Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods

Insani, Muhammad Ilham; Alamsyah, Alamsyah; Putra, Anggyi Trisnawan

doi:10.15294/sji.v5i2.16143

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…In this study, two classification algorithms are used, namely Naive Bayes and Support Vector Machine. Method of Naïve Bayes has 3 stages such as previous research, find the probability value, and looking probabilitasi end of posterior (Insani, et al, 2018). While the Support Vector Machine has a goal to provide a value for the number of occurrences of a word and can classify sentences into positive or negative labels (Giovani et al, 2020).…”

Section: Classificationmentioning

confidence: 99%

Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Shalikha,

Alamsyah

2023

J. Adv. Inf. Syst. Tech

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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%.

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Section: Classificationmentioning

confidence: 99%

Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Shalikha,

Alamsyah

2023

J. Adv. Inf. Syst. Tech

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“…Metode Naive Bayes membentuk suatu probabilistik sederhana yang berdasarkan pada Teorema Bayes pada umumnya, inferensi bayes terutama dengan asumsi kemandirian yang kuat [5]. Teorema Bayes juga dapat digunakan sebagai alat pengambilan keputusan dalam memperbarui tingkat kepercayaan informasi [6].…”

Section: Pendahuluanunclassified

Sistem Pakar dengan Menggunakan Metode Naive Bayes dalam Mengidentifikasi Penyakit Karies pada Gigi Manusia

Hari

Sumijan

2021

JSisfotek

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Caries disease in human teeth is a disease that permanently destroys the inner walls of teeth and forms small holes in the teeth. The Indonesian people lack the knowledge to find information and identify tooth decay, which makes many people unaware of the consequences and dangers of this disease. Tooth decay disease is usually caused by three factors. The first factor is teeth and saliva, which are the hosts of microorganisms in the oral cavity. Bacteria and food are the second and third factors. The purpose of this research is to help the public find information about tooth decay, thus forming a branch of artificial intelligence, the expert system. Artificial intelligence is a science that allows you to build computer systems that display intelligence in different ways. An expert system is a computer program or information system that uses some knowledge of an expert. The methods used in this study are the Naive Bayes method and the model's view controller, which are implemented as a database in the PHP Codeigniter framework and MySQL. The data processed in this study is knowledge about the symptoms of dental caries obtained from experts. The test results of this method provide patients with the knowledge necessary to prevent tooth decay, with an accuracy rate of 83.61%. This expert system helps the public to recognize and obtain information about tooth decay. The Expert System can also be used to take the first step in preventing tooth decay.

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“…There are various types of mental disorders, namely eating disorders, neurodevelopmental disorders, and psychotic disorders [10]. Various in-depth studies of various disorders and their symptoms have been carried out [11]- [15]. According to the symptoms of different mental disorders, we create a knowledge base where users can ask questions, ask their problems, and get the results.…”

Section: Introductionmentioning

confidence: 99%

expert system on diagnosis of mental diseases

Jain

Aggarwal

Singhal

et al. 2023

J. Soft Comput. Explor.

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Mental disorder is one of the most serious problems in today's time. Mental disorders can be classified into different sub-disorders according to changes in human behavior and mental condition. According to reports one out of seven people suffered from mental disorders. In this research paper, our main emphasis is to build an expert system that diagnoses people based on their symptoms, so people can diagnose themselves early before going to the doctor. Expert Systems are one of the most important applications in artificial intelligence that solves complex problems without human help. We provide different rules, facts, and relationships among different symptoms in our knowledge base, from which users can query their problems and get their results. We used SWI-prolog to build an expert system. There are a few types of disorders, such as mental disorders, neurodevelopmental disorders, eating disorders, etc.

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Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods

Cited by 8 publications

References 6 publications

Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Sistem Pakar dengan Menggunakan Metode Naive Bayes dalam Mengidentifikasi Penyakit Karies pada Gigi Manusia

expert system on diagnosis of mental diseases

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