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Jurnal Teknologi Informasi Cyberku
Published by Universitas Dian Nuswantoro
ISSN : 19073380     EISSN : 27472183     DOI : -
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science
Jurnal Teknologi Informasi - Jurnal CyberKU is an open access journal, published by Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro. The journal is intended to be dedicated to the development of Information Technology related to Intelligent System, and Business Intelligence. Topics of interest include, but are not limited to: Artificial Intelligence, Machine Learning, Data Mining, Image Processing, Computer Vision, Text Processing, Signal Processing, Speech Recognition, Software Engineering, Decision Support System, IT Governance, eBusiness, Game Technology, Multimedia, eLearning, Computational Education, Computational Engineering, Mobile Computing, Internet of Things.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 67 Documents
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PREDIKSI TINGKAT LOYALITAS PELANGGAN MENGUNAKAN ALGORITMA C4.5 BERBASIS BACKWARD ELIMINATION Syaifuddin Syaifuddin; Purwanto Purwanto; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Customer loyalty is one of the capital to maintain the company's business strategy in the long run. In thelast two decades of Customer Relationship Management (CRM) has grown to become one of the majortrends in marketing, both in education and in the world practice. CRM is a comprehensive businessstrategy of a company that enables the company to effectively manage the company's relationship with thecustomer. Automatic feature selection algorithm is used with the aim of selecting a subset of the featuresin the dataset in order to reach the maximum level of accuracy in classification. The use of data miningtechniques to predict customer loyalty combines C4.5 algorithm with feature selection BackwardElimination. C4.5 algorithm based backward elimination can improve the accuracy in the prediction ofcustomer loyalty, compared with C4.5 algorithm without feature selection. C4.5 algorithm basedbackward elimination generate income per month attribute, type of subscription, registration fee, the costof the bill, and the old subscription
OPTIMASI PARAMETER ARTIFICIAL NEURAL NETWORK DENGAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK MEMPREDIKSI NILAI TUKAR RUPIAH Khairul Fahmi; Stefanus Santosa; Ahmad Zainul Fanani
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

To predict foreign exchange rate is not easy, accurate prediction is necessary for investor to reduce higrisk about exchange rate volatility. In predicting foreign exchange rate is used Artificial Neural NetworkBackpropagation as a model that applied. There are several parameters to implement Artificial NeuralNetwork that must be determined as training cyclel, learning rate, and momentum, the problem is the lackof standard guidelines in determining the parameters that will be used, therefore in this method used theexperimental method. So that we need a method that can resolve the problem, then that the parametersobtained become more optimal. Solutions that can be applied is to apply the genetic algorithm (GA) onArtificial Neural Networks, in order to optimize the value of training cycle, learning rate and momentumparameters. The results are the application of optimization techniques with Genetic Algorithm canfacilitate the search for optimal parameter values and reduce error (RMSE) or increase the value of theaccuracy of the Artificial Neural Network algorithm, thus the model obtained can be used by investor topredict foreign exchange rate.
PREDIKSI PENYAKIT KANKER PAYUDARA MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Supriyadi Supriyadi; Vincent Suhartono; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Breast cancer is a malignant tumor that begins in the cells of the breast. A malignant tumor is a group of cancer cells that can grow and invade surrounding tissues or spread (metastasize) to distant areas of the body. This disease occurs almost entirely in women, but men can also get it. The hypothesis of this study is the method of Artificial Neural Network which is expected to increase the accuracy in the prediction of breast cancer patients. Results of testing to be performed by measuring method and compared with the Artificial Neural Network algorithm C.45. The dataset taken from UCI with a total number of 699 and it is found benign tumors or as many as 458 (65.5%) whereas malignant cancer or 241 (34.5%), with 699 data and 10 attributes which are processed are the thickness of breast cancer, cell size, cell shape, adhesion Margina, single epi cell size, cell nuclei, bland chromatin, normal nucleoli, myth, and the class of breast cancer benign and malignant breast cancer. From various experiments conducted with the Artificial Neural Network algorithm best results are with 500 Cycle Training and Learning Rate 0.5 to obtain an accuracy value of 95.57%, 93.00% presicion, recall 94.62% and AUC 0.986 with time 38s. So based on grouping by comparing the accuracy and AUC values of experiments shows that the algorithm has a classification Artificial Neural Network with a very good, and when compared with the C4.5 algorithm with the result 0.963 is better than Artificial Neural Network algorithm. To be able to increase the level of accuracy of previous studies that only 93.00% to 95.57% gain research or an increase of 2.57%. For computing the level of accuracy with 94.42% and the standard reached by using computational experiments that change the value of Learning Rate it generated 95.57%, an increase of 1.42%.
PREDIKSI HARGA KOMODITAS EMAS DAN BATUBARA DI PASAR DUNIA DENGAN ALGORITMA SUPPORT VECTOR MACHINE Eko Pudjianto; Purwanto Purwanto; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Changes in commodity prices of gold and coal in the world market is very influential on the Indonesian government's policy, especially in the country's revenue in the foreign exchange sector. By predicting the price of gold and coal in the world market expected the government to determine important strategy especially in the fields of mining, trade (exports), Energy and Mineral Resources in Indonesia. By applying the method of SVM (Support Vector Machine) can be found a configuration that is able to predict the prediction of gold and coal prices in the coming period.Data processing using SVM algorithm based on k - fold validation , C (cost) and its kernel , then searched the level RMSE (root mean square error) is the smallest. RMSE is the smallest design that is used in predicting the price of gold and coal. Gold commodity price prediction method with RMSE (root mean square error) is at best 43 509 + / - 37 487 with data input 7 (seven) months earlier , k - fold 10 , C (cos ) of 0.3 and using a kernel -type dot . So the commodity price forecast gold in the world market for the period December 2013 amounted to U.S. $ 1,298.33 and for coal commodities with RMSE (root mean square error) is best at 3,185 + / - 3,591 with data input 2 (two) months earlier , k - 10 fold , C (cost) of 0.3 and using a kernel-type dot. So the prediction of coal commodity prices on the world market for the period from December 2013 is U.S. $ 81.58
SEGMENTASI OBJEK SEMI-OTOMATIS MENGGUNAKAN METODE REGION MERGING MAXIMAL SIMILARITY BERBASIS ALGORITMA MEAN SHIFT DAN NORMALIZED CUTS Indaryanto Abdul Syukur R A Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The process of segmenting objects in compuer studies vision is very important because it is the basis for advanced image processing such as for object recognition. Automatic object segmentation is difficult to image with a complex background. Semi-automatic segmentation by region merging method is the correct method to perform segmentation of objects in a complex background. Semi-automatic method requiring interaction from the user in the form of markers that mark the object and the background to be segmented. Region merging method requires input in the form of low-level image segmentation results. The mean shift algorithm is ideal to use in low-level segmentation process, being able to split the image into many regions by keeping the borders of the object but the image has a weakness over the region segmentation, the resulting numbers are very overrated. The algorithm was able to overcome the problem normalizedd cuts over segmentation. Sequence region merging process with the help marker object and background markers from the user and maximal similarity based comparison of the color histogram of each region. From the results of experiments on 75 image dataset in getting the region merging method based on the input image of the mean shift + algroritma normalized cuts are very accurate in the object segment. This is evidenced by the value of bit error rate lower, reaching 0.09454 more accurate than the region merging algorithm simply mean shift are getting value for bit error rate of 0.20515. Improved accuracy obtained was 11%. Problems over segmentation also resolved, as evidenced by a decrease in the number of segmented region reached an average 69 %.
IMPLEMENTASI HIDDEN MARKOV MODEL UNTUK APLIKASI PENGENALAN SUARA DAN UCAPAN SEBAGAI SISTEM PENGAMANAN UNTUK PERANGKAT KOMPUTER/LAPTOP MENGGUNAKAN LINEAR PREDICTIVE CODING (LPC) Nasrullah Nasrullah; Ernawati Ernawati; Endina Putri Purwandari
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The security of computer devices is very necessary to keep the data in it can be securely maintained. In this research, security application for windows login process that uses user's voice and speech is built. The method used is Hidden Markov Model (HMM) method to identify matches user speech that is to say words that have meaning. Later it also used Linear Predictive Coding (LPC) as a feature extraction to recognize the user voice characteristic who uttered these words. Applications that built with both methods is a desktop application for the training and testing of user's voice and speech and the startup application for login process on computer devices. In this research, LPC methods implementation produces the same speech recognition to a different user by 100%, speech recognition that is different from the same user by 100%. Whereas HMM method application generates voice recognition based on the number of training by 84%, voice recognition based on the number of words spoken by 100%. For voice and speech recognition with a different tone of voice by the same user with average as many as 94%. This application is built with Delphi 7 programming language.
VISUALISASI PROSES DALAM GENERATOR LISTRIK DENGAN PENDEKATAN KOGNITIF-BEHAVIORISTIK UNTUK PEMBELAJARAN SISWA SMK Agus Setyawan; Edi Noersasongko; Stefanus Santosa
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Power generation is one of the learning materials in Competence Expertise Electricity, methods of tutorials, lectures and question and answer and practices originating in the textbook or modules used in learning. Obstacle field practice that is in addition to equipment is limited, some generators have been damaged either due to aging or installation errors of the students on practice time. It is also to know more clearly about the working principle of electric generators in this case is the magnetic field and rotor rotation that can generate electricity learners understand the difficulty because the occurrence is in the generator with a closed circuit. In reality the practice field to meet the ideal lesson about electrical generators required is expensive and the more generators are turned on at the same time will generate noise that can interfere with other learners. Appropriate learning method is to use the visualization method in this case is by displaying the symbols or tools that illustrate the process of installation of electricity generators and the actual electricity generation process, including the parts of the generator, such as: rotor, stator, the anchor and the commutator. Research done by making the model visualization in the form of interactive multimedia animations and equipped with control equipment that can be operated by users, so users can choose what you want to proceed. From the data showed that the media system in the form of generator power this gives a positive contribution to the understanding of student learning.
RANCANG BANGUN APLIKASI PENCARIAN CITRA BATIK BESUREK BERBASIS TEKSTUR DENGAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX DAN EUCLIDEAN DISTANCE Fathin Ulfah Karimah; Ernawati Ernawati; Desi Andreswari
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Image retrieval using text input is considered less effective because the search results sometimes do not represent the input. Thus, it is necessary to create a search application that processes image input to obtain the results representing the input image. The objective of this study is to build such application for Besurek Batik images of various patterns, a type of batik representing the traditional fabric of Bengkulu with MatLab R2012a programming language The models used to identify image patterns and to determine of similarity between tested images and training images are consecutively Gray Level Co-occurrence Matrix method oriented to the direction of 0º, 45º, 90º, and 135º and Euclidean Distance method, while the approach models used to develop and to design the system are respectively the Waterfall model and Data Flow Diagrams (DFD). The final result of this study is the image retrieval application based on the texture with the recall levels of 37.75 % and precision of 77 % with respect to the test of one batik besurek pattern and the recall levels of 30.26 % and precision of 82 % with respect to more than one pattern
PENERAPAN PEMBOBOTAN ATRIBUT PADA ALGORITMA NAIVE BAYES UNTUK ANALISIS SENTIMEN REVIEW APLIKASI ANDROID DARI GOOGLE PLAY Aris Tri Jaka Harjanta; Abdul Syukur; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Fast growing technology corelate with the demand of faster information access. Recently, the information technology dominate by android (open source) based smartphone, it makes many application developer build the application base on this operating system. With so many existing applications, users need a reference to see the application in general, although it has been provided a facility user review for this application, large number of users review are make the user difficult to be able read one by one. Thus it is necessary to know how the sentiment classification of users on the application. In this experiment, algorithms naïve bayes classifications applied are shown to have good performance on large data and have proven reliable in a variety of domains. As well as adding a attribute weighting use algoritm of weight by correlation, weight by chi squered statistical and weight by SVM on the data, so expect a good accuracy of the sentiment analysis android application to use in Indonesian sentiment.
PREDIKSI LOYALITAS PELANGGAN TELEKOMUNIKASI MENGGUNAKAN LOGISTIC REGRESSION DENGAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION Stefanus Santosa; Fenilinas Adi Artanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

For many companies, finding a reason to lose customers, measurement of customer loyalty and regain customers have become essential, including for telecommunication companies. The telecommunications company is one of the industry, where the customer really needs special attention because it is very influential in maintaining the stability of the company's revenue. The telecommunications industry has always faced the threat of financial loss resulting from customer loyalty. The customer who leaves the service is usually called churners. Find churners can help telecommunications companies in retaining customers and keep the company financially. This study used Logistic Regression algorithm with feature selection Particle Swarm Optimization to predict customer loyalty telecommunications. The test results obtained using ANN algorithm accuracy value amounted to 94.80%, and Logistic Regression Algorithm with Particle Swarm Optimization feature selection shows the value of accuracy of 97.65%, and the AUC value of 0.99, then the Logistic Regression algorithm with feature selection Particle Swarm Optimization can improve the accuracy of prediction telecommunications customer loyalty

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