IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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Sarcasm Detection For Sentiment Analysis in Indonesian Tweets
Get PDFTwitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%
Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad
Get PDFStenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods
Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes
Get PDFInstagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets
Data Integrity and Security using Keccak and Digital Signature Algorithm (DSA)
Get PDFData security is a very important compilation using cloud computing; one of the research that is running and using cloud technology as a means of storage is G-Connect. One of the developments made by the G-Connect project is about data security; most of the problems verification of the data sent. In previous studies, Keccak and RSA algorithms have implemented for data verification needs. But after a literature study of other algorithms that can make digital signatures, we found what is meant by an algorithm that is better than RSA in rectangular speeds, namely Digital Signature Algorithm (DSA).DSA is one of the key algorithms used for digital signatures, but because DSA still uses Secure Hash Algorithm (SHA-1) as an algorithm for hashes, DSA rarely used for data security purposes, so Keccak is used instead of the hash algorithm on DSA. Now, Keccak become the standard for the new SHA-3 hash function algorithm. Because of the above problems, the focus of this research is about data verification using Keccak and DSA. The results of the research are proven that Keccak can run on DSA work system, obtained a comparison of execution time process between DSA and RSA where both use Keccak
Identification of Rice Variety Using Geometric Features and Neural Network
Get PDF Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm
TOPSIS and SLR methods on the Decision Support System for Selection the Management Strategies of Funeral Land
Get PDFThe funeral land is one of the public facilities that must be provided by Local Government to support community activities. The need for funeral land in Lubuklinggau continues to increase while the availability of funeral land is decreasing, this is because the number of deaths of the population continues to increase every year. Forecasting the land availability of funeral for the coming year and applying the management strategies of funeral land can overcome the needs of the cemetery. Forecasting the land availability of funeral using Simple Linear Regression. TOPSIS to choose the management strategies of funeral land. Forecasting uses two variables that are the variable number of the population deaths and the variable amount of funeral land in the last 5 years. Forecasting results will be used as one of the assessment criteria in the decision support system for selection of the management strategies of funeral land. The alternative of the funeral management strategy that will be applied and assessed in accordance with Local Regulation of Town of Lubuklinggau. The highest value of the end result of the system will be used as a recommendation for the selection of management strategies.
Cased Based Reasoning to Identify Cause Conflicts in Marriage
Get PDFThe function of KUA in the activities surrounding the religion of Islam, including providing service and guidance in the area of present services in terms of marriage and reconcilement for Muslims, provide services and guidance in the field of development of Sakina, family consultation conflict or household problems, and so on. Integration between the computer and artificial intelligence into the post-wedding consulting services is one approach in overcoming the limitations of the expert (religious instructor).This research aims to identify conflict in marriage by applying Naive Bayes algorithm at the stage of determining the groups of test data (retrieve), then entered the stage of the search process of the highest similarity value by using the Nearest Neighbor algorithm (reuse). The data source and the test data used are divided into two groups, namely marriage, and history data consultation, While the group conflicts are identified will be divided into five classes, namely an employment factor, the factor of age, educational factors, factors the number of weddings, and social status.Testing is performed by the use of 12 data, consisting of 11 data cases and 1 test data. At the stage of determination of group conflict acquired test data included in group one i.e. F001 (factor of the job), so at the stage of looking for value similarities used only the base case of the class F001 i.e. KK001, KK003, and KK008. The KK001 similarity has a value of 0476, KK003 of 0882, and KK008 of 0142. The case with most high similarity value will be stored as a base case. If the value similarity obtained less than the threshold value that is 0.8, then the solution of the case will be revised by experts. The results of the calculation accuracy, using 35 new test data that gets the value of 82.86%
Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit
Get PDFSocial media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf
Genetic Algorithm for lecturing schedule optimization
Get PDFScheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity
Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs
Get PDF Dental panoramic radiographs heavily depend on the performance of the segmentation method due to the presence of unevenly illumination and low contrast of the images. Conditional Spatial Fuzzy C-mean (csFCM) Clustering have been proposed to achieve through the incorporation of the component and added in the FCM to cluster grouping. This algorithm directs with consideration conditioning variables that consider membership value. However, csFCM does not consider Intuitionistic Fuzzy Set to take final membership and final non-membership value into account, the effect does not wipe off the deviation by illumination and low contrast of the images completely for improvement to skip some scope. In this current paper, we introduced a new image segmentation method namely Conditional Spatial in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs. Our proposed method adds hesitation function aiming to settle the indication of the knowledge lack that belongs to the final membership function to get a better segmentation result. The experiment result shows this method achieves better segmentation performance with misclassification error (ME) and relative foreground area error (RAE) values are 4.77 and 4.27 respectively