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All Journal JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) Jurnal Teknik Informatika (JUTIF)
Muhammad Ihsan Fawzi
Universitas Jenderal Soedirman
Computer Science & IT Decision Sciences, Operations Research & Management

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Dampak Adopsi TI Terhadap Kinerja Perusahaan Multinasional Muhammad Ihsan Fawzi; Apol Pribadi Subriadi
JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) Vol 8, No 2 (2022): JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) MEI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jkbm.v8i2.7320

Abstract

This study aims to classify and provide an overview of the factors that influence the adoption of IT in multinational companies and the impact of IT adoption on the performance of multinational companies. The data for this study were obtained from literature searches published from 2009 to 2021. The searches were conducted on scientific journals published internationally in Elsevier or Science Direct, Emerald Insight, Willey, Taylor and Francis, and the Academy of Taiwan Information Systems Research. The search results related to themes and variables, obtained about 150 articles and selected 40 articles relevant to IT adoption in multinational companies and the impact of IT adoption on the performance of multinational companies. The results showed that technological factors, organizational factors, and environmental factors influenced IT adoption, although technological factors and organizational factors influenced IT adoption more than environmental factors. In addition, IT adoption has a positive impact on the performance of multinational companies, especially economic performance and operational performance. Even so, several studies say that IT adoption does not have a positive impact on the performance of multinational companies. The results of this study also show that to find out the factors that influence IT adoption in multinational companies is to use the TOE framework. IT adoption also has a positive impact on the performance of multinational companies, especially in terms of economic performance and operational performance.
UNRAVELING OF MEN'S FRAGRANCE PREFERENCES ON ONLINE MARKETPLACES: A MACHINE LEARNING STUDY USING DBSCAN CLUSTERING AND LINEAR REGRESSION Alkaf, Zakiyyan Zain; Fawzi, Muhammad Ihsan; Sastyawan, Murti Wisnu Ragil; Putera, Radita Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4187

Abstract

The perfume industry is undergoing significant growth, driving the need to understand consumer preferences, particularly in men’s fragrances, to optimize business strategies. This study aims to analyze and uncover men’s fragrance preferences, using machine learning techniques. A dataset of approximately 1,000 men's perfume records from Kaggle was utilized, where systematic methodologies were employed. Data preprocessing involved handling missing values, removing duplicates, standardizing categorical entries, and performing feature engineering by extracting geographic information from item locations. Exploratory Data Analysis (EDA) was conducted to uncover data distribution. Clustering analysis using DBSCAN revealed consumer segments. Additionally, regression analysis was used to predict sales based on price and location, employing a linear regression model evaluated with metrics like Mean Squared Error (MSE). The findings indicate that price exhibits a complex relationship with sales; while affordable products drive higher sales volumes, premium-priced items cater to a niche yet impactful market segment. Geographic location plays a pivotal role in sales patterns. Clustering analysis reveals two distinct consumer segments: one driven by price sensitivity and another oriented towards premium preferences, influenced by regional factors. Regression analysis demonstrated a negative correlation between price and sales volume, with a coefficient of -1.81, while availability positively influenced sales with a coefficient of 8.36. Despite a moderate model fit (R² = 0.17), the analysis highlights key market dynamics. These insights emphasize the importance of leveraging data-driven strategies to develop targeted marketing campaigns, optimize inventory management and refine market segmentation.
Co-Authors Alkaf, Zakiyyan Zain Apol Pribadi Sastyawan, Murti Wisnu Ragil