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All Journal Sistemasi: Jurnal Sistem Informasi Scientific Journal of Reflection : Economic, Accounting, Management and Business
Hidayah, Natari Dia Alika
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Strategi Optimalisasi Produksi dan Keuntungan Ayam Geprek Menggunakan Metode Simplex: Studi Kasus Ayam Geprek AA Ray Hidayah, Natari Dia Alika; Yanti, Evida; Pranata, Rio; Williandi, Bambang; Yehezkiel, Steven
SCIENTIFIC JOURNAL OF REFLECTION : Economic, Accounting, Management and Business Vol. 8 No. 1 (2025): SCIENTIFIC JOURNAL OF REFLECTION: Economic, Accounting, Management, & Business
Publisher : Sekolah Menengah Kejuruan (SMK) Pustek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37481/sjr.v8i1.1034

Abstract

This research aims to assist Ayam Geprek restaurants in optimizing the production of Ayam Geprek using the simplex method. The primary objectives are: 1) To determine the ideal number of Ayam Geprek to be produced to maximize profits, 2) To analyze whether the current production of Ayam Geprek has already achieved maximum profitability, 3) To assess the efficiency of raw material and resource utilization in the production process, and 4) To evaluate the impact of the simplex method on profit changes and resource utilization. Data for the research was collected through direct observation, involving information about raw materials, labor, and customer demand. The analysis was conducted using QM for Windows software to determine the optimal results. The findings indicate that the simplex method can help improve efficiency, utilize resources more effectively, and maximize profits.Warung Makan Ayam Geprek AA Ray faces challenges in managing production optimally to maximize profits. Based on both manual calculations and the simplex method using QM for Windows, it was found that the maximum profit of 850,000 can be achieved by producing 0.5 units of Ayam Geprek (X1) and 3 units of Lele Geprek (X2).
Analysis and Implementation of Linear Regression and Decision Tree Methods to Predict Sales at Rayyan Bakery, Simpang Marbau Hidayah, Natari Dia Alika; Munthe, Ibnu Rasyid; Juledi, Angga Putra; Nasution, Marnis
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.6124

Abstract

The development of information technology and data analytics has encouraged business actors to leverage historical data as a basis for decision-making. In the small and medium enterprise (SME) sector, particularly in the culinary field, the ability to predict sales is a crucial aspect of production planning and stock management to ensure operational efficiency. Rayyan Bakery Simpang Marbau, as a bakery SME, faces challenges due to fluctuating sales that have traditionally been managed based on experience rather than systematic data analysis. The main problem addressed in this study is the absence of a data-driven sales prediction method that can assist the business owner in estimating sales accurately. Therefore, a predictive approach that utilizes historical sales data is required to support managerial decision-making. This study employs linear regression and decision tree methods. The analyzed data consist of historical sales records of Rayyan Bakery Simpang Marbau over a specific period. Linear regression is used to model the linear relationship between sales variables, while the decision tree captures non-linear patterns and produces easily interpretable decision rules. The performance of both methods is analyzed and compared based on the accuracy of the predictions they generate. The results indicate that both linear regression and decision tree methods can be effectively used to predict sales; however, the decision tree provides greater flexibility in capturing fluctuating sales patterns. These findings are expected to assist Rayyan Bakery in production planning and stock management, as well as serve as a reference for applying sales prediction methods in similar SMEs.
Co-Authors Juledi, Angga Putra Munthe, Ibnu Rasyid Nasution, Marnis Pranata, Rio Williandi, Bambang Yanti, Evida Yehezkiel, Steven