IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN
Get PDF The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN
PLO User Interface based on Telegram Bot
Get PDFInstant messaging services usually integrate a notification system on their users’ devices, as phone calls and short message service (SMS) systems do. Telegram is – as far as we are aware – the only popular instant messaging service that uses open source code. Telegram also provides APIs for their users, enabling the development of a bot system that allows instant messaging application to access information. Here, we study the potential use of Telegram bot as a user interface to a paperless office (PLO) system developed in our institution. We found that Telegram bot improves communication among the users; however, as the amount of messages increased, the server becomes overloaded. This limitation suggests that future works need to be directed to improving the efficiency of the bo
Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)
Get PDFThreats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%)
Automatic Text Summarization Based on Semantic Networks and Corpus Statistics
Get PDFOne simple automatic text summarization method that can minimize redundancy, in summary, is the Maximum Marginal Relevance (MMR) method. The MMR method has the disadvantage of having parts that are separated from each other in summary results that are not semantically connected. Therefore, this study aims to compare summary results using the MMR method based on semantic and non-semantic based MMR. Semantic-based MMR methods utilize WordNet Bahasa and corpus in processing text summaries. The MMR method is non-semantic based on the TF-IDF method. This study also carried out summary compression of 30%, 20%, and 10%. The research data used is 50 online news texts. Testing of the summary text results is done using the ROUGE toolkit. The results of the study state that the best value of the f-score in the semantic-based MMR method is 0.561, while the best f-score in the non-semantic MMR method is 0.598. This value is generated by adding a preprocessing process in the form of stemming and compression of a 30% summary result. The difference in value obtained is due to incomplete WordNet Bahasa and there are several words in the news title that are not in accordance with EYD (KBBI)
A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification
Get PDFThe sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%
The Development of IoT Compression Technique To Cloud
Get PDFThe main problem of data transmission is how to reduce the length of data packet delivery, so it can reduce the time of sending data. One method that can be used to reduce the data size is by compressing the data size. Data compression is a technique for compressing data to get the data with smaller size than the original size so that it can shorten the data exchange timeThis study aims to develop the data compression techniques by modifying and combining the coding and modelling techniques based on the RAKE algorithm. This study testing experiments use 4 different methods in 5 different time-periods to determine the value of the compression, decompression efficiency parameters, and the data transmission time parameters.The result of this study is the data coding technique that using decimal to binary converter data and the modeling technique by calculating the residue from the sensor value will produce data in small sizes and get a compression efficiency value of 45%. For coding techniques using ASCII and modeling techniques with XOR operations will produce bigger size data and the compression efficiency value of 71%. In testing data decompression, the decompression efficiency value of 100%, there is no data loss
Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study
Get PDFArtificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD
Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System
Get PDF Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets
Comparison of Motion History Image and Approximated Ellipse Method in Human Fall Detection System
Get PDFThis paper compares two different method in human fall detection system namely motion history image and approximated ellipse. Research has been done in small studio with 4 CCTV camera as video data recorder, whereas video data are processed using MATLAB software. The experiment was carried out using three object’s fall direction and two type of falling movement. The fall direction is consist of front, side, and back fall. Whereas the falling movement is consist of direct and indirect fall movement. Meanwhile, the object’s initial position is standing and size of captured object is constant. The result is motion history image has accuracy 74.26% for direct falling movement, and 75.69% for indirect falling movement. Whereas approximated ellipse has accuracy 56.85% for direct falling movement, and 61.81% for indirect falling movement. Therefore, motion history image is better than approximated ellipse in human fall detection system
DSS for Selection of Coffee Plants against a Land Using ANP and Modification Of Profile Matching
Get PDFBased on BPS data, the growth of plantation crop production in NTB Province in 2011 to 2016 was recorded to have decreased by an average of 3.3 thousand tons annually. Coffee plants in particular are 0.1 thousand tons on average, the lack of public interest in planting coffee properly on land owned so that it impacts on land use that is not in accordance with its potential which will result in decreased productivity and erosion of land quality [1]. The first study of land suitability analysis for coffee plantations used a matching method in robusta coffee with a matching method producing a class (S1) of 0,46% [2] the second using a matching method on robusta coffee producing a class (S1) of 0,015% [3] These results indicate the ability of each land is different so that the results of the analysis vary. This study applies the ANP method and modified matching profile where the level of recommendations of coffee plants on the ability of land in East Lombok Regency through validation based on coffee production data from the East Lombok District Agricultural Service produces a match in rank 1 of 87,5% and 75% with non-modified profile matching