At private label universities in Indonesia, new students are still the main thing in terms of achieving university operational income. This study intends to group the data of ITTelkom Surabaya students by utilizing the data mining process using the k-means clustering method, then the results of the clustering are forecasted using simple linear regression to be able to predict the achievement of new students as the effect variable and year as the causative variable. The results of this study consist of 5 variables, namely student province, student study program, income of student parents, student parent work and student ethnicity, each of which consists of 4 clusters, then each cluster is predicted for achievement 3 the coming year 2022,2023,2024. It can be concluded that the highest combination of student/parent student profiles was obtained from East Java province, information systems study program, parents' income of 5-10 million per month, the occupation of other parents and the ethnicity of students from Java. The highest forecasting results are found in the income variable of students' parents in cluster 3 with predictions of 1292 students in 2024. It is hoped that with clustering and forecasting based on this research, ITTelkom Surabaya can make the right decision as a basis for decision making to determine strategy in promoting the campus.
The rapid development of technology and communication in recent years with the emergence of 4G technologies followed by the latest technology, 5G, has spoiled internet users in Bali. The addition of Bali internet users continues to increase. However, there has been a decline in Indosat Ooredoo's revenue in Bali, despite the addition of new sites. The decline not only occurred in revenue but also in terms of market share, Indosat Ooredoo's market share in Bali had reached 16.12%, but the market share continued to decline, pressed by Telkomsel and XL. It is necessary to know STP on the existing site, to facilitate the selection of the site with effective and efficient marketing strategy. This research is using mixed methods analysis, which is a combination of qualitative and qualitative methods. Qualitative methods are used to determine the EFE, and IFE variables, indicators and weights obtained from experts through FGDs, deep interviews and surveys. The research resulted in IFE having 2 variables with 8 indicators and EFE having 3 variables with 12 indicators. Determination of segmentation using K-Means Clustering based on existing variables resulted in 7 segments. The coordinates of each segment were calculated and mapped into the Hooley strategy matrix. The results revealed that segment 4 is focus segment which contain 131 sites.
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