OPTIMASI KLASIFIKASI DATA KINERJA AKADEMIK MAHASISWA MENGGUNAKAN SVM BERBASIS ALGORITMA GENETIKA

For a college, especially a private university, students are the main component that supports the survival of the college. An educational database containing information about students is useful for predicting student academic performance. Several studies on the classification of academic performance have been conducted, it is clear that classification problems generally exist in the number of attributes, too many unnecessary attributes will increase computational time and reduce accuracy. The combination of PSO + SVM has proven to be more effective than SVM in various types of datasets. Therefore, this study will try to compare SVM-GA for the classification of student academic performance so that students who have good and bad academic performance can be seen. The data used is the academic performance data of the midwifery students of Ngudi Waluyo University, 2012-2014. The highest accuracy of SVM-GA is the accuracy of 93.55% and AUC 0.977. The previous SVM method had an accuracy of 90.51% and AUC 0.963. Based on the AUC value, the performance of the proposed SVM-GA method is in the "Perfect" group.Â

Peningkatan Akurasi Kelayakan Kredit Menggunakan Particle Swarm Optimization

Abstrak— Penentuan kelayakan kredit adalah sebuah proses untuk menentukan apakah seorang nasabah termasuk kredit baik atau kredit buruk. Dengan demikian teknik data mining yang tepat digunakan adalah klasifikasi. Naive Bayes Clasifier (NBC), Decision Trees (DT), dan Support Vector Machines (SVM) adalah algoritma yang digunakan untuk klasifikasi, namun akurasinya kurang maksimal. Particel Swarm optimization (PSO) yang digunakan untuk pembobotan atribut dapat meningkatkan kinerja dari algoritma klasifikasi. Penelitian ini membandingkan algoritma NBC-PSO, DT-PSO dan SVM-PSO. Dataset yang digunakan adalah German Credit Data. Proses validasi menggunakan tenfold-cross validation, sedangkan pengujian modelnya menggunakan confusion matrix dan kurva ROC. Hasil eksperimen menunjukan kinerja dari masing-masing algoritma meningkat ketika digabungkan dengan PSO, namun membutuhkan waktu eksekusi yang relatif lebih lama. Kata kunci— Kelayakan kredit, klasifikasi, data mining, NBC, Decision trees, SVM, PS

OPTIMASI KLASIFIKASI DATA KINERJA AKADEMIK MAHASISWA MENGGUNAKAN SVM BERBASIS ALGORITMA GENETIKA

For a college, especially a private university, students are the main component that supports the survival of the college. An educational database containing information about students is useful for predicting student academic performance. Several studies on the classification of academic performance have been conducted, it is clear that classification problems generally exist in the number of attributes, too many unnecessary attributes will increase computational time and reduce accuracy. The combination of PSO + SVM has proven to be more effective than SVM in various types of datasets. Therefore, this study will try to compare SVM-GA for the classification of student academic performance so that students who have good and bad academic performance can be seen. The data used is the academic performance data of the midwifery students of Ngudi Waluyo University, 2012-2014. The highest accuracy of SVM-GA is the accuracy of 93.55% and AUC 0.977. The previous SVM method had an accuracy of 90.51% and AUC 0.963. Based on the AUC value, the performance of the proposed SVM-GA method is in the "Perfect" group.

Peningkatan Akurasi Kelayakan Kredit Menggunakan Particle Swarm Optimization

Abstrak— Penentuan kelayakan kredit adalah sebuah proses untuk menentukan apakah seorang nasabah termasuk kredit baik atau kredit buruk. Dengan demikian teknik data mining yang tepat digunakan adalah klasifikasi. Naive Bayes Clasifier (NBC), Decision Trees (DT), dan Support Vector Machines (SVM) adalah algoritma yang digunakan untuk klasifikasi, namun akurasinya kurang maksimal. Particel Swarm optimization (PSO) yang digunakan untuk pembobotan atribut dapat meningkatkan kinerja dari algoritma klasifikasi. Penelitian ini membandingkan algoritma NBC-PSO, DT-PSO dan SVM-PSO. Dataset yang digunakan adalah German Credit Data. Proses validasi menggunakan tenfold-cross validation, sedangkan pengujian modelnya menggunakan confusion matrix dan kurva ROC. Hasil eksperimen menunjukan kinerja dari masing-masing algoritma meningkat ketika digabungkan dengan PSO, namun membutuhkan waktu eksekusi yang relatif lebih lama. Kata kunci— Kelayakan kredit, klasifikasi, data mining, NBC, Decision trees, SVM, PS

KOMPARASI NEURAL NETWORK DAN SUPPORT VECTOR MACHINE UNTUK DATA TIME SERIES DAN NON-TIME SERIES

Abstrak— Neural Network dan Support Vector Machine merupakan metode datamining yang sering digunakan. Penelitian ini bertujuan untuk mengetahui performa dari Neural Network dan Support Vector Machine yang diterapkan pada data time series dan non-time series. Sehingga terlihat perbedaan dan keunggulan dari kedua metode tersebut. Data yang digunakan merupakan dataset publik, “Australian Credit Approval dan Polar Ice Data”. Untuk tahap validasi model menggunakan 10fold cross-validation dan proses evaluasi model menggunakan Root Mean Square Error (RMSE). Hasil percobaan membuktikan bahwa pada data time series SVM lebih unggul dari NN dilihat dari kinerja dan waktu eksekusinya, sedangkan pada data non-time series NN lebih unggul. Hasil akhir evaluasi percobaan data time series berbanding terbalik dengan hasil percobaan data non-time series..   Kata kunci— Time series, Non-time series, Neural Nerwork, Support Vector Machine, klasifikasi kelayakan kredit, Prediksi Polar Es

Pembobotan Atribut PSO Untuk Klasifikasi Data Kinerja Akademik Mahasiswa

An educational database containing information about students is useful for predicting student academic performance. Mujiyono, Sri in 2017 has proven that PSO improves SVM performance for predicting student academic performance. This study aims to prove that PSO can improve the performance of the NBC, C4.5, SVM and NN classification methods for the classification of student academic performance. The results of this study prove that PSO can improve the performance of all the classification methods used. With PSO optimization, NN defeats the accuracy of SVM

KELAYAKAN KREDIT BANK MENGGUNAKAN C4.5 BERBASIS PSO

Abstract— Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 algorithm and the neural network. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy value of C4.5. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show that the accuracy of C4.5 increased from 72.3% to 75.50% after being combined with PSO. Keywords: Credit, German Credit Data, C4.5-PSO. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVMÂ

Pembobotan Atribut PSO Untuk Klasifikasi Data Kinerja Akademik Mahasiswa

An educational database containing information about students is useful for predicting student academic performance. Mujiyono, Sri in 2017 has proven that PSO improves SVM performance for predicting student academic performance. This study aims to prove that PSO can improve the performance of the NBC, C4.5, SVM and NN classification methods for the classification of student academic performance. The results of this study prove that PSO can improve the performance of all the classification methods used. With PSO optimization, NN defeats the accuracy of SVM

PSO-SVM Untuk Klasifikasi Daun Cengkeh Berdasarkan Morfologi Bentuk Ciri, Warna dan Tekstur GLCM Permukaan Daun

Abstract— Of the two types of superior varieties cultivated cloves, clove types of zanzibar is the best kind. However, when not flowering of the three types of clove leaves indistinguishable from the image. This study uses 4 morphological features of shape, 3 color features and 10 most commonly used GLCM features and apply SVM for classification with Particle Swarm Optimization (PSO) optimization method to improve the accuracy of clove plant classification based on leaf surface image. Results of research on the top surface image classification leaf clovers, PSO-SVM method proposed is shown to have a higher accuracy compared with PSO-SVM method than previous research (Novichasari, S.I., 2015) with an accuracy of 90.5% and AUC 0.944. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SV

PSO-SVM Untuk Klasifikasi Daun Cengkeh Berdasarkan Morfologi Bentuk Ciri, Warna dan Tekstur GLCM Permukaan Daun

Abstract— Of the two types of superior varieties cultivated cloves, clove types of zanzibar is the best kind. However, when not flowering of the three types of clove leaves indistinguishable from the image. This study uses 4 morphological features of shape, 3 color features and 10 most commonly used GLCM features and apply SVM for classification with Particle Swarm Optimization (PSO) optimization method to improve the accuracy of clove plant classification based on leaf surface image. Results of research on the top surface image classification leaf clovers, PSO-SVM method proposed is shown to have a higher accuracy compared with PSO-SVM method than previous research (Novichasari, S.I., 2015) with an accuracy of 90.5% and AUC 0.944. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SV