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All Journal Jurnal CoreIT Variable Research Journal
Roby Novianto
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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ANALISIS PENGARUH USER EXPERIENCE (UX) TERHADAP KEPUASAN PENGGUNA DALAM SISTEM INFORMASI PERPUSTAKAAN DIGITAL Sonianto; Roby Novianto
VARIABLE RESEARCH JOURNAL Vol. 1 No. 02 (2024): JULI 2024
Publisher : Media Inovasi Pendidikan dan Publikasi

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Abstract

Pengalaman pengguna (User Experience/UX) merupakan faktor kunci yang mempengaruhi kepuasan pengguna dalam menggunakan sistem informasi, termasuk perpustakaan digital. Penelitian ini bertujuan untuk menganalisis pengaruh UX terhadap kepuasan pengguna dalam konteks perpustakaan digital. Metode penelitian yang digunakan adalah survei kuantitatif dengan kuesioner yang disebarkan kepada pengguna perpustakaan digital di beberapa universitas di Indonesia. Data dianalisis menggunakan metode statistik deskriptif dan inferensial. Hasil penelitian menunjukkan bahwa UX memiliki pengaruh signifikan terhadap kepuasan pengguna. Aspek UX seperti kemudahan penggunaan, kecepatan akses, dan tampilan antarmuka yang menarik secara positif mempengaruhi kepuasan pengguna. Temuan ini mengindikasikan bahwa pengembangan sistem perpustakaan digital harus mempertimbangkan aspek UX untuk meningkatkan kepuasan pengguna.
Comparison Of The Performance Of K-Nearest Neighbors And Naive Bayes Algorithms For Stroke Disease Prediction baskoro baskoro; Roby Novianto; Bambang Triraharjo
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

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Purpose: Stroke is a critical global health issue requiring early and accurate prediction to mitigate severe outcomes. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naive Bayes algorithms in predicting stroke disease, addressing the challenge of imbalanced datasets and improving prediction accuracy for better clinical decision-making.Methods/Study design/approach: The research followed the CRISP-DM model, utilizing a dataset of 5,110 patient records with 12 attributes from Kaggle. Data preprocessing included handling missing values and normalization. The KNN and Naive Bayes algorithms were implemented using RapidMiner, with performance evaluated through cross-validation, confusion matrices, and ROC-AUC curves.Result/Findings: The KNN algorithm achieved an accuracy of 94.50%, but exhibited low precision (7.89%) and recall (1.20%) for stroke-positive cases due to dataset imbalance. Naive Bayes yielded an accuracy of 88.83% with an AUC of 0.767, demonstrating better probability modeling but similar challenges in minority class detection. Both algorithms highlighted the impact of data imbalance on predictive performance.Novelty/Originality/Value: This study provides a comparative analysis of KNN and Naive Bayes for stroke prediction, emphasizing the need for data balancing and optimization techniques. The findings underscore the potential of these algorithms in healthcare applications while suggesting future improvements through ensemble methods or alternative algorithms like Random Forest.
Co-Authors Bambang Triraharjo Baskoro Baskoro Sonianto