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Septiani, Nanda
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KEEFEKTIFAN PERMAINAN “PLAY BIG CITY ADVENTURE” DALAM MENINGKATKAN KEMAMPUAN PEMAHAMAN KOSA KATA BAHASA INGGRIS Tri Andini, Nindi; Sanali, Mutantri; Novianti, Dina; Septiani, Nanda
Prosiding Dedikasi: Pengabdian Mahasiswa Kepada Masyarakat Vol. 3 No. 2 (2024): PROSIDING DEDIKASI MARET
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengabdian masyarakat identik dengan Perguruan Tinggi yang senantiasa mengamalkan Tri Dharma, salah satunya adalah pengabdian masyarakat. Pengabdian masyarakat yang dilakukan oleh mahasiswa universitas pamulang dilakukan di Taman Baca Masyarakat Teras Ceria. Pengabdian ini bertujuan untuk meningkatkan efektivitas pemahaman kosa kata Bahasa Inggris pada siswa tingkat dasar. Latar belakangnya adalah rendahnya kemampuan pemahaman kosa kata di kalangan anak- anak siswa mendasar dimana yang dapat mempengaruhi keterampilan berbahasa mereka secara keseluruhan. Tujuan pengabdian kepada masyrakat yang dilakukan oleh mahasiswa universitas pamulang ini adalah mengimplementasikan metode pembelajaran inovatif yang dapat meningkatkan pemahaman kosa kata. Metodenya melibatkan penggunaan teknologi, permainan pendidikan, dan pendekatan kontekstual. Hasil pengabdian menunjukkan peningkatan signifikan dalam pemahaman kosa kata siswa, terbukti dari peningkatan nilai ujian dan partisipasi aktif dalam diskusi berbahasa Inggris. Kesimpulannya, metode ini efektif untuk meningkatkan pemahaman kosa kata siswa. Saran berfokus pada integrasi metode serupa dalam kurikulum pendidikan formal dan memberikan dukungan lanjutan untuk pengembangan keterampilan berbahasa siswa. Kata Kunci: kosa kata; kelancaran; kemampuan bahasa
Factor Affecting Employee Motivation to Increase Performance of Sharia Bank in Indonesia on Islamic Perspective Mariyanti, Tatik; Septiani, Nanda; Dolan, Ellen; Afif, Muhammad
APTISI Transactions on Management (ATM) Vol 7 No 2 (2023): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v7i2.1860

Abstract

There is still a lack of market share for Islamic banking in Indonesia, so the purpose of this study is to analyze the effect of leadership style, job satisfaction, and work environment on employee motivation to improve the performance of Islamic banks in Indonesia. The methodology used is quantitative with the SEM Smart PLS analysis tool. The study was conducted on 200 employees of Islamic banks in Indonesia as representatives of 14 Sharia Commercial Banks (BUS) and 20 Sharia Business Units (UUS). The results of this study are the positive influence of leadership style, job satisfaction, and work environment on employee motivation and the influence of employee motivation on the performance of Islamic banks in Indonesia. Novelty in this study is leadership style, job satisfaction, and work environment in accordance with Islamic principles will be able to increase employee motivation at work, so that the performance of Islamic banks will be better and the market share of Islamic banking will also increase.
Utilization of Machine Learning for Stunting Prediction: Case Study and Implications for Pre-Matrical and Pre-Conceptive Midwifery Services Aini, Qurotul; Rahardja, Untung; Sutedja, Indrajani; Spits Warnar, Harco Leslie Hendric; Septiani, Nanda
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.
Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Miftah, Mohammad; Immaniar Desrianti, Dewi; Septiani, Nanda; Yadi Fauzi, Ahmad; Williams, Cole
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; Septiani, Nanda; Ramzi Zainum Ikhsan; Kareem, Yasir Mustafa; 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.
Design and Build Academic Website with Digital Certificate Storage Using Blockchain Technology A. Faaroek, Safiani; Saulina Panjaitan, Aropria; Fauziah, Zaleha; Septiani, Nanda
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 3 No 2 (2022): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v3i2.562

Abstract

A certificate is a form of award that is obtained by someone after completing a competency test or certain learning. Certificates must be generated and stored in a safe and secure manner to prevent alteration of content or even falsification. Blockchain technology is a technology that allows secure storage processes at low costs. Security is guaranteed because everyone can take part in storing data with a distributed ledger. Based on the results of research and system design, it can be concluded that the process of making blockchain technology as a medium for issuing certificates and their validation can be made using Ethereum's program, namely Geth, and storing data using smart contracts issued on the blockchain network. The results of the reliability testing of the system show that the system has successfully processed 200 transactions in approximately 8 seconds. For scalability testing, it is estimated that 10 million blocks require a storage capacity of 22.6 GB to become a node or miner on this blockchain network.
Co-Authors AA Sudharmawan, AA Agustian, Harry Daeli, Ora Plane Maria Desrianti, Dewi Immaniar Dina Novianti Dolan, Ellen Dwi Andayani, Dwi Ekawati, Anita Erwin Prasetya Toepak Fauziah, Zaleha Immaniar, Dewi Kareem, Yasir Mustafa Madani, Muchlishina Marviola Hardini Miftah, Mohammad Mirdad, Khaizure Mochamad Heru Riza Chakim Muhammad Afif Mulyati Nasution, Erni Juliana Al Hasanah Ninda Lutfiani Nur Azizah Pérez, Carlos Qurotul Aini Ramzi Zainum Ikhsan Rusilowati, Umi Safiani A. Faaroek Sanali, Mutantri Saulina Panjaitan, Aropria Singh Bist , Ankur Spits Warnar, Harco Leslie Hendric SRI RAHAYU Sriliasta Bangun, Cicilia Supriyanti, Dedeh Sutedja, Indrajani Tatik Mariyanti, Tatik Tri Andini, Nindi Untung Rahardja Victorianda Wahyu Nugroho Wei Ming, LI Williams, Cole Yadi Fauzi, Ahmad Yusrawati Yusrawati Zainum Ikhsan, Ramzi Zarasky, Dwiki Zulkarnain, Andy