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-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 Optimasi Svm Dalam Kasus Kelayakan Kredit Bank

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 and SVM algorithms. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy of SVM. 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 SVM accuracy increased from 74.6% to 76.50% after combined with PSO

Pembobotan Atribut Pso Untuk Optimasi Svm Dalam Kasus Kelayakan Kredit Bank

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 and SVM algorithms. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy of SVM. 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 SVM accuracy increased from 74.6% to 76.50% after combined with PSO

Complex identification of plants from leaves

Se presenta una propuesta para el reconocimiento de hojas muy semejantes en su apariencia.The automatic identification of plant leaves is a very important current topic of research in vision systems. Several researchers have tried to solve the problem of identification from plant leaves proposing various techniques. The proposed techniques in the literature have obtained excellent results on data sets where the leaves have dissimilar features to each other. However, in cases where the leaves are very similar to each other, the classification accuracy falls significantly. In this paper, we proposed a system to deal with the performance problem of machine learning algorithms where the leaves are very similar. The results obtained show that combination of different features and features selection process can improve the classification accuracy

Photometric stereo for three-dimensional leaf venation extraction

© 2018 Elsevier B.V. Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity – whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments

Klasifikasi Jenis Tanaman Mangga Gadung dan Mangga Madu Berdasarkan Tulang Daun

Daun merupakan salah satu bagian tanaman yang dapat menjadiacuan klasifikasi, karena memiliki perbedaan fitur pada setiap jenis mangga. Penerapan Unconstraint Hit or Miss Transformation (UHMT) dengan empatstructuring element (SE) mengakibatkan tingginya kompleksitas komputasional.Sehingga, penelitian ini bertujuan untuk menyederhanakan kompleksitaskomputasi dengan menerapkan satu dari sembilan SE pada UHMT. Pola fiturtulang daun diperoleh dengan menerapkan Local Binary Pattern (LBP),kemudian hasil fitur yang diperoleh dihitung dengan entropy. Hasilmenunjukkan bahwa rata-rata nilai entropy yang tinggi dikategorikan padamangga madu. Pengujian klasifikasi membandingkan akurasi pada K-foldCross Validation, dengan nilai K-fold adalah 5, 8, dan 10. Akurasi tertinggidengan menggunakan 10-fold Cross Validition yaitu 78,5 %

The temporal build-up of hummingbird/plant mutualisms in North America and temperate South America

Background: Global Plants, a collaborative between JSTOR and some 300 herbaria, now contains about 2.48 million high-resolution images of plant specimens, a number that continues to grow, and collections that are digitizing their specimens at high resolution are allocating considerable recourses to the maintenance of computer hardware (e.g., servers) and to acquiring digital storage space. We here apply machine learning, specifically the training of a Support-Vector-Machine, to classify specimen images into categories, ideally at the species level, using the 26 most common tree species in Germany as a test case. Results: We designed an analysis pipeline and classification system consisting of segmentation, normalization, feature extraction, and classification steps and evaluated the system in two test sets, one with 26 species, the other with 17, in each case using 10 images per species of plants collected between 1820 and 1995, which simulates the empirical situation that most named species are represented in herbaria and databases, such as JSTOR, by few specimens. We achieved 73.21% accuracy of species assignments in the larger test set, and 84.88% in the smaller test set. Conclusions: The results of this first application of a computer vision algorithm trained on images of herbarium specimens shows that despite the problem of overlapping leaves, leaf-architectural features can be used to categorize specimens to species with good accuracy. Computer vision is poised to play a significant role in future rapid identification at least for frequently collected genera or species in the European flora

Automatic Plant Detection Using HOG and LBP Features With SVM

Plants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun.  They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.