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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Azhari SN
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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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%.
Determining Community Structure and Modularity in Social Network using Genetic Algorithm Taufan Bagus Dwi Putra Aditama; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 3 (2020): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57834

Abstract

 Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as email networks and social networks. The popularity determines the structure of a community because it can analyze the structure.Meanwhile, to determine the structure of the community by maximizing the value of modularity is difficult. Therefore, a lot of research introduces new algorithms to solve problems in determining community structure and maximizing the value of modularity. Genetic Algorithm can provide effective solutions by combining exploration and exploitation.This study focuses on the Genetic Algorithm which added a cleanup feature in the process. The final results of this study are the results of a comparison of modularity values based on the determination of the community structure of the Genetic Algorithm, Girvan and Newman Algorithm, and the Louvain Algorithm. The best modularity values were obtained using the Genetic Algorithm which obtained 0.6833 results for Zachary's karate club dataset, 0.7446 for the Bottlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset.
Ontology-based Chatbot to Support Monitoring of Server Performance and Security By Rule-base Fauzan Ishlakhuddin; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.58588

Abstract

The server is a computer program or a device that provides functionality for other programs or devices, called "clients". Generally, server computers have many resources that can be used by one or more clients through the network with specific permissions and requirements. Therefore, the server needs a monitoring system that can monitor server activity and notify if problems occur. This research focuses on developing a notification and question and answer system to connect the network admin with the monitoring system via chatbot. The developed chatbot can send notifications to the admin if an error occurs and can answer questions about the server's condition. The question and answer system developed implements natural language processing for Indonesian. The process of understanding questions is by classifying each word (token) based on language knowledge stored in the ontology. Then the classification results are processed by rule-base to produce conclusions to take monitoring data and compiled into answers. The test results show that the developed system can auto-notify if any problem in a server, and can answer questions by accuracy 95%.
Financial Distress Prediction with Stacking Ensemble Learning Muhammad Fadhlil Hadi; De-Ron Liang; Tri Kuntoro Priyambodo; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76575

Abstract

Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.
Topic Modeling on Online News.Portal Using Latent Dirichlet Allocation (LDA) Mohammad Rezza Fahlevvi; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.74383

Abstract

The amount of News displayed on online news portals. Often does not indicate the topic being discussed, but the News can be read and analyzed. You can find the main issues and trends in the News being discussed. It would be best if you had a quick and efficient way to find trending topics in the News. One of the methods that can be used to solve this problem is topic modeling. Theme modeling is necessary to allow users to easily and quickly understand modern themes' development. One of the algorithms in topic modeling is the Latent Dirichlet Allocation (LDA). This research stage begins with data collection, preprocessing, n-gram formation, dictionary representation, weighting, topic model validation, topic model formation, and topic modeling results.            Based on the results of the topic evaluation, the. The best value of topic modeling using coherence was related to the number of passes. The number of topics produced 20 keys, five cases with a 0.53 coherence value. It can be said to be relatively stable based on the standard coherence value.
Co-Authors De-Ron Liang Fauzan Ishlakhuddin Mohammad Rezza Fahlevvi Muh Amin Nurrohmat Muhammad Fadhlil Hadi Taufan Bagus Dwi Putra Aditama Tri Kuntoro Priyambodo