Sistem Pakar Diagnosa Penyakit Mata dengan Fuzzy  Logic dan Naïve Bayes

Putra, Putu Ananta Dama; Purnawan, I Ketut Adi; Putri, Desy Purnami Singgih

doi:10.24843/jim.2018.v06.i01.p04

Cited by 13 publications

(15 citation statements)

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“…Sistem ini dapat mengambil keputusan layaknya seorang pakar yang mengambil keputusan [7][3]. Sistem ini bekerja dengan langkah-langkah kerja sebagai berikut : akuisisi pengetahuan mengidentifikasi object-atribut-value, menetapkan basis pengetahuan, perancangan basis data, formalisasi sistem pakar,perancangan dan pengembangan perangkat lunak, serta validasi system [8], [9].…”

Section: Sistem Pakarunclassified

Implementasi Metode Case Based Reasoning Untuk Mendeteksi Kerusakan Televisi

Wulandari

Nasution

Munandar

2021

mib

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The process of deteriorating television should indeed be done by an expert who is a television expert himself, but because television is a tool that is no longer common for people to know him, many people also have television in their respective homes. Even for television repairs, it is quite expensive, so some people who have televisions can do television maintenance at home. The lack of knowledge possessed by the community can lead to wrong handling of television maintenance / maintenance and this has a fatal impact. Hopefully the existence of this system can help the community in diagnosing the damage to their televisions. In that case they need not bother to call for repairmen or bring in a television repair shop. Here the authors provide solutions to solve the problems that often arise on television. In this study, it discusses how to care for television officers. The research objective is to analyze a desktop-based expert system program that contains the knowledge of an expert / doctor whose truth is believed to have the ability to be able to diagnose the disease from the symptoms of damage that has been damaged by television damage quickly and precisely. The stages of research carried out in this study include literature study, data collection, system design, system creation, system testing. Case Based Reasoning is a method used to build a knowledge-based system. The source of system knowledge is obtained by collecting the handling of cases by an expert / expert. Therefore, many problems in television damage are usually due to the negligence of the television owner himself. The first step in solving a problem is by first identifying the scope of the problem to be resolved, this also applies to any Artificial Intelligence (AI) programming. The results obtained in the study for the diagnosis of conjunctivitis were the value of old cases and new cases which obtained a high weight value, namely 1 from the third case

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Section: Sistem Pakarunclassified

Implementasi Metode Case Based Reasoning Untuk Mendeteksi Kerusakan Televisi

Wulandari

Nasution

Munandar

2021

mib

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“…Sehingga sistem tersebut dapat menjawab masalah-masalah yang sesuai dengan kepakaran sistemnya. Terdapat penelitian tentang sistem pakar dalam diagnosa pada penyakit manusia, sistem tersebut menggantikan pakar penyakit tersebut kepada sistem [8], [9], [10], [11], [12]. Selain pada manusia sistem pakar juga dilakukan untuk menggantikan pakar tanaman [13], [14].…”

Section: Sistem Pakarunclassified

Penerapan Metode Forward-Backward Chaining pada Sistem Pakar Pencegahan dan Pengobatan Penyakit Sapi

Irawan

Widarma²,

Siregar³

et al. 2021

JATI

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Increasing the population of cattle in Aek Nagali village, especially in hamlet V, really helps the economy of the community, the health of the livestock raised by breeders is important for the animals themselves. It is difficult for veterinarians in Aek Nagali village so that it is the custom of people in the village where when their cows are sick and dying they are immediately slaughtered and the meat is traded. Even though this does not mean that the cow is suitable for consumption or not because there is a cow disease that can be transmitted to humans when eating beef. The purpose of this research is to build an expert system-based system that can provide an overview or decision. The method used is Forward Chaining and Backward Chaining in diagnosing cow disease. The discussion is focused on applying Forward Chaining and Backward Chaining rules and applying them to the system. From the results of research, the backward and forward methods provide information on how to prevent and treat cattle disease so that breeders can know what steps to take.

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“…The results obtained are Pain Intensity Prediction Expert System (PIPES) can be implemented for the construction industry, sand mining sites, and other worksites where the work materials can be moved using a shovel manually. The use of fuzzy logic itself has been widely implemented to diagnose a disease, such as an eye disease [14], diphtheria disease [15], polyneuropathy [16], ENT disease [17], and oral and dental disease [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

Wanti¹,

Puspitasari

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Diagnosis of autism spectrum disorder (ASD) can use a fuzzy inference system. The use of fuzzy logic method to obtain ASD diagnosis results according to experts based on the limits of factors/symptoms of the disease and all the rules obtained from experts. Recommendations for therapy and preventive actions can be given by experts after knowing the results of the diagnosis of ASD using the fuzzy logic method. This study serves to diagnose ASD by optimizing each degree of membership in the fuzzy logic method with the Mamdani method approach which is involved in the autism detection process involving 96 patient data. The Mamdani method itself can process an uncertain value from the user/patient into a definite value whose membership degree can be determined and adjusted to the conditions of the problem. Optimization was carried out on the degree of membership for all variables involved in the process of diagnosing ASD, namely social interaction, social communication and imagination and behavior patterns. The results of this study indicate a relatively small level of fuzzy calculation error with a precision value of 94.4%, a recall precision value of 65.4% and an error rate value of 3.05%. Calculation of accuracy shows a result of 90.59%.

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Sistem Pakar Diagnosa Penyakit Mata dengan Fuzzy Logic dan Naïve Bayes

Cited by 13 publications

References 0 publications

Implementasi Metode Case Based Reasoning Untuk Mendeteksi Kerusakan Televisi

Implementasi Metode Case Based Reasoning Untuk Mendeteksi Kerusakan Televisi

Penerapan Metode Forward-Backward Chaining pada Sistem Pakar Pencegahan dan Pengobatan Penyakit Sapi

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

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