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Rancang Bangun Perangkat Lunak Lelang Vendor Barang dan Jasa Berbasis Website Malau, Yesni; Pudjiarti, Eni; Setiadi, Ahmad; Hidayat, Wahyutama Fitri
Jurnal Sistem Informasi Akuntansi Vol. 6 No. 2 (2025): Periode September 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v6i2.10727

Abstract

Conventional vendor selection in a project often faces various problems, such as granting exclusive rights to certain vendors, wasting time and costs, and a lack of transparency that can lead to potential corruption. This study aims to develop a website-based vendor selection system using the Codeigniter framework to improve order and transparency in the vendor selection process. The methods used include collecting user requirements data, designing the system using the PHP programming language and the Codeigniter framework, and comprehensive system testing through a Black Box Testing approach. The test results show that all features of the website-based procurement system function as expected with 100% valid results. This software aims to reduce unethical practices and increase user satisfaction and productivity by providing a faster and more transparent procurement process. This study provides a real solution to improve the vendor selection tender process and is expected to be widely implemented to support order and transparency in procurement across various sectors. In addition, this system is also expected to improve data accuracy and accelerate the decision-making process in procurement.
Pemodelan dan Prediksi Harga Emas Menggunakan Metode ARIMA pada Data Time Series Yesni Malau; Eni Pudjiarti; Fintri Indriyani; Riswandi Ishak; Asep Sayfulloh; Wahyutama Fitri Hidayat
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.12455

Abstract

This study aims to analyze and predict gold price movements using a time series approach with the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this research are historical daily gold closing prices from 2020 to 2026 obtained from Investing.com, consisting of 1,568 data. The research stages include data collection, preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis, parameter estimation, diagnostic checking, and model accuracy evaluation. The results indicate that the data are stationary with a p-value < 0.05. Based on the identification and model selection process, the ARIMA (3,0,3) model was identified as the best model with an Akaike Information Criterion (AIC) value of 15449.326. Model evaluation results show an RMSE of 120.86, MAE of 95.02, and MAPE of 5.48%. The MAPE value below 10% indicates that the model has good accuracy in predicting gold prices. Therefore, the ARIMA model can be used as an effective approach to predict gold price movements based on historical data.