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This Author published in this journals
All Journal Jurnal Minds: Manajemen Ide dan Inspirasi Journal of Accounting and Management Innovation Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton JURAKUNMAN (JURNAL AKUNTANSI DAN MANAJEMEN) Jurnal Pengabdian Kepada Masyarakat Jurnal Ekonomi Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Journal of Accounting and Management Innovation Jurnal Ekonomi dan Bisnis Digital (MINISTAL) Jurnal Multidisiplin Madani (MUDIMA) Indonesian Journal of Business Analytics (IJBA) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Asian Journal of Management Analytics Jurnal Manajemen dan Pemasaran Jasa Formosa Journal of Computer and Information Science Indonesian Journal of Advanced Research (IJAR) Klabat Accounting Review Jurnal Ilmiah Accusi
Depari, Genesis Sembiring
Universitas Sumatera Utara
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Marketing Mix and Repurchase Intention of Cafe Industry During Covid-19: A Statistical and Data Mining Analysis Alfonsius, Alfonsius; Depari, Genesis Sembiring; Huang, Jen Peng
Jurnal Minds: Manajemen Ide dan Inspirasi Vol 8 No 2 (2021): December
Publisher : Management Department, Universitas Islam Negeri Alauddin Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/minds.v8i2.22171

Abstract

The COVID-19 pandemic has hit the world and bring unprecedented changes to all industries, including the Micro Small Medium Entrepreneur (MSME) of coffee shop businesses. This study aimed to determine the effect of the marketing mix on customer satisfaction to increase repurchase intention in modern coffee outlets. In addition, statistical modeling, segmentation analysis, and variables weighting using Support Vector Machine were performed. Eventually, product, price, and place positively and significantly affect customer satisfaction, while the promotion does not. Customer satisfaction was found to increase the repurchase intention of the customers. The segmentation analysis and SVM weighting attributes support the product quality as crucial to the marketing mix. It is implied that marketers should devise a marketing approach differently to different clusters since each cluster has distinct characteristics.
Social Media Performance And Evaluation : An Approach To Business Analytics Concept Genesis Sembiring Depari; Adat Muli Peranginangin; Albert Owen; Luis Jonatan; Winnie Lauren
Jurnal Ilmiah Accusi Vol. 5 No. `1 (2023): Jurnal Ilmiah Accusi Vol 5(1) Mei 2023
Publisher : Program Studi Akuntansi Universitas Simalungun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36985/axmbd208

Abstract

The growth of the internet has resulted in the digitalization of data, which has led to the emergence of big data opportunities. Significant amounts of digital data leave traces of what customers see, read, do, and judge, as well as information about their interests and preferences, resulting in a large amount of data that may be mined for learning experiences. Data mining, statistical algorithms, and machine learning approaches are used in descriptive, predictive, and prescriptive analytics to analyze, forecast, and optimize what is the most take effect, future trends, events, and behaviors based on various data types. A decision support system is widely demanded in tackling this problem, especially in understanding the interactions based on the type, and time from the Facebook post about branding data sets. This work attempts to offer descriptive, predictive, and prescriptive analytics to determine whether a post is worth paying for and promoting. This study is sought for deeper observations of posts on Facebook that get a lot of interaction and loyal users by the best algorithm compared with naive Bayes and decision tree which is using Random Forest with 90.35 % accuracy
Understanding digital remittance adoption: The role of credibility, ease of use, and promotion with technological anxiety as a moderator in Indonesia Genesis Sembiring Depari; Steven; Jen Peng Huang
Jurnal Manajemen dan Pemasaran Jasa Vol. 18 No. 1 (2025): Maret
Publisher : Lembaga Penerbit Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v18i1.21593

Abstract

This study examines the influence of credibility, perceived ease of use, and promotion on users' behavioral intention in using digital remittance services in Indonesia, with a focus on the moderating role of technological anxiety. It aims to understand how these factors shape user engagement and whether technological anxiety alters these relationships. A structured survey was conducted among Top Remit users to collect data on their perceptions and experiences. Findings indicate that credibility, ease of use, and promotion have a positive influence on behavioral intention, highlighting the importance of trust, platform simplicity, and effective marketing. While technological anxiety directly affects behavioral intention, it does not significantly moderate the relationships between credibility, ease of use, and promotion. Enhancing credibility, user experience, and balanced promotions fosters user adoption. The study offers practical insights for digital service providers to enhance user satisfaction and overcome technological barriers.
Predictive Analysis for the Early Detection of Depression in Adolescents Based on Social Media Usage Patterns Genesis Sembiring Depari; Julpan Daniel Simatupang
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16904

Abstract

This study examines the use of predictive analytics for the early detection of depression among teenagers based on social media usage patterns and behavioral indicators. The research utilizes a secondary dataset consisting of 1,200 adolescent records, including variables such as daily social media usage duration, sleep duration, stress level, anxiety level, addiction tendency, academic performance, physical activity, and depression classification labels. A quantitative approach was applied using machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine. The dataset was processed through data cleaning, encoding, normalization, exploratory data analysis, feature selection, model development, and model evaluation. The results show that sleep duration, daily social media usage, stress level, anxiety level, and academic performance are important predictors of teen depression.
Co-Authors Abiemanyoe, Satrio Adat Muli Peranginangin Albert Owen Albert Owen Albert Owen Alfonsius Alfonsius Bukidz, Danny Philipe Candice Yap Yap Cut Alya Fachriza Efin Shu Elisabet Tambunan Elisabeth Marlina Sari Lintong Felix Emmanuel Huang, Jen Peng Indra Indra Jen Peng Huang Jerica Wijaya Joceline Chow Jocellyn, Jocellyn Julpan Daniel Simatupang Luis Jonatan Luis Jonatan Luis Jonatan Natasya Ginting Roberto Purba Ryanto Winata Sari, Mira Satrio Abiemanyoe Shu, Efin Sri Vandayuli Riorini Steven Sylvia Sylvia Winnie Lauren Winnie Lauren Winnie Lauren