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All Journal Jurnal Jurusan Ilmu Keolahragaan Semantik Kepelatihan Olahraga E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Teknologi Informasi Cyberku Jurnal Riset Rumpun Ilmu Kedokteran (JURRIKE) Journal of Student Research Community: Jurnal Pengabdian Pada Masyarakat Nusantara Sporta: Jurnal Pendidikan dan Ilmu Keolahragaan Journal Innovation In Education Scientific Journal of Informatics Mutiara Pendidikan dan Olahraga
Catur Supriyanto
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Journal : Jurnal Teknologi Informasi Cyberku

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PENERAPAN ALGORITMA C4.5 BERBASIS ADABOOST UNTUK PREDIKSI PENYAKIT JANTUNG Abdul Rohman; Vincent Suhartono; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 1 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Heart disease is the occurrence of partial or total blockage of a blood vessel over, as a result of the self peyumbatan deep chemical energy supply to the heart muscle is reduced, resulting in impaired balance between supply and needs .Research in predicting heart disease have been carried out by several previous investigators. In this study will be done for heart disease prediction algorithm using C4.5 and improved the performance of C4.5 algorithm using Adaboost method is implemented on the data of heart disease patients. From the test results by measuring method using a C4.5-based Adaboost, confusion matrix, and the ROC curve, it is known that C4.5 algorithms yield accuracy values 86,59%, AUC values obtained after 0.957 and optimized by using the method to be 92,24% Adaboost, the AUC to 0.982. by looking at the accuracy and AUC values after the optimizations, the algorithmbased C4.5 classification Adaboost into the category of groups is very good, because AUC values between 0.90 – 1.00
ANALISIS DAN PERANCANGAN MODEL FUZZY UNTUK SISTEM PAKAR PENDETEKSI TINGKAT KESUBURAN TANAH DAN JENIS TANAMAN Amiril Mukminin; Heru Agus Santoso; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 1 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Tillage that is not appropriate to the characteristics of plant type can easily causethe plants to wilt and plant growth is not maximal. These factors areoften the main cause of crop failure that is not known by the farmers. Therefore, an expert system is designed to detectthe soil fertility for types of plant using the fuzzy logic, which is expected to help the farmers in choosing the right types of plant with an appropriate of certain level of soil fertility. The measurement results obtained have been appropriate with the calculations and criteria of the land that has been entered.
PREDIKSI KECEPATAN ANGIN MENGGUNAKAN MODEL ARTIFICIAL NEURAL NETWORK BERBASIS ADABOOST Abdul Syukur; Catur Supriyanto; Akhmad Khanif Zyen
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Prediction is an attempt to predict the future by examining the past. This prediction consists of the bias estimation of the magnitude of future several variables, such as sales, on the basis of knowledge of the past, present, and experience. Adaboost is one of the optimization algorithm which can improve the accuracy of a predictive value. Previous research examines the exchange rate prediction of wind speed using back propagation Artificial Neural Network algorithm. The purpose of this study is intended to improve the accuracy of prediction of wind speed previously predicted using Artificial Neural Network Backpropagation algorithm then improved the prediction accuracy using adaboost algorithm during the process of training and added back propagation Artificial Neural Network algorithm in the learning process.The results showed that the prediction accuracy of the wind speed values previously predicted using Artificial Neural Network back propagation algorithm with an accuracy of prediction error at sample time per 10 minute predictions of 0.31576596 managed to reduce the value of the accuracy of the prediction error using adaboost algorithm during training and coupled Artificial Neural Network algorithm Backpropagation learning process with an accuracy of prediction error amounting to 0.15945762.
OPTIMASI PREDIKSI TINGKAT PRODUKSI BAWANG MERAH NASIONAL MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK BERBASIS ALGORITMA GENETIKA Fajriyanto Fajriyanto; Abdul Syukur; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Bawang merah merupakan kebutuhan masyarakat yang terus meningkat seiring dengan pertambahan jumlah penduduk dan harga belinya. Oleh sebab itu, untuk mengimbangi kebutuhan agar selalu terpenuhi maka jumlah produksinya harus seimbang. Menurut Direktorat Bina Hortikultura(1980), bahwa bawang merah adalah salah satu yang memberikan preoritas utama untuk pengembangan produksi Hortikultura secara nasional. Data produksi bawang merah dari tahun 1969-2014 produksi pertahun bersifat fluktuatif disebabkan oleh meningkatnya populasi Sementara lahan yang tersedia semakin sempit. Oleh sebab itu, prediksi produksi bawang merah Nasional dibutuhkan. Metode Backpropagation merupakan metode popular untuk Teknik prediksi yang mempunyai nilai RMSE terbaik. Akan tetapi, metode Backpropagation Neural Network mempunyai beberapa kelemahan, oleh sebab itu dibutuhkan sebuah metode optimasi, salah satunya dengan metode optimasi Algoritma genetika. Penelitian ini menggunakan data produksi bawang merah Nasional yang diperoleh dari Direktorat Jendral Holtikultura untuk proses training dan testing dengan menggunakan metode Backpropagation Neural Network dan Algoritma genetika untuk optimasi input weight.Pada panelitian ini metode Backpropagation Neural Network dengan algoritma genetika sebagai optimasi inputan menghasilkan nilai RMSE 0.062 terbaik, sedangkan metode Backpropagation Neural Network tanpa optimasi algoritma genetika menghasilkan nilai RMSE 0.089.
KLASIFIKASI PESAN SMS MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN SELEKSI FITUR GENETIC ALGORITHM Indah Munitasri; Stefanus Santosa; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Short Message service (SMS)is mobile communication that interest advertiser for its effective deliveries with cheap operational cost compare to printed media. Some spam SMS do not need mailing list to reach their customers. But, spam SMS could create higher respons from emails spam. Spam SMS includes promotion,scamming,and fraud.To overcome this problem,anti-spam filtering are needed to detect spam and non-spam SMS. Some anti-spam filtering algoritm such as Decission Tree, Naïve Bayes (NB),Support Vector Machine (SVM),and Neural Network. This research used Naïve Bayes classifier or known as multinominal Naïve Bayes is a simplification from Bayes algoritm which is suitable for text or documents classification.This study will make additional Genetic Algorithms in the process of selecting attributes that will be used in the classification process with Naïve Bayes algorithm. Genetic Algorithms can be used as an attribute of the overall voter attributes obtained from the process of feature extraction. NB compared to NB and GA produced significant accuracy result, NB gained 89.39% accuracy rate, but GA gained 89.73% accuracy rate. So, there is an increase in 0.34 % after adding GA. NB and GA can be applied to the classification of SMS messages, because Naïve Bayes algorithm is an algorithm that does not consider the relationship between attributes to one another (independence). So, when there is a data set with hundreds of attributes, all of those attributes will be counted by Naïve Bayes, by adding a Genetic Algorithm as a feature selection, which determines the attributes that are relevant in order to optimize the classification accuracy. It is expected to apply feature selection using Particle Swarm Optimization (PSO) for the next research because there is no evolution in the operator, for example, mutation and crossover on Genetic Algorithms (GA,) and PSO is more flexible in maintaining the balance between global and local searches on its search space.
PENGELOMPOKAN ARSIP UNIVERSITAS MENGGUNAKAN ALGORITMA K-MEANS DENGAN FEATURE SELECTION CHI SQUARE Sitti Munifah; Abdul Syukur; 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

The information in every activity is very fundamental either in technical activities or in decision making.One of those is information in the form of data records or archives. Considering the importance of therole of an archive more actively in supporting the activities, then, it needs to manage the archive betterthrough the application of information and communication technology aspect, particularly on the processof archives storage. By using electronic media in archives management, then it can give easiness instoring the archives. Related to the things discussed and due to the increasing of document numbers in thetext form which quiet large on University archives chamber, it makes the document clustering important.The document clustering is a right way and has purpose of distributing the document into some groups,which have text similarity level, term wighting and distance similarity that used at the time of archivesstoring subjectively. The objective of this writing is to clustering of archives document in the archivesstoring system, increasing of clustering document performance through term weighting TF-IDF andselection feature method. The results showed that the use of selection feature method and K-MeansAlgorithm on clustering analysis, to process the archives storing seen that there was an increasing ofaccuracy level on Manhattan Distance which previously selection feature added as 61.39% with timetaken was as 69 seconds, become 73.86% on weighting TF-IDF through selection feature of Chi Squarewith time taken needed in the process was as 9 seconds
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
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
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.
Co-Authors ABDUL ROHMAN Abdul Syukur Abu Salam Achmad Ridwan Achmad Widodo Ahmad Ilham Akhmad Khanif Zyen Amalia Amiril Mukminin Ananda Michael Firdaus Andun Sudijandoko Anindya Mar'atus Sholikhah Ardytha Luthfiarta Aris Tri Jaka Harjanta, Aris Tri Assiva, Mochamad Amry Brighaza Gayoeh Arsyanadi Cinantya Paramita Defri Kurniawan Desy Ika Puspitasari Dhita Aulia Octaviani Dwi Cahyo Kartiko Edi Noersasongko Eko Pudjianto Fajar Jaya Rosadi Fajriyanto Fajriyanto FATKUR ROHMAN KAFRAWI Faza Dhi’fan Wicaksono HERI WAHYUDI Heru Agus Santoso Himawan Wismanadi Indah Munitasri Iqbal Naufal Ahmad Irmantara Subagio Khalivio Rahmyanto Putra Khoirul Mawahib Lutfhi Abdil Khuddus Made Pramono Pudjiijuniarto Pudjiijuniarto Pulung Nurtantio Andono Purwanto Purwanto Ragil Ar Rasyid Ricardus Anggi Pramunendar Rochmatul Anjani Romi Satria Wahono ROY JANUARDI IRAWAN Rudy Donny Liklikwatil Sitti Munifah Soni Sulistyarto Stefanus Santosa Supriyadi Supriyadi syaifuddin syaifuddin Testa Adi Nugraha Utomo, Danang Wahyu Vincent Suhartono