Improve infrastructure and access to education

Oct 13, 2025 by thesweetdesigns.com 121 views

Cluster Analysis to Group Regencies/Cities Based on Indicators of People's Welfare in the Area of North Sumatra Province

Introduction

Cluster analysis is a statistical technique used to identify patterns and relationships between objects and variables. In the context of North Sumatra Province, this technique can be applied to group regencies and cities based on indicators of people's welfare. The goal of this study is to use cluster analysis to identify groups of regencies and cities with similar characteristics and to provide insights for policymakers to design more effective interventions.

Research Methodology and Results

The average linkage method was used in this analysis to group regencies and cities in North Sumatra Province based on the level of people's welfare. This method focuses on calculating the average distance between objects, which helps to identify the similarity and significant differences between regencies and cities. The results of this analysis group 33 regencies and cities into four different clusters.

Cluster 1: Nias Islands Region

The first cluster consists of Nias, South Nias, West Nias, and North Nias regencies. These four districts are located in the Nias Islands region, which shows that they have similar social and economic characteristics. Perhaps due to cultural and geographic linkages, they have almost identical welfare challenges. The Nias Islands region is known for its unique culture and traditional practices, which may contribute to the similarities in welfare indicators.

Cluster 2: Diverse Regencies and Cities

The second cluster is a larger group, covering 28 regencies and cities such as Mandailing Natal, South Tapanuli, Central Tapanuli, North Tapanuli, Toba Samosir, and Labuhan Batu. In this cluster, we can see diversity, ranging from rural to urban areas. This shows the difference in the level of development that may be influenced by access to resources, education, and infrastructure. Regencies and cities in this cluster may have different welfare challenges due to their varying levels of development.

Cluster 3: Deli Serdang

The third cluster consists of only Deli Serdang. With a fairly large population and as one of the districts close to Medan, Deli Serdang shows different characteristics. Possibly, rapid economic growth and rapid infrastructure development have an influence on the level of welfare of its population. Deli Serdang is known for its agricultural and industrial activities, which may contribute to its unique welfare profile.

Cluster 4: Medan

Meanwhile, Medan as the provincial capital became the only member in the fourth cluster. Medan is the center of economic, education, and government in North Sumatra, so it is not surprising that it has a higher welfare index than other regions. As the capital city, Medan has access to more resources and opportunities, which may contribute to its higher welfare level.

Analysis and Understanding of People's Welfare

Based on this grouping, further analysis of the factors that influence welfare in each cluster can be done. For example, access to quality education, the availability of employment, and adequate infrastructure can contribute greatly to the level of community welfare. In the future, it is essential for local governments and related stakeholders to understand the results of this analysis in order to design more effective policies.

Policies for Each Cluster

For example, regencies that are members of cluster 2 can be focused on improving infrastructure and access to education, while Deli Serdang and Medan can strengthen their economic sector with new innovations and investments. By understanding the unique characteristics of each cluster, policymakers can design targeted interventions to address the specific welfare challenges of each region.

Conclusion

Cluster analysis provides valuable insights on the conditions of welfare in North Sumatra Province. By identifying groups based on the same characteristics, the government and related parties can design more focused and effective interventions. Through appropriate efforts, we can hope that the level of welfare in all districts and cities can increase equally, reduce the gap, and improve the quality of life of the community.

Recommendations

Based on the results of this analysis, the following recommendations can be made:

Improve infrastructure and access to education: Regencies and cities in cluster 2 should focus on improving infrastructure and access to education to address the welfare challenges of their populations.
Strengthen economic sector: Deli Serdang and Medan should strengthen their economic sector with new innovations and investments to address the welfare challenges of their populations.
Design targeted interventions: Policymakers should design targeted interventions to address the specific welfare challenges of each region, based on the unique characteristics of each cluster.
Monitor and evaluate: The government and related stakeholders should monitor and evaluate the effectiveness of these interventions to ensure that they are achieving the desired outcomes.

By following these recommendations, we can hope that the level of welfare in all districts and cities can increase equally, reduce the gap, and improve the quality of life of the community.
Cluster Analysis to Group Regencies/Cities Based on Indicators of People's Welfare in the Area of North Sumatra Province: Q&A

Introduction

Q&A

Q: What is cluster analysis?

A: Cluster analysis is a statistical technique used to identify patterns and relationships between objects and variables. It is a type of unsupervised learning algorithm that groups similar objects or variables together based on their characteristics.

Q: How was the cluster analysis conducted in this study?

A: The average linkage method was used in this analysis to group regencies and cities in North Sumatra Province based on the level of people's welfare. This method focuses on calculating the average distance between objects, which helps to identify the similarity and significant differences between regencies and cities.

Q: What are the four clusters identified in this study?

A: The four clusters identified in this study are:

Cluster 1: Nias Islands Region - Nias, South Nias, West Nias, and North Nias regencies.
Cluster 2: Diverse Regencies and Cities - 28 regencies and cities such as Mandailing Natal, South Tapanuli, Central Tapanuli, North Tapanuli, Toba Samosir, and Labuhan Batu.
Cluster 3: Deli Serdang - Deli Serdang regency.
Cluster 4: Medan - Medan city.

Q: What are the characteristics of each cluster?

A: The characteristics of each cluster are:

Cluster 1: Nias Islands Region - Similar social and economic characteristics, unique culture and traditional practices.
Cluster 2: Diverse Regencies and Cities - Diversity, ranging from rural to urban areas, different levels of development.
Cluster 3: Deli Serdang - Rapid economic growth, rapid infrastructure development, unique welfare profile.
Cluster 4: Medan - Higher welfare index, access to more resources and opportunities.

Q: What are the implications of this study for policymakers?

A: The implications of this study for policymakers are:

Design targeted interventions: Policymakers should design targeted interventions to address the specific welfare challenges of each region, based on the unique characteristics of each cluster.
Improve infrastructure and access to education: Regencies and cities in cluster 2 should focus on improving infrastructure and access to education to address the welfare challenges of their populations.
Strengthen economic sector: Deli Serdang and Medan should strengthen their economic sector with new innovations and investments to address the welfare challenges of their populations.

Q: What are the limitations of this study?

A: The limitations of this study are:

Data availability: The study is limited by the availability of data on people's welfare indicators in North Sumatra Province.
Methodological limitations: The study uses a single method (average linkage) to group regencies and cities, which may not capture the complexity of the relationships between variables.

Conclusion