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All Journal ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS) International Journal of Computer Science and Humanitarian AI
Edy Irwansyah
Bina Nusantara University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Sistem Informasi Geografis (SIG) untuk Zonasi Daerah Bahaya Kerusakan Bangunan Akibat Gempa Bumi: Studi Kasus pada Kota Banda Aceh dan Sekitarnya Edy Irwansyah; Iqbal S.; M. Ikhsan; R. I Made Oka Yoga
ComTech: Computer, Mathematics and Engineering Applications Vol. 3 No. 1 (2012): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v3i1.2463

Abstract

This study aims to develop a geographic information system software that has the ability to develop hazard area zoning of building damage due to earthquake, especially in Banda Aceh and the surrounding areas using peak ground acceleration (PGA) value approach. Analysis and design methods are implemented in this study. The analytical method consists of two stages, namely seismic data collection period 1973 - 2011 by magnitude more than 5 on the Richter scale and the calculation of earthquake acceleration on bedrock using the attenuation function of Crouse. The design method comprises several structured stages, which are designing: data flow diagram (DFD), entity relationship diagram (ERD), menus, screens, and state transition diagrams (STD). The main conclusions of this study is that a GIS -based local zoning of earthquake hazard risk can be built and developed with calculation and classification approach of the peak ground acceleration (PGA). In addition, there is a relationship significant spatial found by comparing the results with the zoning patterns of building damage in the earthquake of 2004.
Analisis Faktor yang Mempengaruhi Angka Buta Huruf Melalui Geographically Weighted Regression: Studi Kasus Propinsi Jawa Timur Andiyono Andiyono; Rokhana Dwi Bekti; Edy Irwansyah
ComTech: Computer, Mathematics and Engineering Applications Vol. 4 No. 1 (2013): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v4i1.2788

Abstract

Analysis of factors that influence the rate of illiteracy can provide important information for education. One such factor is the development of information and communication technology (ICT). Characteristics of illiteracy in East Java showes a spatial pattern. Therefore, to obtain the influencing factors Geographically Weighted Regression spatial modeling (GWR) is utilized. Modeling results indicate that the factors that influence the rate of illiteracy in every location are different. In general, factors influencing literacy rate is the percentage of households which have mobile phone and the percentage of households which access the internet at home. 
Hubungan Algoritma Bezier dan B-Spline pada Fungsi Harmonisnya untuk Menciptakan Bentuk Kurva Sesuai Keinginan Djunaidy Santoso; Zulfany Erlisa Rasjid; Edy Irwansyah
ComTech: Computer, Mathematics and Engineering Applications Vol. 2 No. 2 (2011): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v2i2.2821

Abstract

Computer graphics is a science widely used in any areas. Its up-to-date technique is applied to solve image problems related to needs of customer and decision-making. Images can be analyzed so as produce good form which its accuracy is still under researches using CAD and CAM. This study aims to associate algorithm Hermite, Bezier, B-Spline on its harmony function by creating a proper image to a form that can be used in drawing engineering for decision making. This study produces an adequate algorithm for image design. 
Semantic Segmentation for Aerial Images: A Literature Review Yongki Christian Sanjaya; Alexander Agung Santoso Gunawan; Edy Irwansyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 3 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i3.6737

Abstract

Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the world of research by conducting a review of Semantic Image Segmentation which aims to provide a big picture related to the latest developments related to Semantic Image Segmentation. In addition, we also provide the results of performance measurements on each of the Semantic Image Segmentation methods that we discussed using the Intersectionover-Union (IoU) method. After that, we provide a comparison for each semantic image segmentation model that we discuss using the results of the IoU and then provide conclusions related to a model that has good performance. We hope this review paper can facilitate researchers in understanding the development of Semantic Image Segmentation in a shorter time, simplify understanding of the latest advancements in Semantic Image Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future
Comparing SVM and Naïve Bayes Classifier for Fake News Detection Nurhasanah Nurhasanah; Daniel Emerald Sumarly; Jason Pratama; Ibrahim Tan Kah Heng; Edy Irwansyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 3 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i3.8670

Abstract

Fake news has been evolving into a problem that is getting even more challenging. Technology has been misused to spread false information about many things, such as war, pandemics, and the stock market. Unfortunately, this issue is not a big deal for some people without conscious consumption of that news. Hence, being part takes a role in combating the spread of false information using the advancement of technology. This study proposed two methods of machine learning model, Support Vector Machine (SVM) and Naïve Bayes, to classify fake news. Furthermore, to assert the applicability of models by examining news articles dataset which contain two labels, reliable and unreliable news. The higher accuracy is 0.96 using the SVM model
Implementation of Random Forest Algorithm in Handling Imbalanced Data: A Study on Default Models and Hyperparameter Tuning Ivan William Lianata; Kang Nicholas Darren Nugroho; Yosua Nathanael; Neilson Christopher; Edy Irwansyah
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14417

Abstract

The healthcare industry has benefited greatly from the quick development of artificial intelligence, especially machine learning (ML). Unbalanced data is a significant problem in medical classification, as it can impair model performance, particularly when it comes to identifying important minority classes like patients with particular diseases. The purpose of this research is to evaluate how well two ensemble-based algorithms—Random Forest and Gradient Boosting—perform when dealing with data imbalance in diabetes prediction. Age, body mass index, smoking history, HbA1c level, blood glucose level, and other demographic and medical variables are included in the dataset, which was acquired from Kaggle. Data preprocessing, train-test splitting, model implementation with default parameters, and hyperparameter tuning with Grid Search and Cross Validation comprise the methodology. Accuracy, precision, recall, F1-score, and AUC-ROC metrics were used to assess the model's performance. Both models achieved high accuracy above 97%, according to the results. Following tuning, Random Forest achieved 97.06% accuracy, 0.974 AUC, and 0.99 positive-class precision; however, recall somewhat declined, possibly resulting in underdiagnosis. Gradient Boosting, on the other hand, showed consistent performance with an AUC of 0.9791 and an F1-score of 0.81. These results demonstrate that model performance can be enhanced by hyperparameter tuning; however, algorithm selection should be based on the needs of the application, especially in medical settings where striking a balance between sensitivity and diagnostic precision is crucial.
Co-Authors Alexander Agung Santoso Gunawan Andiyono Andiyono Daniel Emerald Sumarly Djunaidy Santoso Ibrahim Tan Kah Heng Iqbal S. Ivan William Lianata Jason Pratama Kang Nicholas Darren Nugroho M. Ikhsan Neilson Christopher Nurhasanah Nurhasanah R. I Made Oka Yoga Rokhana Dwi Bekti Yongki Christian Sanjaya Yosua Nathanael Zulfany Erlisa Rasjid