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Journal of Management and Informatics
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29617731     EISSN : 29617472     DOI : 10.51903
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
management and business economics involving operational management, management of human resources, finance management, marketing management, social and economic management
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Sistem Informasi Menejemen
Articles 71 Documents
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Leveraging Machine Learning for Talent Acquisition: Predicting High-Performance Candidates in Human Resource Management Wahyuning, Sri; Sudibyo, Sukemi Kamto
Journal of Management and Informatics Vol. 3 No. 1 (2024): April Season| JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v3i1.44

Abstract

This study explores the application of machine learning (ML) in human resource (HR) management to enhance the recruitment process by predicting high-performing candidates. The research addresses gaps in traditional recruitment methods, which are often time-consuming and susceptible to subjective bias. By employing a Random Forest algorithm, this study utilizes a dataset of 10,000 records, encompassing attributes such as education, work experience, psychometric assessments, and interview evaluations. Data were divided into 70% training and 30% testing sets to ensure robust model evaluation. The findings demonstrate that the Random Forest model achieved a prediction accuracy of 87%, outperforming traditional methods and other ML models like Logistic Regression. The model's ability to identify key attributes contributing to candidate performance underscores its potential for data-driven decision-making in HR management. However, challenges such as data bias, algorithmic transparency, and resistance to technological change were identified as barriers to implementation. This research contributes to the theoretical and practical understanding of ML in HR by offering a predictive model that balances accuracy with interpretability. Practical implications include strategies for integrating ML into existing HR systems, emphasizing the importance of explainable AI to foster trust among practitioners. The study concludes that ML-based recruitment can significantly improve efficiency, objectivity, and the quality of hiring decisions, paving the way for more innovative and strategic HR practices.
Optimization of Supply Chain Processes in the Retail Sector: A Data-Driven Simulation Approach for Inventory Management Huda , Haris Ihsanil; Kusumo, Haryo; Endaryati , Eni
Journal of Management and Informatics Vol. 3 No. 1 (2024): April Season| JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v3i1.47

Abstract

Efficient supply chain management in the retail sector is crucial for cost reduction and enhancing customer satisfaction. This study analyzes and optimizes inventory management using a data-driven simulation approach. By leveraging data on customer demand patterns, replenishment cycles, and delivery times, the research develops simulation models to evaluate inventory strategies under various scenarios, including high demand and seasonal fluctuations. The findings reveal optimal inventory strategies that minimize holding costs while maintaining product availability. Key performance metrics, such as fill rate and stockout levels, show significant improvements when simulation-based strategies are implemented. For example, under high-demand scenarios, the proposed model achieved a 20% reduction in holding costs and a 15% increase in fill rates compared to traditional methods. These results highlight the importance of integrating simulation techniques into retail supply chain management to enhance decision-making and operational efficiency. This research contributes to the literature by providing a practical framework for inventory optimization and offers actionable recommendations for retail managers to adopt technology-driven approaches. Future research should explore applying these methods to retail sectors with diverse demand characteristics.  
Ethical Challenges in AI-Driven Decision-Making: Addressing Bias and Accountability in Business Applications Munifah, Munifah; Wibawa , Eka Satria; Purwantini , Kasih
Journal of Management and Informatics Vol. 3 No. 1 (2024): April Season| JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v3i1.48

Abstract

The adoption of artificial intelligence (AI) in business decision-making has revolutionized operations but introduced critical ethical challenges, particularly in bias and accountability. This study investigates the sources of bias in AI-driven systems and evaluates current accountability frameworks in business contexts. A mixed-methods approach is employed, combining a comprehensive literature review with in-depth interviews with business leaders across technology, finance, and healthcare sectors. The findings reveal that algorithmic and data biases are prevalent, arising from imbalanced training datasets and opaque algorithmic processes. Existing accountability mechanisms are often insufficient, with responsibility dispersed among developers, managers, and regulators. Practical strategies, such as third-party audits and algorithmic transparency initiatives, are emerging but require further refinement. This study emphasizes the need for robust ethical frameworks, including guidelines like Fairness Accountability Transparency Ethics (FATE), to mitigate bias and ensure responsible AI usage. Key recommendations include the adoption of transparent AI models, enhanced regulatory oversight, and targeted training for stakeholders on AI ethics. These insights contribute to the ongoing discourse on ethical AI deployment and provide actionable pathways for businesses aiming to navigate the ethical complexities of AI.
The Impact of Occupational Safety, Workload, and Compensation on Employee Job Satisfaction in the Mining Industry: A Case Study of PT BCKA Haiedar, Muhammad Hafis; Kholifah, Siti
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.53

Abstract

This study investigates the impact of workplace safety, workload, and compensation on employee job satisfaction at PT BCKA, a prominent mining company in Indonesia. Employee job satisfaction is essential for fostering productivity, loyalty, and retention, which are critical in maintaining operational excellence in high-risk industries like mining. The research adopts a quantitative descriptive approach and employs multiple linear regression analysis to assess data collected from 91 employees through stratified random sampling. Data was gathered using a structured questionnaire containing 40 items measuring workplace safety, workload, compensation, and job satisfaction. Instrument reliability and validity were confirmed using Cronbach's Alpha and Pearson correlation. The findings indicate that workplace safety, workload, and compensation significantly influence employee job satisfaction, explaining 93% of its variance (R² = 0.93). Workplace safety positively impacts satisfaction, emphasizing the importance of secure work environments, while workload exhibits a negative effect, highlighting the need for balanced task allocation to mitigate stress. Compensation positively correlates with satisfaction, underscoring the value of equitable and competitive remuneration systems. Among these factors, workload is identified as the most dominant, reflecting its critical role in shaping employee satisfaction. This study offers practical insights into improving job satisfaction at PT BCKA. It recommends enhancing workplace safety protocols, optimizing workload distribution, and implementing transparent and fair compensation systems. Although the study focuses on a single mining company, future research could expand its scope to other industries and incorporate additional variables, such as career development and organizational culture, to provide a comprehensive understanding of job satisfaction.
Ethical Implications of AI-Driven Recruitment: A Multi-Perspective Study on Bias and Transparency in Digital Hiring Platforms musrifah, Fadlul; Hasanah, Ika Ariani
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.140

Abstract

Integrating Artificial Intelligence (AI) into digital recruitment platforms has introduced significant enhancements in efficiency and decision-making, alongside complex ethical challenges regarding fairness, transparency, and accountability in candidate evaluation. This study investigates how leading AI-driven recruitment platforms articulate and operationalize ethical principles and whether these commitments are effectively translated into practice. Employing a qualitative exploratory design, the research analyzes official white papers, privacy policies, and AI ethics statements from LinkedIn, HireVue, Pymetrics, and ModernHire. Data was examined using AI-assisted text mining and thematic content analysis to identify ethical discourse patterns and assess the depth of implementation. The findings indicate that moral terms such as “fairness” and “bias” are cited frequently, with LinkedIn referencing them 27 times and HireVue 19 times. A comparative transparency assessment yielded scores of 8.5 out of 10 for LinkedIn, 7.2 for HireVue, 6.8 for Pymetrics, and 4.3 for ModernHire, while formal mechanisms for candidate appeals were absent on most platforms. This study contributes to the field by revealing a persistent gap between stated ethical ideals and operational practices in AI recruitment and by recommending the adoption of explainable AI, transparent auditing frameworks, and international regulatory standards. Such measures are essential to foster more accountable, equitable, and humane AI-based hiring processes.
Evaluating Green Supply Chain Practices in Southeast Asia: A Text Mining Approach on Corporate Sustainability Reports Anjani, Andi Dwi; Nur Aida, Iqlillah; Muhammad, Faishal
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.141

Abstract

This study addresses the growing imperative for environmentally responsible supply chain management in Southeast Asia and the challenges of assessing corporate sustainability disclosures. Although companies increasingly produce sustainability reports, the extent to which these documents reflect genuine green practices remains unclear. This research systematically evaluates how five major Southeast Asian firms, including Unilever SEA, Nestlé Indonesia, Indofood, Danone, and ThaiBev, articulate green supply chain initiatives in reports published between 2022 and 2023. Employing a qualitative exploratory design, the study integrates document analysis with text mining and thematic coding; approximately 33,000 words from the five reports were processed, yielding 1,300 occurrences of green supply chain terms categorized into three themes: eco-packaging, green logistics, and carbon tracking. The results reveal a pronounced imbalance: eco-packaging comprised 54 percent of keywords (n = 702), green logistics 29 percent (n = 377), and carbon tracking 17 percent (n = 221). Unilever’s 9,300-word report contained 350 mentions of eco-packaging, while Danone’s 5,900-word report featured 310; carbon tracking averaged under 45 references per report. The study introduces a replicable text mining framework for ESG disclosure analysis and underscores the need for more balanced reporting, including Scope 3 emissions data. Future mixed-method approaches that combine computational analysis with qualitative validation are advocated. The findings provide evidence for policymakers and investors to refine ESG guidelines and highlight the potential of computational tools to enhance corporate accountability in sustainability reporting
Data-Driven Decision Making in MSMEs: Leveraging Free Analytics Tools for Financial Planning and Efficiency Pertiwi, Joana Putri; Hana, Afrida Ummu
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.146

Abstract

As a key enabler of enhancement in financial performance and sustainability, Data-Driven Decision-Making (DDDM) within the digital transformation discourse has been helpful for MSMEs. It is unfortunate that in many developing economies, MSMEs become hindered by informal practices due to limited resources, low digital literacy, and complicated perceptions of analytics tools. In this study, we will investigate the practical application of free digital platforms- Microsoft Excel, Google Data Studio, and Canva Analytics support financial planning and operational efficiency in MSMEs. The research applies a descriptive-applicative approach to create realistic financial data representing the fictitious operations of an MSME-from daily sales, operational costs, to promotional expenses over 30 days. Results show that simple dashboards can lead to some critical insights, for instance, weekly net cash flow that peaked at IDR 2,150,000 in Week 3 and IDR 1,980,000 in Week 5, which means greater operational efficiency. A simulated digital promotion campaign saw a Return On Investment (ROI) of 220%, thereby reinforcing the importance of sales and marketing analytics. Furthermore, the operational expense accounted for about 65% of the total expenses, thus showing room for cost optimization. The findings substantiate the fact that with a little training, MSMEs can now take their financial decisions away from intuition and into data-driven decisions using tools that are freely available online. This study presents a framework that is replicable and scalable in the same resource-constrained environments, with enough practical insights for policymakers and MSME development programs who wish to promote digital financial literacy and performance monitoring.
Personalized Digital Marketing Strategies: A Data-Driven Approach Using Marketing Analytics Na, Im Ha; Jae, Yoo In; Hwa, Park In
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.149

Abstract

The rapid development of digital technology has transformed marketing strategies, enabling companies to leverage big data analytics to enhance personalized marketing approaches. With the increasing volume of customer interaction data collected from various digital platforms, businesses can now gain deeper insights into consumer preferences and behaviors. This study aims to analyze the impact of big data analytics on personalized digital marketing and evaluate the role of data visualization in improving decision-making processes. The research employs an exploratory approach by analyzing secondary data from multiple digital sources, including e-commerce platforms, social media, and company websites. The study applies data-driven segmentation models and machine learning-based predictive analytics to assess customer engagement and conversion rates. The findings reveal that implementing big data analytics leads to a 48.57% increase in customer engagement and a 132% improvement in conversion rates compared to traditional marketing methods. Furthermore, the integration of data visualization techniques enables marketers to interpret complex consumer patterns effectively, contributing to a 46.67% rise in average transaction value per customer. These results indicate that data-driven personalization significantly enhances marketing effectiveness and customer loyalty. This research contributes to the field by providing empirical evidence on the advantages of utilizing big data analytics in digital marketing and highlighting the importance of interactive dashboards for real-time customer trend analysis. Future research is encouraged to explore the automation of personalized marketing through machine learning algorithms and the optimization of real-time data-driven strategies.
The Effectiveness of E-Government Services in Enhancing Public Trust: A Comparative Study Across ASEAN Countries Ramadhani, Ditya Putri Safira; Sulaiman, Haikal Rahmat; Anggraeni, Andita Wulan; Aisyah, Siti
Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i1.150

Abstract

The implementation of e-government has become a key strategy for enhancing public trust in government institutions, particularly in ASEAN countries with varying levels of digital transformation. However, the extent to which e-government adoption influences public trust remains unclear due to disparities in digital infrastructure, public engagement, and policy frameworks. This study examines the relationship between e-government implementation and public trust in ASEAN, identifying key influencing factors. This research employs a quantitative approach using panel data analysis of e-government indices and public trust levels from 2015 to 2023. The dataset includes ten ASEAN countries, covering variables such as the E-Government Development Index (EGDI), digital participation rates, and GDP per capita. The results indicate a significant positive correlation (r = 0.78, p < 0.01) between e-government adoption and public trust, with countries having higher digital participation rates (above 60%) experiencing greater trust improvements. Furthermore, transparency and service reliability (β = 0.64) are more influential than economic factors such as GDP per capita (β = 0.32) in shaping public trust. These findings highlight the crucial role of digital service quality and citizen engagement in fostering public trust beyond economic growth. This study contributes to the literature by emphasizing the need to strengthen digital infrastructure and public participation in e-government initiatives. Future research should explore the socio-political aspects of e-government adoption to provide a deeper understanding of its long-term impact on governance.
A New Theoretical Framework For Analyzing The Social And Economic Impacts Of Artifical  Intelligence Within The Digital Economy Oktavia, Anis; Wibowo, Agus
Journal of Management and Informatics Vol. 4 No. 2 (2025): August Season | JMI : Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i2.156

Abstract

The use of artificial intelligence (AI) in the digital economy has drastically changed business processes and public services. However, in another way, this technology further exacerbates social and economic disparities. Disadvantaged groups such as low-skilled workers, micro and small enterprises (MSMEs), and groups less accessible to digital technology are largely not equally endowed with AI technology and digital infrastructure. This research aims to establish a new theoretical framework for comprehending the social and economic impacts of AI uptake in the digital economy, particularly on vulnerable groups. Employing a qualitative case study approach, this study is literature-reviewed and document-analyzed and based on five sociological theories at its essence: Social Stratification, Social Inequality, Social and Cultural Capital, Modern Stratification, and Network Society. The results of the study show that utilization of AI works to benefit individuals or groups with digital literacy skills and technological access, while reinforcing marginalization of those with fewer resources. This situation amplifies inherent structural inequality and creates a new layer in the form of digital stratification. The conceptual framework derived from this study presents an integrated and multi-disciplinary way of comprehending the far-reaching social implications of AI implementation. Apart from identifying the potential setbacks of technological exclusion, this research is also a springboard upon which to design more equitable and inclusive AI policy. By connecting classic sociological theory to present-day digital dynamics, this research presents a new contribution in the guise of analytical tools for assessing justice and inclusion in an AI economy.

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