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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 JURPIKAT (Jurnal Pengabdian Kepada 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 Jurnal Pengabdian Masyarakat Nusantara OBOR: Oikonomia Borneo
Aldi Bastiatul Fawait Fawait
Universitas Widya Gama Mahakam Samarinda
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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.
EDUKASI PENTINGNYA DATA SCIENCE DI DUNIA PENDIDIKAN UNTUK MENINGKATKAN KUALITAS PENDIDIKAN Yudhi Fajar Saputra; Muh Jamil; Sitti Rahmah; Aldi Bastiatul Fawait; Milkhatun Milkhatun
Jurnal Pengabdian Masyarakat Nusantara Vol. 3 No. 1 (2024): Jurnalpkm.org
Publisher : Open Edutech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63183/591627

Abstract

Salah satu domain dari Artificial Itellegence adalah data science, dimana dapat meramalkan, memprediksi, dan merekomendasikan melalui data yang sudah diolah. Data science memiliki peran yang signifikan dalam dunia pendidikan, beberapa peran utama data science dalam konteks pendidikan adalah analisis kinerja siswa, pengelolaan kurikulum (pengembangan kurikulum dan optimasi materi pembelajaran atau bahan ajar), prediksi dropout dan intervensi dini, pengelolaan sumber daya sekolah (perencanaan keuangan dan manajemen tenaga pendidik), evaluasi kinerja sekolah, dan pengembangan teknologi pendidikan. Penerapan data science dalam pendidikan dapat membantu meningkatkan efektivitas pembelajaran, mengoptimalkan pengelolaan sumber daya, dan memberikan solusi yang lebih adaptif sesuai dengan kebutuhan individu, namun sayangnya data science belum banyak dikenal pada kalangan masyarakat luas khususnya di dunia pendidikan, maka dari itu Para pelajar dan akademisi perlu diperkenalkan dengan data science. Sebagai usaha untuk melaksanakan pengabdian kepada masyarakat, maka dilaksanakan edukasi mengenai data science yang dilaksanakan oleh dosen Universitas Widya Gama Mahakam Samarinda sebagai nara sumber. Materi yang disampaikan terdiri dari alasan mengapa diperlukannya data science, pengenalan dasar data science, persyaratan menjadi data scientist, data mining dan big data yang merupakan bagian penting dari data science dan contoh pengolahan data science. Tahapan pelaksanaan kegiatan terdiri dari persiapan, sosialisasi, pelaksanaan, dan evaluasi
DISEMINASI PEMBELAJARAN DARING KOLABORATIF Yudhi Fajar Saputra; Sulung Alfianto Akbar; Aldi Bastiatul Fawait; Hidayatus Sibyan; Muhamad Fuat Asnawi
Jurnal Pengabdian Masyarakat Nusantara Vol. 3 No. 2 (2024): Jurnalpkm.org
Publisher : Open Edutech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63183/591632

Abstract

Pembelajaran daring kolaboratif telah menjadi pendekatan penting dalam dunia pendidikan, terutama dalam mendukung pembelajaran jarak jauh yang efektif. Artikel ini mengulas proses diseminasi pembelajaran daring kolaboratif antar perguruan tinggi menggunakan platform Sistem Pembelajaran Daring Indonesia (SPADA) sebagai upaya untuk meningkatkan kualitas pembelajaran berbasis teknologi di berbagai institusi pendidikan di Indonesia khususnya pada perguruan tinggi di Universitas Widya Gama Mahakam Samarinda. Kegiatan diseminasi ini dirancang untuk melatih dosen dan mahasiswa dalam memanfaatkan fitur-fitur kolaboratif yang tersedia di SPADA. Proses diseminasi teridir dari tiga tahapan, mulai dari perencanaan, pemaparan dan diskusi tentang pembelajaran kolaboratif menggunakan platform SPADA, dan tindak lanjut. Pelatihan dilakukan dalam bentuk lokakarya yang diikuti oleh 45 peserta dari berbagai latar belakang pendidikan, termasuk perwakilan dari berbagai program studi pada perguruan tinggi Universitas Widya Gama mahakam Samarinda. Materi pelatihan mencakup pengelolaan kelas daring, desain tugas kolaboratif, strategi peningkatan interaksi antara pengajar dan peserta didik, serta penerapan kerja sama antar perguruan tinggi dalam pembelajaran daring. Hasil tindak lanjut menunjukkan bahwa peserta mengalami peningkatan pemahaman dan keterampilan dalam memanfaatkan SPADA untuk pembelajaran kolaboratif. Selain itu, diseminasi ini berhasil mendorong penerapan metode pembelajaran berbasis kolaborasi yang lebih interaktif dan adaptif terhadap kebutuhan peserta didik, serta memperkuat hubungan antar perguruan tinggi. Temuan ini memberikan kontribusi penting bagi pengembangan model pembelajaran daring di Indonesia dan dapat menjadi acuan untuk kegiatan serupa di masa depan
SHARING KNOWLEDGE: PROSPEKTIF DATA SCIENCE DI MASA MENDATANG PADA DUNIA PENDIDIKAN Yudhi Fajar Saputra; Muh Jamil; Aldi Bastiatul Fawait; M. Fajar Rizky; Sri Nur Hidayati; Bayu Pamungkas
Jurnal Pengabdian Masyarakat Nusantara Vol. 4 No. 1 (2025): Pengabdian Berbasis Dampak I: Melampaui Batas Teori ke Aksi Nyata
Publisher : Open Edutech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63183/619362

Abstract

Era revolusi industri 4.0 telah membawa perubahan signifikan dalam kebutuhan keterampilan dan tenaga kerja, di mana data science menjadi salah satu bidang yang berkembang pesat. Data science memiliki peran penting dalam pengolahan, analisis, dan interpretasi data untuk mendukung pengambilan keputusan berbasis informasi. Namun, kesadaran dan pemahaman tentang data science di kalangan siswa Sekolah Menengah Kejuruan (SMK) masih sangat terbatas. Oleh karena itu, kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan wawasan awal tentang prospek data science di masa mendatang kepada siswa SMK, sehingga mereka lebih siap menghadapi tantangan dunia kerja yang semakin digital. Kegiatan ini mencakup sosialisasi teori dasar data science, aplikasi praktis dalam kehidupan sehari-hari khususnya dibidang pendidikan, dan menjelaskan bagaimana best praktis dalam menerapkan data science. Melalui pendekatan interaktif dan edukatif, siswa dapat memahami pentingnya data science sebagai keterampilan masa depan dan terinspirasi untuk mendalami bidang ini lebih lanjut. Hasil dari kegiatan ini berupa peningkatan literasi siswa terhadap teknologi data, kesadaran akan peluang karier dalam data science, serta kesiapan mereka menghadapi tantangan era digital. Program ini juga menjadi langkah awal dalam membangun ekosistem pembelajaran data science di lingkungan pendidikan kejuruan.
Penerapan Arsitektur U-Net untuk Segmentasi Semantik Citra Buah dengan Latar Belakang Kompleks Jamil, Muh Jamil; Wati, Asiah; Rahmah, Sitti; Fawait, Aldi Bastiatul
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 1 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i1.26518

Abstract

Perkembangan ilmu Computer Vision telah berhasil membuat terobosan baru di berbagai bidang, salah satunya adalah di bidang pertanian. Hal ini semakin membuka peluang bagi pertanian cerdas berbasis IoT untuk dapat diterapkan guna mengoptimalisasi pekerjaan petani. Salah satu langkah awal yang bisa diterapkan adalah segmentasi citra, tujuannya adalah untuk mendeteksi area tertentu seperti buah, daun dan objek lain yang memiliki oklusi dan latar belakang kompleks pada lingkungan perkebunan secara langsung. Penelitian yang dikerjakan mengusulkan metode segmentasi semantic berbasis Deep Learning yaitu dengan memadukan ketangguhan dari U-Net dan juga VGG16 dalam melakukan proses segementasi buah melon pada lingkungan perkebunan secara langsung. Model segmentasi yang dirancangan terbukti mampu melakukan proses segmentasi dengan sangat optimal dengan score loss minimum sebesar 0.0157 dan score IoU maksimum sebesar 0.9922 pada proses train dan testing model, selain itu model yang dirancang mampu bekerja secara optimal dengan score IoU sebesar 0.9893 pada validasi akhir model. Penelitian yang dikerjakan bukan hanya berkontribusi sebagai rujukan akademis terkait dengan penerapan computer vision pada bidang pertanian cerdas, tetapi juga dapat berkontribusi menciptakan dataset baru bagi penelitian lanjut di masa mendatang.
Co-Authors Abiyajid Bustami Adriyanto, Feri Alfiah, Agry Alfian Ma’arif ALYA MASITHA Anton Yudhana Arief Yanto Rukmana Arifin, Merlina Lidiana Asno Azzawagama Firdaus Bayu Pamungkas Costa, Apolonia Diana Sherly da Dadang Muhammad Hasyim Darmun, Darmun Dedi Zulkarnain Pulungan Dony Andrasmoro Edwin Pramudya Eko Prasetio Widhi Fahmi, Miftahuddin Fajar Saputra, Yudhi Fakhri, La Jupriadi Furizal Furizal Furizal, Furizal Gilaa, Thitus Hasyim, Andi Hendratri, Bhaswarendra Guntur Hersiyati Palayukan Hidayatus Sibyan Huda, Syafa'at Ariful Hussain, Sara Insyroh, Nazaruddin Irawan, Dodi Jamil, Muh Jamil, Muh Jamil Judijanto, Loso Kariyamin, Kariyamin Kohar , Abdul La Jupriadi Fakhri La Jupriadi Fakhri Loso Judijanto M. Fajar Rizky Maghfiroh, Hari Mardiati Mardiati Merlina Lidiana Arifin Merlina Lidiana Arifin Milkhatun, Milkhatun Muh. Jamil Muh. Jamil Muhamad Fuat Asnawi Muhammad Kunta Biddinika Nadia Keril Saputri Nelson Sompa Arifin Nursalim Nursyam, Aisyah Puteri Aprilani Rahmah, Sitti Rahmah Rahmawati, Rahmawati Ramelan, Agus Reviandari Widyatiningtyas, Reviandari Rizky Wardhani Rosmasari Rosmasari, Rosmasari Rusdi Umar Saputra, Yudhi Fajar Saputri, Nadia Keril Sitti Rahmah Sitti Rahmah Sitti Rahmah Soelistianto, Farida Arinie Sok, Vann Sri Nur Hidayati Sugiarto Sugiarto Sugiarto S Sulung Alfianto Akbar Sunardi, Sunardi Suwarno, Iswanto Syaifullah, Ahmad Syekh Budi Syam Virasanty Muslimah Wartono, Tono wati, asiah Yana Mulyana Yudhi Fajar Saputra Yudhi Fajar Saputra Yulindawati Yusriati, Yusriati Zhang Li