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All Journal Jurnal Armada Informatika JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi
Muhammad Arizal Dwisakti
Universitas Budi Darma
Computer Science & IT Control & Systems Engineering

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Evaluation of User Acceptance of Online Transportation Applications Using User Acceptance Testing Muhammad Arizal Dwisakti; Suci Ramadhani; Pristiwanto
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.278

Abstract

This study evaluates user acceptance of online transportation applications using a questionnaire-based User Acceptance Testing (UAT) approach. The assessment covers vehicle ordering, location tracking, payment, navigation, processing speed, ease of use, comfort, and accessibility. Data were collected online from 30 active users through ten statements measured on a five-point Likert scale. Instrument validity was examined using the Pearson Product Moment correlation, while internal consistency was assessed using Cronbach’s Alpha. All items exceeded the critical correlation value of 0.361, with coefficients ranging from 0.4039 to 0.7380. The Alpha coefficient of 0.8148 indicated good instrument reliability. The observed score was 1,214 out of a maximum of 1,500, producing a UAT percentage of 80.93%. Under the interpretation interval adopted in this study, the application category was very feasible and reflected a high level of user acceptance. However, the result represents aggregated user perceptions and does not demonstrate that every function is technically error-free. The findings provide an initial evaluation, while future work should examine a specific application through controlled task scenarios, a larger sample, and item-level analysis.
Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah Muhammad Arizal Dwisakti; Muhammad Ridho Pramana; Riski Juliandri; Taronisokhi Zebua
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.66

Abstract

House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain relatively high due to the wide variation in house prices. The correlation analysis results reveal that the number of bathrooms and the distance to the city center have a relatively stronger relationship with house prices compared to other variables. This study demonstrates that linear regression can be used as an initial approach for house price prediction; however, it still has limitations in explaining overall price variations. Therefore, future research is expected to improve prediction performance by incorporating additional variables or applying more advanced modeling methods.
Co-Authors Muhammad Ridho Pramana Pristiwanto Riski Juliandri Suci Ramadhani Taronisokhi Zebua