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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 10 Documents
 1 
Search results for , issue "Vol 14, No 2 (2020): April" : 10 Documents clear
Adwords Keyword Set Selection Decision Support System Using AHP and TOPSIS Method Sholikin Ady Chandra; Edi Winarko; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.50731

Abstract

CV. Gitani Creative Agency is a company engaged in the field of creative agency providing digital marketing service. Google Adwords is a platform used by the company to run this service. Keyword set selection is critical to the performance of ads. However, finding the right keyword set is not an easy task. The company needs to consider various criteria to get the optimal advertising results. Decision support system (DSS) is needed as an objective reference in the process of keyword set selection. The criteria for decision-making are click, impressions, cost, and avg. CPC.AHP method is used to compare the value of each criteria and then generate priority weights of each criteria. While TOPSIS method is used for alternative ranking. The combination of these methods aims to improve the performance of TOPSIS method.The result of this study shows that the combination of AHP and TOPSIS methods can be used to determine the best keyword set for ads. Based on the testing results, DSS can do alternative ranking correctly in accordance with the results of manual calculation and it is also flexible to the changes in criteria and alternatives.
Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia Dian Mustikaningrum; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51086

Abstract

 Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation  is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.
Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network MHD Theo Ari Bangsa; Sigit Priyanta; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51646

Abstract

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.
Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection Auliya Rahman Isnain; Agus Sihabuddin; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51743

Abstract

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.
Determination of Temporal Association Rules Pattern Using Apriori Algorithm Shona Chayy Bilqisth; Khabib Mustofa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51747

Abstract

A supermarket must have  good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules  functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.
Clustering followers of influencers accounts based on likes and comments on Instagram Platform Puji Winar Cahyo; Muhammad Habibi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.53028

Abstract

The promotion of goods or services is now facilitated by the dissemination of information through Instagram. Dissemination of information is usually done by influencers or promotional accounts. The account used certainly has a lot of followers. Because of the large amount of follower data in that account, it can be grouped into the same characters. This is done to determine the potential for promotion using social media accounts. This study uses data from 2 popular accounts. The first account is an artist with the username ayutingting92. The second account is Infounjaya, the official promotion account from Jenderal Achmad Yani University, Yogyakarta. The results of grouping can divide follower data into two cluster groups with different interactions. The basic difference between the two groups is the number of likes and comments. The infounjaya account analysis results showed that of 4,906 followers, only 3,211 followers were actively involved in the interaction, 1,695 followers were passive followers who did not like or did not comment on the interaction. Meanwhile, the results of the ayutingting92 follower cluster show that out of 1 million sample data followers, only 13,591 followers were actively involved in the interaction of likes and comments, 986,409 were passive followers.
Application of Blockchain Technology for iLearning Student Assessment Sudaryono Sudaryono; Qurotul Aini; Ninda Lutfiani; Firman Hanafi; Untung Rahardja
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.53109

Abstract

Blockchain is the core technology used to create cryptocurrencies, such as Bitcoin. As one part of the fourth industrial Revolution since the invention of steam engines, electricity, and information technology, blockchain technology has begun to be applied in areas such as finance, judiciary, and trade. Blockchain technology uses decentralized, distributed and transparency techniques for data security. This research aims to determine the implementation of blockchain technology in the field of education, especially in the Data Security section. The study uses two methods namely the mind mapping method and literature review. The results of this study showed that with the presence of blockchain technology, the data is more secure from attacks from both inside and outside because it passes through two levels of security, namely encryption and decentralized data.
Blockchain Technology into Gamification on Education Qurotul Aini; Untung Rahardja; Alfiah Khoirunisa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.53221

Abstract

As we know, Indonesia has begun to enter the era of revolution 4.0 which in that era there were many changes in all fields including the presence of blockchain technology which began to be in demand. Including in the field of education, the changes that occur in the world of education today are so significant with the commencement of the abandonment of teaching methods that still use conventional methods. Keep track of tasks by book, face to face communication, which of course will cause many losses in a certain period of time, as many tasks that have long been buried become difficult to find when needed, and the possibility of manipulation of tasks is still great. The learning method is considered as a boring and insecure way, where students cannot explore learning because the collected files can still be manipulated by other parties. So from now on the application of learning methods is changed by the concept of gamification which relies on blockchain technology. The gamification learning method was created to compensate for the times when students preferred to play games rather than learning, therefore the gamification method could be applied to management education in higher education.
Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation Nur Annisa Tresnasari; Teguh Bharata Adji; Adhistya Erna Permanasari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.54507

Abstract

Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children.  To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases
Traffic Density Classification Using Twitter Data and GPS Based On Android Application Mohammad Afrizal; Idham Ananta Timur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.55761

Abstract

Increasing the number of vehicles in Special Region of Yogyakarta caused by congestion occurred at various traffic points in Special Region of Yogyakarta. The solution to reducing congestion is by increasing the use of public transportation within the city, but it still not in demand by the public. Optimizing daily activities, community always tries to avoid the traffic density on the road to be bypassed.Some research on social media has been used to detect traffic density anomalies. However, the system still cannot provide traffic density information on roads that will be passed by the user because it is just a mapping. Based on this problem, this study aims to classify the traffic density on the road that will be passed by users in the Special Region of Yogyakarta into the category of high traffic and low traffic by utilizing Twitter and GPS data.The results show that Android Applications are able to classify traffic density on the road to be traversed using Geonames.org API. Using the naïve bayes classification algorithm, the system can classify traffic density on 14 streets with an average accuracy of 77.5%, 90% precision, 79.1% recall, and 82.8% f-score.

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