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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 6 Documents
 1 
Search results for , issue "Vol 2 No 1 (2020): April" : 6 Documents clear
Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model
Journal of Advances in Information Systems and Technology Vol 2 No 1 (2020): April
Publisher : Universitas Negeri Semarang

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Abstract

In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.
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.

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