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All Journal Journal of Information Systems and Business Technology
Remo Teguh Yudianto
Universitas Pamulang
Computer Science & IT Library & Information Science

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Analisis Dan Perancangan Sistem Informasi Manajemen Aset Berbasis Web Menggunakan Standar ISO/IEC 25010 Diharphyta Ningrum Hadijoyo; Remo Teguh Yudianto; Chairul Anwar
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Asset management conducted manually frequently leads to challenges, including mistakes in documentation, redundant data, slow information distribution, and complications in asset tracking. These issues highlight the necessity for an information system that can handle asset information in a more unified, quicker, and precise way. This research aims to examine and construct a web-based asset management system utilizing the Prototype method and system modeling with the Unified Modeling Language (UML). The Prototype method was selected because it allows for progressive development of the system through initial design creation, user evaluation, and ongoing enhancements based on evolving requirements. The phases of the research involve determining user requirements, designing the system with UML, developing a prototype, testing, and assessing software quality as per the ISO/IEC 25010 standard. This standard evaluates the quality attributes of the system, which include functional suitability, usability, reliability, performance efficiency, security, compatibility, maintainability, and portability. The results of the research show that the proposed system can improve the efficiency of asset management through enhanced data recording, searching, categorization, and reporting capabilities. As a result, this system is anticipated to contribute significantly to asset management in organizations or companies.
Analisis Prediksi Harga Laptop Menggunakan Algoritma Linear Regression dan Random Forest Regressor Adam Malik Saputra; Remo Teguh Yudianto; Almy Rizky Akbar
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Laptop prices fluctuate greatly because of varying hardware specifications, making it tough for both buyers and sellers to estimate costs. Utilizing machine learning methods offers a useful way to forecast laptop prices based on their characteristics. This research seeks to examine and compare how well the Linear Regression and Random Forest Regressor algorithms can predict laptop prices. The dataset in use includes multiple laptop specifications such as RAM, CPU, GPU, storage size, display dimensions, and operating system. The research process consisted of data cleaning, analyzing feature correlations, encoding categorical data, dividing the data into training and testing sets, training the models, and assessing their performance. The metrics employed for evaluation were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared (R²). Findings reveal that the Random Forest Regressor surpassed Linear Regression in forecasting laptop prices. Random Forest recorded an MAE of 174. 50, an RMSE of 251. 49, and an R² score of 83. 72%, while Linear Regression achieved an MAE of 277. 16, an RMSE of 354. 12, and an R² score of 67. 72%. Additionally, the Actual vs Predicted analysis showed that the predictions made by Random Forest were more aligned with real laptop prices. Consequently, the Random Forest Regressor is regarded as the more efficient model for predicting laptop prices due to its superior accuracy and enhanced ability to understand complex relationships among the features.
Co-Authors Adam Malik Saputra Almy Rizky Akbar Chairul Anwar Diharphyta Ningrum Hadijoyo