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All Journal Jurnal Ilmiah FIFO
Fathin, Muhammad Askar
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Computer Science & IT

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User Requirements Analysis for Government’s Budget Information System Using Kano’s Model Al Ghozali, Isnen Hadi; Fathin, Muhammad Askar; Handoko, Andy Rio
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.002

Abstract

This study finds out what attributes still require attention based on Kano’s Model analysis, which has to be used to prioritize software requirements in the government's or agency’s budget software. This research prioritizes software requirement attributes using Kano's Model. This research used an application by INTRAC as the basis for preparing a questionnaire distributed to 75 civil servants. From this research, it can be concluded that there are 15 sub-elements (including 25 features) classified as one-dimensional and four sub-elements (including eight features) classified as Indifferent. According to the Blauth Formula and continuous data analysis, the result shows a one-dimensional pattern. Based on the CS Coefficient, nine features are prioritized for development (especially the process budget revision). Development on the dimensions of interface requirements and functional requirements has the potential to increase the end user's satisfaction.
A Comparative Study of Machine Learning with Statistical Feature Selection for Risk Detection of Diabetic Al Ghozali, Isnen Hadi; Fathin, Muhammad Askar; Handoko, Andy Rio
Jurnal Ilmiah FIFO Vol 17, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i2.001

Abstract

Elevated glucose levels in the circulation are indicative of diabetes, a chronic medical condition. Prolonged unregulated blood glucose levels pose a significant risk of severe consequences, including renal failure, myocardial infarction, and lower limb amputation. The objective of this study is to conduct a comparative analysis of SVM, Naive Bayes, XGBoost, Random Forest, and ANN models in order to forecast the occurrence of diabetes. The research methodology comprises seven primary stages: (1) literature review, (2) data collection, (3) exploratory data analysis (EDA), (4) data preprocessing, (5) feature selection, (6) model development, and (7) model evaluation and comparison. The XGBoost model is the most suitable option, as indicated by the model evaluation results. The XGBoost model achieved a precision of 0.88, a recall of 0.87, and an accuracy of 0.8690. The XGBoost model has a RMSE of 0.3620 and a MSE of 0.1310.
Co-Authors Al Ghozali, Isnen Hadi Handoko, Andy Rio