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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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[Sentiment Analysis of Novel Review Using Long Short-Term Memory Method ]]>
<![CDATA[Muh Amin Nurrohmat]]>;
<![CDATA[Azhari SN]]>
<![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.41236
<![CDATA[The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.]]>
<![CDATA[A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification ]]>
<![CDATA[Styawati Styawati]]>;
<![CDATA[Khabib Mustofa]]>
<![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.41302
<![CDATA[The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.]]>
<![CDATA[System Security Awareness Planning Model Using The Octave Method Approach ]]>
<![CDATA[Zaied Saad Shouran]]>;
<![CDATA[Nur Rokhman]]>;
<![CDATA[Tri Kuntoro Priyambodo]]>
<![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.43922
<![CDATA[Awareness of the security of information systems is an important thing to note. In this study, we will discuss planning models of awareness about information system security using Octave models or methods. The analytical method used is qualitative descriptive analysis. The results of the study show that the Octave model can increase awareness about the importance of security in an information system and companies that implement it will be able to improve their performance in the future.]]>
<![CDATA[DSS for Selection of Coffee Plants against a Land Using ANP and Modification Of Profile Matching ]]>
<![CDATA[Indra Pratistha]]>;
<![CDATA[Retantyo Wardoyo]]>
<![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.46490
<![CDATA[Based on BPS data, the growth of plantation crop production in NTB Province in 2011 to 2016 was recorded to have decreased by an average of 3.3 thousand tons annually. Coffee plants in particular are 0.1 thousand tons on average, the lack of public interest in planting coffee properly on land owned so that it impacts on land use that is not in accordance with its potential which will result in decreased productivity and erosion of land quality [1]. The first study of land suitability analysis for coffee plantations used a matching method in robusta coffee with a matching method producing a class (S1) of 0,46% [2] the second using a matching method on robusta coffee producing a class (S1) of 0,015% [3] These results indicate the ability of each land is different so that the results of the analysis vary. This study applies the ANP method and modified matching profile where the level of recommendations of coffee plants on the ability of land in East Lombok Regency through validation based on coffee production data from the East Lombok District Agricultural Service produces a match in rank 1 of 87,5% and 75% with non-modified profile matching.]]>
<![CDATA[DSS for "E-Private" Using a Combination of AHP and SAW Methods ]]>
<![CDATA[Ni Komang Yanti Suartini]]>;
<![CDATA[I Made Agus Wirawan]]>;
<![CDATA[Dewa Gede Hendra Divayana]]>
<![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.46625
<![CDATA[Private tutoring was non-formal education and it was needed to help student in learning.There were already tutoring system developed where the selection of private tutors was done by filtering peocess. However, filtering process was not suitable with needs and desires of students.Besides the filtering process, to support the solution in making decisions on the selection of private tutors on the E-Privat system it also used the Decision Suport System (DSS) concept, namely a combination of AHP and SAW methods. AHP method was used to find the weights in each criterion, and the ranking calculation with the SAW method.E-Privat aimed to help parents / students in choosing private tutors that suit the needs and desires of students by involving multi-criteria and various alternative. This system was also developed to help private tutors to get the opportunity to fill out private lessons. The testing process results showed that the system had been successful and suitable for used. There were 5 testing processes : (1)black box testing, (2)white box testing, (3)accuracy test which showed a percentage of 87%, and (4)user's response test whichused the SUS method showed a percentage 92.08% with best imaginable category.]]>
<![CDATA[Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes ]]>
<![CDATA[Nur Azizul Haqimi]]>;
<![CDATA[Nur Rokhman]]>;
<![CDATA[Sigit Priyanta]]>
<![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.47046
<![CDATA[Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.]]>
<![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[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[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.]]>
