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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Startupreneur Business Digital (SABDA Journal) Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Ninda Lutfiani
Satya Wacana Christian University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences Other

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Integration of IoT and Blockchain for Business Data Security Uki Hares Yulianti; Yul Ifda Tanjung; Untung Rahardja; Ninda Lutfiani; Adele Valerry
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1040

Abstract

The background of this study is based on the growing reliance of businesses on digital data driven by digital transformation, which demands higher standards of data security and transparency. The IoT and Blockchain are recognized as key technologies that can address these issues, yet empirical research exploring their combined roles remains limited. The objective of this research is to examine the role of the IoT in strengthening business data security, the role of Blockchain in enhancing data transparency, and the effect of integrating both technologies on business data management. This study adopts a quantitative approach, with data gathered through questionnaires distributed to business practitioners who have implemented digital technologies. The data were analyzed using descriptive statistical methods and simple inferential analysis to identify relationships among the research variables. The results show that the IoT positively influences business data security through real time monitoring, while Blockchain improves data transparency and integrity through its immutable recording mechanism. Moreover, the integration of the Internet of Things and Blockchain produces a stronger impact on data security and transparency compared to their individual use. The study concludes that the adoption and integration of the Internet of Things and Blockchain provide effective strategies for organizations to enhance business data security and transparency, while also fostering stakeholder trust and supporting business sustainability in the digital era.
Improving MSME Competitiveness through Visual Design and Digital Branding Training Suryari Purnama; Felix Sutisna; Ninda Lutfiani; Dimas Aditya Prabowo; Syahyono Syahyono
ADI Pengabdian Kepada Masyarakat Vol 6 No 2 (2026): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i2.1457

Abstract

The strategic role of visual design and digital branding training in enhancing the competitiveness of micro, small, and medium enterprises (MSMEs) has become increasingly important in the digital economy era. Many MSMEs still face limitations in market reach and weak brand identity due to inadequate visual communication skills and minimal implementation of digital branding strategies. These challenges reduce their competitiveness in an increasingly competitive online marketplace. This community service program aims to analyze the impact of structured training in visual design and digital branding on improving brand perception, market visibility, and sales performance of MSMEs. This program employs a mixed-method approach by combining pre-test and post-test surveys, in-depth interviews, and direct observation. The participants consisted of 50 MSME actors who took part in a a three-month training program covering logo design, product packaging, social media branding, and digital marketing content creation. Quantitative data were analyzed using paired statistical tests, while qualitative data were examined through thematic analysis. The results indicate a significant improvement in brand consistency, visual attractiveness, digital engagement, and online sales growth after the participants completed the training program. Participants also reported increased confidence in managing brand assets and implementing digital communication strategies. It can be concluded that visual design and digital branding training represent an effective intervention to strengthen the competitiveness of MSMEs, support sustainable business growth, and promote the development of a resilient creative economy in the era of digital transformation.
Enhancing Social Value through Orange Technology Adoption in Creative Industry Micro Enterprises Ninda Lutfiani; Hindriyanto Dwi Purnomo; Heru Riza Chakim; Syahrul Mu’Arif Wahid; Oliver Sauntos
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1353

Abstract

Social media–based digital transformation has become an essential strategy for MSMEs to expand their market reach and strengthen consumer loyalty in the digital economy era. This article presents a conceptual review and empirical synthesis of digital business transformation strategies in MSMEs that utilize social media as the core channel for marketing and customer service. By integrating the Technology Acceptance Model (TAM), Customer Engagement theory, and the Resource-Based View, this paper proposes a strategic framework consisting of (1) digital capabilities, (2) content and engagement, (3) digital after-sales services, and (4) a collaborative ecosystem (platforms and micro-influencers). The literature synthesis indicates that interactive social media activities and responsive services are consistently associated with increased customer engagement and brand loyalty among MSMEs. Practical recommendations and future research directions are provided to support MSMEs in implementing loyalty-oriented digital transformation.
Strategy of Production Efficiency and Improving the Quality of Wooden Sofa Legs in the Manufacturing Industry Untung Rahardja; Ninda Lutfiani; Muhamad Alfi Duwi Juliansah; Ethan Aptman
Startupreneur Business Digital (SABDA Journal) Vol. 4 No. 2 (2025): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v4i2.867

Abstract

This study relates to the challenges faced by the wood furniture industry, specifically in the production of wooden sofa legs, which encounter issues with production efficiency and product quality. Many small and medium-sized enterprises still rely on manual processes that are time-consuming and result in products with varying quality. This research aims to formulate production efficiency strategies that can improve product quality without compromising costs or quality standards. The method used is a descriptive qualitative approach with a case study of a small furniture industry in Pacitan, East Java, through observations, interviews, and documentation, and data analysis using the Miles and Huberman model. The gap in this research lies in the lack of in-depth studies on the production of wooden sofa legs in the context of small and medium-sized industries in Indonesia, particularly in the application of lean manufacturing principles and quality control. The novelty of this research is the integration of lean manufacturing principles and quality control tailored to the specific conditions of small furniture industries, providing strategic recommendations that are practical for industry players. The results and discussion show that the application of the 5S system and production layout improvements successfully increased efficiency and reduced waste, while the implementation of strict quality control at each stage of production improved product consistency. The study also found that worker training and the application of Standard Operating Procedures (SOPs) significantly improved product quality.
A Framework for Mining Customer Data in Management Information Systems Untung Rahardja; Ninda Lutfiani; Agung Rizky; Yul Ifda Tanjung; Richard Evans
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/m5qymx32

Abstract

The exponential growth of customer data within Management Information Systems (MIS) has generated an urgent need for structured analytical approaches capable of transforming raw information into valuable insights that support decision-making across various organizational processes. This study aims to develop a comprehensive and systematic framework for mining customer data in MIS by integrating preprocessing procedures, machine learning algorithms, and model evaluation techniques into a unified analytical workflow. Using the Design Science Research methodology, the framework was designed based on existing data mining standards, developed through iterative refinement, and demonstrated using a customer-behavior dataset processed with clustering, classification, and association rule mining techniques. The findings reveal that the proposed framework improves data quality, enhances segmentation accuracy, and strengthens predictive capability, enabling MIS to deliver deeper insights into customer behavior, purchasing tendencies, and potential churn risks. Experimental results show that combining K-Means, Random Forest, and Apriori algorithms yields more comprehensive and reliable patterns compared to using a single analytical technique. The outcomes of this research highlight the practical significance of applying an integrated data mining approach in MIS, allowing organizations to optimize marketing strategies, personalize services, and make more informed managerial decisions. Overall, this study contributes to the field by offering a scalable, adaptable, and effective framework for implementing customer data mining within real-world MIS environments.
Trustworthy Machine Learning Evaluation Framework for Robust and Interpretable Intelligent Systems Ninda Lutfiani; Sutarto Wijono; Rifqa Nabila Muti; Yasir Mustafa Kareem
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1067

Abstract

Artificial intelligence (AI) deployment in critical domains requires machine learning systems that are not only accurate but also robust, interpretable, fair, and aligned with responsible governance principles. However, conventional machine learning evaluation approaches often prioritize predictive performance and computational efficiency while giving limited attention to ethical accountability, transparency, regulatory compliance, and sustainability. This study aims to develop a trustworthy machine learning evaluation framework for robust and interpretable AI systems. The focus of the study is the evaluation of intelligent systems across healthcare, finance, and transportation, where reliability and accountability are essential for real-world deployment. A qualitative case study approach was employed through expert interviews, literature analysis, document review, and cross-domain case comparisons to identify key evaluation dimensions. The findings show that trustworthy evaluation should integrate technical indicators, including accuracy, robustness, and interpretability, with broader dimensions such as fairness, accountability, governance compliance, and social responsibility. The proposed framework provides a structured model for assessing intelligent systems beyond conventional performance metrics. It also supports better consistency in interpretability assessment, stronger fairness evaluation, and improved alignment with international AI governance expectations. This study contributes to the development of responsible AI by offering a practi- cal evaluation framework that can guide researchers, developers, and institutions in designing machine learning systems that are reliable, transparent, and socially accountable. The framework has implications for sustainable and compliant AI implementation in high-impact sectors.
Co-Authors Adele Valerry Agung Rizky Dimas Aditya Prabowo Ethan Aptman Felix Sutisna Heru Riza Chakim Hindriyanto Dwi Purnomo Muhamad Alfi Duwi Juliansah Oliver Sauntos Richard Evans Rifqa Nabila Muti Suryari Purnama Sutarto Wijono Syahrul Mu’Arif Wahid Syahyono Syahyono Uki Hares Yulianti Untung Rahardja Yasir Mustafa Kareem Yul Ifda Tanjung Yul Ifda Tanjung