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Catur Supriyanto
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Software Pembelajaran Mandiri pada Latihan Stabilitas Berbasis Medicine Ball Catur Supriyanto,
Kepelatihan Olahraga Vol 6, No 3 (2011)
Publisher : Kepelatihan Olahraga

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Abstract

Stability Ball exercises in not only used for medical purpose but it is also used for the the athletes. These exercises are used to improve the stability, fitness and reduce fatiques. This research is a research and development research. It is conducted from April up to Juli 2009 at the Sport and Coaching Education Department in the Faculty of Physical Education and Sport Science UNESA Surabaya. This research develops the materials, the video tutorial software and then later tests the software.  The statistical result shows that the software significantly improves students’ understanding toward the stability ball exercises.
Klasifikasi Kualitas Kayu Kelapa Menggunakan Gray-Level Co-Occurrence Martix Berbasis Backpropagation dan Algoritma Genetika Ricardus Anggi Pramunendar; Catur Supriyanto
Semantik Vol 4, No 1 (2014): Semantik 2014
Publisher : Semantik

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Abstract

Indonesia merupakan negara kedua terbesar yang memiliki tanaman pohon kelapa (Cocos nucifera), batang pohon kelapadapat diproses menjadi kayu sebagai bahan pembuat mebel dan konstruksi bangunan. Kualitas kayu kelapa yaitu kekuatandan keawetan ditentukan oleh banyak faktor salah satunya adalah dari pola kerapatan serat (vascular bundle) pada kayukelapa. Saat ini untuk menentukan kualitas kayu kelapa dengan melihat kerapatan serat hanya dapat dilakukan oleh seorangahli dibidangnya. Karena pola pemotongan pada batang kelapa, kayu kelapa dibagi menjadi tiga kelas yang dilihat darikerapatan serat kayu, yaitu kerapatan tinggi, kerapatan sedang dan kerapatan rendah. Untuk menghasilkan produk yang baikdiperlukan penentuan kualitas bahan baku (kayu) yang sesuai dengan kebutuhan.Kata kunci : Kualitas kayu kelapa, GLCM, Backpropagation, Algoritma Genetika
Prediksi Rentet Waktu Jangka Pendek Harga TBS Berbasis Algoritma Backpropagation Neural Network Desy Ika Puspitasari; Abdul Syukur; Catur Supriyanto
E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Vol 4 No 1 (2015): e-jurnal JUSITI
Publisher : Universitas Dipa Makassar

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Abstract

Prediksi harga tandan buah segar adalah suatu proses menganalisa dan menentukan harga tandan buah segar di masa yang akan datang. Dengan analisis teknikal, prediksi harga tandan buah segar di masa datang dapat ditentukan dari pembelajaran pola harga tandan buah segar tersebut di masa lampau. Data prediksi yang digunakan adalah harga tandan buah segar kelapasawit Kalimantan Timur dari tahun 2008-2012. Backpropagation merupakan salah satu metode pembelajaran pada artificial neural network yang mempopulerkan sebuah cara untuk melatih unit – unit hidden. Parameter-parameter yang mempengaruhi kinerja BPNN yang akan digunakan dalam penelitian ini adalah learning rate, momentum, iterasi (training cycles) dan windowing. Masing-masing parameter akan dibandingkan kinerja prediksinya dengan mengukur masing-masing Root Mean Square Error (RMSE). Penentuan pengambilan nilai learning rate, momentum, training cycles dan konsep windowingpredict series sangat mempengaruhi kinerja neural network, dalam mencapai hasil yang diharapkan. Hasil penelitian menunjukkan bahwa penggunaan parameter learning rate 0.1, momentum 0.6, training cycles 500, input neuron 15, hidden layer 9 dan windowing 15, menghasilkan tingkat rata-rata error yang lebih baik dalam memprediksi harga tandan buah segar dengan nilai RMSE terkecil yaitu 70,015. 
Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Memprediksi Harga Komoditi Karet Rudy Donny Liklikwatil; Edi Noersasongko; Catur Supriyanto
E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Vol 7 No 2 (2018): e-jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v7i2.252

Abstract

Komoditi dan harganya karet mengalami perubahan yang fluktuatif dan menunjukkan pola yang tidak stasioner, di sisi lain pengambilan keputusan bisnis memerlukan data yang akurat dan terukur. Algoritma k-NN merupakan algoritma yang merupakan algoritma unsupervised, dan terbukti baik pada data mining. Sedangkan Particle Swarm Optimization (PSO) menunjukkan performa optimasi yang lebih baik dibandingkan dengan metode yang lain. Penelitian ini  bertujuan untuk merancang metode prakiraan yang dapat memperkirakan tingkat harga dan volume permintaan untuk TSR 20. Prakiraan dilakukan menggunakan Jaringan Syaraf Tiruan dengan algoritma propagasi balik, dimana data yang digunakan adalah data perkembangan pasar TSR pada bursa berjangka SICOM.. Berdasarkan tiga indikator pelatihan yang dijadikan acuan dalam pemilihan arsitektur terbaik, spesifikasi ke 15 tidak perlu melakukan pelatihan sampai epoch maksimum. Dalam teknik PSO terdapat beberapa cara untuk melakukan pengoptimasian diantaranya meningkatkan bobot atribut  terhadap semua atribut atau variable yang dipakai, menseleksi atribut dan fitur seleksi. Hasil penelitian dari prediksi harga komoditi karet dengan menggunakan model k-NN mendapatkan nilai RMSE sebesar 0,087 sedangkan bila menggunakan k-NN yang dioptimasi dengan menggunakan Particle Swarm Optimization didapatkan nilai  RMSE sebesar 0,082 lebih baik dibandingkan dengan hanya menggunakan k-NN saja.
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
Co-Authors ABDUL ROHMAN Abdul Syukur Abu Salam Achmad Ridwan Achmad Widodo Ahmad Ilham Akhmad Khanif Zyen 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 Defri Kurniawan Desy Ika Puspitasari Dhita Aulia Octaviani Dwi Cahyo Kartiko Edi Noersasongko Eko Pudjianto Fajriyanto Fajriyanto FATKUR ROHMAN KAFRAWI Heru Agus Santoso Himawan Wismanadi Indah Munitasri Irmantara Subagio Khoirul Mawahib Made Pramono Pudjiijuniarto Pudjiijuniarto Pulung Nurtantio Andono Purwanto Purwanto Ragil Ar Rasyid Ricardus Anggi Pramunendar Rochmatul Anjani Romi Satria Wahono Rudy Donny Liklikwatil Sitti Munifah Soni Sulistyarto Stefanus Santosa Supriyadi Supriyadi syaifuddin syaifuddin Utomo, Danang Wahyu Vincent Suhartono