PERAMALAN PENJUALAN ROTI MENGGUNAKAN METODE ARIMA/SARIMA DAN RANDOM FOREST (Studi Kasus : Superkue Cake & Bakery Outlet Ciomas)

Fikri Ibrahim Athallah, . (2025) PERAMALAN PENJUALAN ROTI MENGGUNAKAN METODE ARIMA/SARIMA DAN RANDOM FOREST (Studi Kasus : Superkue Cake & Bakery Outlet Ciomas). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf
Download (360kB)
[img] Text
AWAL.pdf
Download (1MB)
[img] Text
BAB 1.pdf
Restricted to Repository UPNVJ Only
Download (746kB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only
Download (510kB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only
Download (666kB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only
Download (2MB)
[img] Text
BAB 5.pdf
Download (238kB)
[img] Text
DAFTAR PUSTAKA.pdf
Download (222kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only
Download (148kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only
Download (1MB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only
Download (23MB)
[img] Text
ARTIKEL KI.pdf
Restricted to Repository staff only
Download (907kB)

Abstract

Superkue Cake & Bakery Outlet Ciomas operates in the food and beverage industry characterized by unpredictable demand and high volatility, resulting in surplus inventory or stock shortages that pose financial risks. The outlet has not utilized forecasting methods, contributing to missed sales opportunities. This research evaluates sales forecasts for bread products (Roti Unyil, Premium Chocolate, and Premium Coconut) by comparing ARIMA and Random Forest Regressor (RFR) methods. Sales data from January 2024 to August 2025 were analyzed. Models were tested for weekly, four-day, and two-day forecasting using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. Results show RFR significantly outperforms ARIMA across all products and periods. RFR's MAPE ranges from 16.59% to 33.76%, while ARIMA ranges from 35.29% to 92%. RFR proves more effective for volatile demand contexts and shows stronger adaptability to external factors like promotions and holidays. RFR emerges as the preferred forecasting method, demonstrating consistent superior performance for bread sales under volatile conditions. Future research should consider longer timeframes, additional external variables, and comparative analysis with algorithms like XGBoost and Neural Networks for enhanced forecasting performance.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2110312002] [Pembimbing: Alina Cynthia Dewi] [Penguji 1: Muhamad As’adi] [Penguji 2: Yulizar Widiatama]
Uncontrolled Keywords: Forecasting, Sales, ARIMA, Random Forest Regressor
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions: Fakultas Teknik > Program Studi Teknik Industri (S1)
Depositing User: FIKRI IBRAHIM ATHALLAH
Date Deposited: 24 Feb 2026 02:32
Last Modified: 24 Feb 2026 02:36
URI: http://repository.upnvj.ac.id/id/eprint/41927

Actions (login required)

View Item View Item