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All Journal Economic Reviews Journal Populer: Jurnal Penelitian Mahasiswa
Khairun Nadiah
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Penerapan Regresi Linier Dalam Memprediksi Upah Minimum Provinsi Di Indonesia Bright Nine Ginting; Khairun Nadiah; Grace Oktavia; Daniel Sembiring
Populer: Jurnal Penelitian Mahasiswa Vol. 3 No. 1 (2024): Maret : Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v3i1.1662

Abstract

This research aims to evaluate the effectiveness of linear regression as a forecasting tool to estimate the Provincial Minimum Wage (UMP) in Indonesia. Utilizing UMP data from various provinces during the period 2002-2022, this study employs linear regression to analyze the factors influencing UMP determination. The predicted UMP for North Sumatra in 2023 demonstrates a high level of accuracy (R-squared = 0.9678), affirming the potential of linear regression as an effective tool to understand regional economic dynamics. The research provides a crucial foundation for policymakers in regional economic planning and suggests avenues for further investigation, including exploring alternative prediction methods and analyzing the impact of UMP regulation policies.
Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript Angginy Akhirunisa Siregar; Citra; Khairun Nadiah; Hermawan Syahputra; Fanny Rahmadani
Economic Reviews Journal Vol. 3 No. 3 (2024): Economic Reviews Journal
Publisher : Masyarakat Ekonomi Syariah Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/mrj.v3i3.322

Abstract

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.
Co-Authors Angginy Akhirunisa Siregar Bright Nine Ginting citra Daniel Sembiring Fanny Rahmadani Grace Oktavia Hermawan Syahputra