ANALISIS POLA DAN PERAMALAN PENJUALAN ROTI MENGGUNAKAN METODE RANDOM FOREST UNTUK MENDUKUNG PERENCANAAN PRODUKSI (Studi Kasus: Pabrik Roti X)

Mirda Farica Utama, . (2026) ANALISIS POLA DAN PERAMALAN PENJUALAN ROTI MENGGUNAKAN METODE RANDOM FOREST UNTUK MENDUKUNG PERENCANAAN PRODUKSI (Studi Kasus: Pabrik Roti X). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf

Download (114kB)
[img] Text
AWAL.pdf

Download (486kB)
[img] Text
BAB 1.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only

Download (3MB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only

Download (10MB)
[img] Text
BAB 5.pdf

Download (582kB)
[img] Text
DAFTAR PUSTAKA.pdf

Download (767kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only

Download (207kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only

Download (436kB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only

Download (23MB)
[img] Text
ARTIKEL KI.pdf
Restricted to Repository staff only

Download (1MB)

Abstract

Bakery X is a bread manufacturing company facing fluctuating demand, making it difficult to determine production quantities that match market needs. This study aims to analyze bread sales patterns, develop a forecasting model using Random Forest Regression, evaluate its performance, and use the forecasting results to support production planning and raw material inventory control. Historical sales data from January to April 2026 were grouped into four product families: Big Size, Plain, Sweet & Soft, and Dry Bread. The research included Exploratory Data Analysis (EDA), data preprocessing, feature engineering, model development, hyperparameter tuning using GridSearchCV, and evaluation using MAE, RMSE, MAPE, and R². Sales patterns were influenced by the day of the week, season, and holidays. The Random Forest Regression model achieved MAPE values ranging from 7.19% to 18.75% across the four product families. The forecasting results were used to determine production planning and inventory control using the Min-Max Stock method, resulting in a safety stock of 41 kg, a reorder point of 762 kg, a maximum inventory of 1,482 kg, and an order quantity of 725 kg (29 sacks) per ordering cycle. Therefore, Random Forest Regression effectively supports production planning and inventory control at Bakery X.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2210312063] [Pembimbing: Siti Rohana Nasution] [Penguji 1: Donny Montreano] [Penguji 2: Amenda Septiala Tarigan]
Uncontrolled Keywords: Random Forest Regression, Sales Forecasting, Raw Material Inventory
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TS Manufactures
Divisions: Fakultas Teknik > Program Studi Teknik Industri (S1)
Depositing User: MIRDA FARICA UTAMA
Date Deposited: 28 Aug 2026 07:48
Last Modified: 28 Aug 2026 07:48
URI: http://repository.upnvj.ac.id/id/eprint/34447

Actions (login required)

View Item View Item