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Design And Implementation of Document Similarity Search System For WEB-Based Medical Journal Management Mardi Siswo Utomo; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 1 (2011): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2000

Abstract— Document similarity can be used as a reference for other information searches similar. So as to reduce the time-re-appointment for information following a similar document. Document similarity search capability is usually implemented on the features 'related articles'.Similarity of documents can be measured with a cosine, with preprosesing conducted prior to the document that will be measured. The indexing process and the measurement takes a relatively long excecution time. Problems with a web-based application to conduct the process and measuring the similarity index is a limited execution time, so the processing index and similarity measure in web-based application needs its own programming techniques.Problems with a web-based application to conduct the process and measuring the similarity index is a limited execution time, so the processing index and similarity measure in web-based application needs its own programming techniques.The purpose of this research is to design and create a software that give capability for web-based database management system of medical journals in Indonesian language to find other documents similar to the current document in reading at the time.The results of this research is the mechanism autoreload javascript and session cookies and can break down the process and measurement index similaritas into several small sections, so the process can be performed on web-based applications and the number of relatively large documents.Results with the cosine similarity measure in the case of Indonesian-language medical journal “Media medika Indonesiana” has a fairly high accuracy of 90%. Keywords— document similarity, cosine measure, web-based application.

Analisis Fitur Kalimat untuk Peringkas Teks Otomatis pada Bahasa Indonesia Badrus Zaman; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 2 (2011): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2019

Abstract— Automatic Text Summarization (ATS) is a technique to create a summary of the document automatically by using computer applications to produce the most important information from the original document. Features are required to perform weighting of sentences, including Log-TFISF (term frequency index sentence frequency), sentence location, sentence overlap, title overlap and sentence relative length. This research conducted an analysis of five features in order to determine the weights of each feature that will get the results of a coherent summary. The five features are implemented in automated text summarization system in Indonesian language that was developed using the method of relative importance of topics. Results from experiments show that sentence location feature has the highest F-Measures namely 0.46 and then consecutive sentence overlap, title overlap, sentence relative length and Log-TFISF, with a value of 0.42, 0.42, 0.35 and 0.32. Relative weights of feature extraction consecutive from the largest are sentence location, sentence overlap, title overlap, sentence relative length and Log-TFISF with a value of 0.25, 0.22, 0.22, 0.19 and 0.12. These relative weights are implemented on ATS, so we get accuracy of 70.62%. It is more accurate 2,86% than without relative weights which accuracy of 67,72%.. .Keywords— Automatic Text Summarization (ATS), Log-TFISF, sentence location, sentence overlap, title overlap, sentence relative length, bahasa Indonesia

Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification Sandi Fajar Rodiyansyah; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 6, No 1 (2012): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2144

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58% by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification Sandi Fajar Rodiyansyah; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 1 (2013): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3048

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58% by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time Agustinus Jacobus; 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.3491

AbstrakSistem deteksi intrusi adalah sebuah sistem yang dapat mendeteksi serangan atau intrusi dalam sebuah jaringan atau sistem komputer, umum pendeteksian intrusi dilakukan dengan membandingkan pola lalu lintas jaringan dengan pola serangan yang diketahui atau mencari pola tidak normal dari lalu lintas jaringan. Pertumbuhan aktivitas internet meningkatkan jumlah paket data yang harus dianalisis untuk membangun pola serangan ataupun normal, situasi ini menyebabkan kemungkinan bahwa sistem tidak dapat mendeteksi serangan dengan teknik yang baru, sehingga dibutuhkan sebuah sistem yang dapat membangun pola atau model secara otomatis.Penelitian ini memiliki tujuan untuk membangun sistem deteksi intrusi dengan kemampuan membuat sebuah model secara otomatis dan dapat mendeteksi intrusi dalam lingkungan real-time, dengan menggunakan metode support vector machine sebagai salah satu metode data mining untuk mengklasifikasikan audit data lalu lintas jaringan dalam 3 kelas, yaitu: normal, probe, dan DoS. Data audit dibuat dari preprocessing rekaman paket data jaringan yang dihasilkan oleh Tshark.Berdasar hasil pengujian, sistem dapat membantu sistem administrator untuk membangun model atau pola secara otomatis dengan tingkat akurasi dan deteksi serangan yang tinggi serta tingkat false positive yang rendah. Sistem juga dapat berjalan pada lingkungan real-time. Kata kunci— deteksi intrusi, klasifikasi, preprocessing, support vector machine AbstractIntrusion detection system is a system for detecting attacks or intrusions in a network or computer system, generally intrusion detection is done with comparing network traffic pattern with known attack pattern or with finding unnormal pattern of network traffic. The raise of internet activity has increase the number of packet data that must be analyzed for build the attack or normal pattern, this situation led to the possibility that the system can not detect the intrusion with a new technique, so it needs a system that can automaticaly build a pattern or model.This research have a goal to build an intrusion detection system with ability to create a model automaticaly and can detect the intrusion in real-time environment with using support vector machine method as a one of data mining method for classifying network traffic audit data in 3 classes, namely: normal, probe, and DoS. Audit data was established from preprocessing of network packet capture files that obtained from Tshark. Based on the test result, the system can help system administrator to build a model or pattern automaticaly with high accuracy, high attack detection rate, and low false positive rate. The system also can run in real-time environment. Keywords— intrusion detection, classification, preprocessing, support vector machine

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

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%.

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— 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.

Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang) Robynson Amseke; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6540

AbstrakSalah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya timdalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif.Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 (56,35% dan 55,03%). Kata kunci—classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit AbstractOne of the causes of non-performing loans come from the internal, that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation(CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques. Preprocessed initial-credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm(57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software(56,35% and55,03%, respectively). Keywords—classification based on association, CBA, data mining, classification, credit risk

Algoritma CPAR untuk Analisa Data Kecelakaan (Studi pada Kepolisian Daerah Sulawesi Tenggara) Natalis Ransi; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6547

AbstrakKecelakaan lalu lintas (laka lantas) di Sulawesi Tenggara perlu mendapatkan penanganan yang efektif karena menyebabkan korban meninggal dunia yang terus meningkat setiap tahunnya. Salah satu langkah penanganan adalah analisis karakteristik laka lantas yang berhubungan dengan korban meninggal dunia. Analisis karakteristik laka lantas dapat dilakukan dengan pendekatan faktor penyebab kecelakaan, jenis kecelakaan, dan waktu kejadian.Penelitian ini mengaplikasikan algoritma Classification based on Predictive Association Rules (CPAR) pada data mining untuk analisa karakteristik laka lantas. Algoritma CPAR menghasilkan Class Association Rules (CARs), selanjutnya CARs digunakan untuk mendeskripsikan karakteristik laka lantas yang berhubungan dengan korban meninggal dunia.Hasil penelitian diperoleh bahwa faktor yang menyebabkan korban meninggal dunia pada kasus laka lantas adalah faktor manusia (berkendara dibawah pengaruh alkohol dan berkendara melebihi batas kecepatan) dan faktor lingkungan fisik (prasarana jalan yang rusak dan jalan dengan tikungan tajam). Jenis kecelakaan (tunggal dan depan-depan), waktu kejadian (tanggal 8-14, hari Senin dan Selasa, jam 13:00-18:59), jenis kendaraan (sepeda motor) dan merek kendaraan (Honda), berpotensi menimbulkan korban meninggal pada kasus laka lantas. Pengendara sepeda motor rentan menjadi korban pada kasus laka lantas. Pengujian akurasi menggunakan 10-fold cross validation Hasil pengujian menunjukkan bahwa rata-rata akurasi algoritma CPAR lebih tinggi yaitu 48,75% dibandingkan dengan algoritma PRM yaitu 41,13%. Kata kunci— data mining, algoritma CPAR, kecelakaan lalu lintas Abstract Traffic accident in Southeast Sulawesi needs to get treatment more effective. One of the handling is analysis of traffic accident characteristic and then it was related to the death. Analysis of trafiic accident characteristics can be done with the approach factors the cause of the accident, the kind of an accident, and time genesis.This Research apply CPAR algorithm on the data mining to analyze the characteristics of traffic accident. CPAR Algorithm produce Class Association Rules (CARs) that used to describe traffic accident characteristics related to the death.Results of research, that the factors that caused the victim died in traffic accident is human factors (driving under the influence of alcohol and driving exceed the speed) and environmental factors physical (road infrastructure and damaged roads with elbow). Types of accidents (in the singular and home-front), time genesis (on 8-14, reported Monday and Tuesday, hours 1:00 pm-6:59 pm), the type of vehicle (motorcycle), potentially causing the death toll in the case laka then. Motorcycle drivers are prone to fall victim in that case laka then. Testing accuracy using 10-fold cross validation test result show that on average these accuracy algorithm CPAR 48.75%, higher than the algorithm PRM 41.13%. Keywords— data mining, CPAR algorithm, traffic accident

Klasifikasi Data NAP (Nota Analisis Pembiayaan) untuk Prediksi Tingkat Keamanan Pemberian Kredit (Studi Kasus : Bank Syariah Mandiri Cabang Luwuk Sulawesi Tengah) Sumarni Adi; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 1 (2015): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6635

AbstrakSetiap bulannya bank syariah mandiri cabang luwuk menerima proposal kredit (NAP) dari nasabah dalam jumlah yang terus meningkat dan perlu respon yang cepat. Dengan demikian, perlu dikembangkan sistem untuk melakukan data mining dari tumpukan data tersebut yang akan digunakan untuk kepentingan tertentu, salah satunya adalah untuk menganalisis resiko pemberian kredit.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi tingkat keamanan pemberian kredit dengan menerapakan algoritma Naïve Bayes Classificatio. Naive bayes classifier merupakan pendekatan yang mengacu pada teorema Bayes yang menkombinasikan pengetahuan sebelumnya dengan pengetahuan baru, sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Sebelum dilakukan klasifikasi, data debitur melalui preprocessing. Kemudian dari preprocessing ini dilakukan klasifikasi dengan naive bayes classifier, sehingga menghasilkan model probabilitas klasifikasi untuk prediksi kelas pada debitur selanjutnya. Teknik pengujian akurasi model diukur menggunakan boostrap, dan menunjukkan bahwa nilai akurasi terkecil 80% dihasilkan pada sampel data 100, dan menghasilkan nilai akurasi terbesar 98,66% pada sampel data 463. Kata kunci— akurasi, naive bayes, data mining, klasifikasi, preprocessing, NAP AbstractEvery month the Mandiri Syariah Bank Branch Office of Luwuk receives a very large number of proposal credit. Thus, the system should be developed to perform data mining of the heap data to be used for specific purpose, one of which is for the risk analysis of credit allowance. Data mining techniques used in this study for classification level prediction of credit allowance by applying a naïve Bayes Classification algorithm . Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. Prior to classification, data of debitur has been through a preprocessing. Then the weight is to perform classification with naive bayes classifier. After the data is classified, so produce probabilitas of model classification for prediction class to next debitur. Testing techniques the accuracy of the model was measured by bosstrap, and shows that the smallest value of accuracy is 80% produced in the 100 data sample, and the largest value of accuracy 98,66% on a data sample of 463. Keywords— accuracy, naive bayes, data mining, classification, preprocessing, NAP

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