Implementasi Sistem Pakar Diagnosis Penyakit Diabetes Mellitus Menggunakan Metode Fuzzy Logic Berbasis Web

Masykur, Fauzan

doi:10.21456/vol2iss1pp011-017

Cited by 4 publications

(4 citation statements)

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“…Didalam kotak hitam tersebut terdapat metode atau cara yang dapat digunakan untuk mengolah data dari input menjadi output kedalam bentuk informasi yang baik. Pada gambar 5 terdapat pemetaan yang menggambarkan bahwa sebelum inpur menjadi output terdapat kotak hitam diantaranya yang berarti bawa data atau infomrasi inputan akan masuk kedalam kotak hitam yang berisi metode dan cara untuk mengolah data kemudian dikirim ke output jika data yang diolah telah selesai dan menghasilkan informasi [13].…”

Section: Fuzzy Logic Methodsunclassified

Model Sistem Pakar Fuzzy Logic Method untuk Menentukan Status Akreditasi pada Sistem Informasi SAPTI Universitas Kristen Satya Wacana Berbasis Web

Lazuardi

Prasetyo

2018

Indonesian Journal of Computing and Modeling

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Tujuan dari penelitian ini adalah menerapkan metode sistem pakar Fuzzy Logic Method pada Sistem Akreditasi Program Studi Internal (SAPTI) Universitas Kristen Satya Wacana berbasis teknologi web. Metode tersebut digunakan untuk menentukan status akreditasi pada setiap program studi yang direpresentasikan dalam bahasa pemrograman PHP, MySQL, Javascript, dan HTML. Status akreditasi ditentukan berdasarkan aturan-aturan variabel penentu. Selanjutnya dimasukkan kedalam basis pengetahuan sistem pakar dan dilakukan perhitungan secara matematis dengan menggunakan metode inferensi fuzzy Tsukamoto. Berdasarkan tahapan tersebut menghasilkan status akreditasi dengan beberapa kategori rentang nilai yaitu 0%-70% merupakan status gagal akreditasi, 70%-82.5% revisi akreditasi, dan 82.5%-100% sukses akreditasi.

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Section: Fuzzy Logic Methodsunclassified

Model Sistem Pakar Fuzzy Logic Method untuk Menentukan Status Akreditasi pada Sistem Informasi SAPTI Universitas Kristen Satya Wacana Berbasis Web

Lazuardi

Prasetyo

2018

Indonesian Journal of Computing and Modeling

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“…Pada penelitian ini, dibangun sebuah sistem pakar untuk membantu diagnosis penyakit Diabetes Melitus dengan Metode Sugeno. Dalam pembentukan himpunan fuzzy dibutuhkan variabel-variabel pendukung penegakan diagnosis penyakit tersebut [8].…”

Section: Pendahuluanunclassified

The Design of Expert System for Determining the Initial Diagnosis of Tropical Infectious Diseases in Indonesia With Naive Bayes Method-Based Android

Permana

Suyadnya

Khrisne

2019

JUTEI

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Tropical infectious diseases are frequent, serious and concerning for the people in Indonesia. Tropical infectious diseases can be fatal and cause death. But if we diagnose them earlier and get proper treatment, the story can be changed. In this research will make a mobile application using Naive Bayes and Forward Chaining methods for early diagnosing tropical infectious diseases including typhoid fever, dengue fever, tuberculosis, malaria, and measles. The process of this application will start with input of the symptoms felt by users, after the data collected, system will calculate the data with Naive Bayes formula. This application using 147 data training from interviewed with the experts. Based on the tests by System Usability Scale method shows above average users rating 73.875 %, which means the results of the application are acceptable. And Confusion Matrix method shows performance of the application as high as 76.74 %.

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“…To build a good expert system, there are some components needed, such as: user interface, knowledge base, inference machine, and working memory [6].…”

Section: B Expert Systemmentioning

confidence: 99%

Diagnosis of diabetes mellitus using extreme learning machine

Pangaribuan¹,

Suharjito

2014

2014 International Conference on Information Technology Systems and Innovation (ICITSI)

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In 2010, Global Status Report on NCD World Health Organization (WHO) reported that 60 percent of deaths in the world caused by the non-communicable diseases, and one of the non-communicable diseases that consumed a lot of attention was diabetes mellitus. Diabetes is a serious threat to the health development, because diabetes is a disease that caused most other diseases (complications), such as blindness, kidney failure, heart disease, diabetic foot (gangrene) so it had to be amputated, up to the most serious is strokes. Estimated in 2030, Indonesia will have 21.3 million people with diabetes mellitus. An increasing number of diabetes caused by the late diagnosis of this disease. Therefore, we need a new forecast which could be an invaluable tool in determining whether someone has diabetes or not. So many methods used to generate accurate predictions; one of them is artificial neural network. This study will implement a new method of neural network, namely the Extreme Learning Machine (ELM). Extreme learning machine is a feed-forward artificial neural network with one or more hidden layers known as single hidden layer feed-forward neural. Based on the results of experiments conducted, it appears that Extreme Learning Machine is able to provide a good prediction accuracy results with a very good prediction rate.

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Implementasi Sistem Pakar Diagnosis Penyakit Diabetes Mellitus Menggunakan Metode Fuzzy Logic Berbasis Web

Cited by 4 publications

References 0 publications

Model Sistem Pakar Fuzzy Logic Method untuk Menentukan Status Akreditasi pada Sistem Informasi SAPTI Universitas Kristen Satya Wacana Berbasis Web

Model Sistem Pakar Fuzzy Logic Method untuk Menentukan Status Akreditasi pada Sistem Informasi SAPTI Universitas Kristen Satya Wacana Berbasis Web

The Design of Expert System for Determining the Initial Diagnosis of Tropical Infectious Diseases in Indonesia With Naive Bayes Method-Based Android

Diagnosis of diabetes mellitus using extreme learning machine

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