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All Journal International Journal of Business, Economics, and Social Development International Journal of Research in Community Services International Journal of Quantitative Research and Modeling Mutiara: Multidiciplinary Scientifict Journal
Januaviani, Trisha Magdalena Adelheid
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Clustering Faktor Stres Pada Mahasiswa Aktif Menggunakan Algoritma K-Means dan K-Modes Wijaya, Jeffry; Januaviani, Trisha Magdalena Adelheid
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 2 (2024): Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.137

Abstract

Stres merupakan bagian dari kehidupan manusia yang tidak bisa dihindari. Stres adalah permasalahan kesehatan terbesar yang dialami seluruh kalangan, salah satunya adalah mahasiswa. Stres dipengaruhi dari faktor eksternal maupun internal. Beberapa faktor yang menyebabkan terjadinya stres pada mahasiswa, diantaranya: lingkungan, akademis, finansial, keluarga, pertemanan, percintaan, kesehatan, kepercayaan diri, karir, kegiatan/hobi, dan lain-lain. Menggunakan metode Algoritma K-Means dan K-Modes, faktor-faktor tersebut dapat dibagi menjadi berbagai klaster yang memiliki karakterisik serupa sebagai pemicu terjadinya stres pada mahasiswa. Clustering dilakukan menggunakan bahasa pemrograman R yang menunjukan hasil: Terdapat 3 cluster menggunakan algoritma K-Means, diantaranya: cluster 1 memiliki 103 data (41,036%), cluster 2 memiliki 72 data (28,685%), dan cluster 3 memiliki 76 data (30,279%). Cluster 1 memiliki responden terbanyak dibandingkan dengan cluster lainnya dengan karakteristik responden yang mengalami stres akibat faktor perkuliahan, individual, keluarga, dan finansial. Penelitian ini mendapatkan 4 cluster ketika menggunakan algoritma K-Modes, diantaranya: cluster 1 memiliki 92 data (36,65%), cluster 2 memiliki 59 data (23,51%), dan cluster 3 memiliki 13 data (5,18%), dan cluster 4 memiliki 87 data (34,66%). Cluster 3 memiliki responden tersedikit dibandingkan dengan cluster lainnya dengan karakteristik responden yang mengalami stres dari faktor individual dan sosial.
Clustering Faktor Stres Pada Mahasiswa Aktif Menggunakan Algoritma K-Means dan K-Modes Wijaya, Jeffry; Januaviani, Trisha Magdalena Adelheid
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 2 (2024): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.137

Abstract

Stres merupakan bagian dari kehidupan manusia yang tidak bisa dihindari. Stres adalah permasalahan kesehatan terbesar yang dialami seluruh kalangan, salah satunya adalah mahasiswa. Stres dipengaruhi dari faktor eksternal maupun internal. Beberapa faktor yang menyebabkan terjadinya stres pada mahasiswa, diantaranya: lingkungan, akademis, finansial, keluarga, pertemanan, percintaan, kesehatan, kepercayaan diri, karir, kegiatan/hobi, dan lain-lain. Menggunakan metode Algoritma K-Means dan K-Modes, faktor-faktor tersebut dapat dibagi menjadi berbagai klaster yang memiliki karakterisik serupa sebagai pemicu terjadinya stres pada mahasiswa. Clustering dilakukan menggunakan bahasa pemrograman R yang menunjukan hasil: Terdapat 3 cluster menggunakan algoritma K-Means, diantaranya: cluster 1 memiliki 103 data (41,036%), cluster 2 memiliki 72 data (28,685%), dan cluster 3 memiliki 76 data (30,279%). Cluster 1 memiliki responden terbanyak dibandingkan dengan cluster lainnya dengan karakteristik responden yang mengalami stres akibat faktor perkuliahan, individual, keluarga, dan finansial. Penelitian ini mendapatkan 4 cluster ketika menggunakan algoritma K-Modes, diantaranya: cluster 1 memiliki 92 data (36,65%), cluster 2 memiliki 59 data (23,51%), dan cluster 3 memiliki 13 data (5,18%), dan cluster 4 memiliki 87 data (34,66%). Cluster 3 memiliki responden tersedikit dibandingkan dengan cluster lainnya dengan karakteristik responden yang mengalami stres dari faktor individual dan sosial.
The Effect of Macroeconomic Variables on Indonesia's Import Value Using the OLS Method Januaviani, Trisha Magdalena Adelheid; Kalfin; Hutabarat, Aned Miranda; Nikita; Musdaifah, Selvy; Nacong, Nasria
International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i3.1070

Abstract

This study analyzes the factors influencing Indonesia’s import value during the period 2021–2025 using the Ordinary Least Squares (OLS) method. To ensure the validity of the model, a series of classical assumption tests were conducted in accordance with the Best Linear Unbiased Estimator (BLUE) criteria, including tests for normality, multicollinearity, heteroscedasticity, and autocorrelation. The data were obtained from official publications of the Central Statistics Agency (BPS) and other relevant sources. The estimation results demonstrate that the independent variables, namely the exchange rate (X₁), national income (X₂), foreign exchange reserves (X₃), inflation rate (X₄), and interest rate (X₅), exert varying effects on Indonesia’s import value, with certain variables exhibiting significant influence while others remain insignificant. The model is free from violations of the classical assumptions, thereby meeting the criteria of the Best Linear Unbiased Estimator (BLUE). Keywords: Import Value, OLS, Classical Assumption Tests, Macroeconomics
Determination of Mount Eruption Insurance Premiums in Indonesia Based on Collective Risk and Level of Risk Spread Kalfin, Kalfin; Sukono, Sukono; Januaviani, Trisha Magdalena Adelheid; Siregar, Bakti
International Journal of Business, Economics, and Social Development Vol. 5 No. 1 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i1.576

Abstract

Volcano eruption insurance is an insurance product that provides financial protection to policy holders who experience losses due to volcanic eruptions. This insurance product is still rarely developed in Indonesia, even though this country is very vulnerable to natural disasters. Therefore, this research aims to carry out a simulation of determining volcanic eruption disaster insurance premiums based on collective risk and the level of risk distribution. The data used in this research is the frequency of events and economic losses due to volcanic eruptions. Event frequency and loss data are analyzed using a collective risk model. Apart from that, determining insurance premiums also takes into account loading factors and the level of risk distribution from volcanic eruption disaster data. From the results of the analysis, it was found that natural disaster insurance premiums had increased, along with an increase in the loading factor provided. In addition, insurance premium expenses are influenced by the collective risks faced by customers. The greater the collective risk faced, the greater the insurance premium that customers must bear. Based on the results of the estimates carried out, it is hoped that this research can provide an overview to the Indonesian government in estimating the Mount Meletus insurance scheme. Meanwhile, insurance companies can get an idea of determining insurance premiums according to conditions in the field.
Talent Classification: Recognizing and Developing Personal Potential Optimally at Tarsisius Vireta High School Januaviani, Trisha Magdalena Adelheid; Kalfin, Kalfin; Arifin, Alicia; Gunawan, Naftali Brigitta
International Journal of Research in Community Services Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v5i2.607

Abstract

Knowing students' talents and interests from an early age is very important to help them develop their potential more effectively. Every individual is unique, and the process of identifying talents and interests requires patience and a deep understanding of the student. With a good approach, we can help students develop their potential optimally. This research aims to explore the potential of students at Tarsisius Vireta High School using a talent classification approach. In the educational context, recognizing and developing students' potential is the key to achieving personal success and positive contributions to society. The research method used is qualitative with data collection techniques through questionnaires given before (Pre Test) and after (Post Test) the seminar given to students. The data obtained becomes evaluation material in determining how much students know the talents each student has. Based on the results of the analysis, it was found that there was a significant increase in students' awareness of their talents and interests, as well as their ability to identify and categorize talents. With a deeper understanding of students' potential, it is hoped that education at Tarsisius Vireta High School can be more optimal in developing students' talents and interests, so that they are ready to face the future with confidence and honed abilities.
Co-Authors Arifin, Alicia Bakti Siregar Gunawan, Naftali Brigitta Hutabarat, Aned Miranda Kalfin Kalfin, Kalfin Musdaifah, Selvy Nasria Nacong Nikita Sukono . Wijaya, Jeffry