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Taqwa Hariguna
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IJIIS: International Journal of Informatics and Information Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : https://doi.org/10.47738/ijiis
Core Subject : Science,
Computer Science & IT
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 7, No 3: September 2024" : 5 Documents clear
A Multiple Linear Regression Approach to Predicting AI Professionals’ Salaries from Location and Skill Data Maidin, Siti Sarah; Yi, Ding; Ayyasy, Yahya
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.213

Abstract

The rapid growth of Artificial Intelligence (AI) industries worldwide has increased the demand for skilled professionals and highlighted the need to understand salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes, and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677 USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary outcomes (p 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation policies, professionals assessing market value, and educators aligning training programs with evolving industry demands.
Analyzing the Impact of Company Location, Size, and Remote Work on Entry-Level Salaries a Linear Regression Study Using Global Salary Data Khosa, Joe; Mashao, Daniel; Subekti, Fajar
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.215

Abstract

This research explores the key factors influencing entry-level salaries in the global labor market of 2024, emphasizing the roles of company location, organizational size, and the extent of remote work in shaping compensation levels. Drawing on the Global Salary 2024 dataset from Kaggle, which comprises over 5,600 observations across multiple industries and geographic regions, the study applies a multiple linear regression model executed in Python via Google Colab to quantitatively examine salary disparities. The results indicate that company location and size significantly affect entry-level earnings, underscoring how regional economic contexts, cost-of-living variations, and organizational capacity continue to drive wage formation. Conversely, the remote work ratio exhibits a negligible and statistically insignificant effect, implying that flexibility in work arrangements has yet to translate into measurable financial value for early-career professionals. Furthermore, introducing job title as a control variable enhances the model’s explanatory power, reaffirming the influence of individual skill specialization and job function in determining compensation outcomes. These findings reinforce human capital theory while extending it by incorporating contextual and organizational dimensions relevant to the digital labor economy. For job seekers, the study offers data-driven insights to guide career decisions and salary expectations across regions, while employers may utilize the results to formulate fair and competitive pay strategies in an increasingly interconnected workforce. Ultimately, this study provides a comprehensive understanding of how structural and individual factors interact to shape entry-level salary dynamics in the modern digital era.
Determinants of Consumption Behavior Among the Millennial Generation Saputra, Aina Aldi; Sarmini, Sarmini; Widiawati, Chyntia Raras Ajeng; Yunita, Ika Romadoni
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.216

Abstract

This study examines the factors influencing consumption behavior among the millennial generation, emphasizing the effects of family size, education level, and income on food, non-food, and total household expenditure. As digitalization and demographic shifts continue to redefine modern lifestyles, understanding millennial consumption patterns offers valuable insights into changing welfare dynamics and economic structures. Employing a quantitative associative approach, data were collected from 120 millennial households through structured questionnaires and interviews, complemented by secondary data from the Central Statistics Agency (BPS). Multiple linear regression analysis was used to evaluate both simultaneous and partial relationships among variables, while descriptive statistics were applied to illustrate the respondents’ socioeconomic characteristics. The findings show that family size, education, and income collectively have a significant influence on consumption across all categories. Partially, family size and income significantly affect food-related spending, whereas education does not exhibit a notable impact in this segment. In contrast, for non-food and total consumption, all three variables display a positive and significant relationship, suggesting that higher income and education levels encourage more diversified expenditures. Moreover, non-food consumption (57.19%) surpasses food consumption (42.81%), supporting Engel’s Law and indicating improved living standards alongside a shift toward lifestyle diversification. Nonetheless, the proportion of non-food expenditure remains moderate, reflecting cautious financial behavior amid lingering post-pandemic income constraints. These findings align with Keynesian and Life-Cycle consumption theories, illustrating how income stability, education, and life-stage factors shape millennial consumption decisions. Overall, this study underscores the evolving nature of millennial households toward technology-driven, experience-based, yet financially mindful consumption patterns, providing implications for policymakers and businesses to enhance income resilience, digital literacy, and sustainable consumption growth in the digital economy.
A Quantitative Study on User Experience Dimensions and Their Impact on User Satisfaction in Indonesian Mobile E-Commerce Saputra, Afif Dwi; Tarigan, Riswan E.; Wijaya, Yoana Sonia
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.217

Abstract

This research examines how user experience (UX) dimensions influence user satisfaction in Indonesia’s mobile e-commerce ecosystem. As mobile shopping continues to dominate digital transactions, understanding the relationship between UX and user satisfaction becomes crucial for maintaining platform competitiveness. Adopting a quantitative explanatory approach, the study gathered data from 100 active users of leading e-commerce platforms such as Shopee, Tokopedia, and Lazada through an online questionnaire. The instrument was based on the User Experience Questionnaire (UEQ) framework, encompassing six dimensions—Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty—with user satisfaction serving as the dependent variable measured via validated Likert-scale indicators. Analytical procedures included descriptive analysis, reliability and validity tests, and multiple linear regression using SPSS version 26. The findings reveal that five out of six UX dimensions significantly and positively affect user satisfaction (p 0.05). Among them, Perspicuity and Efficiency exert the strongest influence, underscoring the importance of intuitive interface design and smooth, error-free transaction processes. Dependability, Attractiveness, and Stimulation also play notable roles, indicating that both functional performance and emotional engagement contribute to favorable user experiences. Conversely, Novelty—though positively associated—does not reach statistical significance, implying that while users appreciate innovation, they prioritize clarity and reliability. The regression model yields an R² value of 0.742, suggesting that UX dimensions collectively account for 74.2% of the variance in user satisfaction. Overall, the study affirms that UX is a decisive factor in shaping user satisfaction and loyalty in mobile e-commerce environments. It enriches existing UX scholarship by providing empirical evidence from Indonesia’s fast-growing digital market. Practically, the results encourage developers to emphasize usability, dependability, and aesthetic design to maintain user engagement. Future studies are recommended to integrate trust, emotional attachment, and emerging technologies such as artificial intelligence and augmented reality to obtain a more comprehensive understanding of user satisfaction in digital commerce.
A Quantitative Study on Social Media Usage Patterns and Their Effects Among Internet Users Prasetya, Tegar Yudha; Hery, Hery; Haryani, Calandra
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.218

Abstract

This research conducts a quantitative analysis of social media usage habits and their effects among internet users, utilizing a secondary dataset of 999 respondents drawn from the Social Media Usage Survey available on Kaggle. Employing a descriptive–survey design, the study adopts a quantitative approach to examine behavioral tendencies, demographic variations, and relationships among variables such as usage duration, user motivation, privacy awareness, and intentions to reduce social media activity. Data analysis was performed using Python, incorporating descriptive statistics, crosstab analysis, and visual analytics through the Pandas, Matplotlib, and Seaborn libraries. The findings reveal that social media is deeply embedded in everyday routines, with users averaging 3.5 hours of screen time per day. Instagram, Facebook, and Twitter/X emerge as the most frequently used platforms, serving purposes that include entertainment, information access, and business promotion. Video-based content dominates user preferences, reflecting the broader global media consumption trend. Additionally, 69% of respondents acknowledge that social media influences their purchasing behavior, while 65% express moderate to high levels of privacy concern. Notably, about 68% of users report an intention to reduce their screen time, indicating a growing awareness of the need for digital balance. Correlation analysis shows that individuals with longer screen durations and heightened privacy concerns are more likely to intend reducing their usage, suggesting that excessive engagement may drive self-regulatory behavior. These insights illustrate the dual nature of social media—as a medium for empowerment and connectivity, yet simultaneously a potential source of psychological fatigue. Overall, this study contributes empirical evidence supporting efforts to foster healthy and responsible digital engagement, enriching the broader discourse on digital well-being, online literacy, and sustainable technology use in the modern digital landscape.

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