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All Journal Aptisi Transactions on Management ADI Journal on Recent Innovation (AJRI) International Journal of Engineering, Science and Information Technology International Journal of Cyber and IT Service Management (IJCITSM) Startupreneur Business Digital (SABDA Journal) Journal of Computer Science and Technology Application
Nanda Septiani
University of Raharja
Religion Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Influence Of Third Party Funds on Credit Distribution Husin; Cicilia Sriliasta Bangun; Toni Suhara; Nanda Septiani; Alexander Williams
ADI Journal on Recent Innovation Vol. 4 No. 1 (2022): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v4i1.749

Abstract

The purpose of this research is to identify the influence of Third-Party Funds, Non-Performing Loans (NPL), and Return on Assets on Credit Distribution. As well as the role of ROA which becomes mediation in PT. XYZ. This study uses samples in the form of publication balance sheet reports from PT. XYZ in the period 2019-2021. This research is a quantitative research using SEM PLS techniques through the help of smart pls 3.0 software. The results of the study found that DPK significantly affects PK, with a statistical T value of 6,556 > 1.96 and the original sample of -0.773. The absence of influence of NPL on PK due to its statistical T value of 0.868 < 1.96 and the original sample of -0.146 has a negative relationship to credit distribution. ROA has no direct influence on PK with a statistical T value of 0.006 < 1.96 and the original sample of -0.002 has a negative relationship meaning to PK. DPK had a significant negative influence on ROA with a statistical T value of 2,966 > 1.96 and the original sample of -0.657 having a negative relationship direction. There is no effect of NPL on ROA with a Statistical T value of 0.205 < 1.96 and the original sample of 0.056 has a positive relationship to ROA. NPL has no influence over PK through ROA mediation with its Statistical T value of 0.001<1.96.  
Apply the Search Engine Optimization (SEO) Method to determine Website Ranking on Search Engines Fifin Alfiana; Nimatul Khofifah; Tarisya Ramadhan; Nanda Septiani; Wahyuningsih Wahyuningsih; Nadia Nur Azizah; Nova Ramadhona
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 3 No. 1 (2023): April
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v3i1.126

Abstract

In today's digital age, the internet has become an essential tool for various activities, including business promotion. One effective way to promote your business online is by creating a website that offers useful information to visitors. However, simply having a website is not enough; it is crucial to ensure that your website ranks high on search engine results pages (SERPs) in order to attract more visitors. One of the most effective methods to optimize a website for search engines is through search engine optimization (SEO) techniques. These techniques aim to improve a website's visibility on SERPs by using various tactics, such as keyword research, content optimization, and link building. However, it is important to note that SEO optimization is not a one-time task but rather an ongoing process. It requires constant attention and effort to maintain high search engine rankings and ensure the continued success of your online business. By optimizing your website, you can improve its visibility on search engines, attract more visitors, and ultimately drive business growth. It is important to remain vigilant in implementing SEO techniques to maintain high search engine rankings and ensure the continued success of your online business.
Enhancing Educational Management through Social Media and E-commerce-Driven Branding Susy Alestriani Sibagariang; Nanda Septiani; Anthony Rodriguez
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 5 No. 2 (2025): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v5i2.222

Abstract

Intensified digital competition is forcing organizations across diverse sectors, from the B2B carton box industry to Educational Management, to seek differentiation through strategic digital branding. This is particularly true for industrial firms often trapped by commoditization. However, existing literature frequently analyzes social media and e-commerce in silos, creating a research gap regarding their synergistic impact on internal management. This study aims to fill this gap by empirically validating a holistic model that links the strategic integration of these digital platforms to brand enhancement, management quality, and sustainable competitive advantage. The novelty of this research lies in demonstrating this complete causal pathway within an industrial B2B context, offering a transferable framework for other fields. Using a quantitative approach, survey data from 150 respondents in the carton box industry were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirmed all five hypotheses, revealing that strategic integration is an exceptionally strong predictor of brand enhancement (β = 0.979). The findings cruciallydemonstrate that brand enhancement is the primary mediating variable; its effect on improving management quality and securing a competitive advantage is substantially stronger than other direct paths. This study concludes that the most effective mechanism for organizations, industrial companies, or institutions in educational management, to achieve market leadership in the digital era is by strategically using integrated digital tools to first cultivate a powerful and trusted brand.
Digital Business Student Development for Entrepreneurs with Software Nanda Septiani; Ankur Singh Bist; Cicilia Sriliasta Bangun; Ellen Dolan
Startupreneur Business Digital (SABDA Journal) Vol. 1 No. 1 (2022): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (815.699 KB) | DOI: 10.33050/sabda.v1i1.74

Abstract

Era revolution 4.0, universities throughout Indonesia are computer technology and the economy. This article provides sufficient detail about the course's pedagogical design and practical implementation to serve as a model for how entrepreneurship and business issues can be integrated into a software engineering program. Courses are evaluated using learning diaries and questionnaires, as well as principal lecturer learning in each of the three sample courses The aim of this course is to provide students with an introduction to lean startup methods for ideas/innovations and further product and company development. This course will teach students about the software industry, entrepreneurship, teamwork, and lean startup methodologies and This article provides sufficient detail about the course's pedagogical design and practical implementation to serve as a model for the Course to be evaluated using learning and questionnaires.
Leveraging IPFS for Scalable and Secure Data Storage in Blockchain-Based DApps Richard Andre Sunarjo; Nanda Septiani; Dwi Nur Ramadha; Afif Aditya Darmawan; Omar Arif Al-kamari
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i1.2527

Abstract

The rapid expansion of blockchain-based Decentralized Applications (DApps) has intensified challenges related to scalable, secure, and cost-efficient data storage, as conventional on-chain storage is unsuitable for large data volumes due to high gas costs and performance limitations, while centralized off-chain solutions undermine decentralization and increase security risks. This study aims to evaluate the effectiveness of integrating the IPFS as a decentralized storage layer within an Ethereum-based DApp architecture to enhance scalability, data integrity, and operational efficiency. Using an experimental systems engineering approach, a fully functional DApp prototype was developed by integrating a React.js frontend, Ethereum smart contracts written in Solidity, and a local IPFS node for off-chain file storage. Empirical performance testing was conducted to measure file upload and retrieval latency, CID (Content Identifier) consistency, smart contract execution time, and gas consumption on the Ethereum testnet. The results demonstrate that IPFS integration significantly reduces on-chain storage load while maintaining strong data integrity, as evidenced by 100% CID consistency across all test scenarios. Although upload and retrieval times increased proportionally with file size, the system achieved success rates above 95% with stable performance, while gas costs remained low because only CIDs were recorded on-chain. These findings indicate that IPFS provides a scalable, secure, and cost-efficient decentralized storage solution for blockchain-based DApps, enabling the development of more data-intensive and resilient DApps, with future research opportunities focusing on incentive-based pinning mechanisms, advanced encryption, and cross-chain storage integration.
Optimizing Student Engagement and Performance usingAI-Enabled Educational Tools Khaizure Mirdad; Ora Plane Maria Daeli; Nanda Septiani; Anita Ekawati; Umi Rusilowati
CORISINTA Vol 1 No 1 (2024): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i1.22

Abstract

Education is the primary pillar in the progress of modern society. With the development of artificial intelligence (AI) technology, its potential to advance the learning process has become a major focus. This research focuses on the integration of AI-based educational tools to enhance student engagement and academic performance. Through experimental design with a control group, students were divided into two groups: one using AI tools while the other followed conventional methods. Students from various educational levels participated in this research. Data were collected through questionnaires and academic evaluations to compare the outcomes between the two groups. Data analysis was conducted using SmartPLS, enabling the evaluation of the impact of AI tools on student learning. The results indicate that AI integration enables a more personalized and responsive approach to the unique needs of students. It is expected that AI technology in education will bring significant changes in how students engage and achieve academic success. This research expands the understanding of the potential of AI in improving the education process. The integration of AI technology in learning is a progressive step toward a more adaptive and effective education system, preparing students for success in an increasingly connected and complex world
Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Mohammad Miftah; Dewi Immaniar Desrianti; Nanda Septiani; Ahmad Yadi Fauzi; Cole Williams
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.56

Abstract

Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.
Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Nanda Septiani; Ramzi Zainum Ikhsan; Yasir Mustafa Kareem; Untung Rahardja
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.115

Abstract

The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
Utilization of Machine Learning for Stunting Prediction: Case Study and Implications for Pre-Matrical and Pre-Conceptive Midwifery Services Qurotul Aini; Untung Rahardja; Indrajani Sutedja; Harco Leslie Hendric Spits Warnar; Nanda Septiani
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1488

Abstract

Stunting, a global health challenge, affects millions of children, particularly in low- and middle-income countries, and has lasting consequences on cognitive development, physical growth, and overall well-being. Early prediction and intervention are crucial for reducing stunting, especially before conception and during early pregnancy. This paper explores the utilisation of machine learning (ML) for predicting stunting risk in the context of pre-maternal and pre-conceptive midwifery services. By analysing a case study, the research assesses the effectiveness of various machine learning algorithms in identifying stunting risk factors, including maternal health, nutrition, socioeconomic status, and environmental conditions. Using healthcare and demographic data, the study develops predictive models to assist midwives in assessing stunting risks during pre-conception and prenatal phases. The findings demonstrate that ML models, particularly random forest and support vector machine algorithms, outperform traditional risk assessment methods, providing higher accuracy and earlier detection of stunting risk. These models enable midwives to deliver personalised care and targeted interventions, optimising maternal and child health outcomes. The study also highlights the broader implications of integrating machine learning into midwifery services, including improved decision-making, resource allocation, and healthcare efficiency. In conclusion, this research underscores the transformative potential of machine learning in predicting stunting risk and enhancing the effectiveness of pre-maternal and pre-conceptive midwifery services, offering a promising approach to mitigating the global burden of stunting.
Co-Authors Afif Aditya Darmawan Ahmad Yadi Fauzi Alexander Williams Anita Ekawati Ankur Singh Bist Anthony Rodriguez Bangun, Cicilia Sriliasta Chua Toh Hua Cole Williams Dewi Immaniar Desrianti Dwi Nur Ramadha Elisa Ananda Natalia Ellen Dolan Fifin Alfiana Harco Leslie Hendric Spits Warnar Husin Indrajani Sutedja Ipang Sasono Khaizure Mirdad Mohammad Miftah Nimatul Khofifah Nova Ramadhona Nur Azizah Omar Arif Al-kamari Ora Plane Maria Daeli Qurotul Aini Ramzi Zainum Ikhsan Rani Nuraeni Richard Andre Sunarjo Rusilowati, Umi Sibagariang, Susy Alestriani Sri Rahayu Tarisya Ramadhan Toni Suhara Untung Rahardja Wahyuningsih Wahyuningsih Yasir Mustafa Kareem