RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT

SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides an accurate results. The focus about most SVM optimization research is the optimization of the the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with Particle Swarm Optimization (PSO) on SVM using the analysis of input data’s movement. This method performed the optimization of the weight of the input data and RBF kernel’s parameter at once based on the analysis of the movement of the input data which was separated from the process of determining the margin on SVM. The steps of this method were the parameter initialization, optimal particle search, kernel’s parameter computation, and classification with SVM. In the optimal particle’s search, the cost of each particle was computed using RBF function. The value of kernel’s parameter was computed based on the particles’ movement in PSO. Experimental result on Breast Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization method could improve the accuracy of SVM significantly. This method of RBF kernel optimization had a lower complexity compared to another SVM optimization methods that resulted in a faster running time

Deteksi Penyakit Mata Pada Citra Fundus Menggunakan Convolutional Neural Network (CNN)

Pada tahun 2020, terdapat 1,1 milyar orang yang mengalami kehilangan penglihatan di seluruh dunia. Jumlah ini diproyeksikan akan terus bertambah hingga mencapai 1,76 milyar orang pada tahun 2050. Penyebab utama kebutaan untuk anak-anak dan remaja adalah penyakit mata, yang dapat dicegah apabila dilakukan deteksi dan penanganan lebih dini. Oleh sebab itu, pada penelitian ini diusulkan metode berbasis Convolutional Neural Network (CNN) untuk mendeteksi penyakit mata pada citra fundus. Metode yang diusulkan menggunakan metode transfer learning dengan arsitektur jaringan MobileNetV2 sebagai base model. Arsitektur head model yang diusulkan, yang terdiri dari lapisan global average pooling dan diikuti oleh 2 lapisan fully-connected, mampu memberikan akurasi yang paling tinggi dan efisiensi paling baik dibandingkan dengan arsitektur head model lainnya. Eksperimen pada dataset citra fundus yang terdiri dari 601 citra dengan berbagai macam penyakit mata menunjukkan bahwa metode yang diusulkan mampu memberikan performa yang baik dengan nilai akurasi sebesar 72%, precision sebesar 72%, recall sebesar 72%, dan F1-score sebesar 72%. Hasil eksperimen menunjukkan bahwa metode yang diusulkan dapat memberikan akurasi yang lebih tinggi dan lebih efisien dibandingkan dengan menggunakan arsitektur CNN lainnya, seperti ResNet50V2, InceptionV3, InceptionResNetV2, VGG16, dan VGG19

Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively

A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers

Website Development for Publication and Marketing of ITS-Assisted Halal Product MSME

The high and increasing number of MSMEs and the importance of socialization and guidance related to halal product policies is one of the factors for the establishment of the Center for Halal Studies (PKH) of Institut Teknologi Sepuluh Nopember (ITS). PKH ITS aims to assist MSMEs in obtaining halal certification and marketing their products. In this community service activity, it is proposed to develop the ITS PKH website (http://halal.its.ac.id/) for of publication and marketing of ITS-assisted MSME’s halal products. The stages in this community service activity include preparation, implementation, documentation, and reporting. The ITS PKH website that we developed contains a complete profile of ITS assisted MSMEs equipped with a QRCode. This unique QRCode leads to the MSME profile page on the ITS PKH website and has been utilized by ITS-assisted MSMEs by being pasted on the website/social media/product packaging of each MSME. This website also has a “Ask Halal” feature to help people search and check halal products. Currently, there are 134 MSMEs spread throughout Indonesia who use the ITS PKH website that we developed. With the features provided by the ITS Halal Study Center website, it is hoped that MSMEs will find it easier to market their products because users find it easy to get and find information related to MSME products.The high and increasing number of MSMEs and the importance of socialization and guidance related to halal product policies is one of the factors for the establishment of the Center for Halal Studies (PKH) of Institut Teknologi Sepuluh Nopember (ITS). PKH ITS aims to assist MSMEs in obtaining halal certification and marketing their products. In this community service activity, it is proposed to develop the ITS PKH website (http://halal.its.ac.id/) for of publication and marketing of ITS-assisted MSME’s halal products. The stages in this community service activity include preparation, implementation, documentation, and reporting. The ITS PKH website that we developed contains a complete profile of ITS assisted MSMEs equipped with a QRCode. This unique QRCode leads to the MSME profile page on the ITS PKH website and has been utilized by ITS-assisted MSMEs by being pasted on the website/social media/product packaging of each MSME. This website also has a “Ask Halal” feature to help people search and check halal products. Currently, there are 126 MSMEs spread throughout Indonesia who use the ITS PKH website that we developed. With the features provided by the ITS Halal Study Center website, it is hoped that MSMEs will find it easier to market their products because users find it easy to get and find information related to MSME products

Metode Pembobotan Hibrida untuk Ekstraksi Frasa Kunci Bahasa Arab

Banyaknya informasi membuat proses pengindeksan dan pencarian inti dari dokumen menjadi permasalahan yang rumit. Sebagian besar dokumen yang tersedia tidak dilengkapi dengan kata kunci terkait. Hal ini sehingga memaksa pembaca untuk membaca seluruh dokumen untuk mendapat gambaran penuh dari konten seluruh dokumen. Ekstraksi frasa kunci otomatis yang menggunakan Algoritma YAKE memberi solusi cepat ekstraksi frasa kunci menggunakan fitur lokal dari sebuah dokumen. Namun, penggunaan fitur lokal saja membuat hasil ekstraksi menjadi kurang relevan karena diperlukan istilah signifikan yang muncul di dokumen lain. Masalah lain yang muncul adalah terdapat beberapa fitur lokal yang tidak dapat digunakan untuk bahasa Arab, misalnya huruf kapital. Pada penelitian ini, diusulkan metode pembobotan kata yang mengintegrasikan fitur statistik lokal dari sebuah dokumen dan fitur eksternal dari dokumen lain untuk sistem ekstraksi kata kunci. Metode ini dapat digunakan secara efektif pada bahasa Arab dan dapat digunakan pada bahasa lain yang tidak memiliki huruf kapital serta untuk dokumen-dokumen yang tidak terstruktur seperti berita atau karya ilmiah. Dari hasil uji coba telah dibuktikan bahwa performansi metode ini lebih baik daripada metode pembanding yaitu YAKE dan TF-IDF

MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging

Metode Pembobotan Hibrida untuk Ekstraksi Frasa Kunci Bahasa Arab

Banyaknya informasi membuat proses pengindeksan dan pencarian inti dari dokumen menjadi permasalahan yang rumit. Sebagian besar dokumen yang tersedia tidak dilengkapi dengan kata kunci terkait. Hal ini sehingga memaksa pembaca untuk membaca seluruh dokumen untuk mendapat gambaran penuh dari konten seluruh dokumen. Ekstraksi frasa kunci otomatis yang menggunakan Algoritma YAKE memberi solusi cepat ekstraksi frasa kunci menggunakan fitur lokal dari sebuah dokumen. Namun, penggunaan fitur lokal saja membuat hasil ekstraksi menjadi kurang relevan karena diperlukan istilah signifikan yang muncul di dokumen lain. Masalah lain yang muncul adalah terdapat beberapa fitur lokal yang tidak dapat digunakan untuk bahasa Arab, misalnya huruf kapital. Pada penelitian ini, diusulkan metode pembobotan kata yang mengintegrasikan fitur statistik lokal dari sebuah dokumen dan fitur eksternal dari dokumen lain untuk sistem ekstraksi kata kunci. Metode ini dapat digunakan secara efektif pada bahasa Arab dan dapat digunakan pada bahasa lain yang tidak memiliki huruf kapital serta untuk dokumen-dokumen yang tidak terstruktur seperti berita atau karya ilmiah. Dari hasil uji coba telah dibuktikan bahwa performansi metode ini lebih baik daripada metode pembanding yaitu YAKE dan TF-IDF

Alveolar Bone Detection from Dental Cone Beam Computed Tomography using YOLOv3-tiny

Cone Beam Computed Tomography (CBCT) is a medical imaging technique widely used in dentistry including dental implant planning. To determine the size of the dental implant, it is necessary to detect the alveolar bone at the implant site. In this study, we propose automatic detection of alveolar bone from CBCT images of teeth using the YOLOv3-tiny method. The YOLOv3-tiny network architecture consists of a seven-layer convolution networks and six max-pooling layers in the Darknet-53 network with two output branch scale predictions. CBCT images of teeth obtained from 4 patients consisted of 800 coronal slices of 2D grayscale images, containing 830 alveolar bone annotations. Before the training process, the ground truth image annotation was made in the form of a bounding box on the alveolar bone object. The detection results of the YOLOv3-tiny model were compared with the detection results of the YOLOv3 and YOLOv2-tiny models. The results of the experiment on 640 training images and 160 testing images showed that YOLOv3-tiny outperformed YOLOv2-tiny with mAP of 98.6% and 96.73%, respectively. Meanwhile, shows the same good result as YOLOv3