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All Journal Building of Informatics, Technology and Science Infotekmesin
Egia Rosi Subhiyakto
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Pengembangan Aplikasi Penyewaan Mobil Menggunakan Metode Prototyping dengan Online Payment Gateway Midtrans Radig Gedhe Prihatmoko; Egia Rosi Subhiyakto
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2129

Abstract

Car rental has become one of the most profitable industries, as cars have become an essential means of transportation for various societal needs, such as family activities and work. Many car rental companies still rely on conventional methods for promotions and reservations. There is one car rental place whose rental and promotion system still uses WhatsApp chat and telephone or comes directly to the rental. A website-shaped system is crucial as a company platform to showcase available vehicles, enable online transactions for rentals, and facilitate easy and convenient payments. The development of this website involves prototyping with PHP and MySQL programming languages, and the payment system incorporates the Midtrans payment gateway. The system is tested using Blackbox and User Acceptance Testing (UAT). This website's development aims to simplify the booking process for customers and enhance the company's ability to manage vehicle data and payments efficiently. Following the system's construction, testing is conducted, with the results of the UAT indicating a user satisfaction score of 90.8%. Based on the evaluation, the majority of users strongly agree with the developed application.
Evaluasi KNN dan Logistic Regression untuk Klasifikasi Diabetes dengan Preprocessing Terstandarisasi: Trade-off Kinerja dan Interpretabilitas Alif Zayyin Kamandani; Egia Rosi Subhiyakto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9534

Abstract

Although K-Nearest Neighbors (KNN) and Logistic Regression have been widely used in diabetes classification, studies that systematically combine a standardized preprocessing pipeline—including median imputation, feature standardization, and stratified data splitting—and evaluate the trade-off between predictive performance and model interpretability remain limited. This study aims to compare the performance of both algorithms in classifying diabetes status using the Pima Indians Diabetes dataset, which consists of 768 samples with eight numerical attributes. The research stages include data exploration, handling missing values using median imputation, feature standardization using StandardScaler, and stratified data splitting with a ratio of 80:20. Model evaluation is conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. The experimental results show that KNN with an optimal parameter of K=21 achieves an accuracy of 75.97%, an F1-score of 61.86%, and a ROC-AUC of 0.8120, while Logistic Regression achieves an accuracy of 70.78%, an F1-score of 54.55%, and a ROC-AUC of 0.8130. Although KNN demonstrates higher predictive performance, Logistic Regression provides advantages in interpretability through model coefficients, where the variables Glucose (β=1.1825) and BMI (β=0.6887) are identified as the main predictors of diabetes risk. These findings indicate a clear trade-off between accuracy and interpretability, suggesting that KNN is more suitable for high-accuracy prediction tasks, while Logistic Regression is more appropriate in clinical contexts requiring transparency and model accountability.
Co-Authors Alif Zayyin Kamandani Radig Gedhe Prihatmoko