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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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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Transfer Learning of Pre-trained Transformers for Covid-19 Hoax Detection in Indonesian Language Lya Hulliyyatus Suadaa; Ibnu Santoso; Amanda Tabitha Bulan Panjaitan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66205

Abstract

Nowadays, internet has become the most popular source of news. However, the validity of the online news articles is difficult to assess, whether it is a fact or a hoax. Hoaxes related to Covid-19 brought a problematic effect to human life. An accurate hoax detection system is important to filter abundant information on the internet.  In this research, a Covid-19 hoax detection system was proposed by transfer learning of pre-trained transformer models. Fine-tuned original pre-trained BERT, multilingual pre-trained mBERT, and monolingual pre-trained IndoBERT were used to solve the classification task in the hoax detection system. Based on the experimental results, fine-tuned IndoBERT models trained on monolingual Indonesian corpus outperform fine-tuned original and multilingual BERT with uncased versions. However, the fine-tuned mBERT cased model trained on a larger corpus achieved the best performance.
Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification Amalia Nur Anggraeni; Khabib Mustofa; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66965

Abstract

The continuous growth of the internet has led to the use of social media for various purposes increase. For instance, some irresponsible parties take advantage of the comment feature on social media platforms to harm others by providing spam comments on the shared object. Furthermore, variation of comments creates many features to be processed, thereby negatively impacting the performance of a classification algorithm. Therefore, this study aims to solve the problem associated with spam comments by comparing filter and wrapper based feature selection using text classification techniques. Data collected from training and test data of 4944 and 100 comments showed that the best accuracy, precision, recall, and f-measure of MNB are 96%, 100%, 92%, and 95.8%. The best accuracy is achieved using feature selection by combining Chi-Square and Sequential Forward Selection methods with a subset of 500 features. Furthermore, the accuracy increase in the MNB and SVM classifications are 8% and 4%. This research concludes that the combination of feature selection improves the classification performance of Indonesian language spam comments.
Exploring MSMEs Cybersecurity Awareness and Risk Management : Information Security Awareness Yerik Afrianto Singgalen; Hindriyanto Dwi Purnomo; Irwan Sembiring
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67010

Abstract

The use of information technology in the management of Micro, Small, and Medium Enterprises (MSMEs) is not limited to business performance and productivity but also aspects of data security and transactions using various mobile, website, and desktop-based applications. This article offers an idea to explore cybersecurity awareness and risk management of MSME actors who adopt information technology. The research method used is qualitative with a case study approach in the Coffeeshop X business and the Y Souvenir business in Salatiga City, Central Java, Indonesia. The data collection technique used in-depth interviews, observation, and document studies. These findings indicate that Cybersecurity Awareness, especially information security awareness, can be reviewed based on knowledge, attitudes, and behavior. Risk management can be review based on supply risk, operational risk, and customer risk. Cybersecurity Awareness and Risk Management in MSMEs is holistic and cannot be generalized, so it needs to be discussed contextually based on case studies. In the context of Coffeeshop X and Souvenir Y, the level of Cybersecurity Awareness (knowledge, attitude, behavior) is not always linear. In addition, risk management is more dominant in the customer risk dimension, compared to supply risk and operational risk. 
Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding Putri Rizki Amalia; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67306

Abstract

Someone's opinion on a product or service that is poured through a review is something that is quite important for the owner or potential customer. However, the large number of reviews makes it difficult for them to analyze the information contained in the reviews. Aspect-based sentiment analysis is the process of determining the sentiment polarity of a sentence based on predetermined aspects.This study aims to analyze an Indonesian restaurant review using a combination of Convolutional Neural Network and Contextualized Word Embedding models. Then it will be compared with a combination of Convolutional Neural Network and Traditional Word Embedding models. The result of aspect-classification on three models; BERT-CNN, ELMo-CNN, and Word2vec-CNN give the best results on the ELMo-CNN model with micro-average precision of 0.88, micro-average recall of 0.84, and micro-average f1-score of 0.86. Meanwhile, the sentiment-classification gives the best results on the BERT-CNN model with a precision value of 0.89, a recall of 0.89, and an f1-score of 0.91. Classification using data without stemming have almost similar results, even better than using data with stemming.
Covid-19 Hoax Detection Using KNN in Jaccard Space Ema Utami; Ahmad Fikri Iskandar; Wahyu Hidayat; Agung Budi Prasetyo; Anggit Dwi Hartanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67392

Abstract

Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief & Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief & Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.
Decision Support System for Laptop Selection Using AHP Method and Profile Matching Muhammad Mukharir; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67811

Abstract

 Laptop is a desktop personal computer (PC) whose dimensions are reduced to increase flexibility in its use. However, the large number of products will make it difficult for consumers to choose a laptop that suits the needs of consumers who want to buy it.The purpose of this research is to help buyers who want to buy laptop products according to their needs by making a Decision Support System (DSS). There are 12 criteria considered in this research, price, processor, RAM capacity, hard disk capacity, SSD capacity, V-RAM capacity, maximum RAM upgrade capacity, laptop weight, screen size, screen type, screen refresh rate, and screen resolution. Choosing a laptop product there is a criterion value of a laptop product and a value of preference criteria from the buyer as a decision maker. Also the criteria values on laptop products have different contributions to the overall value of the laptop product. Thus, the methods used are Analytical Hierarchy Process (AHP), Profile Matching (PM) with linear interpolation, and Simple Addictive Weighting (SAW) to determine the recommended options. Lastly, SPK that has been made will be able to provide recommendations best alternative choices and best suit the needs of buyers for selecting laptop products.
Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network Kurnia Muludi; Mohammad Surya Akbar; Dewi Asiah Shofiana; Admi Syarif
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66016

Abstract

Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;
Text Summarization in Multi Document Using Genetic Algorithm Nirwana Hendrastuty; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66026

Abstract

Automatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79.
Sentiment Analysis With Sarcasm Detection On Politician’s Instagram Aisyah Muhaddisi; Bambang Nurcahyo Prastowo; Diyah Utami Kusumaning Putri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66375

Abstract

Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of  sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method  then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.
DSS for Keyboard Mechanical Selection Using AHP and Profile Matching Method Amelia Dita Handayani; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67813

Abstract

Mechanical keyboards are designed with various shapes, variations, and specifications that are different from other types of keyboards. The mechanical keyboard itself has an aesthetic function that allows users to customize it. There are various specifications on mechanical keyboards, causing various considerations, which can make it difficult for users to choose a mechanical keyboard that fits the desired criteria. Supported by observations in the Indonesia Mechanical Keyboard Group (IMKG), some users are still limited in their knowledge of mechanical keyboard products available in Indonesia, also, currently there is no solution that can handle this problem.Based on these problems, in this research, an DSS is built that can help overcome these problems, by providing recommendations for a mechanical keyboard according to the wishes of the user. DSS is implemented in web form using the AHP method for the weighting process and Profile Matching for the scoring process. The criteria used are determined by conducting a survey regarding the specifications that are the priority considerations in choosing a mechanical keyboard.At the end of the study, the DSS that was successfully built was able to provide mechanical keyboard priority recommendations according to user preferences and get an average evaluation result of 36.17 out of a total maximum value of 40.

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