Cluster Oriented Image Retrieval System with Context Based Color Feature Subspace Selection

This paper presents a cluster oriented image retrieval system with context recognition mechanism for selection subspaces of color features. Our idea to implement a context in the image retrieval system is how to recognize the most important features in the image search by connecting the user impression to the query. We apply a context recognition with Mathematical Model of Meaning (MMM) and then make a projection to the color features with a color impression metric. After a user gives a context, the MMM retrieves the highest correlated words to the context. These representative words are projected to the color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace selection, the system then clusters the image database using Pillar-Kmeans algorithm. The centroids of clustering results are used for calculating the similarity measurements to the image query. We perform our proposed system for experimental purpose with the Ukiyo-e image datasets from Tokyo Metropolitan Library for representing the Japanese cultural image collections

Lyric Text Mining Of Dangdut: Visualizing The Selected Words And Word Pairs Of The Legendary Rhoma Irama’s Dangdut Song In The 1970s Era

Dangdut is a new genre of music introduced by Rhoma Irama, Indonesian popular musician who was the Legendary dangdut singer in the 1970s era until now. The expression of  Rhoma Irama’s lyric has themes of the human being, the way of life, love, law and human right, tradition, social equality, and Islamic messages. But interestingly, the song lyrics were written by Rhoma Irama in the 1970s were mostly on the love song themes. In order to prove this, it is necessary to identify the songs through several approaches to explore the selected word and the relationship between word pairs. If each Rhoma Irama’s lyric is identified in text mining field, the lyric text extraction will be an interesting knowledge pattern. We collected the lyric from web were used as datasets, and then we have done the data extraction to store the component of lyric including the part and line of the song. We successfully applied the most word frequencies in the form of data visualization including bar chart, word cloud, term frequency-inverse document frequency, and network graph. As a results, several word pairs that often was used by Rhoma Irama in writing his song including heart-love (19 lines), heart-longing (13 lines), heart-beloved (12 lines), love-beloved (12 lines), love-longing (11 lines)

Semantic Madurese Batik Search with Cultural Computing of Symbolic Impression Extraction and Analytical Aggregation of Color,Shape and Area Features

Lack of information media about Madurese batik Causes low awareness of younger generation to maintain the production of Madurese batik. Actually, Madurese Batik also has a high philosophy, which the motif and colour reflect the character of the Madurese. Madurese Batik has useful motif as a mean of traditional communication in the form of certain cultural symbols. We collected images of Madurese Batik by identifying the impression of Madurese Batik motif taken from several literature books of Madurese Batik and also the results of observation of experts or craftsmen who understand about Madurese Batik. This research proposed a new approach to create on application which can identify Madurese Batik impression by using 3D-CVQ feature extraction methods to extract color features, and used Hu Moment Invariant for feature feature extraction. Application searching of Madurese Batik image has two ways of searching, those are based on the image input Madurese Batik and based on the input of impression Madurese batik. We use 202 madurese batik motifs and use search techniques based on colors, shapes and aggregations (color and shape combinations).  Performance results using based on image queries used: (1) based on color, the average precision 90%, (2) based on shape, the average precision 85%, (3) based on aggregation, the average precision 80%, the conclusion is the color as the best feature in image query. While the performance results using based on the impression query are:  (1) based on color, the average value of true 6.7, total score 40.3, (2) based on shape, the average value of true 4.1, total score 24.1, and (3) based on the aggregation, the average value of true 2.5, the total score is 13.8, the conclusion is the color as the best feature in impression query

Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability

Cluster-Based News Representative Generation with Automatic Incremental Clustering

Nowadays, a large volume of news circulates around the Internet in one day, amounting to more than two thousand news. However, some of these news have the same topic and content, trapping readers among different sources of news that say similar things. This research proposes a new approach to provide a representative news automatically through the Automatic Incremental Clustering method. This method began with the Data Acquisition process, Keyword Extraction, and Metadata Aggregation to produce a news metadata matrix. The news metadata matrix consisted of types of word in the column and news section of each line. Furthermore, the news on the matrix were grouped by the Automatic Incremental Clustering method based on the number of word similarities that arised, calculated using the Euclidean Distance approach, and was done automatically and real-time. Each cluster (topic) determined one representing news as a Representative News based on the location of the news closest to the midpoint/centroid on the cluster. This study used 101 news as experimental data and produced 87 news clusters with 85.14% precision ratio

Pemodelan Evakuasi Pejalan Kaki di Ruang Koridor dengan Cellular Automata Studi Kasus Gempa Bumi

Pemodelan Sistem Evakuasi Pejalan Kaki adalah sebuah sistem pemodelan untuk memodelkan pergerakan objek manusia untuk memberikan analisis pada kejadian evakuasi dalam studi kasus bencana Gempa Bumi. Evakuasi di dalam kondisi bencana adalah topik yang sangat penting untuk dikembangkan di Indonesia. Indonesia adalah salah satu negara yang memiliki tingkat frekuensi terjadinya bencana cukup tinggi hal ini berkaitan dengan posisi Geografis Indonesia. Model pendekatan yang digunakan untuk pergerakan objek manusia adalah Cellular Automata. Cellular Automata adalah salah satu model yang cukup sederhana dan banyak digunakan untuk memodelkan berbagai macam pemecahan permasalahan. Cellular Automata dapat digunakan  untuk memodelkan pergerakan Objek. Hasil dari peletian ini adalah sebuah Aplikasi berbasis bahasa pemrograman JAVA untuk memodelkan pergerakan objek manusia di dalam ruangan berkoridor. Input dari aplikasi adalah parameter diffusion dan decay yang akan memberikan analisa pemodelan pergerakan objek. Dengan menggunakan Cellular Automata sebagai pemodelan pergerakan manusia, maka dalam penelitian ini akan dapat memberikan gambaran pergerakan manusia dalam ruangan jika terjadi bencana Gempa Bumi dan waktu Evakuasi

Semantic Songket Image Search with Cultural Computing of Symbolic Meaning Extraction and Analytical Aggregation of Color and Shape Features

The term "Songket" comes from the Malay word "Sungkit", which means "to hook" or "to gouge". Every motifs names and variations was derived from plants and animals as source of inspiration to create many patterns of songket. Each of songket patterns have a philosophy in form of rhyme that refers to the nature of the sources of songket patterns and that philosophy reflects to the beliefs and values of Malay culture. In this research, we propose a system to facilitate an understanding of songket and the philosophy as a way to conserve Songket culture. We propose a system which is able to collect information in image songket motif variations based on feature extraction methods. On each image songket motif variations, we extracted philosophy of rhyme into impressions, and extracting color features of songket images using a histogram 3D-Color Vector quantization (3D-CVQ), shape feature extraction songket image using HU Moment invariants. Then, we created an image search based on impressions, and impressions search based on image. We use techniques of search based on color, shape and aggregation (combination of colors and shapes). The experiment using impression as query : 1) Result based on color, the average value of true 7.3, total score 41.9, 2) Result based on shape, the average value of true 3, total score 16.4, 3) Result based on aggregation, the average value of true 3, total score 17.4. While based using Image Query : 1) Result based on color, the average precision 95%, 2) Result based on shape, average precision 43.3%, 3) Based aggregation, the average precision 73.3%. From our experiments, it can be concluded that the best search system using query impression and query image is based on the color.Keyword : Image Search, Philosophy, impression, Songket, cultural computing, Feature Extraction, Analytical aggregation

Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm

Radical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1]

Data Mining Approach for Breast Cancer Patient Recovery

Breast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisher’s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between Naïve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient