Musli Yanto
Universitas Putra Indonesia YPTK

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Optimization artificial neural network classification analysis model diagnosis Gingivitis disease Vicky Ariandi; Musli Yanto; Annisak Izzaty Jamhur; Firdaus Firdaus; Riandana Afira
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1648-1656

Abstract

Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.
Machine learning classification analysis model community satisfaction with traditional market facilities as public service Hadi Syahputra; Musli Yanto; Muhammad Reza Putra; Aulia Fitrul Hadi; Selvi Zola Fenia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1744-1754

Abstract

Traditional markets are public service facilities that can be utilized by thecommunity. The market function is used place where sellers and buyers meetin conducting transactions. This study aims to build a machine learningclassification analysis model in measuring community satisfaction withtraditional market facilities. The analytical methods used include Fuzzy.multiple linear regression (MRL), artificial neural network (ANN), anddecision tree (DT). Fuzzy is used to generate a pattern of rules in determiningthe level of satisfaction. MRL serves to measure and test the correlation ofrules that have been formed. The ANN method is used to carry out theclassification analysis process based on learning. In the final stage. DT is usedto describe the decision tree of the analysis process. This study presents theresults of machine learning analysis which is very good in determiningsatisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzylogic which can develop a classification rule pattern of 32 patterns. MRL alsoshows a significant correlation level of 81.1% based on the indicator variables.Overall, the machine learning classification analysis model can provideknowledge to be considered in the management of traditional markets aspublic service facilities.