Clustering Analysis of MAMA 2024 Song of the Year Nominees Based on Musical Elements and Popularity Indicators

One of the main goals of education is to develop potential and to educate individuals. Education is an effort to help the souls of students both physically and mentally towards a better human civilization. This research was conducted to group districts/cities in Maluku province based on educational status using hierarchical cluster analysis with the single linkage, average linkage and ward's methods. The results showed that clustering using the single linkage and average linkage methods was almost the same, while clustering using the ward's method had quite different results. Clustering using the single linkage and average linkage methods shows that Ambon city is the only city that always has its own cluster and does not join other districts/cities. This shows that the city of Ambon has an education status that is quite different from other districts/cities in Maluku Province. Regencies/cities that are always in the same cluster, namely cluster 1, both in the single linkage method, average linkage and the ward's method are Tanimbar Islands Regency, Southeast Maluku Regency, Central Maluku Regency and Tual City

Analysing rainfall patterns in Mizoram from 1998 to 2017 reveals diverse trends. The highest average rainfall occurred in 2004, reaching 292.8 mm, while 2014 marked the lowest at 151.77 mm. Siaha District experienced the highest average rainfall (3020.2 mm), while Champhai District had the lowest (1663 mm). In 2017, the Kendall method showed correlations between temperature and relative humidity, rainfall and relative humidity, but not between rainfall and temperature. Cluster analysis, a technique partitioning datasets into cohesive groups, was applied to Mizoram’s district-wise rainfall data using single, complete, and average linkage methods. The Single Linkage Method formed one large cluster with under 26% similarity and the shortest distances between data points. The complete linkage method divided districts into two clusters with under 26% similarity and maximal inter-cluster distance. The Average Linkage Method merged all districts into one cluster with under 26% similarity and minimised inter-cluster distances. Comparing the techniques, Single and Complete Linkage Methods proved most effective for Mizoram’s district-wise rainfall data. With only eight districts, forming additional clusters remained limited. This analysis highlights the significance of rainfall patterns in agricultural ecosystems and the utility of statistical methodologies like cluster analysis in understanding long-term trends.

Poverty refers to the condition where a person cannot meet the basic necessities based on the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in North Sumatra Province in 2021 from 8.75% to 9.01%. However, this increase is exclusive to North Sumatra Province, which has Indonesia's 3rd largest number of districts/cities. This study discussed mapping the North Sumatra Province region based on 10 poverty factor variables. The 10 variables are life expectancy, health complaints, poverty line, Gross Regional Domestic Product (GRDP), population growth rate, Expected Years of Schooling (EYS), Human Development Index (HDI), labor force participation rate, open unemployment rate, and district/city minimum wage. The Hierarchical Clustering analysis was employed to compare single, complete, and average linkage methods. The best method was determined based on the pseudo-F statistic value. 4 clusters had complete linkage methods, each of which possessed varied characteristics. Cluster 1 contains cities with the lowest poverty rate, including Medan City and Pematang Siantar City. Cluster 2 consists of cities with low poverty rates, while Cluster 3 consists of cities with high poverty rates. Cities that are included in Cluster 4 have very high poverty rates, including South Nias District and Pakpak Bharat District. The clusters present significant poverty rate gaps among North Sumatra Province regions.

National hydrological network data between 1970 and 2002 for 96 stations across 25 watersheds were used to monitor annual trends in 13 variables: streamflow rates, water temperature, pH, electrical conductivity (EC), sediment concentration and eight nutrient levels (Na, K, Ca+Mg, CO3, HCO3, Cl, SO4 and boron). The dataset was analysed with multiple linear regression (MLR) models, principal components analysis (PCA) and hierarchical cluster analysis (CA). The Turkish watersheds have experienced a significant increase in pH, K, CO3 and a significant decrease in streamflow rate and sediment concentration between 1970 and 2002, with considerable spatial variations. There was also an increasing trend in streamwater temperature, at a rate of 0.05°C yr–1 (p > 0.05). The MLR models had high r 2 values of 69.6% to 99.9% at p ≤ 0.001 for 12 out of the 13 variables, with r 2 of 42% for boron (p < 0.05). PCA reduced the dimensionality of the dataset to four principal components that explain most (81.7%) of the variance. CA was able to distinguish six geographically associated groupings of watersheds with similar attributes in concordance with the climate zones of Turkey, despite the use of different clustering methods (complete, McQuitty, average, centroid and single linkage methods). Multivariate analyses of dynamic watershed characteristics provide the basis on which preventive and mitigative measures can be tailored to secure and enhance watershed health and sustainability.

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.

Hierarchical cluster analysis is a statistical analysis used to group data based on their similarities. The single linkage, complete linkage and average linkage methods can be used to group data using distance techniques. There is a large difference in the number of poor people in urban and rural areas in South Sulawesi Province, so an analysis is needed to classify areas that have the same characteristics based on poverty indicators. For this reason, these three methods are used. However, the results of this analysis are only based on the similarity measure based on the distance technique used. Thus, the multiscale bootstrap method is used to obtain the validity of the resulting clusters. The results of the research using these three methods are four clusters with different characteristics. By using multiscale bootstrap, it is found that in single linkage there are four valid clusters, for complete linkage there is only one valid cluster and on average linkage there are three valid clusters. So it is found that single linkage is the best method in classifying these cases.

Stunting is a nutrition problem that is still the main focus in developing countries, one of which is Indonesia. One of the instruments designed to measure the performance of the implementation of the stunting reduction acceleration program at the national level is the Stunting-Specific Intervention Index (Indeks Khusus Penanganan Stunting/IKPS). This study aims to group provinces in Indonesia based on a special index for handling stunting consisting of six indicators, namely health, nutrition, housing, food, education, and social protection indicators using the agglomerative hierarchical clustering method. The agglomerative hierarchical clustering method is divided into several methods, including single linkage, complete linkage, average linkage, and ward methods. This study compares the four methods with the aim of obtaining the best cluster solution in the grouping of provinces in Indonesia based on the stunting-specific intervention index. The determination of the best method in agglomerative hierarchical clustering is determined by the value of the cophenetic correlation coefficient. The results show that the average linkage method provides a better cluster solution than other methods. The cluster solution in the average linkage method produces eight clusters, including, cluster 1 consists of one province, cluster 2 consists of nine provinces, cluster 3 consists of twelve provinces, cluster 4 consists of six provinces, cluster 5 consists of one province, cluster 6 consists of one province, cluster 7 consists of three provinces, and cluster 8 consists of one province in Indonesia.

One of the problems of archipelagic countries is the lack of maximum utilization of natural resources which has resulted in some areas being left behind. Indonesia is one of those who experience the impact of the lack of utilization of natural resources in the forestry sector. The non-optimal use of forests for planting forestry plants has made most Indonesians use their land as artificial forests, namely to plant forestry plants. Cluster analysis in this case seeks to classify provinces in Indonesia based on the type of forestry plants cultivated. The method used is hierarchical and non-hierarchical. The hierarchical method uses single linkage and complete linkage methods while the non-hierarchical method uses the K-mean method. By making comparisons between methods, the results obtained are that the single linkage method with 8 clusters is the best method for grouping provinces in Indonesia according to the types of plants cultivated. Of the 34 provinces in Indonesia, cluster 1 consists of 27 provinces, while clusters 2 to 8 each consist of only 1 province.&#x0D; Keywords: Cluster analysis, single linkage, complete linkage, K-mean, forest plantsMSC2020: 62H30

The algorithms of hierarchical cluster analysis are mainly heuristically motivated. This is especially true from the viewpoint of a mathematical statistician, who misses a precise probabilistic model. There are, though, statistical models in hierarchical cluster analysis which use such probabilistic statistical methods. In either approach one can consider hierarchical cluster analysis as a transformation from a dissimilarity matrix to an ultrametric matrix. If the data in a dissimilarity matrix are only disturbed values of the “true” ultrametric matrix, one can consider cluster analysis as estimating the true ultrametric matrix. With the maximum likelihood method one can for example find estimators — here cluster analysis methods — for each error distribution, i.e. for each way in which the data are disturbed. In this way we can develop the single-linkage, modified complete-linkage, median-linkage and average-linkage methods. Rigorous inspection of the models (error distributions) shows the limitations of these (perhaps too) simple models.

Dengue haemorrhagic fever (DHF) is endemic in major cities in Indonesia. West Java Province is one of the provinces in Indonesia with a high number of DHF cases every year. To minimise the spread and increase of DHF cases can be done by controlling the factors that influence it. This study aims to categorise cities/districts in West Java based on factors that influence DHF cases. The data used are population density (Soul/ ), proper sanitation (%), healthy and clean living behaviour (%), access to proper water sources (%), and health index (%) in 27 cities/districts in West Java in 2021. In this study, the method used is the Hierarchical clustering method; namely the single linkage method, the complete linkage method, the average linkage method, and the ward method. The clustering methods are then compared based on the value of their standard deviation. The analysis results show that the best method used is the complete linkage method. The results of clustering areas in West Java based on factors affecting dengue cases obtained three clusters. Cluster 1 is the cluster with the highest level of dengue cases compared to other clusters. Cluster 1 consists of Depok city, Bogor city, Cirebon city, Bandung city, and Cimahi city. The characteristic of cluster 1 is that it has the highest average population density compared to other clusters. Cluster 2 consists of Cirebon, Bekasi, Banjar City, Bogor, Bandung, Karawang, West Bandung, Purwakarta, Kuningan, Majalengka, Pangandaran, Indramayu, Garut, Ciamis, Subang, Sukabumi, Cianjur, Tasikmalaya, and Sumedang. Cluster 2 has the characteristics of having the highest average percentage of households with access to proper sanitation, percentage of households with clean and healthy behaviour, and health index. Cluster 3 is Sukabumi City and Tasikmalaya City. Cluster 3 has characteristics with the lowest average in the percentage of households that have access to proper sanitation and the percentage of households with clean and healthy behaviour.

The agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods are represented by a bottom-up approach where each object starts in its cluster and pairs of clusters are merged as it moves up the hierarchy. In this paper, we present six agglomerative hierarchical clustering methods namely: the single linkage method, complete linkage method, average linkage method, centroid method, median method, and Ward’s method. We also evaluated how these methods work on a practical basis using a matrix of distance pairs of five points. It was observed that the single linkage method through its dendrogram produced the most similarity measure between x_i and x_j, while Ward’s method produced the highest distance measure between x_i and x_j.

The purpose of this study is to evaluate and compare different clustering techniques, including hierarchical cluster analysis (using complete linkage, average linkage, and single linkage methods), Self-Organizing Maps (SOM) clustering, and ensemble clustering, within the framework of integrated cluster analysis combined with Naïve Bayes analysis, specifically applied to cabbage production in Indonesia. The data utilized in this study are on cabbage production from various districts and cities in Indonesia, obtained from the 2023 publications of the Central Statistics Agency (BPS). The variables used in this study are cabbage harvest, cabbage production, area height, and rainfall. The data size used is 157 districts/cities in Indonesia. This research is a quantitative analysis employing integrated cluster analysis combined with Naïve Bayes. Cluster analysis is used to obtain classes in each district/city. Different clustering methods, including hierarchical clustering, Self-Organizing Map (SOM), and ensemble clustering, are compared to determine the best approach for grouping districts based on cabbage production. Naïve Bayes analysis is then used to classify cabbage production in Indonesia and identify the optimal clusters. This comparison aims to find the most effective clustering method for improving grouping accuracy and understanding cabbage production patterns. The best method for classifying cabbage production in Indonesia is the ensemble clustering approach integrated with Naïve Bayes, resulting in three distinct clusters: high, medium, and low production clusters.

Chess is a game that requires a high level of intelligence and strategy. Generally, in order to understand complex move patterns and strategies, the expertise of chess masters is required. With the rapid development in the field of machine learning, the digitization of chess game recordings in Portable Game Notation (PGN) format, and the availability of large and widely accessible data, it is possible to apply machine learning techniques to analyze chess games. This research studies the use of text clustering algorithms, specifically hierarchical clustering and K-means clustering, to categorize chess games based on their moves. We extracted 100 chess games that use certain openings such as French Defence, Queen's Gambit Declined, and English Opening. In the implementation of hierarchical clustering, single, average, and complete linkage methods are used. As a result, our findings show that hierarchical clustering with single linkage is less effective. On the other hand, the average and complete linkage methods, as well as K-means clustering, successfully identify clusters corresponding to the original openings. Notably, K-means clustering showed the highest accuracy in clustering chess games. This research highlights the potential of machine learning techniques in uncovering strategic patterns in chess games, paving the way for deeper insights into game strategies.

The goal of this study was to evaluate different clustering techniques in classifying the vegetable growing locations of Ernakulam (EKM) district of Kerala so that same nutrient recommendation can be prescribed for panchayats coming under the same cluster. Hierarchical clustering (HC) and K –means clustering were performed to group the panchayats based on soil fertility status and thereafter comparison of various clustering procedures was done using Davies – Bouldin (DB) index. Different dissimilarity measures- Euclidean, squared Euclidean, Chebychev distance and Mahalanobis D2 were determined and single linkage, complete linkage and average linkage methods were adopted under these measures. The results revealed that Mahalanobis D2 was the better clustering procedure with seven clusters (DB index: 0.120) followed by average linkage method under Euclidean distance (DB index: 0.306) with seven clusters. Manjapra and Keerampara panchayats remained as individual clusters. Keerampara had strongly acidic soils (pH -5.17) with high available Mg (158 mg kg-1) while Manjapra soils had low Mg availability (19 mg kg-1) and high S content (57 mg kg-1). Kakkad, Kalady and Vengoor came under cluster I which possessed approximately same EC (0.15-0.19 dS m-1), OC (2-2.4%) and Mg (71-73 mg kg-1) content. Chengamanadu and Vengola came under cluster III while Ayyampuzha and Mudakkuzha belong to cluster IV.

Improving the quality of human resources is the main supporting factor in increasing national productivity in various fields and development sectors. The government's productive investment activities that spur the nation's competitiveness in the global era prioritize Indonesia's education development. This study aims to cluster provinces in Indonesia based on educational indicators using the Agglomerative method consisting of the Average Linkage and Ward methods. Data collection is based on documentation techniques obtained from Statistics Indonesia in 2018. Data analysis used hierarchical cluster analysis consisting of data standardization, determining the size of the similarity or dissimilarity between data, the clustering process with a distance matrix, and seeing the characteristics of the cluster results formed. The second clustering method is by doing the initial grouping and determining the excellent cluster based on the average standard deviation ratio to the standard deviation between groups. Clustering results show the Ward method with the number of collections as many as 4 clusters and produces a ratio with a value of 0.01 smaller than the Average Linkage method. It shows that the cluster analysis method using the Ward method has better group accuracy quality than the Average Linkage method.