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Agus Harjoko
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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 476 Documents
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Combination of AHP Method and VIKOR Method For Assesing Sunday School Teacher Devi Valentino Waas; Suprapto Suprapto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.40533

Abstract

The performance appraisal of Sunday school teacher in the Imanuel Lurang congregation aims to measure and distinguish the quality of performance achieved by Sunday school teacher and decide various policies such as giving rewards to every Sunday school teacher with the best performance, and for Sunday school teacher who have poor performance scores will be given a guiding, approach, etc. The number of criteria in determining the quality of Sunday school teacher is not an easy thing to do by manual. Then it is essential that a computerized performance appraisal-based performance  app can speed up the process of progressing to be more effective and efficient. This research develops decision support systems (DSS) that is dynamic using the PHP programming language, by combining the AHP method that has been refined by the VIKOR method. The AHP method is used in determining the weight of each criterion, and the VIKOR method is used for the ranking process. Test results indicate that the system can provide a sequence of alternative Sunday school teacher that will be used as recommendations for decision makers to determine which Sunday school teachers are quality and not qualified.
Sarcasm Detection For Sentiment Analysis in Indonesian Tweets Yessi Yunitasari; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41136

Abstract

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.
GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification Dias Aziz Pramudita; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41215

Abstract

Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM  to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kernel
Sentiment Analysis of Novel Review Using Long Short-Term Memory Method Muh Amin Nurrohmat; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41236

Abstract

The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.
Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions Angga Maulana Purba; Agus Harjoko; Mohammad Edi Wibowo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41259

Abstract

Most of Indonesian organizations either it is government or non government sometime required their member to provide their identity card (E-KTP) as legal document collection in their database. This collection of image usually being used as manual verification method. These document images acquired by each person with their own device, there are variations of angles they are used to acquire the image. This situation created problems in text recognition by OCR softwares especially in text detection part, orientation and noise will affect their accuracy. These cases making the text detection more complex and cannot be solved by simple vertical projection profile of black pixels.  This research proposed a method to improve text detection in identity document by fixing the orientation first, then using MSER regions to form text region. We fix the orientation using the line that made by Progressive Probabilistic Hough Transform. Then we used MSER to obtain all candidate regions and Horizontal RLSA acts as connector between those candidate. The orientation fixing strategy reach average of margin error 0.377o (in 360o system) and the text detection method reach 84.49% accuracy in best condition.
Chatbot in Bahasa Indonesia using NLP to Provide Banking Information Abidah Elcholiqi; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41289

Abstract

FAQs are mostly provided on the company's website to inform their service and product. It's just that the FAQ is usually less interactive and presents too much information that is less practical. Chatbot can be used as an alternative in providing FAQ. In this study, chatbots were developed for BTPN in providing information about their products, namely Jenius. Chatbot developed utilizes natural language processing so that the system can understand user queries in the form of natural language. The cosine similarity algorithm is used to find similarities between queries and patterns in the knowledge base. Patterns with the highest cosine values are considered to be most similar to user queries. It's just that, this algorithm does not pay attention to the structure of the sentence so that it adds checking the structure of the sentence with the parse tree to give weight to the pattern. This chatbot application has been tested by 10 users and it was found that the suitability of the answers with user input was 84%. Therefore the chatbot developed can be used by BTPN to provide Jenius product information to consumers more interactively and practically.
A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification Styawati Styawati; Khabib Mustofa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41302

Abstract

The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.
Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image Muhammad Faqih Dzulqarnain; Suprapto Suprapto; Faizal Makhrus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.42036

Abstract

Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.
Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System Wahyono Wahyono; Joko Hariyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.42299

Abstract

 Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.
Levels of Political Participation Based on Naive Bayes Classifier Rumaisah Hidayatillah; Mirwan Mirwan; Mohammad Hakam; Aryo Nugroho
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.42531

Abstract

Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.

Page 21 of 48 | Total Record : 476

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