PROTOTIPE SISTEM INFORMASI TUNTUNAN PERJALANAN WISATA DI PROVINSI BALI

E-Tourism is a digital platform that connects all tourism stakeholders, and integrates all tourism activities and makes it easy for all travelers to explore tourism charms by accessing applications without being tied to places and times. In Indonesia especially in Bali the development of E-Tourism can be very good. Many sites are available on the internet to be a medium of information and tourist accommodations booking media. In addition, many government and private sites also play a role in the process of providing tourist information, but these sites can be said to be static and simple. These providers only provide text information without prioritizing responsiveness and interactivity to the users.Responsiveness and interactivity are two issues in today's tourism sites. The existing tourism sites in Indonesia are still lacking in responding to the needs or questions of the users and also the interactive functions on the site are felt to be less than satisfactory. Users are only provided an information in the form of photographs and writings about a tourist attraction. Users never know where places can be visited with approximate time, location recommendations, routes and schedules that adjust to user needs. Refers to the problems,we develop an application that able to give recommendation and guidance of tour in Bali area.The system is made on the main needs that have been defined that the system is able to provide travel recommendations. After testing with black box model, the result of the system has been able to meet the needs of the user and provide travel recommendations in accordance with the needs of the user where the travel time-limited, category of place, place of the beginning of the journey and the final place has been fulfilled. The system has given the suitable recommendations based on the criteria that the user wants.

HYBRID GENETIC ALGORITHMDAN ANT COLONY OPTIMIZATIONUNTUK OPTIMISASI METODE MULTILEVEL IMAGE THRESHOLDING

Penggunaan genetic algorithm (GA) sebagai metode multilevel image thresholding dalam segmentasi citra dapat memberikan keuntungan seperti kecepatan proses dan penentuan jumlah threshold serta nilai threshold yang tepat. Akan tetapi, genetic algorithm memiliki beberapa kelemahan dimana salah satunya adalah kemungkinan terjadinya konvergensi yang terlalu dini (premature convergence) dan tidak adanya feedback positive yang tidak menjamin solusi global optimal. Penelitian ini mengajukan metode baru Hybrid GA-ACO untuk optimisasi metode multilevel image thresholdingsehingga dapat mengatasi kelemahan tersebut dengan cara menggabungkan GA dan ant colony optimization (ACO). Penggabungan dilakukan dengan menjadikan posisi dan nilai threshold yang didapatkan pada GA sebagai nilai awal untuk proses algoritma ACO. Hasil pengujian dengan citra sintetis dan citra asli menunjukkan nilai cost function, uniformity, dan misclassification error dari metode hybrid GA-ACO lebih baik dibandingkan dengan algoritma awal GA, yaitu rata-rata 98.87% untuk tingkat uniformity dan 97.72% untuk nilai ME. Nilai cost function metode hybrid GA-ACO yang lebih kecil dibandingkan algoritma GA menunjukkan bahwa metode hybrid GA-ACO dapat mencegah konvergensi dini pada algoritma GA. Dari hasil tersebut dapat disimpulkan bahwa metode hybrid GA-ACO yang dikembangkan merupakan suatu metode multilevel image thresholding yang dapat mencegah konvergensi dini sehingga mencapai konvergensi pada solusi optimal yang bersifat global optimum

Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features

The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros

Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks

Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features

The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros

Framework Rekonstruksi Kraniofasial 3D Menggunakan Deformasi Permukaan Dan Positioning Landmark Otomatis Berdasarkan Surface Curvature Feature

Rekonstruksi kraniofasial atau craniofacial reconstruction (CFR) berbantuan komputer adalah proses yang bertujuan untuk mengestimasi impresi wajah berdasarkan sisa-sisa tengkorak. Proses ini mengadaptasi metode konvensional menggunakan kerangka kerja berbasis model konseptual. Masalah yang ada dalam CFR saat ini adalah (1) anotasi tengkorak masih bergantung pada ahli, (2) pemrosesan tengkorak dalam domain tiga dimensi (3D) memiliki tantangan data volumetrik, dan (3) perlu metode yang didasarkan pada karakteristik morfologi populasi atau statistical model template. Kami mengusulkan sebuah framework rekonstruksi kraniofasial berbasis komputasi yang terdiri dari tiga tahap, yaitu membangun model kraniofasial, deteksi landmark otomatis, dan deformasi permukaan. Machine learning digunakan untuk menarik korelasi antara bentuk permukaan lokal dan bentuk landmark dan secara otomatis mendeteksi posisinya. Fitur permukaan lokal diekstraksi menggunakan Surface Curvature Feature (SCF) sebagai deskriptor 3D. Dengan menggunakan filter berbasis klaster, jarak rata-rata (ke ground truth) dari 20 titik teratas adalah 0,0326 unit, lebih kecil dari radius titik pengambilan sampel 0,05. Filter berbasis klaster lebih baik daripada filter berbasis mass-radius dan secara konsisten memberikan akurasi yang lebih baik, terutama dalam kasus multi-klaster. Data training terdiri dari 140.000 SCF untuk sepuluh kelas landmark. Tahap ketiga, yaitu deformasi permukaan, menyesuaikan bentuk template wajah ke tengkorak berdasarkan korespondensi pasangan landmark wajah-tengkorak. Deformasi permukaan Laplacian memberikan estimasi bentuk alami wajah manusia dengan tetap mempertahankan detail permukaan template wajah. Validasi lima orang ahli dari departemen Antropologi menyatakan bahwa dari hasil rekonstruksi, 91,5% dapat mempertahankan detail template dan diterima sebagai bentuk alami wajah manusia. ================================================================================================================================= Computer-assisted craniofacial reconstruction (CFR) is a process that aims to estimate facial impressions based on skull remains. It adapts conventional methods using a conceptual model-based framework. The existing problems in current CFR are (1) skull annotation still relies on experts, (2) skull processing in the three-dimensional (3D) domain has volumetric data challenges, and (3) there is a need for methods based on population morphological characteristics or statistical model templates. We propose a computationally-based craniofacial reconstruction framework consisting of three stages: building a craniofacial model, automatic landmark detection, and elastic surface deformation. Machine learning draws correlations between local surface shapes as landmarks and automatically detects their positions. Local surface features are extracted using Surface Curvature Feature (SCF) as a 3D descriptor. Using the cluster-based filter, the average distance (to ground truth) of the top 20 points is 0.0326 units, smaller than the sampling point radius of 0.05. The cluster-based filter is better than the mass-radius-based filter. It consistently provides better accuracy, especially in the multi-cluster case. The training data consists of 140,000 SCFs for ten landmark classes. The third stage, surface deformation, adapts the shape of the face template to the skull based on the correspondence of the face-skull landmark pairs. The Laplacian surface deformation provides an estimation of the natural shape of the human face while maintaining the details of the face template surface. Five experts from the Anthropology department stated that from the reconstruction results, 91.5% could preserve the details of the template and is accepted as the natural shape of the human face