cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Agus Harjoko
Contact Email
ijccs.mipa@ugm.ac.id
Phone
+62274 555133
Journal Mail Official
ijccs.mipa@ugm.ac.id
Editorial Address
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 466 Documents
 7   8   9   10   11 
Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network Agus Aan Jiwa Permana; Widodo Prijodiprodjo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3494

Abstract

AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship
Peramalan KLBCampakMenggunakanGabunganMetode JST Backpropagationdan CART Sulistyowati Sulistyowati; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3495

Abstract

Forecasting Measles Outbreak  in an area is necessary because to prevent widespread occurrence in an area. One way that is done in this study is to predict the incidence of measles by using a combination of backpropagation ANN and CART. Backpropagation ANN is used to predict the incidence of measles periodic data, then the CART method used to perform the determination of an outbreak or non-outbreak area.Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules.Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Determination of the number of windows of a backpropagation neural network forecasting on each attribute gives different results and directly affects the forecasting results. ANN can do the forecasting in time series using siliding window with accuracy 90.01% and then CART method can be use for classification with accuracy 83.33%.
Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurence Matrix (GLCM) Refta Listia; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3496

Abstract

AbstrakKanker payudara adalah penyakit yang paling umum dideritaoleh wanitapadabanyak negara. Pemeriksaan kanker payudara dapat dilakukan dengan menggunakan mamografi.Padapenelitianini, pendekatan yang diusulkan bertujuanuntuk mengklasifikasi mammogram berdasarkan tiga kelas yaitukelas normal, tumor jinak, dan tumor ganas. Sistem yang diusulkan terdiri dari empat langkah utamayaitu preprosesing, segmentasi, ekstraksi fitur dan klasifikasi. Padatahappreprosesingakandilakukangrayscale, interpolasi, amoeba mean filter dan segmentasi. Ekstraksi ciri menggunakan Gray Level Cooccurence Matrix (GLCM) danakan dihitung ciri-ciristatistikpada 4 arah (d=1 dan d=2) , GLCM 8 arah(d=1) dan GLCM 16 arah (d=2).Fitur yang digunakanada 5 yaitukontras, energi, entropi, korelasi dan homogenitas. Langkah terakhir adalah klasifikasi menggunakan Backpropagation. Beberapa parameter penting divariasikan dalam proses ini seperti learning rate dan jumlah node dalam lapisan tersembunyi. Hasil penelitian menunjukkan bahwa fitur ekstraksi GLCM 4 arah(denganjarak d=1memiliki akurasi terbaik dalammengklasifikasimammogram yaitusebesar 81,1% dankhususpadaarah akurasi klasifikasidiperolehsebesar 100%.  AbstractBreast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps : preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2),  GLCM 8 angles and GLCM 16 angles.  The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in  hidden layer. The research result suggest that extraction feature in 4 angles ( and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in accuracy is 100%.
Perbandingan Mother Wavelet dalam Proses Denoising pada Suara Rahmat Ramadhan; Agfianto Eko Putra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3497

Abstract

AbstrakTransformasi Wavelet telah digunakan dalam proses denoising pada suara dengan tujuan untuk meningkatkan kualitas dari rekaman suara yang tercampur dengan derau. Jenis-jenis derau yang terlibat antara lain White Gaussian Noise (WGN), White Uniform Noise (WUN) dan Colored Noise. Dalam penelitian ini dilakukan perbandingan terhadap beberapa mother wavelet, diantaranya Daubechies, Coiflet dan Symlet, dalam proses denoising pada sinyal suara yang diberikan WGN, WUN dan Colored Noise. Metode thresholding yang digunakan dalam proses denoising adalah Soft Thresholding dan nilai threshold  berupa Time-Adapted Threshold (TAT) yang diperoleh dengan melakukan estimasi energi untuk membangun sinyal melalui Teager Energy Operator (TEO). Pengujian untuk mendapatkan mother wavelet terbaik dilakukan menggunakan uji Kruskal-Wallis yang dilanjutkan dengan uji Mann-Whitney. Hasil yang diperoleh menunjukkan bahwa Db20, Db30, Db40 dan Coif5 merupakan mother wavelet yang baik untuk mereduksi WGN;Db40, Db20 dan Db30 merupakan mother wavelet yang baik untuk mereduksi WUN dan untuk mereduksi Colored Noise, dapat menggunakan beberapa mother wavelet dalam penelitian ini, kecuali Db30 dan Db40.  Kata kunci—Mother wavelet, denoising, sinyal suara, TAT, Soft Thresholding.  AbstractWavelet Transform was used in denoising process on speech to enhance the quality of speech that courrupted by noise. The kinds of involved noises are White Gaussian Noise (WGN), White Uniform Noise (WUN) and Colored Noise. In this research, the comparison of mother wavelet is performed among Daubechies, Coiflet and Symlet, in denoising process on speech which given by WGN, WUN and Colored Noise. The thresholding method is used in denoising process is Soft Thresholding and threshold value is Time Adapted Threshold (TAT) which obtained by estimating the power for building the signal through Teager Energy Operator (TEO). The testing for obtaining the best moher wavelet is using Kruskal-Wallis test and followed by Mann-Whitney test. The result shows that Db20, Db30, Db40 and Coif5 mother wavelets are better than others to reduce WGN; Db40, Db20 dan Db30mother waveletsare better then the other to reduce WUN and to reduce Colored Noise can use some mother wavelets in this research, except Db30 and Db40. Keywords—Mother wavelet, denoising, speech signal, TAT, Soft Thresholding
Penyembunyian Data pada File Video Menggunakan Metode LSB dan DCT Mahmuddin Yunus; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3498

Abstract

AbstrakPenyembunyian data pada file video dikenal dengan istilah steganografi video. Metode steganografi yang dikenal diantaranya metode Least Significant Bit (LSB) dan Discrete Cosine Transform (DCT). Dalam penelitian ini dilakukan penyembunyian data pada file video dengan menggunakan metode LSB, metode DCT, dan gabungan metode LSB-DCT. Sedangkan kualitas file video yang dihasilkan setelah penyisipan dihitung dengan menggunakan Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR).Uji eksperimen dilakukan berdasarkan ukuran file video, ukuran file berkas rahasia yang disisipkan, dan resolusi video.Hasil pengujian menunjukkan tingkat keberhasilan steganografi video dengan menggunakan metode LSB adalah 38%, metode DCT adalah 90%, dan gabungan metode LSB-DCT adalah 64%. Sedangkan hasil perhitungan MSE, nilai MSE metode DCT paling rendah dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan metode LSB-DCT mempunyai nilai yang lebih kecil dibandingkan metode LSB. Pada pengujian PSNR diperoleh databahwa nilai PSNR metode DCTlebih tinggi dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan nilai PSNR metode gabungan LSB-DCT lebih tinggi dibandingkan metode LSB.  Kata Kunci—Steganografi, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)                           AbstractHiding data in video files is known as video steganography. Some of the well known steganography methods areLeast Significant Bit (LSB) and Discrete Cosine Transform (DCT) method. In this research, data will be hidden on the video file with LSB method, DCT method, and the combined method of LSB-DCT. While the quality result of video file after insertion is calculated using the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experiments were conducted based on the size of the video file, the file size of the inserted secret files, and video resolution.The test results showed that the success rate of the video steganography using LSB method was 38%, DCT method was 90%, and the combined method of LSB-DCT was 64%. While the calculation of MSE, the MSE method DCT lower than the combined method of LSB and LSB-DCT method. While LSB-DCT method has asmaller value than the LSB method. The PNSR experiment showed that the DCT method PSNR value is higher than the combined method of LSB and LSB-DCT method. While PSNR combined method LSB-DCT higher compared LSB method.  Keywords—Steganography, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)
Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine Noviah Dwi Putranti; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3499

Abstract

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif.  Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 %  pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%.  Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter.  AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses  Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.
Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map Ambarwati Ambarwati; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3500

Abstract

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM
Data Mining Untuk Mengetahui Tingkat Loyalitas Konsumen Terhadap Merek Kendaraan Bermotor dan Pola Kecelakaan Lalulintas di DIY Agus Sasmito Ariwibowo; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5205

Abstract

Abstract— The data of vehicle sales and traffic accident can be processed into information that is important for vehicle dealers and the Police Department. Those important information researched are the level of consumer loyalty to the vehicle brands and to predict the vehicle’s brands that will be purchased by a consumer. The study also tries to analyze the traffic accident data to find out is there any link between the occurrence of an accident to a certain brand of vehicle.                This research implementing data mining method called ‘rule based classification’ to establish the sales of vehicles rules by which can be used to classify consumer into group level of brand loyalty and also estimate the brand of the next vehicle’s brand that will be purchased by the consumer. This research will process the data traffic accident by using data mining techniques called Apriori Method. Apriori Method is used to identify a pattern of accidents based on brand, type of vehicles, and the vehicle’s color. The results are used to estimate whether there is any correlation between the occurrences of a traffic accident to a particular brand.                The result can help companies or vehicle dealers to obtain information about the level of the consumer’s brand loyalty to the dealer’s brand and to predict the brand that the consumer would be buy for the next vehicle. The result can also help the Police Department to find out whether there is any correlation between the occurrence of traffic accidents to the brand, type and the color of vehicle. Keywords— rule based classification, apriori, brand loyalty, traffic accident.
Klasifikasi Varietas Tanaman Kelengkeng Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network dan Probabilistic Neural Network Hermawan Syahputra; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5206

Abstract

Pengenalan daun memainkan peran penting dalam klasifikasi tanaman dan isu utamanya terletak pada apakah fitur yang dipilih stabil dan memiliki kemampuan yang baik untuk membedakan berbagai jenis daun. Pengenalan tanaman berbantuan komputer merupakan tugas yang masih sangat menantang dalam visi komputer karena kurangnya model atau skema representasi yang tepat. Fokus komputerisasi pengenalan tanaman hidup adalah untuk mengukur bentuk geometris berbasis morfologi daun. Informasi ini memainkan peran penting dalam mengidentifikasi berbagai kelas tanaman. Pada penelitian ini dilakukan pengenalan jenis tanaman berdasarkan fitur yang menonjol dari daun seperti fisiologis panjang (physiological length), lebar (physiological width), diameter,  keliling (leaf perimeter), luas (leaf area), faktor mulus (narrow factor), rasio aspek (aspect ratio), factor bentuk (form factor), rectangularity, rasio perimeter terhadap diameter, rasio perimeter panjang fisiologi dan lebar fisiologi yang dapat digunakan untuk membedakan satu sama lain. Berdasarkan hasil pengujian, ditunjukkan bahwa hasil pencocokkan daun kelengkeng dengan menggunakan neural network lebih baik dibandingkan dengan hasil pencocokkan daun kelengkeng dengan menggunakan probabilistic neural network. Akan tetapi ekstraksi fitur dengan menggunakan morfologi belum dapat memberikan informasi pembeda yang signifikan bagi pengenalan tanaman varitas kelengkeng berdasarkan daunnya.Keywords— klasifikasi, morfologi daun, neural network, probabilistic neural network
Class Association Rule Pada Metode Associative Classification Eka Karyawati; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5207

Abstract

Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining.  Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms.  This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs).  There are some techniques proposed to improve the rule generation method.  A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset.  It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules.  This technique may reduce the size of generated rules.  Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value.  This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset.  This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset.   However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.

Page 9 of 47 | Total Record : 466

 7   8   9   10   11 

Filter by Year

2006 2025


Reset
Filter By Issues
All Issue Vol 19, No 4 (2025): October Vol 19, No 3 (2025): July Vol 19, No 2 (2025): April Vol 19, No 1 (2025): January Vol 18, No 4 (2024): October Vol 18, No 3 (2024): July Vol 18, No 2 (2024): April Vol 18, No 1 (2024): January Vol 17, No 4 (2023): October Vol 17, No 3 (2023): July Vol 17, No 2 (2023): April Vol 17, No 1 (2023): January Vol 16, No 4 (2022): October Vol 16, No 3 (2022): July Vol 16, No 2 (2022): April Vol 16, No 1 (2022): January Vol 15, No 4 (2021): October Vol 15, No 3 (2021): July Vol 15, No 2 (2021): April Vol 15, No 1 (2021): January Vol 14, No 4 (2020): October Vol 14, No 3 (2020): July Vol 14, No 2 (2020): April Vol 14, No 1 (2020): January Vol 13, No 4 (2019): October Vol 13, No 3 (2019): July Vol 13, No 2 (2019): April Vol 13, No 1 (2019): January Vol 12, No 2 (2018): July Vol 12, No 1 (2018): January Vol 11, No 2 (2017): July Vol 11, No 1 (2017): January Vol 10, No 2 (2016): July Vol 10, No 1 (2016): January Vol 9, No 2 (2015): July Vol 9, No 1 (2015): January Vol 8, No 2 (2014): July Vol 8, No 1 (2014): January Vol 7, No 2 (2013): July Vol 7, No 1 (2013): January Vol 6, No 2 (2012): July Vol 6, No 1 (2012): January Vol 5, No 3 (2011): November Vol 5, No 2 (2011): July Vol 5, No 1 (2011): January Vol 1, No 2 (2007): July Vol 1, No 1 (2006): January More Issue