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All Journal Blend Sains Jurnal Teknik Tsabit
Sibuea, Royhan Umri
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Perancangan Sistem Aplikasi Penjualan dan Layanan Jasa Laundry Sepatu Berbasis Website Sari, Indah Purnama; Syahputra, Abdillah; Zaky, Naufal; Sibuea, Royhan Umri; Zakhir, Zharfan
Blend Sains Jurnal Teknik Vol. 1 No. 1 (2022): Edisi Juli
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.921 KB) | DOI: 10.56211/blendsains.v1i1.67

Abstract

Sistem informasi penjualan adalah salah satu cara mutu untuk meningkatkan kualitas pelayanan dalam kegiatan jual beli melalui internet terutama kualitas informasi yang lebih detail mengenai sebuah produk. Banyak usaha penjualan terutama produk pakaian menggunakan sistem informasi penjualan untuk mempermudah proses penjualan. ShoesLab masih menerapkan sistem penjualan dengan cara manual sehingga masih kurangnya kualitas layanan yang di berikan kepada customer. Peracangan ini bertujuan untuk memudahkan ShoesLab dalam memanajemen proses transaksi, melakukan pendataan mengenai produk dan meningkatkan pendapatan penjualan. Perancangan dan penelitian ini menggunakan metode prototyping dan menggunakan bahasa pemrograman HTML dan PHP serta menggunakan MySql sebagai database. Dengan adanya perancangan sistem informasi penjualan diharapkan akan memudahkan ShoesLab dalam melakukan proses penjualan.
Comparison of Random Forest and XGBOOST Methods on Weather in North Sumatera Sibuea, Royhan Umri; Maulana, Halim
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit44

Abstract

Accurate weather forecasting is crucial for various sectors, including agriculture, transportation, and disaster management. The weather data used includes variables such as humidity, temperature, and wind speed collected from weather stations across North Sumatra. The Random Forest method is an ensemble algorithm based on decision trees known for its ability to handle overfitting and provide accurate results. On the other hand, XGBoost is a boosting technique that improves model performance through iterative learning, correcting errors made by previous models. Research results show that both methods have their respective advantages in terms of accuracy and prediction speed. The Random Forest method yields a Root Mean Squared Error (RMSE) of 0.753732 and a Coefficient of Determination (R²) of 0.736315. In contrast, XGBoost shows a slightly lower RMSE of 0.737818 and a higher R² of 0.747332. It is concluded that XGBoost performs slightly better in minimizing prediction errors (RMSE) and improving model fit to the data (R²) compared to Random Forest.
Co-Authors Halim Maulana Hapzi Ali Syahputra, Abdillah Zakhir, Zharfan Zaky, Naufal