IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network
Get PDFSentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence
Transliteration of Hiragana and Katakana Handwritten Characters Using CNN-SVM
Get PDFHiragana and katakana handwritten characters are often used when writing words in Japanese. Japanese itself is often used by native Japanese as well as people learning Japanese around the world. Hiragana and katakana characters themselves are difficult to learn because many characters are similar to one another. In this study, hiragana and basic katakana, dakuten, handakuten, and youon were used, which were taken from the respondents using a questionnaire. This study used the CNN method which will be compared with a combination of the CNN and SVM methods which have been designed to identify each character that has been prepared. Preprocessing of character images uses the methods of image resizing, grayscaling, binarization, dilation, and erosion. The preprocessed results will be input for CNN as a feature extraction tool and SVM as a tool for character recognition. The results of this study obtained accuracy with the following parameters: 69×69 image size, 3 patience values, val_loss monitor callbacks, Nadam optimization function, 0.001 learning rate value, 30 epochs value, and SVM RBF kernel. If using a system that only uses the CNN network, the accuracy is 87.82%. The results obtained when using a combination of CNN and SVM were 88.21%
Knowledge-Based Systems Selection of Contraceptive Equipment for The Handling of Uncertainty
Get PDF Contraceptives is one of the products of the government program for controlling the population. The government has established the Department of Population Control and family planning and empowerment of women and child protection that specifically manages the dissemination and socialization of the apparatus. But to choose the appropriate contraceptives for himself The Community of people still feel trouble. Not only prospective of common people who feel difficulties, sometimes the KB officers also feel uncertain in giving advice of tool contraceptives. That is because, sometimes the condition of the user does not comply with the existing rules, the latest knowledge about the development of contraception has not been owned by the officer, thus resulting in uncertainty in the suggestion of selection of contraceptives. In this study proposed a knowledge-based system to assist the public in providing an overview of the type of contraceptive equipment suitable for theyself and can be used by the KB officers the as interactive media and in the handling of the uncertainty problem that mentioned before. Then for the handling of uncertatinty problems will use dempster shafer method. dempster shafer method is Chosen because this method can provide an estimate of the value of confidence against a result of the diagnosis, by conducting the calculation of the combination of the same symptoms will be obtained the highest confidence value, or the most dominant. In the testing process, there will be 40 cases compared to the results. This research aims to solve the uncertainty problems of the suggestion the selection of contraceptives tools. The results of this research can provide a consulting medium that is able to provide selection of contraceptives that solve the problem of uncertainty and confidence level of the system to the tool. The test showed an accuracy rate of 95
Gamification-based The Kampus Merdeka Learning in 4.0 era
Get PDFRecently, education has been enlivened by the presence of the Merdeka Campus program initiated by Nadiem Makarim. It uses the Kampus Merdeka concept to learn to follow the development of education in the 4.0 era. This change has become a paradigm for Higher Education to build a Merdeka Campus to learn to face challenges in the 4.0 era. However, the challenge is not easy for universities, so that students join the independent program to learn quickly. This study aims to motivate students to participate in independent learning activities in a collaborative learning system with gamification techniques. Gamification is in the form of reward badges for student achievement in all learning activities carried out. The higher education independent learning system is designed using the library study method and Agile Development with two frameworks, namely Laravel and VueJS. It can be proven from the results of the SUS Score Analysis showing the number 92.5 indicating that the independent learning campus system provides positive benefits by gamification of students being more motivated and ready to face learning challenges in the 4.0 era
Prioritizing Drug Procurement Using ABC, VEN, EOQ And ROP Combination
Get PDFThe availability of drugs is one of the things that must be considered because if there is a deficiency or excess it can cause loss or disruption in patient care. The process to procure drugs that are still being carried out with uncertain considerations will create scheduling irregularities, this will have an impact on inventory costs due to accumulated inventory in warehouses or the absence of these drugs.This study aims to produce a decision support system for drug procurement using a combination of ABC methods, VEN analysis, ROP and EOQ.The test results show that the system can provide 3 recommendations for decision makers with consideration of the results of the ABC and VEN matrices and procurement calculations based on EOQ and ROP. The result of calculating the total Inventory Cost in the case example of the orodine drug based on the pharmacy calculation is IDR 708,500 while the calculation using the Economic Order Quantity method is IDR 689,381 from the calculation results obtained a savings of IDR 19,119
Exploring MSMEs Cybersecurity Awareness and Risk Management : Information Security Awareness
Get PDFThe use of information technology in the management of Micro, Small, and Medium Enterprises (MSMEs) is not limited to business performance and productivity but also aspects of data security and transactions using various mobile, website, and desktop-based applications. This article offers an idea to explore cybersecurity awareness and risk management of MSME actors who adopt information technology. The research method used is qualitative with a case study approach in the Coffeeshop X business and the Y Souvenir business in Salatiga City, Central Java, Indonesia. The data collection technique used in-depth interviews, observation, and document studies. These findings indicate that Cybersecurity Awareness, especially information security awareness, can be reviewed based on knowledge, attitudes, and behavior. Risk management can be review based on supply risk, operational risk, and customer risk. Cybersecurity Awareness and Risk Management in MSMEs is holistic and cannot be generalized, so it needs to be discussed contextually based on case studies. In the context of Coffeeshop X and Souvenir Y, the level of Cybersecurity Awareness (knowledge, attitude, behavior) is not always linear. In addition, risk management is more dominant in the customer risk dimension, compared to supply risk and operational risk.
Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm
Get PDF The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data
Text Summarization in Multi Document Using Genetic Algorithm
Get PDFAutomatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79
Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent
Get PDFClassification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%, and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model
Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification
Get PDF Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study