Geometric Feature Extraction of Batik Image Using Cardinal Spline Curve Representation

Batik is an Indonesian national heritage which has been recognized as a world cultural heritage (world heritage). Batik is widely used as clothing material. The advancement of technology allowed the material optimization in clothing design. Geometrical information of batik image is required in a modul for optimizing clothing design with batik as raw material. Geometric feature extraction of batik image is used to help computer to recognize batik's pattern or motif. This research proposes a method for geometric feature extraction of batik image by using cardinal spline curve representation. The method for geometric feature extraction is divided into 2 processes, i.e., feature extraction for Klowongan and feature extraction for Isen-Isen. Klowongan represents pattern of batik image, whereas Isen-Isen is content patterns of Klowongan. Feature extraction of Klowongan is performed by deleting collinear points from object boundaries until the dominant points are obtained. The dominant points are then used as control points. Feature extraction of Isen-Isen is performed by saving coordinate of every connected components which are also used as control points. Geometry feature of batik image is represented as a set of control points of klowongan and isen-isen. Batik image can be reconstructed by drawing cardinal spline curve using a set of control points in the geometric representation. The experiment shows that the reconstructed images is visually similar with the original batik image

PENGELOMPOKAN POLIGON UNTUK PERMASALAHAN 2D IRREGULAR STRIP PACKING BERDASARKAN CONVEX HULL DANBOUNDING BOX

Strip packing problem (SPP) merupakan permasalahan peletakan sekumpulan objek ke dalam sebuah kontainer persegi dengan panjang minimum. Objek dapat berbentuk regular (persegi, lingkaran, segitiga, dsb) dan irregular (poligon), sedangkan kontainer berbentuk persegi dengan lebar tetap dan panjang tak hingga. Dalam penelitian ini mengusulkan pengelompokkan polygon berdasarkan convex hull dan bounding box untuk menggabungkan beberapa polygon menjadi sebuah polygon baru yang lebih besar. Uji coba menggunakan dataset DAGLI, DIGHE1, FU, JAKOBS2, MAO dan MARQUES menunjukkan bahwa pengelompokan berdasarkan parameter convex hull dan bounding box dapat mengurangi jumlah poligon dengan rata-rata 37%. Kata kunci: 2D Irregular Strip Packing Problem, Pengelompokan Polygon, Convex Hull, Bounding Box

Design and Implementation of Markerless Augmented Reality Application for Cockroach Phobia Therapy Using Adaptive Threshold

Augmented reality (AR) technology is useful for treating several psychological problems, including phobias such as fear of flying, agoraphobia, claustrophobia, and phobia to insects and small animals. However, the currently existing applications for therapy of cockroach phobia that uses AR technology are still very dependent towards the presence of markers, which might lessen the feeling of being in an actual scenario from everyday lives. In this paper, we created a system that is able to use everyday things as a replacement for markers for phobia therapy for cockroach. There are five main processes: getting the live streaming feed from camera, preprocessing, extracting the center point of the objects, tracking the marker-substitute objects, and lastly, instantiating cockroaches randomly after user lifts the objects according to the number and mode of the cockroaches, whether it is moving or not, that are predetermined by the user. The evaluation in this paper includes eight participants that are carefully selected based on their Fear of Spiders Questionnaire (FSQ) score that is translated into Indonesian and modified to accommodate cockroaches instead of spiders. The results is that the system can induce anxiety level on participants with the highest score of 10, which is the highest score in Standard Unit of Discomfort scale (SUDs). While the presence and reality judgment of this paper has the highest score of 7 which is also the highest score in Slater-Usoh-Steed Questionnaire (SUS)

METODE HIBRIDASI ANT COLONY OPTIMIZATION DAN INFORMATION GAIN UNTUK SELEKSI FITUR PADA DOKUMEN TEKS ARAB

Kategorisasi teks telah membuat kemajuan pesat dan menjadi salah satu area penelitian di bidang pengolahan informasi. Tetapi kategorisasi teks memiliki masalah utama yaitu tingginya dimensi fitur sehingga dapat mengurangi kinerja klasifikasi. Karena itu dalam penelitian ini diusulkan sebuah metode untuk seleksi fitur menggunakan metode hibridasi Ant Colony Optimization (ACO) dan Information Gain (IG) pada dokumen teks Arab. Menggunakan dokumen teks Arab karena penelitian dalam bidang ini masih sedikit. Dokumen – dokumen teks Arab ini akan mengalami tahap preprocessing hingga menghasilkan fitur – fitur. Kemudian fitur – fitur tersebut akan diberi nilai IG dan akan digunakan untuk seleksi fitur menggunakan ACO. Informasi heuristik pada metode ACO menggunakan nilai IG yang telah dihitung sebelumnya. Pada percobaan ditunjukkan bahwa metode hibridasi ACOIG dapat mereduksi fitur sebanyak 89%, sedangkan metode ACO hanya 79%. Dan waktu performa yang dibutuhkan metode ACOIG lebih cepat dari metode ACO

Arabic Book Retrieval using Class and Book Index Based Term Weighting

One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic Fiqh (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%

REKONSTRUKSI PERMUKAAN TIGA DIMENSI PADA PHOTOMETRIC STEREO BERBASIS JARINGAN SYARAF

Photometric stereo merupakan metode untuk merekonstruksi permukaan 3 (tiga) dimensi suatu objek. Photometric stereo menggunakan tiga citra objek yang sama, dengan posisi pencahayaan yang berbeda. Namun, photometric stereo juga membutuhkan posisi sumber cahaya yang diketahui secara akurat. Dalam makalah ini digunakan jaringan syaraf untuk melakukan proses photometric stereo terhadap tiga citra dengan hanya mengandalkan nilai intensitas citra tanpa posisi sumber cahaya yang diketahui. Komputasi dilakukan pada tiap-tiap piksel secara independen dengan menggunakan model permukaan Lambertian. Jaringan syaraf ini akan mengekstraksi informasi surface normal objek yang diperoleh pada bobot jaringan syaraf. Informasi surface normal ini kemudian diproses dengan metode enforcing integrability untuk mendapatkan bentuk permukaan 3D objek. Kata Kunci: Rekonstruksi 3D, photometric stereo, jaringan syaraf, enforcing integrability, model Lambertian

Ekstraksi Fitur Berdasarkan Deskriptor Bentuk dan Titik Salien Untuk Klasifikasi Citra Ikan Tuna

Abstract. The manual classification of fish causes problems on accuracy and execution time. In the image of tuna, beside the shape feature, local features is also necessary to differentiate the types of fish especially which have a similar shape. The purpose of this study is to develop a new feature extraction system which integrates point of saline and the shape of descriptor to classify the image of tuna. The input image is then transformed into HSV format. Hue channel is selected for the segmentation process. Shape descriptors are extracted by using Fourier Descriptor (FD) and the saline points are extracted using Speeded Up Robust Features (SURF). The results of local features are performed by Bag of Feature (BOF). Feature integration combines shape descriptor and saline features with appropriate weight. Experimental results show that by integrating features, the classification problems of fish with similar shape can be resolved with an accuracy of classification acquired by 83.33%.Keywords: feature extraction, fourier descriptor, surf, classification, tuna fish imageAbstrak. Klasifikasi secara manual yang dilakukan berdasarkan bentuk, tekstur, dan bagian tubuh ikan dapat menimbulkan permasalahan pada akurasi dan waktu klasifikasi. Pada citra ikan tuna, selain diperlukan fitur bentuk juga diperlukan fitur lokal untuk membedakan jenis ikan terutama yang memiliki bentuk secara visual mirip. Tujuan penelitian ini adalah mengembangkan sistem ekstraksi fitur baru yang mengintegrasikan deskriptor bentuk dan titik salien untuk klasifikasi citra ikan tuna. Segmentasi diawali dengan mengambil kanal Hue pada citra HSV. Deskriptor bentuk diekstrak menggunakan Fourier Descriptor dan titik salien diekstrak menggunakan Speeded Up Robust Features. Untuk menyamakan dimensi dilakukan pemrosesan menggunakan Bag of Feature. Kedua jenis fitur yang sudah diperoleh dilakukan integrasi dengan mempertimbangkan bobot masing-masing fitur. Uji coba dilakukan pada dataset tiga jenis ikan tuna dengan 10-fold cross validation. Hasil uji coba menunjukkan dengan mengintegrasikan deskriptor bentuk dan titik salien permasalahan klasifikasi ikan tuna dengan bentuk yang mirip dapat diselesaikan dengan akurasi klasifikasi sebesar 83,33%.Kata Kunci: ekstraksi fitur, deskriptor fourier, surf, klasifikasi, citra ikan tun

Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

A lot of methods are used to develop on image research. Image detection to relay back new information, widely used in various research field, such as health, agriculture or other field research. Various methods are used and developed to get better results. A combination of several methods is performed for testing as part of the research contribution. In this study will perform the combination results of the process color feature extraction with texture features. In color feature extraction using HSV color space method that gets 72 feature extraction and on texture feature extraction using local binary pattern that gets 256 feature extraction. The process of merging the two extracted results gets 328 new feature extractions. The result of combining color feature extraction and texture feature extraction is further classified. Results from image classification of butterflies get an accuracy score of 72%. The results obtained will be tested performance. The results obtained from performance testing get precision value, recall and f-measure respectively 76%, 72% and 74

KLASIFIKASI MASSA PADA CITRA MAMMOGRAM MENGGUNAKAN KOMBINASI SELEKSI FITUR F-SCORE DAN LS-SVM

ABSTRAKKanker payudara adalah penyakit yang paling umum diderita oleh perempuan pada banyak negara. Pemeriksaan kanker payudara dapat dilakukan menggunakan citra Mammogram dengan teknologi sistem Computer-Aided Detection (CAD). Analisis CAD yang telah dikembangkan adalah ekstraksi fitur GLCM, reduksi/seleksi fitur, dan SVM. Pada SVM (Support Vector Machine) maupun LS-SVM (Least Square Support Vector Machine) terdapat tiga masalah yang muncul, yaitu: Bagaimana memilih fungsi kernel, berapa jumlah fitur input yang dioptimalkan, dan bagaimana menentukan parameter kernel terbaik. Jumlah fitur dan nilai parameter kernel yang diperlukan saling mempengaruhi, sehingga seleksi fitur diperlukan dalam membangun sistem klasifikasi. Pada penelitian ini bertujuan untuk mengklasifikasi massa pada citra Mammogram berdasarkan dua kelas yaitu kelas kanker jinak dan kelas kanker ganas. Ekstraksi fitur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil proses ekstraksi fitur tersebut kemudian diseleksi mengunakan metode F-Score. F-Score diperoleh dengan menghitung nilai diskriminan data hasil ekstraksi fitur di antara data dua kelas pada data training. Nilai F-Score masing-masing fitur kemudian diurutkan secara descending. Hasil pengurutan tersebut digunakan untuk membuat kombinasi fitur. Kombinasi fitur tersebut digunakan sebagai input LS-SVM. Dari hasil uji coba penelitian ini didapatkan, bahwa menggunakan kombinasi seleksi fitur sangat berpengaruh terhadap tingkat akurasi. Akurasi terbaik didapat dengan menggunakan LS-SVM RBF dan SVM RBF baik dengan kombinasi seleksi fitur, maupun tanpa kombinasi seleksi fitur dengan nilai akurasi yaitu 97,5%. Selain itu juga seleksi fitur mampu mengurangi waktu komputasi.Kata Kunci: F-Score, GLCM, kanker payudara, LS-SVM.ABSTRACTBreast cancer is the most common disease suffered by women in many countries. Breast cancer screening can be done using a mammogram image. Computer-aided detection system (CAD). CAD analysis that has been developed is GLCM efficient feature extraction, reduction / feature selection and SVM. In SVM (Support Vector Machine) and LS-SVM (Support Vector Machine Square least) there are three problems that arise, namely; how to choose the kernel function, how many input fea-tures are optimal, and how to determine the best kernel parameters. The number of fea-tures and value required kernel parameters affect each other, so that the selection of the features needed to build a system of classification. In this study aims to classify image of masses on digital mammography based on two classes benign cancer and malignant cancer. Feature extraction using gray level co-occurrence matrix (GLCM). The results of the feature extraction process then selected using the method F-Score. F-Score is obtained by calculating the value of the discriminant feature extraction results data between two classes of data in the data training. Value F-Score of each feature and then sorted in descending order. The sequenc-ing results are used to make the combination of fea-tures. The combination of these features are used as input LS-SVM. From the experiments that use a combination of feature selection affects the accuracy ting-kat. Best accuracy obtained using LS-SVM and SVM RBF RBF with combi-nation or without the combination of feature selection with accuracy value is 97.5%. It also features a selection able to curate the computa-tion time.Keywords: Breast Cancer, F-Score, GLCM, LS-SVM

The Application of Phosphate Solubilizing Microbes Biofertilizer to Increase Soil P and Yield of Maize on Ultisols Jatinangor

Ultisols has problems of low availability of nutrients, especially phosphorus. To improve soil phosphate and P fertilizer efficiency, it is necessary to develop biofertilizer such as phosphate solubilizing microbes. Phosphate solubilizing microbes (PSM) have the capability of dissolving soil phosphorus which have been adsorbed and can mineralize organic P to become inorganic P, hence increasing the avalibility of P in the soil. Phosphate solubilizing bacteria (Pseudomonas mallei and Pseudomonas cepacea) and phosphate solubilizing fungi (Penicillium sp. and Aspergillus sp) were selected based on their ability to dissolve P. The experiment was conducted at Jatinangor, West Java Indonesia to study the application of PSM biofertilizer to increase soil P and yield of maize. Experiment used a Randomized Block Design (RBD) in factorial pattern, consisting of two factors with three replications. The first factor consisted of PSM biofertilizer, which were; without PSM, 5 L ha-1 of PSM and 50 kg ha-1 of PSM.  The second factor was P fertilizer with five levels (0%, 25%, 50%, 75% and 100% dosage of recommendation). The results showed that the application of PSM biofertilizer increased soil phosphate and yield of maize on Ultisol Jatinangor.  The dosage of P inorganic fertilizers was reduced by 50%.Keywords: ultisol, maize, biofertillizer, phospate-solubilizing bacteria