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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Systemic: Information System and Informatics Journal Jurnal Teknologi Dan Sistem Informasi Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Universitas Dian Nuswantoro
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method Irham Ferdiansyah Katili; Mochamad Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4488

Abstract

Indonesia is a country rich in a variety of regional cultures. Regional airspace needs to be preserved so as not to become extinct. One of them is the local culture of Central Java Province, namely Javanese Character. In this modern era, globalization is growing in every country. The impact of globalization is increasingly widespread and developing in society. One effect of globalization is local people prefer foreign language skills to learn local languages. This study, applies the method of character recognition using a new combination workflow that contains Local Binary Pattern (LBP) and Information Gain. Then compare Support Vector Machine (SVM), k-Nearest Neighbor and Naïve Bayes. The LBP method is used to obtain an image's texture or shape characteristics. Information Gain is used for the feature selection algorithm, whereas SVM, k-Nearest Neighbor and Naïve ayes is used for the classification method. From previous research, the information gain method succeeded in increasing the accuracy by 2%. This research compares the SVM classification with another classification method, and the result shows that our proposed can improve classification performance. The best accuracy result using SVM classification gets 87,86%, at ten folds and cell size 64x64.
BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features Kristhina Evandari; M. Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4743

Abstract

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
Co-Authors Abdul Syukur Ade Yusupa Ahmad Akrom Ahmad Akrom Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arifin, Zaenal Catur Supriyanto Catur Supriyanto Catur Supriyanto Dewi Nurdiyah Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Etika Kartikadarma Evanita Evanita, Evanita Fajrian Nur Adnan Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Fikri Diva Sambasri Guruh Fajar Shidik Hasan Asari Hendri Ramdan Ika Novita Dewi Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi khoiriya latifah Kristhina Evandari Kurnia Prayoga Wicaksono M. Arif Soeleman M. Arif Soleman Megantara, Rama Aria Mira Nabila Moch Arief Soeleman Mochamad Arief Soeleman Mochamad Hariadi Moh. Arief Soeleman Muh Nasirudin Karim Noor Wahyudi Nuanza Purinsyira Nur Azise Pulung Nurtantio Andono Pulung Nurtantio Andono Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya Rama Aria Megantara Rama Aria Megantara Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rony Wijanarko Sasono Wibowo Stefanus Santosa Sutini Dharma Oetomo Tamamy, Aries Jehan Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari