Analysis Of The Element Of Work Movement With The Modapts Method To Generate Data On The Time Of Sorting Fresh Fruit Bunches

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Introduction

In the palm oil processing industry, maintaining the quality of raw materials is crucial for the production of high-quality final products. PT. Persada Nusa Nabati Indonesia (PNNI) is a palm oil processing factory that has a great responsibility in ensuring the quality of raw materials. One of the critical processes in this factory is sorting fresh fruit bunches, which is carried out when receiving raw materials. However, the number of fresh fruit bunches received is often fluctuating, directly affecting the time of completing the sorting process. This study aims to analyze the sorting time using the modular arrangement of predetermined time standards (Modapts) method.

Background of the Study

The Modapts method is a technique used to analyze the elements of work movement in a process. By conducting a draft improvement through the elimination of ineffective movements (work elimination) and simplification of work (work simplification), it is expected to increase the efficiency of work time. In this study, the data collection of work time was carried out using data mining techniques with Weka software to find patterns and information in existing sorting databases.

Methodology

The analysis using the Modapts method shows that the average time standard time for sorting is 18,009 seconds. By implementing the proposed improvement that eliminates inefficient movements, it is hoped that the average time of this standard can be reduced to 14.883 seconds. This finding shows how important the optimization of the elements of work movement is to increase productivity.

Furthermore, the processing of data mining produces the most suitable mathematical model to describe the sorting database is a linear regression model, which has a correlation coefficient of 1. This shows a strong relationship between attributes in the sorting database. The Root Mean Squared Error (RMSE) value obtained is 0, which indicates the accuracy of the model is very high.

Data Mining Techniques

In this study, data mining techniques were used to analyze the sorting database. The results show that the most suitable mathematical model to describe the sorting database is a linear regression model. This model has a correlation coefficient of 1, indicating a strong relationship between attributes in the sorting database. The Root Mean Squared Error (RMSE) value obtained is 0, which indicates the accuracy of the model is very high.

Clustering Techniques

Clustering techniques in data mining were used to identify data groups with the shortest sorting time and worker characteristics. The results show that Cluster 2 has the smallest normal time average, which is 9,7364 seconds. These findings provide valuable insights on the effectiveness of each group in carrying out sorting tasks.

Decision Trees

The use of decision trees proves that the classification of fresh fruit bunches to determine the normal time in the work of sorting is strongly influenced by a number of measurable movements using the Modapts method. The correlation coefficient obtained is 0.9997, which shows that there is a very strong relationship between classification and work time.

Conclusion

With the application of this Modapts and Data Mining-Based Analysis method, PT. PNNI can increase efficiency and effectiveness in the sorting process. The results of this study are not only beneficial for the company but can also be used as a reference for other industries that face similar challenges in processing raw materials. Increased efficiency in the production process will contribute directly to the quality of the final product produced.

Recommendations

Based on the findings of this study, the following recommendations are made:

  • The Modapts method should be applied in other processes in the factory to increase efficiency and effectiveness.
  • Data mining techniques should be used to analyze other databases in the factory to identify areas for improvement.
  • The results of this study should be used as a reference for other industries that face similar challenges in processing raw materials.

Limitations of the Study

This study has several limitations, including:

  • The study only analyzed the sorting process in one factory, and the results may not be generalizable to other factories.
  • The study only used data mining techniques to analyze the sorting database, and other techniques may be used to analyze other databases.
  • The study only focused on the efficiency and effectiveness of the sorting process, and other factors may be considered in future studies.

Future Research Directions

Future research directions include:

  • Applying the Modapts method in other processes in the factory to increase efficiency and effectiveness.
  • Using data mining techniques to analyze other databases in the factory to identify areas for improvement.
  • Considering other factors that may affect the efficiency and effectiveness of the sorting process.

Conclusion

In conclusion, this study demonstrates the effectiveness of the Modapts method in analyzing the elements of work movement in the sorting process. The results of this study show that the average time standard time for sorting can be reduced by implementing the proposed improvement that eliminates inefficient movements. The study also demonstrates the use of data mining techniques in analyzing the sorting database and identifying areas for improvement. The results of this study are not only beneficial for the company but can also be used as a reference for other industries that face similar challenges in processing raw materials.

Q: What is the Modapts method?

A: The Modapts method is a technique used to analyze the elements of work movement in a process. It involves conducting a draft improvement through the elimination of ineffective movements (work elimination) and simplification of work (work simplification) to increase the efficiency of work time.

Q: What is the purpose of this study?

A: The purpose of this study is to analyze the sorting time using the Modapts method and to identify areas for improvement in the sorting process.

Q: What data mining techniques were used in this study?

A: The data mining techniques used in this study include linear regression, clustering, and decision trees.

Q: What is the most suitable mathematical model to describe the sorting database?

A: The most suitable mathematical model to describe the sorting database is a linear regression model, which has a correlation coefficient of 1.

Q: What is the Root Mean Squared Error (RMSE) value obtained?

A: The RMSE value obtained is 0, which indicates the accuracy of the model is very high.

Q: What is the correlation coefficient obtained by the decision tree?

A: The correlation coefficient obtained by the decision tree is 0.9997, which shows that there is a very strong relationship between classification and work time.

Q: What are the recommendations of this study?

A: The recommendations of this study include applying the Modapts method in other processes in the factory to increase efficiency and effectiveness, using data mining techniques to analyze other databases in the factory to identify areas for improvement, and using the results of this study as a reference for other industries that face similar challenges in processing raw materials.

Q: What are the limitations of this study?

A: The limitations of this study include the study only analyzed the sorting process in one factory, the study only used data mining techniques to analyze the sorting database, and the study only focused on the efficiency and effectiveness of the sorting process.

Q: What are the future research directions?

A: The future research directions include applying the Modapts method in other processes in the factory to increase efficiency and effectiveness, using data mining techniques to analyze other databases in the factory to identify areas for improvement, and considering other factors that may affect the efficiency and effectiveness of the sorting process.

Q: What are the benefits of this study?

A: The benefits of this study include increasing efficiency and effectiveness in the sorting process, providing valuable insights on the effectiveness of each group in carrying out sorting tasks, and identifying areas for improvement in the sorting process.

Q: What are the implications of this study?

A: The implications of this study include the use of the Modapts method and data mining techniques in other industries that face similar challenges in processing raw materials, and the potential to increase efficiency and effectiveness in other processes in the factory.

Q: What are the contributions of this study?

A: The contributions of this study include the development of a new method for analyzing the elements of work movement in the sorting process, the identification of areas for improvement in the sorting process, and the provision of valuable insights on the effectiveness of each group in carrying out sorting tasks.