Double Exponential Smoothing

menyatakan kepemilikan sertifikat merupakan faktor penting, dan hanya 30% yang menyatakan arah hadap merupakan faktor yang penting. Data properti yang diperoleh dari ERA adalah data transaksi ERA tahun 2008, 2009, 2010, 2011 dan 2012. Wawancara dilakukan dengan Julliani Sidjaya, Branch Manager ERA Jatim Realty Bukit Mas. Faktor-faktor yang mempengaruhi harga properti adalah elevasi tanah, bentuk tanah, arah hadap bangunan, dan lokasi properti. Sedangkan menurut Ben Siahaan, salah satu pengajar di Panangian School of Property, harga properti sangat dipengaruhi oleh: 1. Adanya pusat bisnis didaerah atau lokasi tersebut. 2. Masuknya 1 pengembang besar untuk membangun rumah. 3. Daerah tersebut dalam tahap berkembang. 4. Properti dapat diramalkan nilainya di masa depan asalkan tahu berapa persen kenaikan harga tersebut tiap tahun sehingga dapat disimpulkan kenaikan harga properti tersebut di masa yang akan datang. Studi Literatur Studi literatur dilakukan dengan mempelajari metode Trend Linier dan Hold’s Double Exponential Smoothing. Hold Double Eksponensial Smoothing merupakan suatu metode smoothing yang disesuaikan untuk data yang mempunyai suatu kecenderungan (Makridakis, 1999). Ramalan dari metode Hold Double Eksponensial Smoothing didapat dengan menggunakan dua konstanta pemulusan yaitu α dan γ (Qoyyimah, 2007). Langkah – langkah dalam menghitung Hold’s Double

PD Pamitran is a company that trades electrical goods, neon lights, and household tools. It is not easy to monitor and control the warehouse inventory with many occuring transactions. Other problem is the pilling up of old inventories and difficulty of sales forecasting Additionally, there were problems in monitoring the financial transactions. A desktop application was developed as a solution with C# programming language, SQL Server database, and a Decision Support System for stock forecasting using the "Brown's Double Exponential Smoothing".

The ability of systems and technology used to be one of the main advocates for the company to and be able to compete in the future. Use of the system and technology is not limited to the distribution model and the processing of data during the process, but the existing system should be able to analyze the possibility of a company's ability in the future. In other words, the company should be able to predict the conditions and circumstances, both on the supply of goods, personnel or benefits to be obtained. Forecasting is important in planning and serves as input (input) to many other business decisions. The level of accuracy in forecasting the impact on the supervision and planning of raw materials, while the supervision and planning of raw materials is an important part of the operational management of an enterprise so as to cause a direct influence on income. By using Double Exponential Smoothing it will get an accurate demand forecast. The forecasting data is then used for inventory management with Economic Order Quantity method to determine the amount of inventory that must be met.

Hasil forecasting dengan menggunakan empat metode di atas, menunjukkan bahwa metode moving average 3 dan metode weighted moving average 4 memiliki hasil yang tidak akurat karena nilai tracking signal kedua metode diluar rentang yang ditentukan. Untuk metode single exponential smoothing dan metode double exponential smoothing dapat dikatakan akurat karena memiliki nilai tracking signal yang masih di dalam rentang -4 hingga 4. Maka dari itu, hasil forecasting yang digunakan adalah metode double exponential smoothing karena memiliki nilai MAD yang lebih kecil dibandingkan metode single exponential smoothing.

1. Memberikan solusi peramalan permintaan pada CV.Violette pada masa yang akan datang dengan menggunakan metode double exponential smoothing yang diharapkan mendapatkan nilai peramalan yang akurat. 2. Memberikan jumlah lot persediaan barang dengan menggunakan metode

Wienarti,Isti. 2010. Peramalan (Forecasting) Nilai Ekspor Jawa Tengah Menurut Komoditi dengan Metode Exponential Smoothing Bulan Desember 2009 sampai Juni 2010 . Tugas Akhir, Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Semarang. Pembimbing I Drs. Amin Suyitno, M. Pd dan Pembimbing II Isnarto, S.Pd, M.Si.

Loans take an important role in the economy such as increased productivity, maintain economic stability, development, develop a business, and employment. But not all debtors pay loans in accordance with the applicable provisions so it’s caused Non-Performing Loans. Otoritas Jasa Keuangan (OJK) record Non-Performing Loans at the first quarter of 2016 reached Rp. 113.08 trillion (Kompas, 2016). It is certainly a very serious problem because it can decrease banks profitability. In an effort to suppress the growth of credit, forecasting is useful for predicting Non-Performing Loans. So mobile realtime forecasting applications is needed to monitor the growth rate of bad loans easily. Forecasting results can be used to be taken into consideration in giving loans, decision support, consideration and banks can set up a new strategy to reduce the number of Non-Performing Loans. In the software implementation, Double Exponential Smoothing forecasting methods and Trend Projection methods selected because other forecasting studies gave a good accuracy results. Based on the software that have been built using a loans data in Indonesian bank, forecasting results obtained Double Exponential Smoothing have 5% errors, while Trend Projection method have 12% error. To determine the prediction using the best method to test the statistical test F distribution and the result is Trend Projection method is an appropriate method that used for the forecasting the growth of Non-Performing Loans software.