Journal of Information Systems and Informatics (Journal-ISI)
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Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI
Full text linkThe adoption of data analytics has led to a paradigm shift in business decision-making, moving from intuition-based to data-driven strategies. Specifically in customer analytics, metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSS), and Repeat Purchase Rate (RPR) are widely used to formulate customer retention strategies. Although dashboard applications like Microsoft Power BI support the visualization of these metrics, existing designs lack integrated filtering capabilities based on demographic characteristics such as gender and age group. This study aims to propose a Power BI dashboard application design that integrates NPS, CSS, and RPR with demographic filters to effectively convey customer loyalty, satisfaction, and advocacy. The research methodology includes four stages which are Power BI understanding, data acquisition, data pre-processing, and metric modeling. The dataset was collected by using an online questionnaire in January 2025 (N = 542). It must be validated and transformed before being modeled by using DAX. The proposed dashboard design offers an interactive interface, allowing users to explore insights through chart elements such as bars and pie slices. This design enhances user experience and supports intuitive analysis, making it a valuable tool for smart device retailers and manufacturers to make data-driven decisions. Additionally, the dashboard is adaptable to other business contexts with similar analytical needs. For real-world implementation, the inclusion of Key Performance Indicators (KPIs) for each metric is recommended to ensure that insights are actionable and aligned with business objectives
Group Decision Making Using Mean SAW Borda and Decision Maker-Based Criteria Weighting
Full text linkIn an organization, unfair decisions are often a problem. Therefore, this study creates a group decision-making model to optimize the achievement of fair decisions. This study combines the Mean and Simple Additive Weighting Borda (SAW Borda) methods. The combination of these methods is called Mean SAW Borda. The Mean calculation is used to obtain the average value of decision-makers. The mean value can be the weight of the group decision-making criteria. The weight is the reference for the SAW Borda calculation. This aims to optimize fair decisions. The SAW Borda calculation provides group decisions. This study uses data on the election of a university's head of the Informatics Engineering study program. In the study program, 24 decision-makers gave scores to choose the head of the study program. The Means SAW Borda method calculates the assessment, and the result is that A4 has the highest decision value (1923.26573). The results of the Mean SAW Borda method are the same as those of the conventional election. The conventional method chooses A4 to be the elected candidate. Based on these results, the Mean SAW Borda method can produce fair decisions and is agreed upon by decision-makers
Data Warehousing for Optimizing Healthcare Resource Allocation in Botswana
Full text linkHealthcare resource allocation remains a persistent challenge in Botswana, primarily due to inefficiencies in data management that obstruct equitable distribution and evidence-based decision-making. Traditional allocation approaches in Botswana exhibit severe fragmentation, low interoperability, and an absence of real-time data analytics factors that contribute to service delivery disparities, especially in rural and underserved areas. In contrast, developed countries have leveraged data warehousing to optimize healthcare resource planning, offering Botswana a proven yet untapped strategic opportunity. This study designs and validates a context-sensitive data warehouse methodology, applying the Kimball Lifecycle model as the guiding framework. A mixed-methods design was adopted, incorporating qualitative interviews with 24 healthcare practitioners and administrators across public and private health facilities, along with quantitative surveys assessing the state of 12 existing health data systems. Results reveal systemic shortcomings in data accuracy (average error rates of 22%), timeliness (with a median data update lag of 14 days), and accessibility (only 38% of facilities had centralized access). Post-implementation of the prototype data warehouse, significant improvements were noted: data accuracy increased by 47%, data accessibility across departments rose to 85%, and decision turnaround time was reduced by 33%. The warehousing also demonstrated cost-effectiveness, reducing redundant data handling expenses by an estimated 18% over six months. In conclusion, this study presents a robust, scalable, and locally adaptable data warehousing framework that effectively addresses Botswana’s systemic challenges in healthcare resource allocation
Sentiment Analysis of Consumer Acceptance of Honda’s Digital Marketing Strategy Using Lexicon-Based Algorithm
Full text linkThis study analyzes customer sentiment toward Honda’s digital marketing strategy via the Wahana Honda application. A total of 2,000 customer reviews were collected from the Google Play Store using web-scraping techniques. Text data underwent preprocessing (e.g. cleansing, tokenization, stop-word removal, stemming, and translation into English). Sentiment classification using a lexicon-based approach revealed that 56.7% of reviews were positive, 20.8% neutral, and 22.5% negative. The model demonstrated high precision in identifying negative sentiment, though it showed limitations in classifying neutral opinions due to linguistic ambiguity. These findings highlight the need for more adaptive sentiment models and offer strategic insights for Honda’s digital marketing. Specifically, the analysis can help prioritize improvements in app functionality, excellence service priority, enhance personalized customer engagement, and shape targeted digital marketing strategies based on real user feedback. Leveraging these insights enables Honda to optimize user experience, increase retention, and align digital campaigns with customer expectations
EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition
Full text linkFacial expression recognition (FER) remains a challenging task due to the subtle visual variations between emotional categories and the constraints of small, controlled datasets. Traditional deep learning approaches often require extensive training, large-scale datasets, and data augmentation to achieve robust generalization. To overcome these limitations, this paper proposes a hybrid FER framework that combines EfficientNet B0 as a deep feature extractor with an L2-regularized Support Vector Machine (L2-SVM) classifier. The model is designed to operate effectively on limited data without the need for end-to-end fine-tuning or augmentation, offering a lightweight and efficient solution for resource-constrained environments. Experimental results on the JAFFE and CK+ benchmark datasets demonstrate the proposed method’s strong performance, achieving up to 100% accuracy across various hold-out splits (90:10, 80:20, 70:30) and 99.8% accuracy under 5-fold cross-validation. Evaluation metrics including precision, recall, and F1-score consistently exceeded 95% across all emotion classes. Confusion matrix analysis revealed perfect classification of high-intensity emotions such as Happiness and Surprise, while minor misclassifications occurred in more ambiguous expressions like Fear and Sadness. These results validate the model’s generalization ability, efficiency, and suitability for real-time FER tasks. Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment
Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classificatio
Abstractive Text Summarization to Generate Indonesian News Highlight Using Transformers Model
Full text linkThe increasing volume of information has led to the phenomenon of information overload, a condition where individuals struggle to filter and comprehend information efficiently within a limited time. To address this issue, automatic text summarization serves as an essential approach. This research aims to assess effectiveness of two transformer-based models, IndoT5 and mBART, by comparing their ability to generate abstractive summaries (highlight) of Indonesian news articles. The abstractive approach allows models to generate new sentences with more natural language structures compared to extractive methods. Fine-tuning for both models was conducted using a dataset comprising 10,410 news articles from Tempo.co, each containing full news content and a corresponding highlight used as a reference. ROUGE and BERT-Score metrics were employed in the evaluation process to assess structural and semantic correspondence between the references and the generated summaries. Results show that IndoT5 outperformed in terms of ROUGE-1 (0.43087), ROUGE-2 (0.29143), ROUGE-L (0.39224), BERT-Score Recall (0.89130), and F1 (0.87708), indicating its capability to generate complete and relevant news highlight. Meanwhile, mBART achieved a higher BERT-Score Precision (0.86717) but tended to generate less informative outputs. The findings of this research are expected to aid in enhancing the coherence and efficiency of abstractive summarization systems
Taxicab Entrepreneurs’ Attitude to Continue Using e-Hailing Platforms in South Africa
Full text linkTaxicab entrepreneurs who operate on e-hailing platforms in South Africa face challenges such as earning below minimum wage, lacking employment benefits, working long hours, and experiencing victimisation by traditional taxicab operators. The key question is why these entrepreneurs continue using e-hailing platforms despite unfavourable working conditions. This study proposed that technology adoption factors enable entrepreneurs to overcome challenges and encourage them to keep using e-hailing platforms. Based on this assumption, this study investigated the determinants of technology adoption that influence the attitude of taxicab entrepreneurs to continue using e-hailing platforms in South Africa. The researchers gathered quantitative data from 253 entrepreneurs in Johannesburg, South Africa and tested the hypotheses with multiple regression analysis. The results demonstrated that perceived usefulness, benefits, and security strongly influenced entrepreneurs' willingness to continue operating on e-hailing platforms. However, perceptions of convenience, trust, and perceived ease of use did not affect their decision to use e-hailing services. Theoretically, this study pinpointed the factors that drive and hinder the continued use of e-hailing applications. Practically, the results provide insights into understanding long-term usage, user satisfaction, and the success of e-hailing in developing countries undergoing digital transformation, such as South Africa
Applying the Periodic Review System Method in Progressive Web Apps for E-Commerce Inventory Management
Full text linkRetail businesses, particularly hardware stores, often encounter challenges in order management such as delayed deliveries, inaccurate stock tracking, and limited information transparency factors that hinder operational efficiency and customer satisfaction. This study proposes a web-based order management system utilizing Progressive Web Apps (PWA) technology, developed with the Next.js framework. The Periodic Review System (PRS) method is implemented to calculate reorder points based on actual demand and safety stock levels. System development follows the Waterfall model, with data collected through observation, semi-structured interviews, and literature review. Testing confirms that the application enhances stock accuracy, minimizes delivery delays, supports offline access, and meets SEO performance standards. The implementation significantly improves operational efficiency and holds promise for boosting customer loyalty. The study concludes that PWA-based digital systems are practical, scalable solutions for the MSME sector, with future potential for integration of AI, CRM, and real-time analytics
A Hybrid Framework for Enhancing Privacy in Blockchain-Based Personal Data Sharing using Off-Chain Storage and Zero-Knowledge Proofs
Full text linkBlockchain technology presents transformative opportunities for secure personal data sharing, particularly in healthcare, finance, and identity management. However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). This study introduces a novel hybrid framework that integrates the InterPlanetary File System (IPFS) for off-chain storage with Zero-Knowledge Proofs (ZKPs) to enhance privacy, ensure regulatory compliance, and reduce on-chain storage demands. Employing a Design Science Research (DSR) methodology, the framework was developed and validated using Ethereum and Hyperledger Fabric, guided by insights from a systematic review of 180 studies from 2018 to 2023. Empirical evaluations revealed a 75% reduction in blockchain storage, 98% GDPR compliance, and zk-SNARK proof verification times below one second. The framework also enables GDPR-compliant erasure by removing encrypted off-chain data while preserving on-chain auditability. Despite challenges such as IPFS latency and trusted setup complexities, the solution offers a scalable and privacy-preserving architecture applicable to real-world domains, especially in privacy-critical environments like healthcare and finance by resolving blockchain’s GDPR compliance paradox
Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia
Full text linkThe fitness industry in Yogyakarta is experiencing rapid growth accompanied by intense competition among gym service providers. This has led to an increase in membership churn, negatively impacting business sustainability. This study aims to conduct a comparative analysis of three supervised classification algorithms Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to predict member churn at EightGym Indonesia. The dataset, consisting of 1,287 membership records collected between July 2024 and April 2025, includes features such as visit frequency, subscription duration, membership type, and churn status. The study focuses on predicting members at risk of subscription cancellation using historical data such as visit frequency, subscription duration, membership type, and churn status. The methodology follows the CRISP-DM framework, covering business understanding, data preparation, modeling, evaluation, and deployment stages. Evaluation results indicate that XGBoost delivers the best performance with 95% accuracy, high recall, and F1-score, making it the most effective algorithm for churn prediction in this context. Additionally, the model was implemented in a web-based prototype application to support gym management decision-making. The findings contribute significantly to the application of machine learning for customer retention strategies in the fitness industry and provide a foundation for the future development of predictive decision support systems