Digital dan Sistem Informasi
Kusrini Kusrini
STMIK AMIKOM Yogyakarta
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : Techno

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Classification of Mango Fruit Quality Based on Texture Characteristics of GLCM (Gray Level Co-Occurrence Matrices) with Algorithm K-NN (K-Nearest Neighbors) Wahyu Wijaya Widiyanto; Eko Purwanto; Kusrini Kusrini
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 1 (2019): Techno Volume 20 No.1 April 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i1.3816

Abstract

Proses klasifikasi kualitas mutu buah mangga dengan cara konvensional menggunakan mata manusia memiliki kelemahan di antaranya membutuhkan tenaga lebih banyak untuk memilah, anggapan mutu kualitas buah mangga antar manusia yang berbeda, tingkat konsistensi manusia dalam menilai kualitas mutu buah mangga yang tidak menjamin valid karena manusia dapat mengalami kelelahan. Penelitian ini bertujuan untuk klasifikasi kualitas mutu buah mangga ke dalam tiga kelas mutu yaitu kelas Super, A, dan B dengan computer vision dan algoritma k-Nearest Neighbor. Hasil pengujian menggunakan jumlah k tetangga 9 menunjukan tingkat akurasi sebesar 88,88%.Kata-kata kunci— Klasifikasi, GLCM, K-Nearest Neighbour, Mangga
Image Similarity Searching Use Multi Part Cutting And Grayscale Color Histogram Sofyan Pariyasto; Kusrini Kusrini; Hanif Al Fatta
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 1 (2019): Techno Volume 20 No.1 April 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i1.3817

Abstract

The use of information technology in everyday life continues to increase so rapidly. This is inseparable from the role of researchers, especially in the field of information technology. Information technology has become a necessity so that it is widely used in the fields of education, trade, livestock and even to the agricultural sector. One of the obstacles that is needed is to check all activities involving information systems, especially when there is data in the form of images. Problems that arise are usually needed by humans to check and sort items that have been carried out by humans. This is the background of this research to help reduce activities involving humans. The process of finding the similarity of images in computer vision can be used in several fields such as education, retail, and other fields. In the field of computer vision education can be utilized for the automatic absence process through face recognition, in terms of retailing, it can be used for sorting through object detection. The process of finding similarities between images that are queries and dataset images will be the subject of research, and the process of calculating similarities between queries and datasets will be discussed step by step. The method used in the search process is by calculating the shortest distance between query images and dataset. The steps taken are the extraction feature and then RGB to gray color conversion. The next stage is to cut the image into four parts which will then be calculated the distance of the ecludian. The final part will calculate the performance of the algorithm using the matrix confusion method, so that the test results are in the form of error rates, precision, and accuracy. The trial process uses 30 data using 1000 datasets. In the test results obtained information in the form of recall of 1, 0.66 accuracy and 0.66precision.Keywords: Image Similarity, Histogram Image Grayscale, Ecludiance Distance.
Searching Similarity Digital Image Using Color Histogram Wahyu Wijaya Widiyanto; Kusrini Kusrini; Hanif Al Fatta
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 1 (2019): Techno Volume 20 No.1 April 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i1.3818

Abstract

In the era of globalization and modernization, as now, information technology is widely used in the fields of education, trade, animal husbandry, agriculture and even to the legal sector. One branch of science in the field of information technology that is growing rapidly is computer vision. One of the important roles of computer vision in everyday life is the use of computer vision. This can be applied in terms of face recognition, object detection, and can be applied to group images based on the order of similarity of the image, the ability of computer vision is applied to facilitate human work in selecting from several images to find the most similar images. In this study described the process of finding the similarity of an image with other images through several stages of research flow, the method used is to use RGB values that have been converted to grayscale, then the eucludian distance distance is calculated to determine the value of proximity of an image while calculating performance accuracy algorithm using confusion matrix. The search trial process resulted in an accuracy rate of 0.42, precision of 0.42 and recall 1 of 1000 datasets and 30 random data were taken. Found images that differ in color and shape but when converted into histograms the data has a fairly high similarity to the query. The disadvantage of this research is that images that have histograms similar to queries are displayed as similar images even though the reality is that images are very different from colors and shapes.Keywords: computer vision, similiarity, eucludian distance, grayscale, histogram
Co-Authors Abdul Malik Zuhdi Abdul Rokhim Achmad Yusron Arif Agung Jasuma Agung Nugroho Agus Harjoko Aisha Alfani Aji Susanto Anom Purnomo Andayani Andayani Andi Sunyoto Andria Andria Anggit Dwi Hartanto Aprison Wolla Gole Ari Suhartanto Arif Fajar Solikin Arif Fajar Solikin Armadyah Amborowati Asro Nasiri Assani, Moh. Yushi Azis Wahyudi B, Isdayani Christin Nandari Dengen DHANI ARIATMANTO Dina Maulina Dwinda Etika Profesi Eka Wahyu Pujiharto Eka Yulia Sari Eko Purwanto Elfrida Ratnawati Elik Hari Muktafin Elvis Pawan Emha Taufiq Luthfi Erfan Tongalu Eva Oktaviani Fajar Dwi Insani Fareza Aditiyanto Nugroho Ferry Wahyu Wibowo Firmanda Fasya Hamada Zein HANIF AL FATTA Hasan, Nur Fitrianingsih Henderi . Heri Abijono Irfan Purwanto Jhoanne Fredricka Jimmy H Moedjahedy Junaidi Sabtu Kusnawi Kusnawi Lili Kartikawati M Rudyanto Arief M Vaizul Rahman M. Afriansyah M. Rudyanto Arief M. Syukri Mustafa Marta Ardiyanto Maykel Sonobe Mochamad Fadillah Abdullah Muchamat Zainal Arifin Muhamad Kurniawan Muhammad Agus Muljanto Muhammad Alfariz Muhammad Fahmi Muhammad Fajar Apriyanto Muhammad Firdaus Abdi Muhammad Rudyanto Arief Muhammad Salimy Ahsan Musthofa Galih Pradana Mutiara Dwi Anggraini Neno, Friden Elefri Norhikmah Norhikmah Pamekas, Bondan Wahyu Rahmat Saleh Sukur Retantyo Wardoyo Rifqi Hammad Ririn Putri Damaiyanti Riska Dwi Handayani Siti Nurhayati Siti Solekhah Sofyan Pariyasto Sri Hartati Sri Lestari Rahayu Sri Yanto Qodarbaskoro Wahyu Nur Alimyaningtias Widiyanto, Wahyu Wijaya Wira Dimuksa Wiwi Widayani Yeyen Dwi Atma Yustian Servanda Zenal Muttaqin Zeni Muhamad Noer