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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Riset Informatika
Bety Wulan Sari
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Computer Science & IT Engineering

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Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Prabowo, Donni; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
User Experience Using the Planes Method on the BUKUERP Application Bety Wulan Sari; Donni Prabowo
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.291

Abstract

This study applies the five planes method to comprehensively investigate a particular enterprise resource planning (ERP) application. To improve overall usability and user satisfaction, the organizational requirements component is the specific focus of this study. This research utilizes the five planes method, which consists of five UX design elements: strategy, scope, structure, skeleton, and surface. A review of the methodology, processes, and frameworks of similar research within user experience and user experience analysis is conducted. Each component makes The addressed problems more definite, understandable, and explicit. The System Usability Scale (SUS) is used in this study to examine and assess the procedure for raising user satisfaction. This study explains the significance of a structured approach emphasizing users in the application development, particularly in digitizing an organization's business.
Co-Authors Afrig Aminuddin Donni Prabowo Donni Prabowo, Donni Yoga Pristyanto