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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL REKAYASA TEKNOLOGI INFORMASI Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informasi dan Teknologi Journal of Robotics and Control (JRC) Community Development Journal: Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer DiJITAC : Digital Journal of Information Technology and Communication Ilmu Komputer untuk Masyarakat Sanskara Akuntansi dan Keuangan West Science Social and Humanities Studies Journal of Social Science Utilizing Technology Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Social Science Utilizing Technology Scientific Journal of Engineering Research Journal of Computer Science Advancements Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Multidisciplinary Sustainability Asean
Aldi Bastiatul Fawait Fawait
Universitas Widya Gama Mahakam Samarinda
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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PENGENALAN ARTIFICIAL INTELLIGENCE KEPADA SISWA DI SMA NEGERI 9 SAMARINDA Fawait, Aldi Bastiatul; Rahmah, Sitti; Sugiarto, Sugiarto; Fakhri, La Jupriadi; Jamil, Muh; Saputra, Yudhi Fajar; Arifin, Merlina Lidiana; Saputri, Nadia Keril
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 6 (2024): Vol. 5 No. 6 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i6.38605

Abstract

Kegiatan pengabdian pada masyarakat yang melibatkan sosialisasi mengenai Pengenalan Artificial Intelligence (AI) kepada siswa di lingkungan Sekolah Menengah Atas ini bertujuan untuk memberikan informasi kepada siswa-siswi SMA Negeri 9 Samarinda mengenai perkembangan teknologi terkait AI. Dalam proses sosialisasi ini, tujuannya adalah memberikan pemahaman mengenai kecerdasan buatan yang dirancang khusus untuk menangani permasalahan kognitif yang umumnya terkait dengan kecerdasan manusia, seperti proses pembelajaran, pemecahan masalah, dan pengenalan pola. Manfaat dari kegiatan pengabdian pada masyarakat ini adalah agar siswa-siswi dapat lebih memahami kemajuan teknologi kecerdasan buatan di Indonesia dan memanfaatkan Sumber Daya Manusia (SDM) lokal yang berkualitas, seiring dengan pemahaman bahwa SDM dalam negeri tidak kalah dengan SDM luar negeri. Selain itu, diharapkan bahwa keberadaan Artificial Intelligence dapat berkontribusi dalam pengembangan SDM di negara Indonesia. Hasil Pengabdian Pengenalan Artificial Intelligence Kepada Siswa di Lingkungan Sekolah Menengah Atas pada SMA 9 Negeri Samarinda dilakukan selama satu hari yang dihadiri oleh 34 peserta, 2 orang dosen, dan dibantu oleh mahasiswa program studi Ilmu Komputer Universitas Widyagama Samarinda hasil angket dari peyelenggaraan pengabdian ini didapatkan 7 pertanyaan yang hasilnya diatas 80% peserta yang artinya peserta setuju telah mendapat pengetahuan Pengenalan Artificial Intelligence dengan baik atau penyelenggaraan ini sudah dilaksanakan dengan sangat baik sekali.
Impact of Using Big Data Analisys in Increasing Personalization of Learning Rahmawati, Rahmawati; Nursalim, Nursalim; Alfiah, Agry; Hasyim, Andi; Fawait, Aldi Bastiatul
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.
The Impact of Using Collaborative Learning Platforms on Increasing Student Creativity Wardhani, Rizky; Pulungan, Dedi Zulkarnain; Irawan, Dodi; Gilaa, Thitus; Fawait, Aldi Bastiatul
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.v2i2.1082

Abstract

One of the student-centred learning (SCL) methods is collaborative learning. In collaborative learning, students are required to actively participate in learning together or in groups. And collaborative learning is also based on the needs of students to improve the quality of learning. This research is conducted to find out how the use of collaborative learning platforms can help students become more creative in collaborative activities. By understanding the different types of collaborative learning platforms, teachers and parents are able to incorporate the role of technology in students' learning process. In conducting this research, researchers used quantitative methods in the implementation of the research. The data obtained by researchers was obtained through distributing questionnaires presented by researchers through a goggle from application. The distribution of this questionnaire was carried out by researchers online, which then the results of the acquisition of the distribution of this questionnaire will be processed using an SPSS application.  From this research, the researcher can conclude that the impact of using a collaborative learning platform on increasing student creativity shows positive results. With the use of collaborative learning platform, it can visualise abstract and complex concepts, opening opportunities for students to develop their imagination and creativity through rich visual exposure. Based on the results of this study, it shows that collaborative learning platform can enhance students' creativity as it allows students to interact more actively and interactively during the learning process. In addition, rich visual exposure enables better understanding and enhances students' creativity and imagination.
The Impact of Adaptive Learning Technology on Improving Students’ Concept Understanding Soelistianto, Farida Arinie; Andrasmoro, Dony; Yusriati, Yusriati; Mardiati, Mardiati; Fawait, Aldi Bastiatul
Journal of Computer Science Advancements Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i3.1176

Abstract

Adaptive learning technology is an educational method that uses artificial intelligence and computer algorithms. This learning system can manage students’ interaction pattern during learning activities. The use of adaptive learning technology is able to change students from just receiving information to an active and collaborative part in the learning process. This research was conducted with the aim of improving the quality of education in Indonesia by encouraging teachers to use this technology. This research also aims to provide a better understanding of the potential and weaknesses of adaptive learning technology in improving students’ concept understanding as well as providing stronger guidance for curriculum development and better educational practices.  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 results of the study, it can be seen that the impact of using adaptive learning technology shows that adaptive learning technology can improve the quality of education. Research shows that with the use of adaptive learning technology, it can change teaching methods, learning materials, and can find out the level of learning difficulties faced by these students. From this study, researchers can conclude that the impact of using adaptive learning technology, can improve student understanding and achievement and has the potential to improve the quality of education. with the existence of adaptive learning technology, it is able to increase student involvement and motivation in learning, so that student understanding in learning can be achieved well.
Use of Artificial Intelligence in Predicting Electricity Needs in Smart Cities Fawait, Aldi Bastiatul; Li, Zhang; Hussain, Sara
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1620

Abstract

The rapid urbanization and adoption of smart city technologies have led to increasing complexities in managing electricity demand. Traditional methods of forecasting electricity needs often fail to accommodate the dynamic and real-time nature of energy consumption in smart cities. Artificial Intelligence (AI) offers a promising approach by leveraging machine learning algorithms and predictive analytics to address these challenges. This study explores the use of AI in predicting electricity needs, focusing on its applicability in optimizing energy distribution and reducing inefficiencies in smart city infrastructures. The research aims to develop an AI-based predictive model to forecast electricity demand using historical and real-time data. The methodology involves data collection from smart meters, weather forecasts, and demographic records, followed by training machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks. Performance metrics, including prediction accuracy, computational efficiency, and scalability, were analyzed to evaluate the model's effectiveness. Results indicate that AI-based models outperform traditional forecasting methods, achieving an average prediction accuracy of 92%. Neural Networks demonstrated the highest performance, particularly in handling complex and nonlinear data patterns. The AI model also showcased scalability by adapting to increasing datasets without significant degradation in performance. The study concludes that AI is a transformative tool for predicting electricity needs in smart cities. By enhancing forecast accuracy and enabling efficient energy distribution, AI contributes to sustainable urban development and smarter energy management systems.
Applications of Artificial Intelligence in Weather Prediction and Agricultural Risk Management in India Fawait, Aldi Bastiatul; Aprilani, Puteri; Sugiarto, Sugiarto; Sok, Vann
Techno Agriculturae Studium of Research Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v1i3.1591

Abstract

Agriculture in India is particularly vulnerable to climate change and extreme weather conditions, which can negatively impact productivity and food security. This research was conducted against the background of the importance of developing technology to help farmers in dealing with weather uncertainty and managing agricultural risks. The purpose of this study is to explore the application of artificial intelligence (AI) in accurately predicting weather as well as managing the risks associated with extreme weather in India's agricultural sector. This study uses a descriptive method with a quantitative and qualitative approach, where data is collected through interviews with agricultural experts, analysis of historical weather data, and AI modeling. The results show that the AI application is able to predict weather patterns with an accuracy rate of up to 90%, which helps farmers make more informed decisions regarding planting timing, irrigation, and pesticide use. In addition, AI-based risk management systems allow for early detection of extreme weather, thereby reducing crop losses. The conclusion of the study is that artificial intelligence applications have great potential to improve food security and agricultural productivity in India by helping farmers anticipate weather changes and manage risks more efficiently. However, the adoption of this technology requires adequate training and infrastructure to ensure its optimal use in the field.
The Role of Applied Statistics in Drug Development and Clinical Trials Rahmah, Sitti; Fawait, Aldi Bastiatul; Hasyim, Dadang Muhammad
Research of Scientia Naturalis Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1584

Abstract

Background: The integration of applied statistics in drug development and clinical trials is essential for ensuring the efficacy and safety of new pharmaceuticals. Statistical methods play a critical role in designing studies, analyzing data, and interpreting results, thereby influencing regulatory decisions and clinical practices. Objective: This study aims to examine the role of applied statistics in the drug development process, particularly within clinical trials. The focus is on identifying key statistical techniques and their impact on trial outcomes and decision-making. Methodology: A comprehensive review of literature was conducted, analyzing various statistical methods employed in clinical trials, including sample size determination, randomization techniques, and data analysis methods. Case studies were included to illustrate the application of these methods in real-world scenarios. Results: Findings indicate that robust statistical methodologies significantly improve the reliability of clinical trial results. Proper sample size calculations ensure adequate power to detect treatment effects, while randomization techniques minimize bias. Additionally, advanced data analysis methods enhance the interpretation of trial outcomes, leading to more informed regulatory approvals. Conclusion: This research highlights the indispensable role of applied statistics in drug development and clinical trials. Emphasizing the importance of sound statistical practices not only improves trial integrity but also contributes to the overall success of new drug therapies. Continued advancements in statistical methods will further enhance the efficiency and effectiveness of clinical research.
Implementation of Artificial Intelligence in Cybersecurity Crisis Management Bastiatul Fawait, Aldi; Fakhri, La Jupriadi; Muslimah, Virasanty
Journal of Multidisciplinary Sustainability Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v1i6.1776

Abstract

Background. The growing complexity of cybersecurity threats has led to an increasing demand for faster and more efficient solutions. As cyber threats evolve in sophistication, the implementation of Artificial Intelligence (AI) in cybersecurity crisis management has become highly relevant. AI’s ability to process vast amounts of data quickly and detect patterns that may be undetectable to human operators offers significant potential in combating cybercrime and cyberattacks. Purpose. This study aims to evaluate how AI can enhance the effectiveness of cybersecurity by improving the detection and response to cyber threats. Specifically, the research focuses on understanding AI's role in identifying potential threats more quickly and responding with greater efficiency compared to traditional methods. Method. The research employs a mixed-method approach, combining quantitative data analysis and qualitative interviews. Quantitative data were gathered from cyberattack simulations to measure AI’s effectiveness in detecting and responding to various types of cyber threats. Additionally, qualitative interviews were conducted with cybersecurity experts to gather insights into AI’s practical applications and limitations in real-world scenarios. Results. The findings show that AI significantly accelerates threat detection, improving the overall response efficiency with a success rate of up to 85%. AI is also capable of analyzing large datasets in a short period, enabling faster identification of vulnerabilities and potential threats. However, AI still faces limitations in handling unexpected and novel types of cyberattacks, indicating that it cannot entirely replace human expertise. Conclusion. While AI offers numerous advantages in the field of cybersecurity, it must be integrated with human expertise to address its limitations effectively. AI technology should be continuously updated to adapt to emerging threats. This study contributes to the understanding of AI’s strategic role in cybersecurity and provides valuable direction for further research aimed at overcoming the technology’s weaknesses in threat management.
Co-Authors Adriyanto, Feri Alfiah, Agry Alfian Ma’arif ALYA MASITHA Anton Yudhana Arief Yanto Rukmana Arifin, Merlina Lidiana Asno Azzawagama Firdaus Costa, Apolonia Diana Sherly da Dadang Muhammad Hasyim Darmun, Darmun Dedi Zulkarnain Pulungan Dony Andrasmoro Edwin Pramudya Eko Prasetio Widhi Fahmi, Miftahuddin Fakhri, La Jupriadi Furizal Furizal Furizal, Furizal Gilaa, Thitus Hasyim, Andi Hendratri, Bhaswarendra Guntur Hersiyati Palayukan Huda, Syafa'at Ariful Hussain, Sara Insyroh, Nazaruddin Irawan, Dodi Jamil, Muh Judijanto, Loso Kariyamin, Kariyamin Kohar , Abdul La Jupriadi Fakhri Loso Judijanto Maghfiroh, Hari Mardiati Mardiati Merlina Lidiana Arifin Muh. Jamil Muhammad Kunta Biddinika Nelson Sompa Arifin Nursalim Nursyam, Aisyah PUTERI APRILANI Rahmah, Sitti Rahmah Rahmawati, Rahmawati Ramelan, Agus Reviandari Widyatiningtyas, Reviandari Rizky Wardhani Rosmasari Rosmasari, Rosmasari Rukmana, Arief Yanto Rusdi Umar Saputra, Yudhi Fajar Saputri, Nadia Keril Sitti Rahmah Sitti Rahmah Sitti Rahmah Soelistianto, Farida Arinie Sok, Vann Sugiarto Sugiarto S Sugiarto Sugiarto Sunardi, Sunardi Suwarno, Iswanto Syaifullah, Ahmad Syekh Budi Syam Virasanty Muslimah Wartono, Tono Yana Mulyana Yudhi Fajar Saputra Yusriati, Yusriati Zhang Li