Forecast of Waste Generated and Waste Fleet using Linear Regression Model

Abstract

Sukolilo District has 115,855 inhabitants with a population density of 4,897 inhabitants/square kilometers. The increase of population simultaneously increase the rate of waste generation. The increase in the rate of waste generation affects waste collection. Thus, optimization of waste management in operational, technical, and community participation aspects in Sukolilo District, Surabaya City, is needed. The optimization of waste management require to forecast the population, the amount of waste generation, and its effect on the number of waste transportation fleets in the next 10 years. Therefore, this research presented the data in order to solve the issue of waste management. This research is convenienly offered to be one of the major consideration for the Sukolilo District Government in accordance with the waste management issue. Field surveys were conducted to collect data on population, waste generation, and the condition of the waste fleet. Then, the data was analyzed with linear regression model to estimate the total waste generation and waste fleet. The projected population growth in 2030 is 146,054 people. The calculation of the amount of waste generation based on the formula from the Indonesian Ministry of Environment Regulation in 2030 is 26,655 tons/year. Meanwhile based the result of this study, the simple linear regression equation in 2030 is 65,442 tons/year. The increase in waste generation from 2021 to 2030 reached 35,041 tons, with a percentage of 115%. Thus, that, the calculation of the waste fleet requirements for the next 10 years is 40 waste container tanks and 20 arm roll trucks. Population growth in Sukolilo District is in line with the total amount of waste generation. Therefore, it is necessary to add a waste fleet at that location in the next 10 years. The authors suggest that improving the quality and the quantity of the waste fleet is indeed recommended.

Pengendalian Persediaan Bahan Baku Rotan Menggunakan Metode Eoq Dan Poq

“…Linear Regression merupakan sebuah metode forecasting yang menggunakan pengembangan hubungan matematis antara variable, yaitu variable dependen (Y) atau variable yang dipengaruhi dengan variable indipenden (X) varibel yang mempengaruhi [15].…”

“…Metode ini adalah teknik peramalan rata-ratabergerak yang sederhana dan banyak digunakan untuk menganalisa trend dari fluktuasi data dari urutan waktu tertentu. Penggunaaan bobot didasarkan pada trial dan error, jika data terkini diberikan bobot terlalu besar, maka forecast akan bereaksi berlebihanpada fluktuasi acak, jika diberikan bobot terlalu rendah maka forecast akan tidak sensitif pada perubahan perilaku permintaan [15]. Peramalan dengan metode Weighted Moving Average dilakukan dengan menggunakan aplikasi software QM for windows.…”

“…EOQ sangat berguna untuk menentukan kuantitas pesanan persediaan yang dapat meminimalkan biaya penyimpanan dan biaya dan biaya pemesanan persediaan. EOQ juga berguna untuk mengatasi masalah berkaitan dengan ketidakpastian melalui persediaan pengaman (safety stock) persediaan [15].…”