Article Per Year (5 Year)
p-Index From 2021 - 2026
2.405
P-Index
This Author published in this journals
All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi jurnal ilmiah hospitality Jurnal Ilmiah Manajemen Kesatuan JURNAL ILMIAH SIMANTEK JURNAL ILMIAH KOHESI Jurnal Ekonomi Jurnal Pengabdian Kepada Masyarakat Jurnal Peradaban Masyarakat (JPM) ULINA: Jurnal Pengabdian kepada Masyarakat
SIAHAAN, BERTHA NERPY
Universitas Mandiri Bina Prestasi
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Factors Affecting Public Interest In Applying For Loans To Bank: Case Study on Furniture Entrepreneurs in Medan City Lennaria Lusia Tarigan; Erni Dewi Munte; Bertha Nerpy Siahaan; Duma Lasmaria Siagian; Vikarma Jaya Halawa
Jurnal Ilmiah Manajemen Kesatuan Vol. 12 No. 6 (2024): JIMKES Edisi November 2024
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jimkes.v12i6.2950

Abstract

In daily life, borrowing through financial institutions such as banks is a common activity. This study aims to examine the influence of perceived ease of use, perceived risk, privacy, trust, and perceived benefits on public interest in applying for bank loans. The study population consisted of 2,785 furniture entrepreneurs in Medan City, with 104 respondents selected through purposive sampling. Primary data were collected via questionnaires, supported by secondary data from relevant literature. Data analysis was conducted using Partial Least Squares (PLS) to assess relationships among variables. The findings reveal that perceived ease of use, privacy, trust, and perceived benefits significantly influence interest in borrowing from banks, while perceived risk does not show a significant effect. These results provide valuable insights for future research and contribute to the understanding of financial behavior, particularly in the context of entrepreneurial financing decisions. Keywords: perceived ease of use, risk, privacy, trust, perceived benefits, borrowing interest
Segmentasi Pasar Terhadap Generasi Z Berdasarkan Perilaku Belanja Online Menggunakan Metode K-Means Clustering Meri Nova Marito; Ratna Wati Simbolon; Duma Lasmaria Siagian; Bertha Nerpy Siahaan; Ade Linhar P.; Anugrah Zai
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp244-248

Abstract

The advancement of digital technology greatly affects consumption patterns, especially among Gen Z, towards e-commerce platforms. Gen Z has a high affinity for digital technology. This condition creates a need for more precise market segmentation so that marketing strategies can be tailored to consumer characteristics. This study aims to conduct market segmentation based on online shopping behavior using the K-Means Clustering method. The data used in this study is the Online Retail Dataset obtained from the UCI Machine Learning Repository. The research stages include data preprocessing, attribute selection, data normalization, determination of the number of clusters, and the clustering process using the K-Means algorithm. The variables analyzed include transaction frequency, product purchase quantity, customer transaction value, and transaction time interval. The research results show that customer data can be grouped into several segments with different characteristics, such as active customers with high purchase levels, customers with moderate transactions, and customers with low purchasing activity. Thus, the resulting segmentation can help business actors understand consumer behavior and develop more targeted marketing strategies. In addition, the K-Means Clustering method has been proven effective in grouping customer data based on online shopping patterns. 
Co-Authors Ade Linhar P. Afrida Yanti Surbakti Ali Syah Putra Anugrah Zai Bambang Christoffer Banjarnahor, Saut Maruli Duma Lasmaria Siagian Eddy Eddy Eddy Eddy Gunawan Edward Harnjo Erina Alimin Erina Alimin Erni Dewi Munte Ewin Susanti Halawa Felix Leonardi Harlen Silalahi Ika Julianti Silveria Br Pandiangan Lennaria L. Tarigan Marice Hotnauli Simbolon Meri Nova Marito Monang Tarigan Pasaribu, Maranata Pinondang Hutapea Putri Fadillah Hadi Mirsa Sartana Sartana Sartana Sartana, Sartana SILALAHI, DUMARIANI Simbolon, Fauzi Haris Simbolon, Marice Hotnauli Simbolon, Ratna Wati Sinaga, Sarman Vikarma Jaya Halawa Wanra Tarigan