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All Journal Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Teknologi dan Manajemen Industri Terapan Journal of Computer Science Advancements Jurnal Lentera Insani International Journal of Teaching and Learning (INJOTEL) Scientechno: Journal of Science and Technology Journal of Paddisengeng Technology
Rahmawati
Universitas Almarisah Madani
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Mathematics Other

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Analisis Kinerja Algoritma YOLO dalam Penghitungan Benih Udang Siska Armalivia; Muhammad Abdillah Rahmat; Thiara Tri Funny Manguma; Rahmawati
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 1 (2024): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v7i1.202

Abstract

The two main processes in shrimp cultivation are filling and enlargement. The counting of shrimp seeds is part of the filling activity. However, the counting of shrimp after larvae is still done manually, which means taking the shrimp and counting them manually, which is time-consuming and often results in human error. This research proposes a You Only Look Once (YOLO) method to automatically calculate the number of shrimp larvae. The YOLO method is a deep learning model that can detect an object with high speed and accuracy, even under less ideal lighting conditions. In this study, images of objects were taken using a camera placed on top of a white container containing 2 cm of water and photographed with a backlight system to avoid the reflection of light from inside the water. Testing is done by comparing system calculations and manual calculations. The results showed that the system not only detected larvae but also counted the number of shrimp larvae as well as the mAP validation value on the final model built with YOLO, which was 96.83% It also produced an average accuracy with 30 training data of 76.48%.
IMPLICATIONS OF TECHNOLOGY TRENDS IN EDUCATION: USING BIG DATA FOR LEARNING ANALYTICS Rahmawati
International Journal of Teaching and Learning Vol. 2 No. 5 (2025): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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Abstract

Big Data technology trends have a significant impact on education. With its ability to collect, store, and analyze large amounts of data, Big Data can provide valuable insights into the learning process and help improve its effectiveness. Big Data has a significant role and contribution in learning analytics. With its ability to collect, store, and analyze large amounts of data, Big Data can provide valuable insights into the learning process and help improve its effectiveness. The use of Big Data technology in education has significant implications in learning analytics. The use of Big Data technology in education has the potential to improve learning effectiveness, personalize learning, data-based decision making, develop relevant curricula, and monitor and evaluate the learning process. Big Data has an important role and contribution in learning analytics. With its ability to collect, store, and analyze large amounts of data, Big Data can provide valuable insights into the learning process and help improve its effectiveness.
CURRENT LEARNING MEDIA: USING AUGMENTED REALITY (AR) AS AN ALTERNATIVE DIGITAL LEARNING MEDIA Rahmawati; Maliatul Fitriyasari; M. Rifqi Atsani
International Journal of Teaching and Learning Vol. 2 No. 9 (2025): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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Abstract

Augmented Reality (AR) technology has developed rapidly and become one of the significant innovations in the world of modern education. AR offers an interactive learning experience that combines digital elements, such as 3D objects and animations, into the real world, thereby increasing student engagement and motivation. This study aims to explore the potential use of AR as an alternative digital learning media in various subjects and levels of education. This study shows that AR can help students understand abstract concepts, visualize learning materials in a more concrete way, and support project-based learning. In addition, AR also allows flexible and accessible learning, especially through mobile devices that are widely used by students. However, the implementation of AR in education still faces challenges, such as limited technological infrastructure, lack of teacher competence, high development costs, and difficulties in measuring learning effectiveness. To overcome these challenges, support is needed from various parties, including the government, educational institutions, and technology developers. With effective collaboration, AR has the potential to become an innovative and effective learning media in preparing students to face challenges in the digital era.
Environmental Fiqh as a Framework for Social Sustainability Amiruddin. A; Rahmawati; Nurhaedar; Rahmi; Salmah Harun
Jurnal Lentera Insani Jurnal Lentera Insani (JLI) - Vol. 1 No. 2 (December 2025)
Publisher : Mahkota Science Publishers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65586/jli.v1i2.25

Abstract

When ecological crises reflect the failure of modern ethics, environmental fiqh emerges as a moral challenge to the way humans build social sustainability. The purpose of this study is to formulate and construct environmental fiqh as a framework for social sustainability in Asian Muslim communities by comprehensively explaining the relationship between fiqh norms, social practices, and ecological dynamics. This study uses a qualitative approach, combining a literature review with content analysis, and a normative-sociological approach to critically examine how environmental fiqh can be positioned as a framework for social sustainability that challenges the dominance of the secular-technocratic paradigm. The results indicate that environmental fiqh, when read critically and reconstructed progressively, has the transformative power to challenge the Asian development model based on ecological exploitation and social inequality, while offering a more equitable, long-term framework for social sustainability rooted in Islamic ethical values. By integrating environmental maqāṣid, intergenerational ethics, and revitalised fiqh concepts such as amānah, maṣlaḥah, and ḥisbah, environmental fiqh need not remain a moral discourse, but can become a normative instrument that influences public policy, corrects corporate behaviour, and strengthens civil society movements.
Integration Of Digital Twin Technology and Smart Sensors in Real-Time Monitoring of Structural Health (SHM) of High-Rise Buildings Rahmawati; Amiruddin. A; Nicodemus Rahanra; Mira Wellya Fatma; Wahyudin
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 4 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v4i4.1686

Abstract

The integration of Digital Twin (DT) technology and smart sensors represents a new paradigm in structural health monitoring (SHM) of high-rise buildings. This study explores the synergy between data-driven physical simulation and real-time data collection to improve the accuracy of early damage detection. Using wireless sensor networks integrated with Building Information Modeling (BIM) models, environmental load and structural response data are processed in real time. The analysis results show that DT can reduce the margin of error in structural failure prediction by up to 30% compared with conventional methods. This study concludes that bidirectional data synchronization between the physical and digital worlds is crucial for disaster risk management and the efficient maintenance of modern buildings in dense urban areas.
Impact of Using Big Data Analisys in Increasing Personalization of Learning Rahmawati Rahmawati; Nursalim Nursalim; Agry Alfiah; Andi Hasyim; Aldi Bastiatul Fawait
Journal of Computer Science Advancements Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i2.906

Abstract

In today’s digital era, big data analytics has become a very relevant topic to improve learning personalisation as it can collect and analyse very large and complex data. Big data analytics can lead to a more efficient learning system by collecting and analysing huge and complex data. In education, big data analytics can be used to understand students’ learning behaviour, their needs and preferences, so that learning and learning outcomes can be improved. This research is conducted with the aim of using big data analytics to improve learning personalisation. It also aims to find out the challenges of using big data analytics to improve learning personalisation. The method used in this research is quantitative method. This method is a way of collecting numerical data that can be tested. Data is collected through the distribution of questionnaires addressed to students. Furthermore, the data that has been collected from the distribution of the questionnaire, will be accessible in Excel format which can then be processed with SPSS. From the research results, it can be seen that the big data analysis has shown that the use of more detailed and accurate data can help teachers find students’ special needs and improve learning effectiveness. As a result, teachers can create learning strategies that are better suited to students’ needs and improve their learning outcomes. From this study, we can conclude that the use of big data analytics in improving personalisation allows teachers to understand better the individual needs and preferences of students, so that more suitable learning plans can be developed and student engagement can be improved.
Implementation of Grid Computing in Genomic Data Processing in Biomedical Informatics Rahmawati Rahmawati; Ammar Al-Momani; Sarah Williams
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1618

Abstract

The exponential growth of genomic data in biomedical informatics has necessitated efficient computational methods to process and analyze vast datasets. Traditional computational systems often fall short in handling the scale and complexity of genomic data. This study investigates the implementation of grid computing as a scalable and cost-effective solution for genomic data processing in biomedical informatics. The research aims to evaluate the feasibility and performance of grid computing in enhancing data throughput, reducing computational latency, and improving resource utilization in genomic data workflows. The study adopts a methodological approach that integrates grid computing frameworks, such as Globus Toolkit and Apache Hadoop, into genomic data processing pipelines. Simulated genomic datasets and real-world case studies were employed to benchmark the grid computing system against conventional computational environments. The results demonstrate significant improvements in processing speed, with an average reduction of 40% in computational time, and a 25% increase in resource efficiency. Additionally, the system showcased robust scalability, handling up to 10 times larger datasets without compromising accuracy or reliability. In conclusion, the findings underscore the potential of grid computing to revolutionize genomic data processing, making it a pivotal technology in biomedical informatics. This study highlights the importance of adopting distributed computing paradigms to address the challenges posed by modern bioinformatics demands.
Enhancing Algorithmic Thinking through Computational Tools: A Study on High School Computing Education Rahmawati Rahmawati; Omar Khan; M Syahputra; Amalia Hanifa; Ediaman Sitepu
Scientechno: Journal of Science and Technology Vol. 3 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v3i3.1657

Abstract

The importance of algorithmic thinking in modern education has grown significantly, particularly in the context of computing education in high schools. As computational tools become more accessible, their role in enhancing students' problem-solving abilities has gained considerable attention. This study investigates how computational tools can improve algorithmic thinking among high school students and their overall engagement with computing subjects. The primary objective is to assess the impact of integrating computational tools, such as programming environments and visual coding platforms, on students' development of algorithmic skills. The study adopts a mixed-methods approach, combining quantitative data from pre-and post-tests measuring algorithmic thinking skills, and qualitative data through interviews and classroom observations. A total of 150 high school students from various educational backgrounds participated in the study over one semester. The results indicate a significant improvement in students’ algorithmic thinking abilities after exposure to computational tools, particularly in areas such as problem decomposition, abstraction, and logical reasoning. Additionally, students reported higher levels of motivation and interest in computing subjects. In conclusion, the integration of computational tools in high school computing education not only enhances algorithmic thinking but also fosters greater student engagement. These findings highlight the potential of using technology to bridge the gap between theoretical concepts and practical application in computing education. Further research is encouraged to explore long-term effects and the scalability of these methods across diverse educational settings.
Adaptive Learning Paths: Implementing Machine Learning to Personalize Curriculum Development in Multicultural Classrooms Rahmawati Rahmawati; Amiruddin A; Siska Armalivia
Journal of Paddisengeng Technology Vol. 1 No. 1 (2025)
Publisher : PT. Sinergi Bersahaja Sejahtera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65224/jopate.v1i1.172

Abstract

Background. The integration of technology into education has revolutionized how learning experiences are designed and delivered, especially in culturally diverse classrooms. Machine learning (ML) offers a promising approach to personalizing curriculum development by dynamically adapting content, pace, and instructional strategies based on student data. This study focuses on the application of adaptive learning paths powered by ML in multicultural classroom settings to enhance engagement, equity, and academic achievement. Purpose. This study aims to explain the effectiveness of implementing adaptive learning paths using machine learning algorithms in multicultural classrooms, particularly in enhancing personalized curriculum delivery for diverse student populations. Method. This research used a mixed-methods approach, combining quantitative analysis through SmartPLS modeling and qualitative data from teacher interviews and classroom observations. The quantitative data involved student performance records and interaction logs from 3 culturally diverse high school classrooms, while qualitative data explored the perceived relevance and inclusivity of the adaptive learning system. The path coefficients and thematic analysis were used to evaluate the impact of the adaptive system. Results. The results showed that adaptive learning paths supported by ML significantly improve student engagement and academic performance. The SmartPLS model revealed strong correlations between pedagogical constructs (Alpha), system intelligence (Beta), and outcome evaluation (Gamma). Positive responses from teachers and students also confirmed the system’s effectiveness in delivering personalized and culturally relevant learning experiences. However, the study also noted challenges related to data bias, algorithm transparency, and teacher readiness. Conclusion. Based on the findings, it can be concluded that adaptive learning paths powered by machine learning are effective in personalizing curriculum for multicultural classrooms. They promote inclusive learning by accommodating diverse student needs and cultural backgrounds, although successful implementation requires thoughtful alignment between technology, pedagogy, and context
Co-Authors Agry Alfiah Aldi Bastiatul Fawait Fawait Amalia Hanifa Amiruddin A. Amiruddin. A Ammar Al-Momani Andi Hasyim Ediaman Sitepu FITRIYASARI, MALIATUL M Syahputra M. Rifqi Atsani Mira Wellya Fatma Muhammad Abdillah Rahmat Nicodemus Rahanra Nurhaedar Nursalim Omar Khan Rahmi Rahmi Salmah Harun Sarah Williams Siska Armalivia Siska Armalivia Thiara Tri Funny Manguma Wahyudin