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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Journal : International Journal of Informatics and Computation

 1 
Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House Nurhadi Wijaya
International Journal of Informatics and Computation Vol 1 No 2 (2019): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v1i2.20

Abstract

Occupancy status is one indicator of the rehabilitation and reconstruction program to support eruption victims in Indonesia. It needs to establish the rehabilitation and reconstruction in digital system with structured database. In this paper, we provide dataset 2,146 occupied and 370 unoccupied houses. We utilize a naive Bayes classifier to classify the objects and implement a chi-square algorithm to measure comparison data to actual observed data. This research uses a combination of Naive Bayes and Chi-Square by applying weighting to the dataset attributes. Our study conclude that the combination of the algorithms can achieve a promosing result in classifying the occupancy houses status. The combination of the proposed technique gain 89.59% accuracy and ROC-AUC value 0.839. Therefore, our approach is better than the standard Naive Bayes without combination with the Chi-Square approach
Pooling Comparison in CNN Architecture for Javanese Script Classification Mujastia Feliati Muhdalifah
International Journal of Informatics and Computation Vol 3 No 2 (2021): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v3i2.30

Abstract

Javanese script is evidence of the past culture, which contains various current language learning, including script recognition. However, learning traditional scripts becomes less attractive to the students. Thus, we propose a learning method to enable character recognition among students to deal with the issues. We offer a novel CNN architecture and compare different pooling layers for Javanese script classification. We calculate the separate pooling layer to reduce extensive feature extraction of the image. We present the model comparison results in Javanese character classification to convince our development.
The Design Of Augmented Reality Media Koi Fish Literacy Using Fast Corner Algorithm Mohammad Rofi Rahman
International Journal of Informatics and Computation Vol 3 No 1 (2021): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v3i1.32

Abstract

Ornamental fish that are quite famous and in demand in the market is the koi fish. This fish has a relatively high economic value, and its demand is increasing. There are still many difficulties in maintaining this fish so that it can cause the growth of disease and even death in the fish. It is due to the lack of public attention in terms of literacy about koi fish. Researchers used augmented reality technology to design koi fish literacy media based on these problems using the FAST Corner algorithm. So it is hoped that it could help improve public literacy about koi fish by introducing real-time information. The Fast Corner detection algorithm is helpful to accelerate the computational time when detecting corners in real-time with the markerless Augmented Reality technique. In this technique, the marker used for object tracking has been replaced with pattern recognition or pattern recognition of an object. The study results showed that experiments using this algorithm could track targets with good and faster performance and a maximum level of accuracy.
Implementation of KNN Algorithm for Occupancy Classification of Rehabilitation Houses Nurhadi Wijaya; Joko Aryanto; Kasmawaru Kasmawaru; Anang Faktchur Rachman
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.36

Abstract

The 2010 eruption of Mount Merapi and the resulting rain lava in Central Java's Kab. Sleman DIY and Magelang Regency damaged homes and infrastructure. According to the Head of BNPB Regulation No. 5, the Community Rehabilitation and Reconstruction and Community-Based Settlement program plan is utilized to repair and rebuild properties damaged by the 2011 Merapi eruption. Two thousand five hundred sixteen residences that will stay in the area have been built permanently due to this initiative. Occupancy rates (permanent occupancy) are used by the World Bank's Key Performance Indicators (KPI) to gauge a program's effectiveness. The database has information on how the software was used and proved successful. Databases, essential tools for introducing new data patterns and revealing previously hidden information, are used in data mining. This study applies the KNN algorithm to classify the house's occupancy status data after Mount Merapi's eruption. The accuracy results obtained from the classification of 82.03%, and the performance of the results through the AUC obtained a value of 0.935.
Implementation of Deep Learning for Classification of Mushroom Using CNN Algorithm Imam Mahfudz I'tisyam; Nurhadi Wijaya; Rike Pradila
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.42

Abstract

Mushrooms are a type of low-level plant that lacks chlorophyll. One of the advantages of fungi is that they are commonly utilized as food items in the community. This paper discussed the implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningful features and patterns for accurate categorization. To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization ability to unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Joko Aryanto kasmawaru kasmawaru Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Ordiyasa, I Wayan Putra Wanda, Putra Rike Pradila Sri Hasta Mulyani Trinidya, Fike