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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Innovation Information Technology and Application (JINITA)
Putu Gita Pujayanti
Universitas Pendidikan Ganesha
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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PERANCANGAN TAMPILAN APLIKASI DOMPET DIGITAL BERBASIS MOBILE DENGAN PENDEKATAN HUMAN-CENTERED DESIGN Md Wira Putra Dananjaya; Putu Gita Pujayanti
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3800

Abstract

This research aims to design and develop the user interface of a mobile-based digital wallet application using the Human-Centered Design (HCD) approach. The HCD method is employed to prioritize user needs and preferences in the application interface development. The application prototype includes features such as transaction tracking, budget management, and financial reporting, with the goal of enhancing the user experience in personal financial management. The development process involves design iterations based on user feedback to ensure the interface aligns with user expectations and needs. Usability testing results using the System Usability Scale (SUS) indicate a positive score of 75.6, signifying good acceptance of the application interface. The practical implications of this research are to provide more effective design guidelines for the development of digital wallet applications that consider user comfort and needs.
Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study Md. Wira Putra Dananjaya; Putu Gita Pujayanti
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2540

Abstract

Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Evaluation results on the test data revealed that the Multiple Linear Regression (MLR) model unexpectedly delivered the most superior performance, achieving an R2 value of 0.7324 and the lowest RMSE of 2.0391. Further analysis from non-linear models identified Attendance and Hours_Studied as the two factors with the highest predictive influence. This study concludes that interpretable models like MLR can be highly effective when supported by relevant features, offering practical implications for institutions to develop effective early warning systems by focusing on key, actionable factors.
Co-Authors Dananjaya, Md Wira Putra Md. Wira Putra Dananjaya