Online Newspaper Clustering in Aceh using the Agglomerative Hierarchical Clustering Method

Adek, Rizal Tjut; Dinata, Rozzy Kesuma; Ditha, Ananda

doi:10.52088/ijesty.v2i1.206

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Hierarchical clustering generates a cluster tree, or dendrogram, which creates a cluster hierarchy. Each cluster node in the tree divides the area covered by its parent, as Adek, Dinata, and Ditha (2022) highlighted in their study.…”

Section: Methodsmentioning

confidence: 99%

Unfolding emotions for creating happiness and quality of life in Malaysia's low‐income community using text mining

Shuhidan

Lokman

Hamidi

et al. 2023

Community & Applied Soc Psy

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Determining the true reasons for happiness that exist in different social groups is important, especially in the case of those groups where poverty strongly dominates people's well‐being and happiness. To understand the factors that determine happiness among poor communities, this study collected data in the form of 1,793 responses from low‐income communities, who constitute 40% of Malaysia's workforce. To ascertain the feelings and emotional responses of the target group, primary source data were collected based on a questionnaire survey utilising the Lokman Emotion Importance Quadrant (LEIQ™) model. The data were analysed using text mining techniques based on R‐Language software. According to the findings, happiness in Malaysian low‐income communities is primarily shaped by concerns about family matters such as success, care issues, education and so on. Interestingly, government financial support, money, income and work matters are only fifth in importance, and lastly, the least emphasis was put on the public transportation issue. To ensure the true happiness and wellbeing of people who are struggling financially, policymakers must provide not only financial assistance but also ensure a better future for their families: better healthcare, better incomes, keeping a roof over their heads, etc. This study will be useful to the government, development agencies, NGOs, international organisations and other stakeholders wanting to realise the United Nations' Sustainable Development Goals (SDGs) in Malaysia and similarly affected countries. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

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Section: Methodsmentioning

confidence: 99%

Unfolding emotions for creating happiness and quality of life in Malaysia's low‐income community using text mining

Shuhidan

Lokman

Hamidi

et al. 2023

Community & Applied Soc Psy

View full text Add to dashboard Cite

show abstract

“…Proses ini berlanjut dengan menggabungkan klaster-klaster yang semakin besar hingga semua data tergabung dalam satu klaster utama. Selama proses ini, terbentuklah struktur hirarkis yang menggambarkan hubungan antar klaster, sering kali direpresentasikan dalam bentuk pohon dendrogram atau grafik [21][24] [25][26] [27].…”

Section: Algoritma Agglomerative Hierarchical Clustering (Ahc)unclassified

Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia

Sachrrial,

Iskandar

2023

bits

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The poverty rate in Indonesia has increased from 9.54 percent in March 2022 to 9.57 percent in September 2022 due to inflation and low wages and people's incomes. To overcome this problem, steps such as providing social assistance, creating decent jobs, and increasing wage standards are needed to increase people's purchasing power and reduce poverty in the future. The government needs to pay special attention to provinces with high poverty rates through special programs and efforts to increase income and the economy in these areas. Data Mining is a solution in solving this problem by utilizing the clustering method which is known as the clustering method. The clustering method used in this study is the AHC method and the K-Medoids method. In order to determine the provinces with the highest number of poor people, the AHC and K-Medoids clustering methods will be applied separately so that the final results of each will be analyzed. The results of the analysis show the formation of three clusters with different cluster locations. The application of the AHC method resulted in cluster 2 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 1 with only 3 provinces. While the application of the K-Medoids method resulted in cluster 1 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 2 with only 3 provinces. Although the location of the clusters is different between the two methods, the number of provinces in the cluster is the same so that a cluster with a total of 3 provinces is declared the province with the largest number of poor people.

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“…Additionally, the number of clusters to be used can be chosen, and the cluster merging process can be stopped by utilizing cut-off criteria. Using the appropriate formula, the Manhattan or Euclidean distance between two clusters can be computed [21][32] [33][34] [35]. a.…”

Section: Algorithm For Agglomerative Hierarchical Clustering (Ahc)mentioning

confidence: 99%

K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis

Lubis,

Huang,

Wang

et al. 2023

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Crime is a common phenomenon that often occurs in society and has a negative impact both individually and collectively. Gaining a deeper understanding of crime can help us tackle the problem more efficiently. In an era that is increasingly complex and globally connected as it is now, crime has undergone significant developments and changes. Crime remains a serious threat to our security, integrity, and well-being. Some common types of crime include theft, robbery, fraud, physical abuse, and murder. Crime can happen anytime and anywhere. To tackle crime, data mining techniques can be used to analyze the surrounding situation and gain new knowledge. One approach is to classify provinces based on crime data from previous years so that crime-prone areas can be identified and security measures can be increased. In this study, two grouping methods were used, namely K-Means and AHC using the complete linkage mode. There are 34 provinces in Indonesia which are grouped based on the number of victims of crime from 2019 to 2021. The grouping results using the K-Means method yield two groups with 17 provinces each. However, using the AHC complete linkage method, there is a difference in the number of provinces between cluster 0 and cluster 1 compared to the K-Means results. In addition, there are differences in the location of the province in the cluster between the two methods. In the K-Means method, provincial data is located in cluster 0, while in the AHC method, the province's data is in cluster 1. Thus, this study provides insight into crime in Indonesia and provides information about the grouping of provinces based on crime rates using the K-Means method. Means and AHC

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Online Newspaper Clustering in Aceh using the Agglomerative Hierarchical Clustering Method

Cited by 5 publications

References 22 publications

Unfolding emotions for creating happiness and quality of life in Malaysia's low‐income community using text mining

Unfolding emotions for creating happiness and quality of life in Malaysia's low‐income community using text mining

Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia

K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis

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