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<![CDATA[Performance Comparison of SVM, Naïve Bayes, and KNN Algorithms for Analysis of Public Opinion Sentiment Against COVID-19 Vaccination on Twitter ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.59493
<![CDATA[The emergence of the COVID-19 virus in 2020 has created a new breakthrough in the form of a vaccine as a solution to slow the spread of the virus. However, the COVID-19 vaccine is considered controversial and invites many people to express their views on various media, one of which is social media Twitter. Using Twitter data on the COVID-19 vaccine, sentiment analysis can be performed. Sentiment analysis aims to evaluate whether the tweet contains a positive sentence or sentiment. In this study, the analysis of sentiments on the COVID-19 vaccine on social media Twitter was carried out using the Support Vector Machine (SVM), Naïve Bayes, and k-Nearest Neighbor (KNN) algorithms. SVM has the advantage of being able to identify hyperplanes that maximize the margins between different sentiments. Meanwhile Naïve Bayes is an algorithm that is simple, fast and produces maximum accuracy with training. The KNN algorithm was chosen because it is superior to noise. The performance of the three classification algorithms will be compared, so that it can be seen which algorithm is better in classifying text mining. Sentiment classification results in this study consist of positive sentiment and sentiment classes. The resulting accuracy value will be a benchmark for finding the best test model in the case of sentiment classification. Based on ten tests, the final result of accuracy and best performance using the SVM algorithm with an accuracy value of 96.3% is obtained. Meanwhile, the Naïve Bayes and KNN algorithms have an accuracy of 94% and 91%, respectively. The high accuracy results are supported by the feature extraction TF-IDF the TextBlob library.]]>
<![CDATA[Optimization of the C4.5 Algorithm Using Particle Swarm Optimization and Discretization in Predicting the Results of English Premier League Football Matches ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.59531
<![CDATA[Football is one of the most popular sports. One of the most competitive football competitions is the English Premier League. This study aims to determine the prediction of the results of the football match in English Premier League. The prediction results in the form of home win, away win, and draw. This prediction uses data mining techniques, namely using the C4.5 algorithm as a classification algorithm with Particle Swarm Optimization as a feature selection method and Discretization as a preprocessing method. The dataset used was obtained from the football-data.co.uk website for four league seasons from the 2017/2018 season to the 2020/2021 season with a total of 1,520 instances. In this study, a comparison was made to the methods used to determine the increase in accuracy obtained. Based on ten times the data mining process, the final result of the best accuracy from using the C4.5 algorithm is 57.24%, then the C4.5 algorithm with Discretization gets an accuracy of 65.13%, and the C4.5 algorithm with Discretization and Particle Swarm Optimization gets accuracy of 71.05%. The conclusion is that the use of Discretization and Particle Swarm Optimization can improve the performance of the C4.5 algorithm in predicting the results of English Premier League matches with an increase in accuracy of 13.81%.]]>
<![CDATA[Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.59561
<![CDATA[Menstrual cup is a menstrual hygiene sanitation tool that replaces disposable sanitary napkins for women that reaps many pros and cons in its use. From this, it is necessary to analyze the public's views regarding the use of menstrual cups, which is called sentiment analysis. Sentiment analysis is a process that aims to determine the polarity of the sentiment of a text. This paper performs a classification of menstrual cup sentiment analysis on Twitter using the Naïve Bayes and the Support Vector Machine algorithm. Particle Swarm Optimization is applied to improve the accuracy of both classification algorithms. The final result of the accuracy obtained by the Naïve Bayes algorithm is 92.72% and the Support Vector Machine algorithm is 96.13%. While the accuracy results after Particle Swarm Optimization is applied, for Naïve Bayes it produces an accuracy rate of 95.87%, and Support Vector Machine is 96.68%.]]>
<![CDATA[Implementation of Fuzzy Inference System with Best-Worst Method for Cost Efficiency on Amazon Web Services ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.60569
<![CDATA[This study aims to reduce the cost of using computing services on AWS. Cost reduction is needed because there is a possibility that the total cost of using cloud services exceeds the estimated budget. One type of EC2 that offers a large discount is the Spot Instance. The downside of this type of EC2 is that AWS reserves the right to stop it at any time. The proposed solution is an automation system to select and run EC2 Spot Instance types based on price, discount, amount of memory, and vCPU usage from previous instances. The automation system is built with the implementation of fuzzy inference system and Best-Worst Method (BWM). All input data is obtained using the Boto3 SDK. System deployment is done in Lambda functions. This Lambda function is automatically executed whenever a Spot Instance is terminated by AWS. The EventBridge service will catch the event and then trigger the Lambda to run. System testing was run for 4 (four) days with event simulation using the Send Events feature. From these tests it is known that the automation system can select the appropriate instance and generate a total cost of $3.85 (USD). After calculating the total cost with regular EC2 estimation (On Demand), the cost is reduced by 71.28%. This number proved to be 4.28% greater than previous similar studies.]]>
<![CDATA[Chaotic Whale Optimization Algorithm in Hyperparameter Selection in Convolutional Neural Network Algorithm ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.60595
<![CDATA[In several previous studies, metaheuristic methods were used to search for CNN hyperparameters. However, this research only focuses on searching for CNN hyperparameters in the type of network architecture, network structure, and initializing network weights. Therefore, in this article, we only focus on searching for CNN hyperparameters with network architecture type, and network structure with additional regularization. In this article, the CNN hyperparameter search with regularization uses CWOA on the MNIST and FashionMNIST datasets. Each dataset consists of 60,000 training data and 10,000 testing data. Then during the research, the training data was only taken 50% of the total data, then the data was divided again by 10% for data validation and the rest for training data. The results of the research on the MNIST CWOA dataset have an error value of 0.023 and an accuracy of 99.63. Then the FashionMNIST CWOA dataset has an error value of 0.23 and an accuracy of 91.36.]]>
<![CDATA[Prediction of Life Expectancy of Lung Cancer Patients Post Thoracic Surgery using K-Nearest Neighbors and Bat Algorithm ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.60846
<![CDATA[Lung cancer is one of the deadliest cancers, accounting for 11.6% of cancer diagnoses in the world. Death in lung cancer patients can occur in various ways and one of the treatments for lung cancer patients that can be done is thoracic surgery. Thoracic surgery is generally considered a medium risk procedure, but thoracic surgery has a high risk, one of the risks is that if the patient loses blood which will result in the death of the patient. In this study, the method used to implement predictive life expectancy in post-thoracic surgery patients is the bat algorithm for feature selection and the KNN algorithm for classifying data. The dataset used in this study was obtained from the UCI Machine Learning Repository, namely the thoracic surgery dataset which contains 470 data with 16 attributes. The results of the study in predicting the life expectancy of patients after thoracic surgery were carried out with 3 tests. The first test is testing the population with the best accuracy of 87.23%, the second test is convergent testing with the best accuracy of 87.23% and the third test is the comparison test of KNN which produces the best accuracy of 87.23%. The bat algorithm succeeded in increasing the accuracy of the KNN classification by 5.23% from 81.91%.]]>
<![CDATA[Detection of the Use of Masks as an Effort to Prevent Covid-19 Using Gray Level Co-Occurrence Matrix (GLCM) Based on Learning Vector Quantization (LVQ) ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.61105
<![CDATA[Covid-19 is a disease caused by the SARS-CoV-2 virus. Transmission of Covid-19 can be through the flow of air (aerosol), splashes of liquid (droplets). One of the prevention efforts to break the chain of transmission is to use a mask when interacting with other people. Monitoring and controlling the use of masks will be safer and more efficient when implementing a mask detection system. This study will analyze GLCM for extraction method and LVQ for classification method. The results of GLCM successfully provide statistical features that represent image characteristics well. While the LVQ can provide classification results with a good percentage of accuracy. The results of the best percentage accuracy for the first rank are 83.15% in the composition ratio of 90: 10. Furthermore, the percentage of accuracy for the second rank is 76.03% at the composition ratio of 70: 30 and the third rank is 72.47% at the composition ratio of 80: 20. This indicates that the composition more training data does not guarantee the level of achievement of a higher percentage of accuracy. There is an optimal maximum number of epochs where the number of epochs that exceeds the optimal number of epochs will not experience a change in the percentage of accuracy. For each value the learning rate (alpha) can give the results of the percentage of accuracy with different graphic patterns and will stop at the optimal maximum number of epochs.]]>
<![CDATA[Implementation of Naïve Bayes Method with Certainty Factor for Disease and Pest Diagnosis on Onion Plants ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.61189
<![CDATA[Shallots can be regarded as non-substituted, which is a plant that is used as a food seasoning and herbal medicine. Every year, the demand for shallots is increasing. But along with the ever-increasing demand, it is inversely proportional to the lack of availability. The cause of this is the lack of knowledge about shallot cultivation, including pest and disease disturbances. The purpose of this research is to help farmers diagnose early diseases and pests that attack shallot plants. With the presence of these pests and diseases, a system that contains knowledge from an expert is needed to diagnose early symptoms experienced by plants. In this study, the authors created an expert system for the diagnosis of diseases and pests on shallot plants. Researchers used the Naïve Bayes method as a classification method for each selected symptom. Then the Certainty Factor as a method of determining the value of confidence in the diagnosis results in the first method. In this study, it produced an accuracy rate of 97%.]]>
<![CDATA[Factor Analysis of Continuance Intention to Use QR Code Mobile Payment Services: An Extended Expectation-Confirmation Model (ECM) ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.61468
<![CDATA[QR code mobile payment is a payment method that is quite popular in Indonesia where users only need to open or display a QR code on the m-payment application when making transactions. Users can make payments easily, anywhere and anytime. Apart from the benefits of QR codes on m-payments, there are still obstacles regarding the intention to continue using them. Some users stopped using the QR code service on the m-payment application due to the potential risks involved. The purpose of this study is to find out what factors can affect continued intention to use QR code m-payment. The research model used is the Extended Expectation-Confirmation Model (ECM) by combining ECM and UTAUT and adding trust and perceived risk variables. The number of samples in this study was 313 participants who were users who had used QR code m-payment OVO, GoPay, or ShopeePay with a minimum age of 17 years. The sampling technique used is purposive sampling. This study uses quantitative methods and data analysis with the PLS-SEM approach using SmartPLS version 3. The results of this study are three rejected hypotheses and nine accepted hypotheses. Based on the accepted hypotheses, it shows that social influence, trust, and satisfaction affect continuance intention to use QR code m-payment. Social influence is the biggest factor affecting continuance intention to use QR code m-payment service. These results can be considered for developers and companies such as OVO, GoPay, and ShopeePay.]]>
<![CDATA[Electric Vehicle Routing Problem with Fuzzy Time Windows using Genetic Algorithm and Tabu Search ]]>
<![CDATA[ Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jaist.v4i2.62314
<![CDATA[The distribution of goods becomes a very calculated thing in the economic aspect, especially in the case of wide and complex distribution. The greater the range of distribution of goods, the more precise, fast, and accurate calculations are needed. Specifically, the calculation of the distribution required starts from mileage, total travel time, customer satisfaction level based on customer time windows, and operational costs. Vehicle Routing Problem (VRP) is a solution to the problem of distributing goods from the depot to its customers. This study aims to determine the optimal route. The methods used for VRP optimization are the Genetic Algorithm (GA) and Tabu Search (TS) methods. Fuzzy logic is used to provide leeway on the limitations of the time windows parameters, thus providing a time tolerance in the event of early arrival of the vehicle or delay in delivery. Data processing using the GA-TS combination was carried out as many as two types of trials, namely trials with the same dataset ten times and trials with various types of datasets ten times. The results of the first trial fitness value on E-VRPFTW average increased by 14.39% compared to the results of the E-VRPTW fitness value that did not use fuzzy. The results of the second trial also experienced an average increase of 8.49% compared to the results of the E-VRPTW fitness value that did not use fuzzy. Therefore, the addition of fuzzy logic has an effect in determining the optimum route of E-VRPTW.]]>
