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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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[Group Decision Support System Using The Analytic Network Process and Borda Methods for Selecting ]]>
<![CDATA[Beta Yudha Mahindarta]]>;
<![CDATA[Retantyo Wardoyo]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47219
<![CDATA[The amount of land for the current location of housing development has resulted in developers choosing the location of housing development regardless of the condition of the land, infrastructure, socio-economic. To overcome this problem a computer system is needed in the form of a GDSS that can assist in the selection of Housing Development Locations.This study aims to implement a GDSS with ANP and Borda methods to determine the selection of the right and fast housing development location. GDSS is needed because there are 3 Individual Decision Makers, DM-1 assessing based on Land Conditions, DM-2 assessing Infrastructure-based, DM-3 assess the Socio-Economic and Decision Maker based groups to make the final decision. The ANP method is used to weight the criteria from each alternative location, to the alternative ranking of housing construction locations for each individual Decision Maker. The Borda method is used to combine the results of ranking carried out by the Group Decision Maker so that it gets the final ranking as a determinant of the Location of Housing Development.The final result of this research is a decision support system that can help developers to get a priority recommendation according to the needs of the developer.]]>
<![CDATA[Data Integrity and Security using Keccak and Digital Signature Algorithm (DSA) ]]>
<![CDATA[Muhammad Asghar Nazal]]>;
<![CDATA[Reza Pulungan]]>;
<![CDATA[Mardhani Riasetiawan]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47267
<![CDATA[Data security is a very important compilation using cloud computing; one of the research that is running and using cloud technology as a means of storage is G-Connect. One of the developments made by the G-Connect project is about data security; most of the problems verification of the data sent. In previous studies, Keccak and RSA algorithms have implemented for data verification needs. But after a literature study of other algorithms that can make digital signatures, we found what is meant by an algorithm that is better than RSA in rectangular speeds, namely Digital Signature Algorithm (DSA).DSA is one of the key algorithms used for digital signatures, but because DSA still uses Secure Hash Algorithm (SHA-1) as an algorithm for hashes, DSA rarely used for data security purposes, so Keccak is used instead of the hash algorithm on DSA. Now, Keccak become the standard for the new SHA-3 hash function algorithm. Because of the above problems, the focus of this research is about data verification using Keccak and DSA. The results of the research are proven that Keccak can run on DSA work system, obtained a comparison of execution time process between DSA and RSA where both use Keccak.]]>
<![CDATA[The Development of IoT Compression Technique To Cloud ]]>
<![CDATA[Kartika Sari]]>;
<![CDATA[Mardhani Riasetiawan]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47270
<![CDATA[The main problem of data transmission is how to reduce the length of data packet delivery, so it can reduce the time of sending data. One method that can be used to reduce the data size is by compressing the data size. Data compression is a technique for compressing data to get the data with smaller size than the original size so that it can shorten the data exchange timeThis study aims to develop the data compression techniques by modifying and combining the coding and modelling techniques based on the RAKE algorithm. This study testing experiments use 4 different methods in 5 different time-periods to determine the value of the compression, decompression efficiency parameters, and the data transmission time parameters.The result of this study is the data coding technique that using decimal to binary converter data and the modeling technique by calculating the residue from the sensor value will produce data in small sizes and get a compression efficiency value of 45%. For coding techniques using ASCII and modeling techniques with XOR operations will produce bigger size data and the compression efficiency value of 71%. In testing data decompression, the decompression efficiency value of 100%, there is no data loss.]]>
<![CDATA[Digitalization On Students Scoring System of SMPN 18 Bekasi ]]>
<![CDATA[Fesa Asy Syifa Nurul Haq]]>;
<![CDATA[Nuryuliani Nuryuliani]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47275
<![CDATA[ Information technology has been supporting the development of school services in the world. But there are still many schools does not using the information technology at all - specially in Indonesia, for example at SMPN 18 Bekasi. As usually like another school they only using Ms. Word and Ms. Excel applications. That is make many differences output in format scoring and mistakes while filling score on the students report format. The application of academic information system in this research have developed using PHP, HTML and MySQL as programming language. It named SIADHEL, means Eighteen Academic Information System (Sistem Informasi Akademik Delapan Belas) . The aims of this project is to provide a good tools for students or their parents to receive the exactly, fast and accurate informations of their students scoring. Teachers can use an integrated and accurate tools as facility to provide data for the Principal to make new policies. This application could be opened by every browser platform, so it will make easier for the users to access the program wherever and anytime.]]>
<![CDATA[Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study ]]>
<![CDATA[Muhammad Asaduddin Hazazi]]>;
<![CDATA[Agus Sihabuddin]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47802
<![CDATA[Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD.]]>
<![CDATA[Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN ]]>
<![CDATA[Friska Ayu Listya]]>;
<![CDATA[Nur Rokhman]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.47906
<![CDATA[ The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN .]]>
<![CDATA[The Evaluation QS-WFQ Scheduling Algorithm For IoT Transmission To Cloud ]]>
<![CDATA[Hirzen Hasfani]]>;
<![CDATA[Mardhani Riasetiawan]]>
<![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.48157
<![CDATA[This study using the Weighted Fair Queue scheduling algorithm when the weights can change and calculated based on changes in the average queue size in the buffer. This algorithm divides the priorities of each sensor into three priorities, namely high, medium and low priority. Each queue is given a weight that is adjusted to the resource requirements of each traffic. High priority data will take precedence, but medium and low priority data will remain underserved and guaranteed by network resources.The results of this study show packet loss ratio when the ratio of the number of buffers and the amount of data is 1: 3 with variations in the number of high, medium and low priority buffers 75: 75: 150 and 50: 50: 200 is 0%. The delay time in the high priority and the medium priority buffer has almost the same delay time when data is transmitted, whereas for the low priority buffer increased in the delay time.]]>
<![CDATA[Identification of Rice Variety Using Geometric Features and Neural Network ]]>
<![CDATA[Wahyu Srimulyani]]>;
<![CDATA[Aina Musdholifah]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.48203
<![CDATA[ Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.]]>
<![CDATA[Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs ]]>
<![CDATA[Wawan Gunawan]]>;
<![CDATA[Agus Zainal Arifin]]>;
<![CDATA[Undang Rosidin]]>;
<![CDATA[Nina Kadaritna]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.48699
<![CDATA[ Dental panoramic radiographs heavily depend on the performance of the segmentation method due to the presence of unevenly illumination and low contrast of the images. Conditional Spatial Fuzzy C-mean (csFCM) Clustering have been proposed to achieve through the incorporation of the component and added in the FCM to cluster grouping. This algorithm directs with consideration conditioning variables that consider membership value. However, csFCM does not consider Intuitionistic Fuzzy Set to take final membership and final non-membership value into account, the effect does not wipe off the deviation by illumination and low contrast of the images completely for improvement to skip some scope. In this current paper, we introduced a new image segmentation method namely Conditional Spatial in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs. Our proposed method adds hesitation function aiming to settle the indication of the knowledge lack that belongs to the final membership function to get a better segmentation result. The experiment result shows this method achieves better segmentation performance with misclassification error (ME) and relative foreground area error (RAE) values are 4.77 and 4.27 respectively.]]>
<![CDATA[Modification of Stemming Algorithm Using A Non Deterministic Approach To Indonesian Text ]]>
<![CDATA[Wafda Rifai]]>;
<![CDATA[Edi Winarko]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.49072
<![CDATA[ Natural Language Processing is part of Artificial Intelegence that focus on language processing. One of stage in Natural Language Processing is Preprocessing. Preprocessing is the stage to prepare data before it is processed. There are many types of proccess in preprocessing, one of them is stemming. Stemming is process to find the root word from regular word. Errors when determining root words can cause misinformation. In addition, stemming process does not always produce one root word because there are several words in Indonesian that have two possibilities as root word or affixes word, e.g.the word “beruang”.To handle these problems, this study proposes a stemmer with more accurate word results by employing a non deterministic algorithm which gives more than one word candidate result. All rules are checked and the word results are kept in a candidate list. In case there are several word candidates were found, then one result will be chosen.This stemmer has been tested to 15.934 word and results in an accurate level of 93%. Therefore the stemmer can be used to detect words with more than one root word.]]>
