IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Articles
476 Documents
<![CDATA[An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm ]]>
<![CDATA[Wahyono Wahyono]]>;
<![CDATA[Chasandra Puspitasari]]>;
<![CDATA[Muhammad Dzulfikar Fauzi]]>;
<![CDATA[Kasliono Kasliono]]>;
<![CDATA[Wahyu Sri Mulyani]]>;
<![CDATA[Laksono Kurnianggoro]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.36154
<![CDATA[To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.]]>
<![CDATA[Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm ]]>
<![CDATA[Windyaning Ustyannie]]>;
<![CDATA[Suprapto Suprapto]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.37415
<![CDATA[The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.]]>
<![CDATA[Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest ]]>
<![CDATA[Endang Anggiratih]]>;
<![CDATA[Agfianto Eko Putra]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.37461
<![CDATA[Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.]]>
<![CDATA[Optimization of ARIMA Forecasting Model using Firefly Algorithm ]]>
<![CDATA[Ilham unggara]]>;
<![CDATA[Aina Musdholifah]]>;
<![CDATA[Anny Kartika Sari]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.37666
<![CDATA[ Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.]]>
<![CDATA[Automatic Text Summarization Based on Semantic Networks and Corpus Statistics ]]>
<![CDATA[Winda Yulita]]>;
<![CDATA[Sigit Priyanta]]>;
<![CDATA[Azhari SN]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.38261
<![CDATA[One simple automatic text summarization method that can minimize redundancy, in summary, is the Maximum Marginal Relevance (MMR) method. The MMR method has the disadvantage of having parts that are separated from each other in summary results that are not semantically connected. Therefore, this study aims to compare summary results using the MMR method based on semantic and non-semantic based MMR. Semantic-based MMR methods utilize WordNet Bahasa and corpus in processing text summaries. The MMR method is non-semantic based on the TF-IDF method. This study also carried out summary compression of 30%, 20%, and 10%. The research data used is 50 online news texts. Testing of the summary text results is done using the ROUGE toolkit. The results of the study state that the best value of the f-score in the semantic-based MMR method is 0.561, while the best f-score in the non-semantic MMR method is 0.598. This value is generated by adding a preprocessing process in the form of stemming and compression of a 30% summary result. The difference in value obtained is due to incomplete WordNet Bahasa and there are several words in the news title that are not in accordance with EYD (KBBI).]]>
<![CDATA[Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark ]]>
<![CDATA[Rakhmad Ikhsanudin]]>;
<![CDATA[Edi Winarko]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.38596
<![CDATA[Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.]]>
<![CDATA[Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting ]]>
<![CDATA[Abram Setyo Prabowo]]>;
<![CDATA[Agus Sihabuddin]]>;
<![CDATA[Azhari SN]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.39071
<![CDATA[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.]]>
<![CDATA[Application of Load Balancing with the Nth Method on Multiple Gateway Internet Networks ]]>
<![CDATA[Rasna Rasna]]>;
<![CDATA[Ahmad Ashari]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.39074
<![CDATA[The Performance of a Network Is Necessary by the Office of the Special Jayapura Regent in matters related to networking. One of the technological problems to increase connections in the network is to use three ISPs and become microtik as a balanced load. Each ISP uses load sharing that can be divided evenly in each section. Wireless networks that are connected to distributed systems make load balancing techniques that can be received from a system. Load balancing can be applied to HTTP servers, proxies, databases, and gateways. This research implements a proxy with load balancing method on an internet network that has three gateway lines through a router. Expected to be expected to be expected to be expected to load three ISP. The results of the research on the application of load balancing with the method on several internet gateways in the Jayapura District Regent Office is an inconsistency in bandwidth for each client before the implementation of the Nth method and using the Nth method with ten active clients can used when bandwidth on some clients is not much different and more evenly distributed than without load load balancing Nth.]]>
<![CDATA[TOPSIS and SLR methods on the Decision Support System for Selection the Management Strategies of Funeral Land ]]>
<![CDATA[Yayang Eluis Bali Mawartika]]>;
<![CDATA[Azhari SN]]>;
<![CDATA[Agus Sihabuddin]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.39788
<![CDATA[The funeral land is one of the public facilities that must be provided by Local Government to support community activities. The need for funeral land in Lubuklinggau continues to increase while the availability of funeral land is decreasing, this is because the number of deaths of the population continues to increase every year. Forecasting the land availability of funeral for the coming year and applying the management strategies of funeral land can overcome the needs of the cemetery. Forecasting the land availability of funeral using Simple Linear Regression. TOPSIS to choose the management strategies of funeral land. Forecasting uses two variables that are the variable number of the population deaths and the variable amount of funeral land in the last 5 years. Forecasting results will be used as one of the assessment criteria in the decision support system for selection of the management strategies of funeral land. The alternative of the funeral management strategy that will be applied and assessed in accordance with Local Regulation of Town of Lubuklinggau. The highest value of the end result of the system will be used as a recommendation for the selection of management strategies. ]]>
<![CDATA[Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit ]]>
<![CDATA[Junanda Patihullah]]>;
<![CDATA[Edi Winarko]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.40125
<![CDATA[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.]]>
