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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Arwin Datumaya Wahyudi Sumari
State Polytechnic of Malang
Computer Science & IT

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A Simple Approach using Statistical-based Machine Learning to Predict the Weapon System Operational Readiness Arwin Datumaya Wahyudi Sumari; Dimas Shella Charlinawati; Yuri Ariyanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.58

Abstract

Weapon system operational readiness is a critical requirement to ensure the combat readiness in order to guarantee the state defense sustainability time by time. Weapon systems are only operated by the military and their readiness are programmed every year based on some factors such as the amount of the allocated budget, the weapon system strength, and its circulation. Usually, the weapon system readiness is programmed based on the planner’s experiences that are inherited from time to time. In this research, we proposed a simple approach by using statistical-based machine learning method called linear regression for helping the planner to predict the weapon system operational readiness faced to its affecting factors such as scheduled and unscheduled maintenance. We used a dataset from a randomized primary data for 5 years from year 2016 to year 2020 to predict year 2021. To ensure the performance of the model, two measurements are used namely, Mean Absolute Percentage Error (MAPE) to measure its accuracy and goodness, and R-squared (R2) to measure the ability of the independent variables, the weapon system circulation, influences the dependent variable, the weapon system readiness. From the measurement results, the models, in general, are able to achieve MAPE as much as 1.99% that has interpretation as very accurate prediction with the accuracy of 98.02%. On the other hand, the system is able to achieve R2 as much as 84.15% that means the combination of the independent variables altogether have given a strong influence to the dependent variable. The higher the value of R2 the better the model is. Our research conclude that linear regression is the proper machine learning model for predicting the weapon system operational readiness.
Improving the User Interface and Experience of a Student PortalThrough the Eight Golden Rules Arwin Datumaya Wahyudi Sumari; Fatiha Eros Perdana; Dwi Nugraheny; Sandra Lovrencic
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4542

Abstract

One of the crucial web-based academic service facilities in higher education is the Student Portal. Based on a survey of student users, the existing Student Portal at the Institut Teknologi Dirgantara Adisutjipto (Design A) is visually unappealing. It therefore requires improvement in terms of User Interface (UI) design. The purpose of this study is to enhance the UI and UX of the Student Portal. The method used involved applying the Eight Golden Rules method and the Maze tool to design the UI. The resulting new design (Design B) and the current one (Design A) were tested using A/B testing. This study involved a sample of 41 student users from the Informatics Study Program, as they were considered familiar with UI/UX, along with four staff users selected to represent the overall population of Student Portal users. The instrument that is used to evaluate both designs is the System Usability Scale (SUS). The test results show that Design A received an average score of 55.0, which falls into the ”OK” category with a grade of D. In contrast, Design B, which incorporates the Eight Golden Rules method, achieved an average score of 75.0, placing it in the ”GOOD” category with a grade of B. In conclusion, the application of the Eight Golden Rules method led to a 36.4% improvement inthe UI and UX of the Student Portal.
Co-Authors Dimas Shella Charlinawati Dwi Nugraheny Fatiha Eros Perdana Sandra Lovrencic Yuri Ariyanto