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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) BIMASTER Coding: Jurnal Komputer dan Aplikasi Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer Jurnal Khatulistiwa Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) BULETIN AL-RIBAATH JURIKOM (Jurnal Riset Komputer) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) The Indonesian Journal of Computer Science
Dwi Marisa Midyanti
Universitas Tanjungpura
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health

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Journal : Scientific Journal of Informatics

 1 
Diagnosis of Lung Disease Using Learning Vector Quantization 3 (LVQ3) Midyanti, Dwi Marisa
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25368

Abstract

Lung disease is one of the diseases with the highest number of patients in Indonesia. Lung disease is a disease with many types and symptoms that are almost the same as each other. This study uses an artificial neural network Learning Vector Quantization 3 (LVQ3), to diagnose lung disease. The data used in this study were 113 medical records, with seven types of lung disease, and 27 symptoms of the disease. From the experimental results, the best LVQ3 parameters from this study are using m = 0.15, and the learning rate = 0.15. LVQ3 produces the best accuracy value for training data at 87.5% of 80 data, and accuracy for test data 88% of 33 data.
ADALINE Neural Network For Early Detection Of Cervical Cancer Based On Behavior Determinant Midyanti, Dwi Marisa; Bahri, Syamsul; Midyanti, Hafizhah Insani
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.31064

Abstract

Purpose: Cervical cancer is one of the most common types of cancer that kills women worldwide. One way for early detection of cervical cancer risk is by looking at human behavior determinants. Detection of cervical cancer based on behavior determinants has been researched before using Naïve Bayes and Logistic Regression but has never using ADALINE Neural Network. Methods: In this paper, ADALINE proposes to detect early cervical cancer based on the behavior on the UCI dataset. The data used are 72 data, consisting of 21 cervical cancer patients and 51 non-cervical cancer patients. The dataset is divided 70% for training data and 30% for testing data. The learning parameters used are maximum epoch, learning rate, and MSE. Result: MSE generated from ADALINE training process is 0.02 using a learning rate of 0.006 with a maximum epoch of 19. Twenty-two test data obtained an accuracy of 95.5%, and overall data got an accuracy value of 97.2%. Novelty: One alternative method for early detection of cervical cancer based on behavior is ADALINE Neural Network. 
Diagnosis of Lung Disease Using Learning Vector Quantization 3 (LVQ3) Midyanti, Dwi Marisa
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25368

Abstract

Lung disease is one of the diseases with the highest number of patients in Indonesia. Lung disease is a disease with many types and symptoms that are almost the same as each other. This study uses an artificial neural network Learning Vector Quantization 3 (LVQ3), to diagnose lung disease. The data used in this study were 113 medical records, with seven types of lung disease, and 27 symptoms of the disease. From the experimental results, the best LVQ3 parameters from this study are using m = 0.15, and the learning rate = 0.15. LVQ3 produces the best accuracy value for training data at 87.5% of 80 data, and accuracy for test data 88% of 33 data.
Co-Authors , Syahru Rahmayuda, Haris Febriyanto Ramadhan , Sampe Hotlan Sitorus Abdul Hamid Abdul Hamid Adi Kurnia Agung Jati Swiknyo Arista, Agamita Sasya Cahyani Astrid Lestari Tungadi Caecilia Cantica Gita Mileniae Ahen Chairul Mukmin Cucu Suhery Dedi Triyanto Deni Deni Dian Prawira, Dian Edwin Rianto Erick Alfons Lisangan Farhan Fajar Luthfi Firdaus, Akmal Firdaus, Nurul Fajri 'Aini Hafiz Muhardi Halawa, Yasman Hanafi, Ahmad Abdul Hansen Alexander Rustan Herlian, Wildan Hirzen Hasfani Ikhwan Ruslianto Ilham Syah Ilhamsyah Indrawan, Muhammad Catur Irma Nirmala Junianti, Suci Karika Sari Kasliono Kasliono Kurniawan, Robbi Lestari, Ayu Lathalia Mahfuza, Sri Martha, Shantika Midyanti, Hafizhah Insani Mochammad Imron Awalludin Mulia, Agung Wira Neneng Suriati Nova Andaresta Nurhadi, Ade Muhamad Qadari, Ilham Latul Rahman, Reiza Aditya Rahmi Hidayati Renny Puspita Sari Rikki Pratama Saputro, Arinto Mangku Shantika Martha Suhardi Suhardi Suhardi Syamsul Bahri Syamsul Bahri Tedy Rismawan Tedy Rismawan Tedy Rismawan That, Shau Thomas Adi Putra Tri Floren Doni Uray Ristian Valentino, Viktorius Wahiddatun Nisa Yunizar, Ahmad