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All Journal Bulletin of Computer Science Research Jurnal Teknik Informatika
Darwin, Ricalvin
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Computer Science & IT

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Perbandingan Implementasi Machine Learning Menggunakan Metode KNN, Naive Bayes, dan Logistik Regression Untuk Mengklasifikasi Penyakit Diabetes Dewi Nasien; Darwin, Ricalvin; Cia, Alexander; Leo Winata, Andrean; Go, Jerry; M.C, Richard; Charles Wijaya, Ryan; Charles Lo, Kevin
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i1.640

Abstract

Penyakit diabetes menjadi sorotan karena sifatnya yang kronis, dengan gejala utama berupa peningkatan kadar gula darah di atas batas normal. Diabetes terjadi ketika tubuh tidak dapat efisien mengambil glukosa ke dalam sel untuk diubah menjadi energi, menyebabkan penumpukan gula ekstra dalam aliran darah. Penelitian ini menggunakan ekstraksi fitur dengan Analisis Komponen Utama (Principal Component Analysis - PCA) dengan threshold 80%, menghasilkan 5 fitur utama. Fitur-fitur ini kemudian digunakan sebagai input untuk tiga classifier, yaitu K-Nearest Neighbors (KNN), Naive Bayes, dan Regresi Logistik. Data yang digunakan berasal dari Kaggle, dengan pembagian data 70:30 dan 80:20. Hasil penelitian menunjukkan bahwa metode Naive Bayes memberikan akurasi terbaik, mencapai 79% pada pembagian data 80:20. Oleh karena itu, dapat disimpulkan bahwa algoritma Naive Bayes adalah pilihan terbaik untuk klasifikasi data diabetes dalam penelitian ini.
Implementasi Metode Analitycal Hierarchy Process dan Multi-Objective Optimization by Ratio Analysis Untuk Rekomendasi Laptop Darwin, Ricalvin; Irwan, Irwan; Desnelita, Yenny; Siddik, Muhammad; Gustientiedina, Gustientiedina
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.746

Abstract

Laptops have become essential in the world of work, education, and society. With a laptop, tasks such as creating reports, sending data, learning, and even entertainment become easier. However, the variety of laptops available with different specifications can confuse people when choosing one that suits their profession and status. This confusion often leads to wasted time and the risk of choosing a laptop that does not meet their needs. Therefore, a decision support system (DSS) is needed to provide laptop recommendations based on desired criteria. In this study, the method used is a collaboration of Analytical Hierarchy Process (AHP) and Multi-Objective Optimization By Ratio Analysis (MOORA). AHP is used to calculate the weight of laptop criteria according to desired criteria, while MOORA is used to rank the recommended laptop values suitable for use. The implementation of the AHP and MOORA methods in this study resulted in laptop recommendations that meet the desired criteria and specifications of the community. Based on manual calculations in this study, the top-ranked laptop recommendation is alternative A8, the HP Victus Gaming Laptop 15 with a Yi of 0.424, followed by alternative A2, the HP Pavilion Gaming 15 with a Yi of 0.382. This study is considered successful because the results of manual calculations and those of the system built are consistent. Thus, the implementation of AHP and MOORA methods in a web-based system can be used for laptop recommendations.
Co-Authors Charles Lo, Kevin Charles Wijaya, Ryan Cia, Alexander Desnelita, Yenny Dewi Nasien Go, Jerry Gustientiedina Irwan Irwan Leo Winata, Andrean M.C, Richard Siddik, Muhammad