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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
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<![CDATA[Classification of Human Weight Based on Image ]]>
<![CDATA[Shofwatul 'Uyun]]>;
<![CDATA[Toni Efendi]]>
<![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.35794
<![CDATA[Classification of human weight can be determined by body mass index. The body mass index can be calculated by dividing the height by the square of the body weight. According to researchers, this is less practical, so it needs to make a tool that can be used to determine ideal body weight more practically. One way is to use an Android smartphone camera. The camera is used to capture the image of the human body. Then the image is processed by using digital image processing and by using certain algorithms, so it may conclude the person's ideal weight category. The data used in this study are human photos, body weight and height. There are four stages to determine the weight and height based on the image. First, performing an analysis of the calculation of the derived formulas. Second, analyzing the edge detection algorithm. Third, conducting unit convertion, and fourth, proposing several algorithms to calculate the height and weight used to determine the ideal body weight. The results of the evaluation show that Algorithm C (measuring the width of an object starting with the height of the image adjusting half of the height of the object in the image) is the best algorithm with deviation value of 1.85% of the height and 8.87% of the weight, while the system accuracy rate in determining the ideal body weight has reached 78.7%. ]]>
<![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[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[Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions ]]>
<![CDATA[Angga Maulana Purba]]>;
<![CDATA[Agus Harjoko]]>;
<![CDATA[Mohammad Edi Wibowo]]>
<![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.41259
<![CDATA[Most of Indonesian organizations either it is government or non government sometime required their member to provide their identity card (E-KTP) as legal document collection in their database. This collection of image usually being used as manual verification method. These document images acquired by each person with their own device, there are variations of angles they are used to acquire the image. This situation created problems in text recognition by OCR softwares especially in text detection part, orientation and noise will affect their accuracy. These cases making the text detection more complex and cannot be solved by simple vertical projection profile of black pixels. This research proposed a method to improve text detection in identity document by fixing the orientation first, then using MSER regions to form text region. We fix the orientation using the line that made by Progressive Probabilistic Hough Transform. Then we used MSER to obtain all candidate regions and Horizontal RLSA acts as connector between those candidate. The orientation fixing strategy reach average of margin error 0.377o (in 360o system) and the text detection method reach 84.49% accuracy in best condition.]]>
<![CDATA[Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image ]]>
<![CDATA[Muhammad Faqih Dzulqarnain]]>;
<![CDATA[Suprapto Suprapto]]>;
<![CDATA[Faizal Makhrus]]>
<![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.42036
<![CDATA[Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.]]>
<![CDATA[Comparison of Motion History Image and Approximated Ellipse Method in Human Fall Detection System ]]>
<![CDATA[Mohammad Brado Frasetyo]]>;
<![CDATA[Elvira Sukma Wahyuni]]>;
<![CDATA[Hendra Setiawan]]>
<![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.43632
<![CDATA[This paper compares two different method in human fall detection system namely motion history image and approximated ellipse. Research has been done in small studio with 4 CCTV camera as video data recorder, whereas video data are processed using MATLAB software. The experiment was carried out using three object’s fall direction and two type of falling movement. The fall direction is consist of front, side, and back fall. Whereas the falling movement is consist of direct and indirect fall movement. Meanwhile, the object’s initial position is standing and size of captured object is constant. The result is motion history image has accuracy 74.26% for direct falling movement, and 75.69% for indirect falling movement. Whereas approximated ellipse has accuracy 56.85% for direct falling movement, and 61.81% for indirect falling movement. Therefore, motion history image is better than approximated ellipse in human fall detection system.]]>
