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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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Analysis of E-learning Program's Role in Physical Learning
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

This study aimed to determine the application of e-learning programs, the role of e-learning programs, and how important the e-learning program's role in Physical learning was. This research used descriptive study using 80 students of the odd semester in 2016/2017 and 2017/2018 academic study program. The data obtained by questionnaires then analyzed based on the specified categories. From the data processing, it was proved that e-learning programs could be applied through e-learning modules. The program's role was in the "moderately contribute" category with a 3.44 score (scale 1 -5). The relation of e-learning program with its role in Physical learning based on the theoretical (based on calculations using formulas) mean that for the moderate, high/strong, and very high/very strong category was equal to 91.25%, for low/weak category was equal to 8.75%. Based on the data, it means that for moderate, high/strong, and very high/very strong category was equal to 76.25%, whereas for low/weak and very low/very weak category was equal to 23.75%.
Decision Support System for the Success of Education Program at Secondary School Level using Combination of K-Medoids Clustering and TOPSIS
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

The success of education programs is one of the concepts of educational equity that aims to educate a nation's life. However, the condition of education in Indonesia is not evenly distributed, and this can be seen from the availability and affordability of education services in each province. This research applies a decision support system to determine two categories: provinces that have not achieved a success rate of education program using K-Medoids Clustering and TOPSIS. The K-Medoids Clustering was used to overcome the outliers in the data. The TOPSIS is used to provide decision making based on the best alternative concept. The best alternative concept in TOPSIS has the closest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The number of clusters formed as many as five clusters. The iteration needed to cluster provinces using K-Medoids Clustering is 817 iterations. The third cluster has the largest variable average value and smallest standard deviation value. So, the third cluster shows the best cluster quality. Determination of provincial members into two categories is partially/fully refers to members of best cluster quality and TOPSIS preference value.
Optimization of Naïve Bayes Method using Genetic Algorithm to Diagnose Cattle Disease
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

Technological advances that so fast encourage people to create a breakthrough, one of which is the expert system. An expert system can be used to solve problems in diagnosing disease problems, one of which is cattle disease. Lack of knowledge of breeders regarding this can result in considerable losses to the breeder. An expert system is needed to diagnose cow disease. The method that can be used to create an expert system is the Naïve Bayes method. The naïve Bayes method is a classification with probability methods and statistics to predict future opportunities based on previous experience. But there are weaknesses; namely, the independence feature is often wrong, and the probability estimation results cannot be optimal. To overcome this problem, one way is to use Genetic Algorithms. Genetic algorithms are random search forms that mimic the principle of natural biological evolution processes to find optimal solutions. The number of attributes used is 24 attributes consisting of the name of the disease and 23 symptoms. The accuracy of using the Naïve Bayes method is 90%, while the accuracy of using the Naïve Bayes and Genetic Algorithm is 95%. It can be concluded that there is an accuracy increase of 5%.
Optimization of C4.5 Algorithm Using K-Means Algorithm and Particle Swarm Optimization Feature Selection on Breast Cancer Diagnosis
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

Large data requires methods to explore information so that it can provide solutions to problem solving. The method is the data mining process. In the medical world, data mining is useful in diagnosing a disease such as breast cancer. Data mining has several techniques in exploring hidden data, one of which is a classification with the C4.5 algorithm. The C4.5 algorithm has proven better results than other decision tree algorithms. In the classification process, the results of the accuracy obtained are very important. So, optimization is needed to improve classification accuracy. The C4.5 algorithm optimization is done using the K-Means algorithm for clustering processes in continuous data and the Particle Swarm Optimization feature selection process. This research aims to determine the workings of accuracy optimization in the C4.5 algorithm and the results of accuracy obtained in breast cancer diagnosis. This research uses a dataset of the Wisconsin Diagnostic Breast Cancer (WDBC) UCI Machine Learning Repository. From the results of the research, the proposed method provides an average accuracy is 97,894%. So that provides better accuracy when compared with the C4.5 algorithm, which is 94.152%. Experiments based on the proposed method proved to be able to increase the classification accuracy by 3,742%.
Implementation of Fuzzy Logic Method and Certainty Factor for Diagnosis Expert System of Chronic Kidney Disease
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

Problem analysis from the development of technology that was originally conducted manually, it can now conduct systematically using computerization. An expert system can solve one problem analysis in diagnosing a disease like chronic kidney disease. Fuzzy logic and certainty factor are expert system methods that are often used. The data used in this research was Chronic Kidney Disease, which was obtained from the UCI dataset. The system was developed using the Laravel PHP framework programming language and MySQL database. The system's development used the waterfall method, which was analyzing user needs to the system, conducting design of the system, coding, and testing the system if it achieves what was expected. The combination of fuzzy logic and certainty factor methods worked with several stages, namely fuzzification (CFuser), rule base formation for CF, calculating CFexpert, Calculating CF values, the combination of CF values, Finding CFmaximum. The accuracy level of the system generated from 400 data was obtained 92.25% accuracy for the fuzzy logic method, 97.25% accuracy for the certainty factor, 99% accuracy method for the combination of fuzzy logic and certainty factor methods. While the kappa value for the fuzzy logic method, certainty factor, and the combination of the two methods were respectively 0.84, 0.94, 0.98.
Optimization of Classification Accuracy Using K-Means and Genetic Algorithm by Integrating C4.5 Algorithm for Diagnosis Breast Cancer Disease
Journal of Advances in Information Systems and Technology Vol 3 No 1 (2021): April
Publisher : Universitas Negeri Semarang

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Abstract

Technological development resulted in data proliferating. The data is processed into valid information for daily needs. Data mining is a technique to convert data into useful information. Data mining has been widely used in performing prediction functions, for example, health and medical science. This study using Wisconsin Diagnostic Breast Cancer dataset taken from UCI Machine Learning Repository. The dataset has 32 attributes with 569 samples. This data has a continuous and high dimensional data type, and it makes the C4.5 algorithm need long computation time and extensive storage. This study aims to improve the accuracy of the C4.5 with a combination of K-Means and Genetic Algorithm. These study results compared the accuracy of the C4.5 algorithm before and after applying the combination of K-Means and the Genetic Algorithm for diagnosing breast cancer. The accuracy of C4.5 is 91,228%. Meanwhile, the accuracy of C4.5 after optimized using the K-Means and Genetic Algorithm is 94,824%, with the average number of features are selected 22 features. Thus, the application of K-Means and Genetic Algorithm on the C4.5 Algorithm can improve the accuracy of diagnosing breast cancer by 3,596%.
Accuracy Enhancement in Early Detection of Breast Cancer on Mammogram Images with Convolutional Neural Network (CNN) Methods using Data Augmentation and Transfer Learning
Journal of Advances in Information Systems and Technology Vol 3 No 1 (2021): April
Publisher : Universitas Negeri Semarang

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Abstract

The advancement of computer technology has made it possible for computers to imitate the work of the human brain to make decisions that can be used in the healthcare sector. One of the uses is detecting breast cancer by using Machine Learning to increase the sensitivity and or specificity of detection and diagnosis of the disease. Convolutional Neural Network (CNN) is the most commonly used image analysis and classification method in machine learning. This study aims to improve the accuracy of early detection of breast cancer on mammogram images using the CNN method by adding the Data Augmentation and Transfer method. Learning. This study used a mammography image dataset taken from MIAS 2012. The dataset has seven classes with 322 image samples. The results of accuracy tests of early detection process of breast cancer using CNN by utilizing Data Augmentation and Transfer Learning show several findings: Model VGG-16, Model VGG-19, and ResNet-50 produced the same training accuracy rate of 86%, while for validation accuracy, Model ResNet-50 produced the highest level of accuracy (71%) compared to other models (VGG-16=64%, VGG-19=61%). The use of more image datasets may create better accuracy.
Diagnosis Using Brain Tumors Two-Dimensional Principal Component Analysis (2D-PCA) with K-nearest Neighbor (KNN) Classification Algorithm
Journal of Advances in Information Systems and Technology Vol 3 No 1 (2021): April
Publisher : Universitas Negeri Semarang

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Abstract

The rapid development of computer technology has brought more and more benefits to human life. Currently, computers can make decisions by imitating the human brain to be used in the health sector to play a role in solving existing problems. One of the technologies used is digital image processing technology on MRI images of brain tumors. Brain tumor images have various variations and large dimensions; therefore, an appropriate method is needed to recognize images maximally. Dimensional reduction uses the Two-Dimensional Principal Component Analysis (2DPCA) method. The classification process uses the K-Nearest Neighbor (KNN) method by calculating the euclidean distance (Euclidean Distance). From 3 tests with the number of data 200 images, the results of the accuracy of the 1st test were 90.0% with 60 test data and 140 training data, the second test was 85.0% with 80 test data and 120 training data, and the 3rd test is worth 83.0% with 100 test data and 100 training data. Based on the research above, it can be concluded that the highest accuracy is obtained in the 1st test, while the lowest accuracy is on the 3rd test. The more amount of training data compared to the test data, the greater the accuracy value obtained. This research is expected to be a reference for further research so that the results obtained are more optimal.
Optimization of the C4.5 Algorithm by Using a Genetic Algorithm for the Diagnosis of Life Expectancy for Hepatitis Patients
Journal of Advances in Information Systems and Technology Vol 3 No 1 (2021): April
Publisher : Universitas Negeri Semarang

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Abstract

As technology develops rapidly, the amount of data generated experiencing rapid development, including medical data. Data can help diagnose the life expectancy of people with the disease such as hepatitis using data mining methods in the medical field. In this research, technique data mining uses a classification technique with the C4.5 algorithm and the UCI Machine Learning Repository dataset. This dataset has 19 attributes, 1 class, and 155 samples. C4.5 algorithm is optimized using the Genetic Algorithm feature selection process. This study compares the accuracy of the C4.5 algorithm before and after optimization using a Genetic Algorithm. C4.5 algorithm produces the highest accuracy of 96.23%. Meanwhile, the C4.5 algorithm, after being optimized using Genetic Algorithm, has the highest accuracy of 98.11%. The number of features selected is 15 features. Application of Genetic Algorithms in C4.5 algorithm is proven to improve the accuracy in diagnosing life expectancy of people with hepatitis as much as 1.88%.
Implementation of Expert System to Diagnose Pregnancy Disorders using Fuzzy Expert System Method
Journal of Advances in Information Systems and Technology Vol 3 No 1 (2021): April
Publisher : Universitas Negeri Semarang

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Abstract

The process of problem analysis can be carried out by a computer system that has included a knowledge base and a set of rules from an expert, known as an expert system. One of the problems that the expert system can solve is to diagnose pregnancy disorders. This study aims to determine how to design an expert system by adopting a doctor's expertise with the fuzzy expert system method. The data used in this study were 46 data obtained from the medical records from Tugurejo Hospital in Semarang City. The variables used were general symptoms and pregnancy disorders. The result of this research is the implementation of the fuzzy expert system to diagnose pregnancy disorders. The level of system accuracy generated from the scenario of 26 data as training data and 20 data as test data is equal to 95%.

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