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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Derwin Suhartono
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : ComTech: Computer, Mathematics and Engineering Applications

 1 
Using K-Nearest Neighbor in Optical Character Recognition Veronica Ong; Derwin Suhartono
ComTech: Computer, Mathematics and Engineering Applications Vol. 7 No. 1 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i1.2223

Abstract

The growth in computer vision technology has aided society with various kinds of tasks. One of these tasks is the ability of recognizing text contained in an image, or usually referred to as Optical Character Recognition (OCR). There are many kinds of algorithms that can be implemented into an OCR. The K-Nearest Neighbor is one such algorithm. This research aims to find out the process behind the OCR mechanism by using K-Nearest Neighbor algorithm; one of the most influential machine learning algorithms. It also aims to find out how precise the algorithm is in an OCR program. To do that, a simple OCR program to classify alphabets of capital letters is made to produce and compare real results. The result of this research yielded a maximum of 76.9% accuracy with 200 training samples per alphabet. A set of reasons are also given as to why the program is able to reach said level of accuracy.
Pengembangan Algoritma Fast Inversion dalam Membentuk Inverted File untuk Text Retrieval dengan Data Skala Besar Derwin Suhartono
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.v3i1.2461

Abstract

The rapid development of information systems generates new needs for indexing and retrieval of various kinds of media. The need for documents in the form of multimedia is increasing currently. Thus, the need to store or retrieve now becomes a primary problem. The multimedia type commonly used is text types, as widely seen as the main option in the search engines like Yahoo, Google or others. Essentially, search does not just want to get results, but also a more efficient process. For the purposes of indexing and retrieval, inverted file is used to provide faster results. However, there will be a problem if the making of an inverted file is related to a large amount of data. This study describes an algorithm called Fast Inversion as the development of base inverted file making method to address the needs related to the amount of data.
Design of Distribution Optimization Application using Firefly Algorithm Ngarap Imanuel Manik; Yunggih Nursalim; Derwin Suhartono
ComTech: Computer, Mathematics and Engineering Applications Vol. 8 No. 3 (2017): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v8i3.2567

Abstract

The goal of this research was to optimize the distribution of goods and computerize. The method consisted of problem identification, analysis, implementation, and evaluation. Firefly algorithm was used as a method for optimizing the distribution of goods. The results achieved are the shortest distribution route of goods in accordance with the existing constraints and low cost of distribution. It can be concluded that the research can beused too ptimizethe distribution of goods and tominimize distributioncost.
Aplikasi E-Tour Guide dengan Fitur Pengenalan Image Menggunakan Metode Haar Classifier Derwin Suhartono; William Surya Permana; Antoni Wiguna; Ferlan Gisman Putra
ComTech: Computer, Mathematics and Engineering Applications Vol. 4 No. 2 (2013): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v4i2.2593

Abstract

Smartphone has became an important instrument in modern society as it is used for entertainment and information searching except for communication. Concerning to this condition, it is needed to develop an application in order to improve smart phone functionality. The objective of this research is to create an application named E-Tour Guide as a tool for helping to plan and manage tourism activity equipped with image recognition feature. Image recognition method used is the Haar Classifier method. The feature is used to recognize historical objects. From the testing result done to 20 images sample, 85% accuracy is achieved for the image recognition feature.
Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF) Hans Christian; Mikhael Pramodana Agus; Derwin Suhartono
ComTech: Computer, Mathematics and Engineering Applications Vol. 7 No. 4 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i4.3746

Abstract

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.
Eve: An Automated Question Answering System for Events Information Ivan Christanno; Priscilla Priscilla; Jody Johansyah Maulana; Derwin Suhartono; Rini Wongso
ComTech: Computer, Mathematics and Engineering Applications Vol. 8 No. 1 (2017): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v8i1.3781

Abstract

The objective of this research was to create a closed-domain of automated question answering system specifically for events called Eve. Automated Question Answering System (QAS) is a system that accepts question input in the form of natural language. The question will be processed through modules to finally return the most appropriate answer to the corresponding question instead of returning a full document as an output. Thescope of the events was those which were organized by Students Association of Computer Science (HIMTI) in Bina Nusantara University. It consisted of 3 main modules namely query processing, information retrieval, and information extraction. Meanwhile, the approaches used in this system included question classification, document indexing, named entity recognition and others. For the results, the system can answer 63 questions for word matching technique, and 32 questions for word similarity technique out of 94 questions correctly.
Question Categorization using Lexical Feature in Opini.id Christian Eka Saputra; Derwin Suhartono; Rini Wongso
ComTech: Computer, Mathematics and Engineering Applications Vol. 8 No. 4 (2017): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v8i4.4026

Abstract

This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naïve Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with attribute selection in WEKA achieves the accuracy of 95,94% by using SVM as the classifier.
Psychological Stress Detection Using Transformer-Based Models Derwin Suhartono; Irfan Fahmi Saputra; Andhika Rizki Pratama; Gabriel Nathaniel
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.11105

Abstract

Stress is a significant mental health problem that results in a lack of concentration. It has been more widely identified through social media since people who are under stress usually post about their physical pain and tiredness. However, stress assessment through social media by professionals can be expensive and time-consuming. The research aimed to produce a stress detection system trained using a Twitter dataset to predict stress using the user’s input sentence. The experiments that were done in the research used transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa). The research involved data pre-processing, model training, and model evaluation to ensure high-quality train data. Since the data were imbalanced, data trimming was performed in pre-processing to select data randomly until the balance matched. This process ensured the model’s effectiveness in the training and evaluation stages. The features used in these experiments were features from each pre-trained model. In evaluating the model, accuracy, loss, and F1 score were used as metrics. In the result, for BERT, accuracy reaches 0.848 with an F1 score of 0.847. Meanwhile, RoBERTa has an accuracy of 0.837 and 0.834. The results prove that BERT and RoBERTa can be used to classify stress with accuracy and an F1 score above 0.8. The experiment result shows that the BERT deep learning model can detect stress using the Twitter datasets.
Co-Authors Andhika Rizki Pratama Antoni Wiguna Bagas Pradipabista Nayoga Billy Wibowo Chintya De Christian Eka Saputra Daniel D. Albesta Farhan Putra Salsabil Ferlan Gisman Putra Gabriel Nathaniel Gerardo Axel Lwiantoro Gregorius A. N. Aditantyo Hans Christian Heinrich Reagan Salim Helen Clarissa Irfan Fahmi Saputra Ivan Christanno Jody Johansyah Maulana Michael L. Jonathan Mikhael Pramodana Agus Muhammad Jawad Ngarap Imanuel Manik Nicholas Daniel Pratamaputra Oktovianus Hardiawan Priscilla Priscilla Rini Wongso Ryan Adipradana Ryan Suryadi Veronica Ong William Surya Permana Yunggih Nursalim