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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC)
Ade Eviyanti
Universitas Muhammadiyah Sidoarjo, Sidoarjo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Deteksi Penyakit Epilepsi Melalui Sinyal EEG Menggunakan Metode DWT dan Extreme Gradient Boosting Erlina Agustin; Ade Eviyanti; Nuril Lutvi Azizah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5412

Abstract

Epilepsy is a disorder of the central nervous system due to excessive patterns of electrical activity in the brain. This disease causes patients to experience repeated seizures in one or all parts of the body. Therefore, epilepsy must be detected early so that the patient immediately gets the right treatment so that the condition does not get worse. This study proposes the detection of epilepsy using the Discrete Wavelet Transform method for feature extraction and Extreme Gradient Boosting for classification. Detection results are classified into two classes, namely seizures and non-seizures. The EEG recording data used came from CHIB MIT Hospital Boston which was obtained online. In the classification process, this study uses four comparisons of the percentage of training data and test data as well as tuning parameters which are processed by Randomized Search Cross Validation. The combination of these methods produces the highest accuracy, namely 85.15% which is produced by the percentage of 80% training data and 20% test data. However, these results experienced a high overfitting of 13.54%. As for the most fit results produced by the research, namely an accuracy value of 81% with a training score of 88.65% and a test score of 81.20% resulting from a percentage of 80% training data and 20% test data.
Sistem Pakar Diagnosa Penyakit Angsa Menggunakan Metode Forward Chaining Berbasis Web William Yviis Guko; Ade Eviyanti; Hindarto Hindarto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4835

Abstract

Geese have many benefits for human needs. Starting from meat, eggs, feathers, fat, and other uniqueness such as guarding other livestock if there are animals or strangers approaching their territory. However, in Indonesia, goose utilization is less desirable and only a few do it. This poses a probӏem for goose breeders due to the absence of knowӏedge about the diseases experienced and the handӏing soӏutions. An expert system is an artificiaӏ inteӏӏigence that supports expert decision-making. The forward chaining method provides conclusions through rules derived from existing facts. This Expert System for Diagnosing Swan Diseases Using the Web-Based Forward Chaining Method is designed to make it easier for farmers or users to consult about the diseases experienced and treatment solutions with 90% diagnostic results. This expert system is made based on a website that can be accessed easily and at any time. Based on black box testing, the results obtained show 100% system functionality, so this expert system application can be used.
Co-Authors Erlina Agustin Hindarto Nuril Lutvi Azizah William Yviis Guko