Maulahikmah Galinium

Automatic mood classification of Indonesian tweets using linguistic approach

Research concerning Twitter mining becomes an interesting research topic recently. It is proven by numerous number of published paper related with this topic. This research is intended to develop a prototype system for classifying Indonesian language tweets. The prototype includes preprocessing step, main information retrieval and classification system. This research proposes a system that uses grammatical rule for retrieving main information from the tweet, and then classifies the information to the suitable mood space. The classification algorithm, which is used, is lexicon based classifier. The proposed classification system has 53.67% accuracy for classifying tweets into 12 mood spaces and 75% accuracy for classifying tweets into 4 mood spaces. As the comparison, the same dataset is also classified using SVM and Naïve Bayes.

Geometry learning tool for elementary school using augmented reality

Augmented Reality is a technology that overlay the virtual objects into the real world. With the capabilities of Augmented Reality in combining virtual and real world object in real time, it can be utilized to help education process for elementary students. There are numerous frameworks to build Augmented Reality based application. OpenCv is a computer library vision that implements various tools for processing images. The purpose of this research is to test whether OpenCv is capable to build Augmented Reality based application or not. With the capabilities of OpenCv in detecting color and registering the virtual object in real time, it supports the Augmented Reality based application creation. The research is done by creating the Augmented Reality Geometry Learning Tool prototype system based on OpenCv to help elementary students learning using protractor. The prototype system then is implemented in the elementary school to obtain the response from the students. The response from the student is satisfying and it proves OpenCv is capable to build Augmented Reality based application.

Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach

The exponential growth of the data may lead us to the information explosion era, an era where most of the data cannot be managed easily. Text mining study is believed to prevent the world from entering that era. One of the text mining studies that may prevent the explosion era is text classification. It is a way to classify articles into several predefined categories. In this research, the classifier implements TF-IDF algorithm. TF-IDF is an algorithm that counts the word weight by considering frequency of the word (TF) and in how many files the word can be found (IDF). Since the IDF could see the in how many files a term can be found, it can control the weight of each word. When a word can be found in so many files, it will be considered as an unimportant word. TF-IDF has been proven to create a classifier that could classify news articles in Bahasa Indonesia in a high accuracy; 98.3%.

Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.

Data mining approach for user profile generation on advertisement serving

Advertisement serving on website is a prosperous business with huge market and millions of dollar prospect. By placing right advertisement at right time and place to right people, advertiser can increase their revenue by huge margin. The question is how advertiser and broker can push the right advertisement to the right user. User profiling can be used to analyze users behavior and predict what kind of advertisement should be served to the website user. Data mining approach can be harnessed to help with user profiling process. With data mining technique, users trace can be used as data source for behavior analysis. This research is used to do user profiling based on their browsing history stored on proxy server. Their browsing history serves as the basis of content crawling for content analysis using Multinomial Naïve Bayes classifier based text classification. The result of profiling then will be used as the basis for serving advertisement to user. The result of content analysis is validated by asking users preferences and comparing it with profile generated by classifier engine.

Automatic multi-document summarization for Indonesian documents using hybrid abstractive-extractive summarization technique

This paper discusses the development of multi-document summarization for Indonesian documents by using hybrid abstractive-extractive summarization approach. Multi-document summarization is a technology that able to summarize multiple documents and present them in one summary. The method used in this research, hybrid abstractive-extractive summarization technique, is a summarization technique that is the combination of WordNet based text summarization (abstractive technique) and title word based text summarization (extractive technique). After an experiment with LSA as the comparison method, this research method successfully generated a well-compressed and readable summary with a fast processing time.

Iris localization using gradient magnitude and fourier descriptor

Best way to localize iris inside an image of an eye is still a huge challenge because a standard regarding iris image does not yet exist. The first step of iris segmentation is iris localization. It is very important step because it will ensure the other step is running well. In this paper, a novel method to localize iris in a robust and simple manner is explained. The method is able to localize iris occluded by eyelids. Experiments on publicly available iris database that made by Malaysia Multimedia University Iris Database (MMU1) [1] showed a satisfying result of the proposed method.

Implementation of face recognition algorithm for biometrics based time attendance system

Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil, and compare the result with the measurements stored in the database and return the closest record (facial metrics). Nowadays, there are a lot of face recognition techniques and algorithms found and developed around the world. Facial recognition becomes an interesting research topic. It is proven by numerous number of published papers related with facial recognition including facial feature extraction, facial algorithm improvements, and facial recognition implementations. Main purposes of this research are to get the best facial recognition algorithm (Eigenface and Fisherface) provided by the Open CV 2.4.8 by comparing the ROC (Receiver Operating Characteristics) curve and implement it in the attendance system as the main case study. Based on the experiments, the ROC curve proves that using the current training set, Eigenface achieves better result than Fisherface. Eigenface implemented inside the Attendance System returns between 70% to 90% similarity for genuine face images.

News recommendation in Indonesian language based on user click behavior

Recommendation system has been proposed for years as the solution of information era problem. This research strives to develop an intelligent recommendation system based on user click behavior on news websites. We extracted frequent itemsets and association rules from the web server log of a news website, performed a pre-computation of similarity between news articles, and then proposed a three-level recommendation system: based on association rule discovery, news articles on the same category, and similarity between news articles. By combining collaborative filtering approach and content-based filtering, experiment results show that the technique produces reliable news recommendation.

Contact lens detection for iris spoofing countermeasure

The development of biometric authentication system should be followed by strengthening to spoofing attempts. Among various identifiers, iris has aroused many attentions due to its uniqueness and stability. Nevertheless, the use of iris for biometric authentication is accompanied by spoofing risk, for example using contact lens. In order to handle the spoofing attempts, its detection is an inevitable part of a recognition system, to reduce the risk of forging system. Cosmetic contact lens is one of most common spoofing materials which is hard to be detected. In this study, weighted local binary pattern (w-LBP) and simplified scale invariant feature transform (SIFT) descriptors were used to extract the feature of the iris, in which segmented using gradient magnitude and Fourier descriptor. Simplified SIFT descriptor is extracted at each pixel of iris image and being used to rank the local binary pattern (LBP) sequence of encoding. The features were then presented to support vector machine (SVM) classifier, for positive vs. negative classification. Positive class means that contact lens was used by a person, and vice versa. The experimental results showed that combining SIFT and w-LBP as features for SVM yielded an accuracy of 84%.