muhammad misbakhul, Rindra Yusianto

Prediction of the Three Sub-Districts with the Highest Waste Generation among 14 Sub-Districts in Rembang Regency Using Simple Linear Regression Method

Introduction

Prediction of the three sub-districts with the highest waste generation among 14 sub-districts in rembang regency using simple linear regression method. Predicts top 3 waste-generating sub-districts in Rembang Regency using simple linear regression. Finds Rembang, Sedan, and Kragan as highest, offering solutions.

Abstract

Waste has become a complex and global issue, faced by countries around the world, including Indonesia, regardless of their developmental status. The increasing population in Rembang Regency each year results in a rise in waste production. Since the increase in waste volume is not proportional to consumption, it is necessary to categorize and predict the amount of waste generated across 14 sub-districts. The simple linear regression method has shown good predictive capabilities.The objective of this research is to study the concepts, analyze waste management, develop linear regression models, and evaluate waste generation results. Evaluation results for estimation indicate that the first rank is in Rembang Sub-district with a waste generation amount of 68,398 tons and an increase of 1,063 tons over five years. The second rank is in Sedan Sub-district with a waste generation amount of 43,690 tons and an increase of 877 tons over five years. The third rank is in Kragan Sub-district with a waste generation amount of 49,744 tons and an increase of 805 tons over five years.Based on predictions for the population and waste generation from 2024 to 2028, considering the factors of waste generation and the main factor of increased waste. In this case, the average population increase will be used to estimate the population over the next five years, leading to predictions for population and waste generation from 2024 to 2028. The top three sub-districts with the highest increases in waste are Rembang Sub-district, Sedan Sub-district, and Kragan Sub-district. Therefore, several recommendations can be provided to address the waste generation issues in Rembang Regency.

Review

This paper addresses a highly relevant and pressing issue: waste generation and management, specifically focusing on Rembang Regency, Indonesia. The study's objective to identify and predict the three sub-districts with the highest waste generation among 14 sub-districts using the Simple Linear Regression method is clear and directly tackles a practical problem for local authorities. The abstract effectively highlights the growing challenge of waste due to population increases, setting a strong context for the research. The chosen methodology offers a straightforward approach for initial predictive modeling, making the findings accessible and actionable for preliminary planning and resource allocation. The abstract clearly outlines the application of simple linear regression to project waste generation from 2024 to 2028, primarily linking it to population increases over a five-year period. The explicit presentation of the top three sub-districts—Rembang, Sedan, and Kragan—along with their predicted waste volumes and increases (e.g., Rembang Sub-district with 68,398 tons and an increase of 1,063 tons over five years) provides concrete and useful outputs. While the approach offers a foundational understanding, a more comprehensive abstract would benefit from detailing the specific data sources for population and waste generation, how "average population increase" was calculated, and how the model was validated. The reliance solely on population as the main factor, while intuitive, might oversimplify the multifaceted drivers of waste generation. The identification of the top three sub-districts serves as a critical first step for targeted waste management interventions in Rembang Regency. These findings enable local authorities to strategically focus their efforts and resources on the areas most affected by escalating waste volumes. The mention of "several recommendations" suggests the study successfully translates its predictions into actionable advice for policymakers. For future work, it would be beneficial to consider incorporating additional socio-economic factors, waste composition data, and potentially more advanced predictive models (e.g., multiple regression, time series analysis) to enhance the robustness and accuracy of the predictions. Nonetheless, this study provides a valuable baseline for strategic planning and resource allocation to address the escalating waste problem in the identified high-priority areas.

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