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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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Beyond Descriptive Analytics: Predictive Models For Strategic Marketing Decisions Putri, Nabila; Ainindhira, Aghisti
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.v4i2.165

Abstract

As marketing ecosystems grow increasingly complex, organizations are under pressure to move beyond descriptive analytics and embrace predictive models to enhance strategic marketing decisions. This study investigates the application of predictive analytics, powered by machine learning, in optimizing marketing decision-making processes. Employing a quantitative research approach, we analyzed historical and behavioral data from a digital retail platform over a 12-month period. Several machine learning techniques logistic regression, random forest, and XGBoost were used to build predictive models that estimate customer conversion probabilities and forecast campaign outcomes. The results indicate that predictive models can significantly improve the precision of strategic marketing initiatives, enabling marketers to identify high-value customer segments and allocate resources more efficiently. Compared to traditional methods, the predictive approach led to a measurable uplift in campaign effectiveness and ROI. From a managerial perspective, the study highlights how data-driven strategy and real-time insights can support agile, evidence-based decision-making in competitive markets. Academically, this research contributes to the growing field of predictive marketing analytics by demonstrating the strategic utility of machine learning techniques. In an era where consumer behavior evolves rapidly, leveraging predictive tools is no longer optional it is essential.
Measuring the forecast accuracy in retail MSMEs: A comparative analysis between AI and traditional methods in the era of digital selling Hikmah, Nur; Fauzi, Achmad; Nayyiroh, Frida Ulfatun
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.v4i1.166

Abstract

Accurate sales forecasting is essential for retail Micro, Small, and Medium Enterprises (MSMEs) to optimize operations and inventory planning in the digital economy. This study compares the forecasting accuracy between Artificial Intelligence (AI)-based methods (Random Forest, Decision Tree) and traditional techniques (Moving Average, Exponential Smoothing) using 3,600 transaction records from five retail MSMEs over three months. A quantitative experimental approach was employed to evaluate model performance under real-world conditions, including market fluctuations and seasonal anomalies. Evaluation metrics include Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and cross-validation techniques. The findings indicate that the Random Forest model achieves superior accuracy (MAPE = 8.5%) compared to traditional methods (MAPE = 15.2%). Explainable AI (XAI) using SHAP and LIME further enhances transparency and managerial trust. Although traditional methods offer faster computation and ease of interpretation, AI-based models show resilience against unpredictable sales patterns. This research recommends hybrid adoption strategies that balance predictive power with interpretability for MSMEs with limited technical capacity. The results contribute to the discourse on digital transformation and intelligent forecasting in the MSME sectors.
Effectiveness and Reliability of Artificial Intelligence in Fraud Detection: A Mixed-Method Study on Financial Audit Naseer, Khasif; Ahmed, Hakeem Nazeer
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.v4i1.168

Abstract

Financial statement fraud threatens investor trust at a substantial level in the present market conditions. AI technology, through data pattern analysis, helps financial auditing reach better results when detecting rumors along with anomalies and suspicious trends. This research evaluates artificial intelligence's effectiveness in yeast-free detection systems through several investigative methods. An evaluation of AI systems by professionals indicates their ability to detect financial statement fraud accurately. A quantitative analysis of historical data through AI enables fraud pattern detection according to this study method. The researchers who utilize the qualitative method meet with forensic accountants for their research work. The research delivers both forensic accountants and financial auditors definitive information about the challenges they face and their perspectives toward AI system implementation in audit procedures. The results show that AI is very successful when recognising fraud trends, particularly when using machine learning and deep learning approaches. However, the quality of the data and the settings of the algorithms still have an impact on how reliable AI is. Furthermore, despite ongoing worries about result interpretation and accountability of AI models, qualitative data suggests that auditors generally embrace AI as a tool that speeds up the audit process. According to the study's findings, artificial intelligence (AI) can effectively assist financial audits; however, to improve the validity of fraud detection, it should be used in addition to the analysis of qualified examiners. To increase the accuracy of fraud detection in the future, this study suggests creating more transparent AI models and integrating AI with blockchain technology.
Digital Marketing Ethics in the Age of AI: A Comparative Analysis of Transparency and Consumer Trust in E-Commerce Platforms Hidayat, Muhammad Sayamsul; Muhammad, Wafa; Isdayanti, Putri Laila
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.v4i1.178

Abstract

The increasing integration of Artificial Intelligence (AI) in e-commerce has transformed digital marketing strategies, particularly through personalized recommendation systems. However, this advancement has raised ethical concerns regarding data privacy, algorithmic transparency, and consumer autonomy. In Southeast Asia, where digital platforms rapidly expand, these concerns become more complex due to diverse regulatory landscapes and user expectations. This study aims to analyze and compare the transparency of AI-based recommendation systems across three major e-commerce platforms in the region: Shopee, Lazada, and Tokopedia. A qualitative comparative approach was employed using content analysis of official policy documents, including privacy policies and AI guidelines, sourced from each platform between January and March 2024. The analysis utilized LLM-assisted text mining and thematic coding to identify ethical indicators such as algorithmic transparency, user control, and policy readability. The results reveal significant variation in ethical practices: Shopee scored the highest in all dimensions, including algorithmic transparency (score: 9/10), user control (8/10), and policy readability (9/10). Lazada ranked moderately with scores of 6/10, 4/10, and 6/10 respectively, while Tokopedia scored lowest with 3/10 for transparency, 0/10 for user control, and 4/10 for readability. These disparities indicate that ethical communication and user empowerment remain uneven across platforms. This study contributes to the discourse on digital ethics by highlighting the need for regional standards in AI transparency and promoting user-centric ethical design. The findings provide valuable insights for policymakers, platform designers, and digital consumers navigating trust in algorithm-driven environments.
AI-Driven Sentiment Analysis of Retail Investor Behavior during Market Volatility: A Study of Twitter Data in Southeast Asia Sriasih, Sutriani Dewi; Razak, Farhat Abdul; Ikhsan, Hussein al Ikhsan
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.v4i1.179

Abstract

In recent years, retail investor participation in Southeast Asian capital markets has surged, contributing to increased market volatility and making sentiment analysis a critical factor in understanding price dynamics. This study investigates the relationship between social media sentiment and stock market fluctuations by focusing on Twitter data during periods of market volatility in Indonesia, Thailand, and Malaysia. The objective is to examine how collective investor emotions, as expressed through social media, correlate with daily stock index movements. Employing an exploratory quantitative approach, the study integrates Natural Language Processing (NLP) methods, both lexicon-based tools such as VADER and advanced transformer-based models like BERT and GPT, to classify over 150,000 tweets into positive, negative, and neutral sentiments. Sentiment scores were then aggregated and statistically tested using Pearson correlation with daily stock index returns, specifically the IDX Composite, SET Index, and FTSE Bursa Malaysia. The findings reveal a significant negative correlation between negative sentiment and market returns, particularly in the IDX Composite (r = -0.61, p < 0.05), indicating that pessimistic sentiment is associated with market downturns. Thailand’s SET Index and Malaysia’s FTSE Index showed moderate to weak negative correlations, with r = -0.43 and r = -0.27, respectively. These results highlight the sensitivity of emerging markets to emotionally driven retail behavior. The study concludes that AI-based sentiment analysis offers a valuable early warning tool for market volatility and can complement traditional financial indicators. It recommends developing AI-based sentiment dashboards and enhancing digital financial literacy to mitigate emotional reactivity among retail investors.
Implementation of MIS (Management InformationSystem) to Improve Efficiency and Security of Interbank transactions Using BCA Mobile   (Case Study at Bank BCA Tbk) Handoko, Melyani; Yulianto, Andri Rizko; Jatinurcahyo, R.; Subariyanti, Herudini; Nikmah, Wasilatun; Adawia, Popon Rabia; Yulianto; Armaniah, Henny
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.v4i2.201

Abstract

The rapid advancement of digital technology has revolutionized the financial services industry, compelling banks to adopt more efficient and secure digital platforms. This study explores the implementation of a Management Information System (MIS) to enhance the efficiency and security of interbank transactions through the BCA Mobile application, a leading digital banking solution provided by PT Bank Central Asia (BCA). The research employs a case study approach and gathers primary data from 30 respondents who are active users of BCA Mobile. Using SPSS for validity testing, the results indicate that the efficiency variable (X1) has a strong and significant positive correlation with improved transaction performance, while the security variable (X2) also contributes positively, albeit to a lesser degree. The findings suggest that an integrated MIS—featuring rapid data access, process automation, and biometric authentication—can substantially enhance operational reliability and customer trust. Moreover, the prototype development model used in this study supports user-centered design iterations to optimize usability and system functionality. The research contributes to digital banking literature by demonstrating how a tailored MIS can support financial inclusion, customer satisfaction, and strategic agility in Indonesia’s competitive banking sector. It also offers practical implications for banks seeking to minimize operational risks and improve service quality through system innovation. This study underscores the significance of continual MIS enhancements to sustain customer confidence and address evolving cybersecurity threats in the digital era.
Analysis of Price and  Product Perceived Bolster Purchase Decision: Study Case in Fast Food Restaurant in East Bekasi Area Rahadjeng , Indra Riyana; Handoko, Melyani; Indrarti, Wahyu; Shaura, Rizkiana Karmelia; Rafik, Ahamd; Daniel; Emita, Isyana; Anwar, Dian Mohamad
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.v4i2.202

Abstract

The increasing competition among fast food restaurants in urban areas has made consumers more selective in their purchasing decisions, with price and product quality emerging as key determinants. This study aims to analyze the influence of perceived price and perceived product quality on consumer purchasing decisions at Subway fast food restaurants in East Bekasi. A quantitative approach was applied using a structured questionnaire distributed to 50 consumers. Data were analyzed through multiple linear regression and classical assumption testing. The results indicate that price has a significant and positive effect on purchasing decisions, with a regression coefficient of 1.191 and a partial determination coefficient (R²) of 0.750, suggesting that price alone explains 75% of the decision variance. Similarly, perceived product quality also demonstrates a significant positive effect with a coefficient of 0.469 and R² of 0.724. When combined, both variables explain 72.4% of the variance in purchasing decisions, with the F-test result of 91.673 surpassing the critical value of 3.20. These findings suggest that consumers in East Bekasi prioritize both affordability and quality when choosing fast food. This research contributes to the literature by validating the dual importance of economic and experiential value in fast food marketing strategies, especially in highly competitive suburban environments.
Framework Analysis of Smart House based on Orange Technology use Systematic Literature Sudrajat, A.; Handoko, Melyani; Zahra; Kurniawan, Hendra; Solehudin, Didin; Sari, Dian Indah; Sumantri, Fazhar
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.v4i2.210

Abstract

Now days, the home environment is still much  less supportive of life for the elderly, most elderly living at  home need a companion to help them. Safety, health, happiness  and independence will be difficult for them to get even the  elderly will be further away from the surrounding  environment. Based on population projection 2017 data there  are 23.66 million elderly people in Indonesia and predicted in  2020 there are 27.08 million people elderly. Smart homes that  are currently widely studied have not focused on elderly; most  smart homes only provide a sense of security and convenience  for adult residents. And this will be a problem that until now  has not solved the improvement of human life through  technology to get happiness, care and health especially for the  elderly. In many cases of the elderly, it is easier to send them to  live in a nursing home and that keeps them separated from  their families for the rest of their lives. That's what makes  them less happiness. Orange Technology is a collection of  technological elements to improve human life by getting  happiness, Care and health. This study reviewed the journals  of scientific databases such as IEEE explore, ACM digital  library and Proquest published from 2002 to 2017. From the  search results obtained 54 papers that will answer the scientific  questions of this research. The result of this research is a  framework of smart house that has Sensor, Monitoring,  Wireless, Scalability, Low cost, GPS and ease of installation  and maintenance as components of smart house of orange  technology for elderly.
Determination of Employee Performance: Work Environment and Leadership Style : (Case Study at PT. MPIW Jakarta) Roni , Faizal; Handoko, Melyani; Parancika, Rd Bily; Aris, Mohammad; Ardi, Yahya Mara; Syabrinildi
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.v4i2.216

Abstract

In today’s dynamic industrial environment, optimizing employee performance has become a strategic imperative for organizational sustainability. Among the factors that significantly influence workforce outcomes, the physical work environment and leadership style stand out as pivotal drivers. This study explores how these two variables affect employee performance within PT. MPIW, a prominent manufacturing firm in Jakarta. Employing a quantitative approach, data were obtained through structured questionnaires from 50 purposively selected respondents working in logistics, production, and purchasing divisions. Using IBM SPSS Statistics 21, the analysis revealed compelling insights. The work environment demonstrated a strong and positive influence on employee performance (t = 11.883, p < 0.001), affirming that factors such as spatial adequacy, safety, and interpersonal dynamics play a crucial role in enhancing productivity. Surprisingly, leadership style exerted a negative and statistically significant effect (t = -3.880, p < 0.001), suggesting that the prevailing authoritarian tendencies may undermine employee morale and engagement. The joint influence of both variables was also found to be significant (F = 86.518, p < 0.001), underscoring their combined relevance in shaping performance outcomes. This study contributes a critical perspective on organizational behavior in the manufacturing sector by integrating environmental and managerial dimensions into a unified analytical model. The findings underscore the need for adaptive, participatory leadership approaches and strategic enhancement of workplace conditions to drive sustainable performance improvements.
Exploring the Impact of Artificial Intelligence on Customer Experience Personalization and Marketing Strategy Optimization in Digital Marketing: An Empirical Analysis. Owusu-Mensah , Daniel; Sarfo, Philip Adu; Kusi, Gideon Appiah
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.v4i2.242

Abstract

This study explores the transformative role of Artificial Intelligence (AI) in enhancing customer experience personalization (CEP) and optimizing marketing strategies (MSO) within digital marketing environments. Grounded in the Technology Acceptance Model (TAM), the research incorporates ethical and privacy concerns (EC) as moderating factors influencing AI adoption and its effectiveness. Using survey data from marketing professionals across diverse industries, the study employs Structural Equation Modeling (SEM) to analyze complex relationships among AI use, CEP, MSO, and marketing outcomes. The findings reveal that AI significantly improves customer engagement and marketing performance by enabling tailored interactions, data-driven segmentation, and campaign optimization. Moreover, ethical and privacy concerns positively moderate these effects, underscoring the necessity of responsible AI practices in sustaining consumer trust and regulatory compliance. By offering empirical insights into the interplay between AI, personalization, strategy, and ethics, this study contributes to both theoretical development and practical guidance for businesses navigating digital transformation. The research highlights AI’s strategic potential in creating sustainable competitive advantage while advocating for ethical safeguards in technology-driven marketing.

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