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Maulana Fikri Ahmadi
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Computer Science & IT

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Klasifikasi Penyakit Ginjal Kronis (CKD) dengan Algoritma KNN dan Decision Tree ID3 Nabil Fahlevi Abdi; Maulana Fikri Ahmadi
Journal of Informatics and Advanced Computing (JIAC) Vol 5 No 2 (2024): Journal of Informatics and Advanced Computing
Publisher : Teknik Informatika Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Chronic Kidney Disease is a global health problem that requires diagnosis to prevent complications.According to the Director of Non-Communicable Disease Prevention and Control of the IndonesianMinistry of Health, in Indonesia, Chronic Kidney Failure is the 10th leading cause of death with more than42,000 deaths per year. Chronic kidney disease is a condition in which kidney function gradually declines.Chronic kidney disease can occur due to various factors, including hypertension, diabetes, autoimmunediseases, kidney infections, and kidney stones that are not treated properly. A step that can be used forprevention is to identify the disease with data mining classification. Many methods have been used topredict chronic kidney disease, including the K-Nearest Neighbor (KNN) & ID3 Decision Tree methods. Inthis study, classification was carried out using the KNN and ID3 methods by testing data with variouspercentages of test data, namely 10%, 20%, 30% and 40%. After testing, the highest calculation result ofthe KNN method is in the 30% percentage test data with a value of k = 3, the accuracy obtained reaches99.16%. While in the ID3 Decision tree method, the highest accuracy value is found in the 30% percentageof test data with an accuracy value of 98.33%.Keywords: Chronic Kidney Disease; Classification; K-Nearest Neighbor; Decision Tree ID3
Sistem Pendukung Keputusan Untuk Penentuan Transfer Pemain Sepak Bola Yang Tepat Bagi Sebuah Klub Abdi, Nabil Fahlevi; Maulana Fikri Ahmadi
Journal of Informatics and Advanced Computing (JIAC) Vol 3 No 2 (2022): Journal of Informatics and Advanced Computing (JIAC)
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v3i2.4542

Abstract

Abstrak–Sepak bola merupakan salah satu olahraga yang paling populer di dunia. Sepak bola saat ini bukan hanya tentang permainan saja, melainkan terdapat bisnis yang berjalan di dalamnya terutama dalam transfer pemain. Transfer pemain dilakukan setiap pelatih klub sepak bola untuk meningkatkan kualitas dan performa tim. Namun, proses penentuan transfer pemain membutuhkan proses yang cukup lama, karena dalam penentuan transfer pemain harus sesuai dengan kebutuhan, dan jika salah dalam melakukan transfer pemain akan menyebabkan kerugian. Untuk memudahkan menentukan transfer pemain, dapat dilakukan dengan Sistem Pendukung Keputusan. Penentuan transfer pemain dengan Sistem Pendukung Keputusan dapat dilakukan dengan 3 metode, yaitu Metode Simple Additive Weighting, Metode Eksponensial dan Metode Weight Product. Dari hasil penelitian menggunakan 3 metode tersebut dapat disimpulkan bahwa alternatif P8 merupakan alternatif terbaik yang menjadi pemain yang akan ditransfer oleh klub yaitu Kvaratskhelia.
Klasifikasi Penyakit Ginjal Kronis (CKD) dengan Algoritma KNN dan Decision Tree ID3 Abdi, Nabil Fahlevi; Maulana Fikri Ahmadi
Journal of Informatics and Advanced Computing (JIAC) Vol 5 No 2 (2024): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v5i2.7189

Abstract

Chronic Kidney Disease is a global health problem that requires diagnosis to prevent complications.According to the Director of Non-Communicable Disease Prevention and Control of the IndonesianMinistry of Health, in Indonesia, Chronic Kidney Failure is the 10th leading cause of death with more than42,000 deaths per year. Chronic kidney disease is a condition in which kidney function gradually declines.Chronic kidney disease can occur due to various factors, including hypertension, diabetes, autoimmunediseases, kidney infections, and kidney stones that are not treated properly. A step that can be used forprevention is to identify the disease with data mining classification. Many methods have been used topredict chronic kidney disease, including the K-Nearest Neighbor (KNN) & ID3 Decision Tree methods. Inthis study, classification was carried out using the KNN and ID3 methods by testing data with variouspercentages of test data, namely 10%, 20%, 30% and 40%. After testing, the highest calculation result ofthe KNN method is in the 30% percentage test data with a value of k = 3, the accuracy obtained reaches99.16%. While in the ID3 Decision tree method, the highest accuracy value is found in the 30% percentageof test data with an accuracy value of 98.33%.Keywords: Chronic Kidney Disease; Classification; K-Nearest Neighbor; Decision Tree ID3
Co-Authors Abdi, Nabil Fahlevi Nabil Fahlevi Abdi