COVID-19 Case Growth Prediction Using a Hybrid Fuzzy Time Series Forecasting Model and a Machine Learning Approach

Uky Yudatama1, Solikhin2, Dwi Ekasari Harmadji3, Agus Purwanto4 

1 Department of Informatics Universitas Muhammadiyah Magelang Magelang, Indonesia

2 Department of Informatics Engineering STMIK Himsya Semarang, Indonesia

3 Department of Accounting Universitas Wisnuwardhana Malang, Indonesia

Corresponding author: Solikhin (Solikhin@stmik-himsya.ac.id). 

ABSTRACT The COVID-19 pandemic has evolved into a global health crisis, with Indonesia particularly affected due to its high death rates compared to the rest of Asia. A significant number of unacknowledged, undocumented, or unaddressed cases further exacerbate the situation in Indonesia. Challenges arise from the growing patient population and a scarcity of resources, medical experts, and facilities. This study analyzes the daily development of COVID-19 cases in Indonesia, aiming to estimate the number of confirmed cases, recoveries, and fatalities. Introducing a novel hybrid forecasting model, we utilize the Holt-Winter triple exponential smoothing statistical method and the fuzzy time series rate of change algorithm. We apply the Triple Exponential Smoothing Holt Winter statistical model to predict future periods to the fuzzy time series. Based on the testing results, our proposed hybrid forecasting model demonstrates a very high level of predictive capacity. The acquired data are highly accurate, with a 0.15 percent confirmation rate, 0.15 percent recovery rate, and a 0.20 percent mortality rate, along with an average absolute error of less than 10% for each COVID-19 case. The findings indicate that early awareness by the COVID-19 Task Force of the status of cases is highly advantageous. This awareness can aid in formulating appropriate policies for future planning, organization, and accelerated treatment of COVID-19 in Indonesia. Consequently, successful efforts can be made to slow the emergence and spread of COVID-19 in the country.

KEYWORDS component; COVID 19; Forecasting; Fuzzy Time Series; Rate of Change; Triple Exponential Smoothing

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A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model

Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto

Abstract

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

Keywords

Frequency-based partitioning; Machine learning; Prediction; Rate of change; Transportation public

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DOI: https://doi.org/10.11591/eei.v11i5.3518

Prediction of passenger train using fuzzy time series and percentage change methods

Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto

Abstract

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

Keywords

Double exponential smoothing; Forecasting; Fuzzy time series; Passenger train; Percentage change

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DOI: https://doi.org/10.11591/eei.v10i6.2822

Membangun Sistem Smart Trash Menggunakan Mikrokontroler Motor Servo Panjerino

Yuda Hirmawan¹, Eko Riyanto², Solikhin Solikhin^3*

Abstract

To cultivate good behavior and care for the environment, SD Negeri 2 KuwasenJepara promotes proper waste disposal, but in reality, there are still many students who don't do it. The purpose of this research is to build a smart trash can to socialize waste disposal in an attractive way for students. We use a manual trash can that is integrated with the Arduino Uno. This smart trash system is able to open automatically when it detects movement within <50 cm and vice versa, and can emit a "Thank you for not littering" sound. The performance test results show that the ultrasonic sensor device opens and closes within 3.07 seconds at a distance of 15 centimeters and 3.06 seconds at a distance of 30 centimeters. The feasibility test of the tool obtained a score of ≥76% and an ease of use score of 87.7%.

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DOI: https://doi.org/10.26877/jiu.v9i1.15444