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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Surya Masyarakat Aksioma Education Journal
Munsarif, Muhammad
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Religion Humanities Electrical & Electronics Engineering Engineering Environmental Science Mathematics Public Health Other

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Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Munsarif, Muhammad; Sam'an, Muhammad; Raharjo, Samsudi
Jurnal Surya Masyarakat Vol 6, No 2 (2024): Mei 2024
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.2.2024.269-275

Abstract

The development of artificial intelligence (AI)--based learning models has made significant progress alongside the abundance of data. This enables the creation of complex deep-learning models to tackle increasingly intricate tasks. Evolving machine learning algorithms become a key factor in enhancing AI model capabilities. The demand for smart and efficient solutions from the business sector drives the adoption of AI technology, supported by advances in sensor technology, the Internet of Things (IoT), natural language processing (NLP), and image recognition. This article highlights the potential impact of AI model development on the learning experience, especially at the Elementary (SD), Junior High (SMP), and Senior High School (SMA) levels. Implementing AI models in elementary and secondary schools can support student progress assessment, provide material recommendations based on student understanding, and develop skills. The study discusses a teacher training initiative using ChatGPT to understand and utilize artificial intelligence in education. Training results show that teachers can effectively create varied and engaging learning materials using ChatGPT. Despite AI's benefits, cultural and social values remain irreplaceable, such as ethics towards teachers and social interactions among students. In conclusion, digital literacy training for teachers is essential to enhance their ability to develop modern and effective learning models, with AI as a valuable tool in creating dynamic and interactive learning environments.
Applicaton Of Mathematics In Big Data Analysis To Support Strategic Decision Munsarif, Muhammad; Walid, Abul; Sari, Nila Kartika
Aksioma Education Journal Vol. 1 No. 4 (2024): December-AEJ
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/tq97je41

Abstract

This study aims to investigate the application of mathematical models in big data Analysis and their impact on strategic decision making in various industrial sectors. Using a quantitative approach to the survey, data was collected from 190 respondents from the technology, finance, manufacturing and healthcare sectors. The results showed that the application of mathematical models, such as predictive algorithms and machine learning, contributed significantly to improving the quality of strategic decisions. The study also identified that variables such as human resource competence and technological infrastructure moderate the relationship between big data Analysis and effective decision-making. The technology and finance sectors have proven to benefit the most from the application of math-based big data Analytics, with benefits seen in improved market prediction, risk management, and operational optimization. The findings underscore the importance of integrating mathematical models in data analysis to support data-driven decision-making in the digital age.
Deep residual bidirectional long short-term memory fusion: achieving superior accuracy in facial emotion recognition Munsarif, Muhammad; Ku-Mahamud, Ku Ruhana
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9090

Abstract

Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn-Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.
Peningkatan Kompetensi Guru di Sekolah Menengah Islam Ihsanul Fikri melalui Pelatihan Pengembangan Media Pembelajaran Berbasis Kecerdasan Buatan: Sebuah Studi Mixed-Methods Ramadhani, Rima Dias; Ilham, Ahmad; Sam’an, Muhammad; Fathurohman, Akhmad; Safuan, Safuan; Munsarif, Muhammad; Assaffat, Lukman; Kindarto, Asdani; Anggana, Muhammad Wahyu; Firmasyah, Teguh; Syaifani, M. Amin; Ahyana, Afan Arga; Nugroho, Adi; Kurnia, Janu Yogi
Jurnal Surya Masyarakat Vol 7, No 2 (2025): Mei 2025
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.7.2.2025.207-214

Abstract

The training program on Artificial Intelligence-based Learning Media Development (P3MP-AI) represents a strategic initiative to enhance the quality of education at the Ihsanul Fikri Integrated Islamic High School. In the rapidly evolving landscape of information technology, the integration of Artificial Intelligence (AI) technology into education has become an urgent necessity. This endeavor aims to address the personalized learning needs, overcome human resource and time constraints, and enhance the overall quality of education in achieving educational goals. Through the Active and Interactive Learning Method (MPB-AI) approach, educators engage in various practical and interactive activities, enabling them to comprehend and applykecerdasan buatanconcepts practically. Evaluation of this training program demonstrates significant achievements in enhancing participants' understanding and skills, as well as a high level of creativity and diversity in AI-based learning media development practices. Thus, this training program has successfully achieved its objectives in improving the quality of education at the Ihsanul Fikri Integrated Islamic High School. Recommendations for future activities include extending the duration of implementation for optimal goal attainment and developing similar activities periodically to continually enhance educators' abilities in delivering more effective and efficient learning experiences.
Co-Authors Abul Walid Adi Nugroho Ahyana, Afan Arga Akhmad Fathurohman Anggana, Muhammad Wahyu Firmasyah, Teguh Ilham, Ahmad Kindarto, Asdani Ku-Mahamud, Ku Ruhana Kurnia, Janu Yogi Lukman Assaffat Ramadhani, Rima Dias Safuan Safuan Sam'an, Muhammad SAMSUDI RAHARJO Sam’an, Muhammad Sari, Nila Kartika Syaifani, M. Amin