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Journal of Advances in Information Systems and Technology
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 2715999X     DOI : https://doi.org/10.15294/jaist
Core Subject : Science,
Computer Science & IT
Journal of advances in Information Systems and Technology (JAIST) seeks to promote high quality research that is of interest to the international community.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 83 Documents
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Diagnosis of TBC Disease Using SVM and Feedforward Backpropagation
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.60646

Abstract

Tuberculosis (TBC) is an infectious disease caused by a virus Mycobacterium tuberculosis. One of the organs that is often infected by the virus Mycobacterium tuberculosis is the lungs. This disease is the second largest killer worldwide for infectious diseases after HIV/AIDS (Laily et al., 2015). Therefore, the level of diagnosis accuracy TBC disease needs to be improved using better methods. After the data is collected, then the data is processed in the preprocessing stage and through the normalization process so that the data range can be balanced. Furthermore, the last process is the classification process. In this classification process using two methods, namely Support Vector Machine and Feedforward Backpropagation. The two classification methods are assessed because they are simple and has a fairly precise level of accuracy. But also has a weakness in the selection of appropriate features. Based on research that has been done, using model testing with 10 executions, the accuracy results for Support Vector Machine produces an accuracy of 97.41%, while the results accuracy for Feedforward Backpropagation produces a level of accuracy by 98.51%. This shows that the Feedforward method Backpropagation is considered to improve the accuracy of diagnosis TBC disease.
Audit Information Technology Using COBIT 5 in the Procurement Service Unit (Case Study: SIM UKPBJ Kabupaten XYZ)
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.60793

Abstract

Information Technology (IT) in this all-digital era affects all aspects of human life. Including the field of government. One of them is in the Goods/Services Procurement Section of XYZ Regency, an example of an agency that has implemented IT Governance using the Goods/Services Procurement Service Unit Management Information System (SIM UKPBJ) to integrate existing operational activities. SIM UKPBJ is a means to achieve organizational goals and simplify every process in procuring goods/services. In the UKPBJ SIM, since its inception in form until now, there has never been an evaluation process regarding IT Governance that has been implemented. COBIT 5 is a framework that has a comprehensive scope starting from management and Governance. The COBIT framework is following the current state of SIM UKPBJ, 5 domains have been selected that focus on DSS05 (Manage Security Services), DSS06 (Manage Business Controls), APO11 (Manage Quality), APO12 (Manage Risk), and APO13 (Manage Security). This study used a data collection process through interviews, observations, and questionnaires. Based on the results of the study, the capability level value of each process in the DSS05, DSS06, APO11, APO12, and APO13 domains is at level 4, the predictable process and the maturity level results have an average percentage of 80.5%, namely L (Large achieved). SIM UKPBJ chooses the target level to be performed as 5, namely the Optimizing process. It is necessary to increase the capability level from the current conditions in terms of growing activities with recommendations, namely maximizing policies that are already running well and making innovations in activities to accelerate the achievement of agency goals.
Increasing Accuracy of The Random Forest Algorithm Using PCA and Resampling Techniques with Data Augmentation for Fraud Detection of Credit Card Transaction
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.60865

Abstract

The credit-card transaction analysis uses a random forest algorithm as an algorithm for the classification process. The problem faced from the classification process using credit card fraud filing dataset fraud is an imbalanced data that causes an imbalanced data alignment on the model results from data training. To resolve the problem, a combination of PCA methods and resampling techniques with data augmentation for the optimum process on random forest classification algorithms. The PCA method is used in the preprocessing stage to do the process of transforming data into numerical data and resampling techniques and data augmentation are used in data resamples to bring the data to a balance. The data used is a data card fraud of Europe that has 284807 transactions. Model accuracy measurement was implemented using confusion matrix. The highest accuracy results from a random forest combination using PCA and resampling techniques with data augmentation of 99.9976%.
Performance Comparison of SVM, Naïve Bayes, and KNN Algorithms for Analysis of Public Opinion Sentiment Against COVID-19 Vaccination on Twitter
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.59493

Abstract

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.
Optimization of the C4.5 Algorithm Using Particle Swarm Optimization and Discretization in Predicting the Results of English Premier League Football Matches
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.59531

Abstract

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%.
Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.59561

Abstract

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%.
Implementation of Fuzzy Inference System with Best-Worst Method for Cost Efficiency on Amazon Web Services
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.60569

Abstract

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.
Chaotic Whale Optimization Algorithm in Hyperparameter Selection in Convolutional Neural Network Algorithm
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.60595

Abstract

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.
Prediction of Life Expectancy of Lung Cancer Patients Post Thoracic Surgery using K-Nearest Neighbors and Bat Algorithm
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.60846

Abstract

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%.
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)
Journal of Advances in Information Systems and Technology Vol 4 No 2 (2022): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i2.61105

Abstract

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.

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