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All Journal Scientific Journal of Informatics Journal of Soft Computing Exploration Journal of Student Research Exploration Scientific Journal of Informatics
Muzayanah, Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : journal of soft computing exploration

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Developed an expert system for analysis of covid-19 affected Mishra, Shashank; Yadav, Shivam; Aggarwal, Mukul; Sharma, Yashika; Muzayanah, Rini
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.113

Abstract

The expert system solves problems within a specific area of the knowledge base. Prolog is a logical programming language which works on its knowledge base and effectively can be used to develop an expert system. Covid 19 is a pandemic deices and an expert system can be developed to diagnose this disease with the help of its symptoms that can be used as a knowledge base in Prolog. This expert system can make a fast diagnosis process for the covid 19 which is important to prevent the spread of the virus. Here we developed an expert system using prolog for diagnosis purposes. Like humans, these systems can get better with time as they gain more experience. Expert systems combine their experiences and expertise into a knowledge base that is then used by an inference or rules engine, a set of rules that the software employs, to apply to certain scenarios. Prolog is ideal for use with intelligent systems for a few reasons. Prolog can be viewed as a straightforward theorem prover or inference engine that derives from predefined rules. With the help of Prolog's built-in search and backtracking techniques, simple expert systems can be created.
Optimization of support vector machine using information gain and adaboost to improve accuracy of chronic kidney disease diagnosis Listiana, Eka; Muzayanah, Rini; Muslim, Much Aziz; Sugiharti, Endang
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.218

Abstract

Today's database is growing very rapidly, especially in the field of health. The data if not processed properly then it will be a pile of data that is not useful, so the need for data mining process to process the data. One method of data mining used to predict a decision in any case is classification, where in the classification method there is a support vector machine algorithm that can be used to diagnose chronic kidney disease. The purpose of this study is to determine the level of accuracy of the application of information gain and AdaBoost on the support vector machine algorithm in diagnosing chronic kidney disease. The use of information gain is to select the attributes that are not relevant while AdaBoost is used as an ensemble method commonly known as the method of classifier combination. In this study the data used are chronic kidney disease (CKD) dataset obtained from UCI repository of machine learning. The result of experiment using MATLAB applying information gain and AdaBoost on vector machine support algorithm with k-fold cross validation default k = 10 shows an accuracy increase of 0.50% with the exposure of the result as follows, the support vector machine algorithm has accuracy of 99.25 %, if by applying AdaBoost on the support vector machine has an accuracy of 99.50%, whereas if applying AdaBoost and information gain on the support vector machine has an accuracy of 99.75%.
Comparison of gridsearchcv and bayesian hyperparameter optimization in random forest algorithm for diabetes prediction Muzayanah, Rini; Pertiwi, Dwika Ananda Agustina; Ali, Muazam; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.308

Abstract

Diabetes Mellitus (DM) is a chronic disease whose complications have a significant impact on patients and the wider community. In its early stages, diabetes mellitus usually does not cause significant symptoms, but if it is detected too late and not handled properly, it can cause serious health problems. To overcome these problems, diabetes detection is one of the solutions used. In this research, diabetes detection was carried out using Random Forest with gridsearchcv and bayesian hyperparameter optimization. The research was carried out through the stages of study literature, model development using Kaggle Notebook, model testing, and results analysis. This study aims to compare GridSearchCV and Bayesian hyperparameter optimizations, then analyze the advantages and disadvantages of each optimization when applied to diabetes prediction using the Random Forest algorithm. From the research conducted, it was found that GridSearchCV and Bayesian hyperparameter optimization have their own advantages and disadvantages. The GridSearchCV hyperparameter excels in terms of accuracy of 0.74, although it takes longer for 338,416 seconds. On the other hand, Bayesian hyperparameter optimization has a lower accuracy rate than GridSearchCV optimization with a difference of 0.01, which is 0.73 and takes less time than GridSearchCV for 177,085 seconds.
Co-Authors Aggarwal, Mukul Ali, Muazam Awaliyah, Desi Adrianti Budi Prasetiyo Budi Prasetiyo, Budi Endang Sugiharti, Endang Fadhilah, Muhammad Syafiq Jumanto Jumanto, Jumanto Lestari, Apri Dwi Listiana, Eka Mishra, Shashank Much Aziz Muslim Pertiwi, Dwika Ananda Agustina Sharma, Yashika Tama, Endi Adika Yadav, Shivam