Implementasi Fuzzy Time Series Pada Peramalan Penjualan Tabung Gas LPG Di Ud. Samudera LPG Lhokseumawe

Fyanda, Dwi Auji; Ula, Mutammimul; Asrianda, Asrianda

doi:10.29103/sisfo.v1i1.249

Cited by 3 publications

(3 citation statements)

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“…Logika fuzzy dikenal sebagai logika samar, adalah bidang yang mempelajari seluruh atau kebenaran sebagian. Ini juga dianggap memiliki kemampuan untuk dengan tepat memetakan ruang input ke ruang output tanpa mempertimbangkan faktor-faktor lain yang ada (Fyanda et al, 2017).…”

Section: Pendahuluanunclassified

Implementasi Metode Fuzzy Time Series dalam Peramalan Penjualan Produk Unggulan Perusahaan

Damayanti,

Nurhayati

2024

JUTIN

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This research discusses sales forecasting of two superior products of PT. Pakis Logam Perkasa Indonesia: semi-stainless spatula and electric elbow grater c. One of the household furniture manufacturers in Tulungagung sells its wholesale and retail products. Due to the high demand for semi-stainless spatula and c-elbow electric grater products, the company experienced uncontrolled inbound and outbound sales. Therefore, sales forecasting is imperative. Sales forecasting uses previous data to decide how many products to market. Chen's Fuzzy Time Series model uses a technique for forecasting the demand for semi-stainless spatulas and c-elbow electric grates for the next month based on previous data patterns. The two methods used to analyze data are qualitative analysis and quantitative analysis. The aim of creating this forecasting model is to improve forecasting results, especially in terms of accuracy. Next, the Mean Absolute Percentage Error (MAPE) determines the error value from the forecasting results. The error value for the semi-stainless spatula product is 1.03%, and the c-elbow electric grater is 0.62%. The MAPE test shows that the forecasting results of these two products are excellent.

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Section: Pendahuluanunclassified

Implementasi Metode Fuzzy Time Series dalam Peramalan Penjualan Produk Unggulan Perusahaan

Damayanti,

Nurhayati

2024

JUTIN

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“…Selanjutnya penelitian yang membahas analisis dengan model fuzzy time series Pada Peramalan penjualan tabung gas di Kota Lhokseumawe. Hasil prediksi untuk memperkirakan produksi kebutuhan gas pada tahun berikutnya yang dilihat dari data historis lama [16].…”

Section: Pendahuluanunclassified

Penerapan Fuzzy Times Series dan Regresi Linier dalam Melihat Stok Ketersediaan Beras

et al. 2023

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Salah satu komoditas terbesar Indonesia adalah beras. Dimana Perum Bulog berperan penting dalam menyediakan cadangan beras bagi negara untuk menjaga stabilitas nasional Menjelang panen raya, Perum Bulog harus menyusun strategi stok beras yang terencana dengan baik agar tetap tersedia. Karena harga pasar beras yang tinggi akan memicu ekspor beras dari luar negeri, maka harga gabah bisa turun saat petani panen raya akibat ekspor beras dari luar negeri jika pasokan beras Bulog menjadi langka. Ketika permintaan pasar akan beras tidak dapat dipenuhi, biasanya muncul persoalan ini. Penelitian ini dilakukan untuk meramalkan produksi beras nasional untuk kepentingan. Data yang digunakan dalam penelitian ini adalah data produksi stok beras nasional yang diperoleh dari bps.id dari tahun 2018 sampai dengan tahun 2021. Dimana uji harga beras kualitas premium untuk AFER sebesar 0,74444% dan RMSE sebesar 8,9422. Peramalan harga beras kualitas sedang nilai error AFER sebesar 0,22927% dan RMSE sebesar 1,732. Dan pengujian beras tidak bermutu diperoleh nilai error AFER sebesar 0,23640 dan RMSE sebesar 09,09439. Pengujian dengan menggunakan metode regresi linier diperoleh hasil peramalan kualitas beras premium; kualitas menengah dan luar memiliki hasil peramalan yang sama dengan nilai 87,62%, dengan demikian forecasting untuk harga beras sangat baik. Selanjutnya terlihat bahwa nilai akurasinya diatas 80% yang sangat tinggi dengan melalui pengujian.

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“…Furthermore, fuzzy time series forecasting can predict in the following month the determination of rainfall with Chen's FTS series [12]. The Fuzzy Time Series method can be used in forecasting the demand for the amount of inventory in the following month with the input variables consisting of demand, inventory, and sales [13]. The fuzzy time series method is used in predicting world oil prices and the interval value in the fuzzy time series is very influential on the subsequent prediction results, so that the results of the formation of fuzzy values in the use of intervals are precise and effective in determining further forecasts [14] [15].…”

Section: Introductionmentioning

confidence: 99%

Application of the Fuzzy Time Series Model in Clothing Material Stock Forecasting

Ula,

Yulisda

et al. 2022

JUSIKOM PRIMA

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The application of fuzzy time series is used to view the stock of clothing materials. As for the problem so far, CV Duta Express does not have a model for seeing the stock of complete materials in the warehouse, so the process is not optimal. This will have an impact on orders that come in at the same time and in large quantities. to avoid stock shortages, which resulted in the company experiencing losses. The purpose of this study is to make it easier to predict the stock of clothing materials and to be able to analyze every stock management at CV Duta Express with a fuzzy time series model. The variables used are stock needs, the amount of stock of school clothes, batik clothes, and pants. The research methodology in data collection consists of product type data from 2018–2021 at CV Duta Express Aceh Utara. Data analysis needs consist of school clothes, school pants, and clothes. Next, the fuzzy time series process determines the actual data is the type of school clothes, and what is forecast is sales for January 2018–2021 at the end of December. For a value of 1108 A2 fuzzification value, 172 A4 fuzzification value for batik clothes, and 894 pants with an A1 fuzzification value, then the value of the universe set used is U = [26, 323]. The value of forming a linguistic set is based on the length of the interval U3 = [111,153], U4 = [153,196], and U5 = [196,238]. The result of the fuzzification value from historical data for the value of 172 fuzzification is A4, for data of 133 fuzzification is A3. The formation of Fuzzy Logic Relationship (FLR) values for the period 1/7/2021 to 1/13/2021 is obtained from the data range A4=174 and range A3=125 in each period to be related. The results of forecasting with fuzzy time series testing at the end of December 2021 are 196 stocks of clothes that must be optimized in the following month. The test results in this study are to see if the error value using the AFER model is 0.4511% while the RMSE test value has an error value of 5.0199. After being calculated for the forecast every month, the average obtained for AFER is 0.50154 % and the RMSE is 9.86518.. Keywords : Forecasting, Fuzzy Time Series, stock

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Implementasi Fuzzy Time Series Pada Peramalan Penjualan Tabung Gas LPG Di Ud. Samudera LPG Lhokseumawe

Cited by 3 publications

References 1 publication

Implementasi Metode Fuzzy Time Series dalam Peramalan Penjualan Produk Unggulan Perusahaan

Implementasi Metode Fuzzy Time Series dalam Peramalan Penjualan Produk Unggulan Perusahaan

Penerapan Fuzzy Times Series dan Regresi Linier dalam Melihat Stok Ketersediaan Beras

Application of the Fuzzy Time Series Model in Clothing Material Stock Forecasting

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