Article Per Year (5 Year)
p-Index From 2020 - 2025
3.397
P-Index
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 Journal of Accounting and Management Innovation Jurnal Ekonomi dan Bisnis Digital (MINISTAL) Jurnal Multidisiplin Madani (MUDIMA) Asian Journal of Management Analytics Jurnal Manajemen dan Pemasaran Jasa Indonesian Journal of Advanced Research (IJAR) Klabat Accounting Review Jurnal Ilmiah Accusi
Depari, Genesis Sembiring
Program Studi Manajemen Universitas Pelita Harapan, Medan, Indonesia
Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Veterinary Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan Depari, Genesis Sembiring
Journal of Accounting and Management Innovation Vol 4, No 2 (2020)
Publisher : Universitas Pelita Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting.Keyword: social media advertisement, random forest, support vector machine, data mining 
Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan Depari, Genesis Sembiring
Journal of Accounting and Management Innovation Vol 4, No 2 (2020)
Publisher : Universitas Pelita Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (513.217 KB)

Abstract

The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting.Keyword: social media advertisement, random forest, support vector machine, data mining 
THE INFLUENCE OF ONLINE CUSTOMER REVIEW AND PERCEIVED QUALITY TOWARD CUSTOMER PURCHASE DECISION AT LAZADA ONLINE RETAIL COMPANY GENESIS SEMBIRING DEPARI; Natasya Ginting
Jurakunman (Jurnal Akuntansi dan Manajemen) Vol 15, No 1 (2022): JURAKUNMAN, VOL. 15 NO. 1, JANUARI 2022
Publisher : Sekolah Tinggi Ilmu Ekonomi Surya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48042/jurakunman.v15i1.84

Abstract

Penelitian ini bertujuan untuk melihat pengaruh online customer review dan perceived quality terhadap keputusan pembelian pelanggan di Lazada. Data kuesioner disebarkan kepada 100 pengguna Lazada yang berdomisili di Medan dan Jakarta yang pernah bertransaksi di Lazada minimal satu kali. Metode yang digunakan adalah metode non-probabilitas dengan teknik purposive sampling. Hasil penelitian menunjukkan bahwa online customer review dan perceived quality berpengaruh positif terhadap keputusan pembelian pelanggan di perusahaan Lazada.
Marketing Mix and Repurchase Intention of Cafe Industry During Covid-19: A Statistical and Data Mining Analysis Alfonsius Alfonsius; Genesis Sembiring Depari; Jen Peng Huang
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.
Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan Genesis Sembiring Depari
Journal of Accounting and Management Innovation Vol 4, No 2 (2020)
Publisher : Universitas Pelita Harapan Medan Campus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/ami.v4i2.396

Abstract

The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting.Keyword: social media advertisement, random forest, support vector machine, data mining 
Mahasiswa di Era Pandemi Covid-19: Apakah Mungkin Kuliah Sambil Berbisnis? Genesis Sembiring Depari
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 1 No 2 (2021): November 2021
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (273.133 KB) | DOI: 10.54259/pakmas.v1i2.113

Abstract

The COVID-19 pandemic has affected all aspects of human life. This influence can be in the form of a positive or negative impact. Among students, not a few negative effects have been felt, such as decreased learning outcomes, increased boredom and stress etc. In addition to the negative impacts, some positive impacts can be seen and felt. One of them is the increasing ability of the community to use and utilize internet technology such as video conferencing technology, the use of social media etc. With the increasing ability to use internet technology, the opportunities to market products or services through the internet are also increasing. So that it brings new opportunities for students to be able to start their entrepreneurial activities from an early age when they are still studying in college. This community service activity is carried out through a scientific webinar that focusing on ways and strategies for students to be able to start their business as early as possible in the era of the COVID-19 pandemic. Participants in this community service activity consist of management study program students and the public.
The Influence of Social Media Marketing, Hedonic Shopping Motivation and Electronic Word of Mouth Towards Impulse Purchases for Shopee’s Customers in Medan Candice Yap Yap; Genesis Sembiring Depari
Jurnal Ekonomi dan Bisnis Digital Vol. 1 No. 1 (2022): January 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.316 KB) | DOI: 10.55927/ministal.v1i1.66

Abstract

This paper aims to research regarding the impacts that social media marketing, hedonic shopping motivation and electronic word of mouth has to a Shopee customer’s impulse purchases. The method applied in this study is quantitative and data will be collected by providing online to Shopee’s customers located in Medan, Indonesia. Convenience sampling method will be used and the sample will be distributed to whom the writer can easily distribute to. From the questionnaires distributed, 97 respondent’s data were obtained and they will be processed with the SPSS statistical software program. Result shows that only hedonic shopping motivation will significantly impact the impulse purchases made by Shopee customers in Medan while the others will not much impact to impulse purchases.
Pengaruh Social Media Marketing dan Tagline terhadap Brand Awareness pada Pattern X Medan Satrio Abiemanyoe; Genesis Sembiring Depari
Jurnal Multidisiplin Madani Vol. 1 No. 2 (2021): November 2021
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.387 KB) | DOI: 10.55927/mudima.v1i2.35

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui bagaimana social media marketing (Instagram) dan Tagline memberikan pengaruh terhadap Brand Awareness.Desain penelitian ini dengan menggunakan metode kuantitatif. Teknik mengambil sample yaitu non probability sampling. Teknik Analisa data yang digunakan adalah analisis deskriptif statistic, uji instrument penelitian, Uji Asumsi Klasik, Analisis Regresi Linear Berganda, Uji Koifisien Determinasi dan Uji Hipotesis. Berdasarkan dari hasil penelitian, data telah terdistribusi secara normal, tidak memiliki interkorelasi antar variabel bebas dan bebas dari heteroskedasitas. Kesimpulan yang dapat diambil adalah Pemasaran Sosial Media memiliki pengaruh positif dan signifikan terhadap Brand Awareness dengan hasil itung t (4.269) ›t tabel(1.984). Tagline memiliki pengaruh positif dan signifikan terhadap Brand Awareness. dengan hasil t-itung (4.625) >t tabel(1.984). Selain itu, Sosial Media Marketing dan Tagline memberikan pengaruh yang positif dan signifikan secara serempak terhadap Brand Awareness sebesar 60%.
Real Estate Segmentation: A Model of Real estate Decision Support System Genesis Sembiring Depari
Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Vol 7 No 2 (2021): Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Muhammadiyah Buton

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.097 KB) | DOI: 10.35326/pencerah.v7i2.1126

Abstract

Due to human limitations of computational thinking, the quality of rational decision-making is constrained, and as a result, people encounter bounded rationality. A decision support system is widely demanding in tackling this problem, especially in real estate management. This study focuses on 3 main purposes. Firstly, comparing K-means, X-means and K-medoid algorithm performance in clustering sold house characteristics to be further used for pricing houses. Second, characterizing each cluster for developing a suitable marketing strategy by utilizing machine learning technology. Lastly, providing a managerial implication as a decision support system for assisting stakeholders in making a decision. Eventually, K-means and X-means algorithm show very similar performance. X-means can automatically determine the number of clusters while k-means utilize the elbow method to find the optimum number of clusters. Three clusters were identified as cluster 0, cluster 1, and cluster 2. Cluster 0 was occupied by 85.77% of low house prices. There are two practical implications of this study. Firstly, the results of clustering analysis which reflected in a model of decision support system. Second, an intuitive and comprehensive methodological framework is presented for helping stakeholders designing a decision support system.
Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan Genesis Sembiring Depari
Journal of Accounting and Management Innovation Vol 4, No 2 (2020)
Publisher : Universitas Pelita Harapan Medan Campus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/ami.v4i2.396

Abstract

The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting.Keyword: social media advertisement, random forest, support vector machine, data mining 
Co-Authors Abiemanyoe, Satrio Adat Muli Peranginangin Albert Owen Alfonsius Candice Yap Yap Cut Alya Fachriza Efin Shu Huang, Jen Peng Indra Indra Jen Peng Huang Jerica Wijaya Joceline Chow Jocellyn, Jocellyn Luis Jonatan Natasya Ginting Ryanto Winata Sari, Mira Satrio Abiemanyoe Shu, Efin Sri Vandayuli Riorini Steven Winnie Lauren