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All Journal Jurnal Komputer dan Teknologi Informasi
Juyus Muhammad Adinulhaq
Program Studi S1 Informatika Universitas Muhammadiyah Semarang
Computer Science & IT

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Sistem pendukung keputusan pemilihan smartphone tahun 2022 menggunakan metode simple additive weighting berbasis MATLAB Juyus Muhammad Adinulhaq; Akhmad Fathurohman; Safuan Safuan
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 2 (2023): Sistem Pengambilan Keputusan
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i2.12920

Abstract

This research develops a decision support system using the Simple Additive Weighting (SAW) method based on MATLAB to choose a smartphone in 2022. Based on the specified criteria and data, the best smartphone that managed to get the highest rating is the Xiaomi Redmi Note 12 Pro. This smartphone meets the criteria for price, rating, 5G features, NFC, IR Blaster, RAM capacity, internal memory, battery, screen refresh rate, and rear main camera well. This decision support system provides effective assistance in choosing a smartphone according to user preferences and needs.
Perbandingan Kinerja Akurasi Model Mesin Learning Untuk Prediksi Penyakit Jantung Juyus Muhammad Adinulhaq; Muhammad Sam'an
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 2 (2023): Sistem Pengambilan Keputusan
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i2.12918

Abstract

This research aims to comprehensively analyze heart disease-related data through Exploratory Data Analysis (EDA), identification of correlations between numerical variables, and cluster analysis to uncover patterns in the data. Furthermore, using various machine learning algorithms, such as Logistic Regression, Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors, and Gaussian Naive Bayes, a heart disease prediction model was built. The model evaluation shows that Naive Bayes has the highest test accuracy of 90%, followed by RandomForestClassifier and KNeighborsClassifier which have 85% test accuracy. These findings indicate a good ability to predict heart disease, but further analysis is needed to ensure good generalization to unseen data. This research makes an important contribution to the development of heart disease prediction models and can support early detection and appropriate intervention strategies.
Co-Authors Akhmad Fathurohman Muhammad Sam'an Safuan Safuan