Accuracy Level of Diagnosis of ENT Diseases in Expert System

Putra, Dede Wira Trise; Utami, Ade Oka; Minarni,; Swara, Ganda Yoga

doi:10.35134/komtekinfo.v6i2.51

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Dalam tugas klasifikasi, tujuannya adalah untuk memprediksi label kelas dari sampel yang diberikan berdasarkan sekumpulan fitur atau karakteristik. Misalnya, berdasarkan data tentang usia, jenis kelamin, dan pendapatan seseorang, tujuannya mungkin untuk memprediksi apakah mereka cenderung memiliki pekerjaan bergaji tinggi atau tidak [20].…”

Section: Naïve Bayesunclassified

Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN

2023

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Pemindahan serta pembangunan Ibu Kota Negara Nusantara pada masa Presiden Bapak Ir. H. Joko Widodo akan direlokasi secara bertahap dari tahun 2024 hingga 2045. Dengan hal ini tersebut maka menjadi sebuah perbincangan dan mengundang banyak reaksi, terutama bagi masyarakat Indonesia. Persoalan dalam Pemindahann Ibu Kota Negara merupakan hal yang sensitif sehingga ramai diperbincangkan di media sosial termasuk Twitter. Pada dasarnya media sosial digunakan untuk menyampaikan pendapat atau sebuah ekspresi. Pemanfaatan media sosial ini tentunya menjadi layanan dan fasilitas yang dapat dimanfaatkan untuk sebuah isu politik atau hal-hal yang sedang dibahas. Maka dari itu penelitian ini bertujuan untuk melakukan analisis sentimen masyarakat Indonesia terhadap pemindahan Ibu Kota Nusantara. Dalam penelitian ini metode yang digunakan adalah Naïve Bayes (NB) dan K-Nearest Neighbor (KNN). Hasil penelitian menyajikan hasil komparasi kinerja metode tersebut bahwa metode Naïve Bayes memberikan tingkat akurasi analisis sentimen sebesar 82.27%, nilai Precision sebesar 86.36% dan nilai Recall sebesar 76.93%. Kinerja metode KNN juga menyajikan hasil analisis dengan tingkat akurasi sebesar 88,12%, Precision sebesar 93.%98 dan nilai recall sebesar 81.53%. Berdasarkan hasil analisis tersebut maka proses analisis menggunakan metode KKN mengungguli metode NB dalam mengukur sentimen terhadap pemindahan Ibu Kota Nusantara.

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Section: Naïve Bayesunclassified

Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN

2023

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Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes

2021

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Stroke is a disease caused by brain damage caused by disruption of the blood supply to the brain. At this time in general, people are still not very familiar with how this stroke disease or do not realize the symptoms that may have appeared from the start. People also tend to be hesitant to visit the hospital to check their symptoms and feel they are delaying further examinations. This is certainly a scourge that continues to make the number of strokes increase. In assisting the community in identifying stroke disease, an expert system is needed that is able to identify the type of stroke based on the symptoms felt. The data used in this study were obtained from Brain Hospital. Dr. Drs. M. Hatta Bukittinggi which was later developed into a website-based system using the PHP Framework Laravel programming language and MySQL as the database. The system is built based on the Naive Bayes method which is one of the Expert System methods that has a high accuracy value. The use of this system is expected to be able to provide knowledge to the public about the symptoms that might lead to what type of stroke the user might suffer, so that the user can use the results of the system as a reference to visit the hospital and immediately get more targeted help. This system can perform calculations that match the results of the doctor's diagnosis with an accuracy value of 100% in identifying the type of stroke from 10 data samples used.

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

Hari

Sumijan

2021

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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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Accuracy Level of Diagnosis of ENT Diseases in Expert System

Cited by 3 publications

References 10 publications

Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN

Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN

Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes

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

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