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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 13, No 1 (2019): January" : 10 Documents clear
Cased Based Reasoning to Identify Cause Conflicts in Marriage Arief Ichwani; Suprapto Suprapto
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.24861

Abstract

The function of KUA in the activities surrounding the religion of Islam, including providing service and guidance in the area of present services in terms of marriage and reconcilement for Muslims, provide services and guidance in the field of development of Sakina, family consultation conflict or household problems, and so on. Integration between the computer and artificial intelligence into the post-wedding consulting services is one approach in overcoming the limitations of the expert (religious instructor).This research aims to identify conflict in marriage by applying Naive Bayes algorithm at the stage of determining the groups of test data (retrieve), then entered the stage of the search process of the highest similarity value by using the Nearest Neighbor algorithm (reuse). The data source and the test data used are divided into two groups, namely marriage, and history data consultation, While the group conflicts are identified will be divided into five classes, namely an employment factor, the factor of age, educational factors, factors the number of weddings, and social status.Testing is performed by the use of 12 data, consisting of 11 data cases and 1 test data. At the stage of determination of group conflict acquired test data included in group one i.e. F001 (factor of the job), so at the stage of looking for value similarities used only the base case of the class F001 i.e. KK001, KK003, and KK008. The KK001 similarity has a value of 0476, KK003 of 0882, and KK008 of 0142. The case with most high similarity value will be stored as a base case. If the value similarity obtained less than the threshold value that is 0.8, then the solution of the case will be revised by experts. The results of the calculation accuracy, using 35 new test data that gets the value of 82.86%.
Prediction of Length of Study of Student Applicants Using Case Based Reasoning Ulfi Saidata Aesyi; Retantyo Wardoyo
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.28076

Abstract

 Graduation is important matter in college. Length of study can be used to evaluate curriculum. It affect accreditation score of the sutdy program. Based  on Akreditasi Program Studi Magister Buku V Pedoman Penilaian Instrumen Akreditasi 3rd standard there is rule about students and graduation, such as profile of the graduates including average length of study time and gpa (grade point average) of graduates.In this study, system built to predict Gadjah Mada University Master of Computer Science student applicant’s length of study. It used new case with 13 features from applicant that will be predict as new case, then calculate local similarity using euclidean distance and hamming distance while global similarity using nearest neighbor. Maximum value of global similarity taken as solution while revised will be done if it’s value below threshold.Result of this study show that system can help study program to manage educational process. It show 76% accuracy of 50 data.
PLO User Interface based on Telegram Bot Bambang Nurcahyo Prastowo; Nur Achmad Sulistyo Putro; Oktaf Agni Dhewa
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.29089

Abstract

Instant messaging services usually integrate a notification system on their users’ devices, as phone calls and short message service (SMS) systems do. Telegram is – as far as we are aware – the only popular instant messaging service that uses open source code. Telegram also provides APIs for their users, enabling the development of a bot system that allows instant messaging application to access information. Here, we study the potential use of Telegram bot as a user interface to a paperless office (PLO) system developed in our institution. We found that Telegram bot improves communication among the users; however, as the amount of messages increased, the server becomes overloaded. This limitation suggests that future works need to be directed to improving the efficiency of the bot
Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting Abram Setyo Prabowo; Agus Sihabuddin; Azhari SN
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.39071

Abstract

One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model.
Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit Junanda Patihullah; Edi Winarko
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.40125

Abstract

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.
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%.
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.
Genetic Algorithm for lecturing schedule optimization David Kristiadi; Rudy Hartanto
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.43038

Abstract

Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.
Apps-based Machine Translation on Smart Media Devices - A Review Hary Gunarto
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.43066

Abstract

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.

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