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All Journal Journal of Information Systems and Business Technology
Adam Malik Saputra
Universitas Pamulang
Computer Science & IT Library & Information Science

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Analisis dan Perancangan Sistem Informasi Manajemen Laundry Berbasis Website Menggunakan Standar ISO/IEC 25010 Adam Malik Saputra; Marsheiyna Putri; 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

Pengelolaan bisnis laundry yang masih menggunakan sistem konvensional sering kali menghadapi berbagai hambatan operasional, mulai dari ketidakteraturan pencatatan transaksi hingga ketidakakuratan laporan keuangan. Penelitian ini merancang sebuah sistem informasi manajemen berbasis website sebagai solusi digital untuk meningkatkan efisiensi dan kualitas layanan. Pengembangan sistem dilakukan dengan menerapkan metode Prototype, yang memungkinkan keterlibatan aktif pengguna melalui siklus evaluasi dan perbaikan yang berkelanjutan. Sistem ini dilengkapi dengan fungsionalitas utama seperti manajemen data pelanggan, pelacakan status cucian, serta otomatisasi cetak nota dan laporan pendapatan. Pengujian dilakukan menggunakan standar ISO/IEC 25010 untuk memastikan kualitas perangkat lunak dari aspek fungsionalitas, efisiensi kinerja, penggunaan, dan keandalan. Hasil penelitian menunjukkan bahwa implementasi sistem ini mampu mengoptimalkan operasional bisnis dan meminimalkan kesalahan pencatatan data.
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 Almy Rizky Akbar Chairul Anwar Marsheiyna Putri Remo Teguh Yudianto