Keywords: Subjective well-being, Emotional intelligence, Social supportAbstrak. Penelitian ini bertujuan mengetahui hubungan antara kecerdasan emosi, dukungan sosial, dan kesejahteraan subjektif pada remaja awal.Subjek penelitian ini adalah siswa kelas VIII SMP Negeri 2 Kota S, berusia 12-15 tahun. Alat ukur menggunakan tiga skala: skala kesejahteraan subjektif, skala kecerdasan emosi, dan skala dukungan sosial. Uji hipotesis menggunakan analisis regresi berganda dengan variabel moderator yang disebut juga sebagai Moderated Regression Analysis (MRA). Hasil penelitian menunjukkan nilai R 2 pada model regresi ke-3 lebih tinggi, sebesar 0,667dengan memasukkan interaksi variabel kecerdasan emosi dan variabel dukungan sosial (sebagai variabel moderator), sehingga terbukti variabel dukungan sosial tepat sebagai variabel moderator dan mampu meningkatkan hubungan antara variabel kecerdasan emosi dengan variabel kesejahteraan subjektif.
Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful information about student preference. Data result of the integration will be processed using hybrid clustering using K-means and self organizing map (SOM) algorithm. The hybrid clustering conducted to get promising clustering result along with the visualization of segmentation. This study successfully produces five student segments. It received 1,386 as the Davies-Bouldin index (DBI) value and 2,752 as the quantization error (QE) value which indicates a good clustering result for market segmentation. In addition, the visualization of the clustering result can be seen in a hexagonal map.
People with disabilities still experience many difficulties in their social life. People with disabilities rarely get the opportunity to get a good education and job. Some people still consider diffable as people who have shortcomings so that they must be pitied. This social treatment will affect the level of self-efficacy in persons with disabilities. The diffable is expected to be able to increase self-efficacy by controlling emotions skills so that they do not get trapped in pessimistic thinking. Counseling steps with emotional regulation techniques are offered to overcome the problem of self-efficacy with disabilities. This study aims to find the implementation of counseling services with emotion regulation techniques to increase the self-efficacy of people with disabilities. This research is important because people with disabilities need assistance in increasing self-efficacy due to the social treatment they receive. This study uses literature study techniques by looking for reference sources related to disabilities, self-efficacy, and emotion regulation techniques. The library sources are then combined, to then look for the formulations and conclusions.
The importance of the presence of higher education enables the private sector to participate in organizing academic activities in the form of higher education institutions. This causes the private higher education market to become more competitive, which implies a low number of students. Therefore, market segmentation needs to be applied to college students so that they can help to determine the model of marketing and promotional activities. The stages carried out in this study consisted of data collection, data exploration, and extracting segment. Cluster analysis was applied as a method for extracting segments of students with psychographics variables as partitioning factors. The K-Means algorithm was chosen as the method applied for cluster analysis because it produces better performance when compared to the use of K-Modes. Cluster analysis based on psychographics variables applied to this case succeeded in extracting the segment of the university students into 6 segments.
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