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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Pendidikan IPA (JPPIPA) Martabe : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Surya Masyarakat Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Aksioma Education Journal
Munsarif, Muhammad
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences 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.
Segmentasi Pemain Bola Dengan Arsitektur U-Net Fajar Bima Laksono; Hannan Isnaen, Muhammad; Okta Wijaya, Risky; Munsarif, Muhammad
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 18 No. 2 (2024): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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

Abstract

Segmentasi merupakan teknik pada pengolahan citra digital yang memfokuskan pada pembagian objek ke dalam beberapa bagian dan pemisahan antara region (objek) dengan latar belakang. Dalam konteks ini, Ada tiga jenis karakteristik gambar yang signifikan, yaitu titik, garis, dan tepi. Segmentasi citra sendiri dapat dikelompokkan menjadi tiga kategori utama, yaitu identifikasi objek, identifikasi semantik, dan identifikasi instan.. Pada penelitian ini, fokusnya adalah pada segmentasi pemain bola menggunakan pendekatan deep learning, khususnya dengan metode Convolutional Neural Network (CNN) dan arsitektur U-Net. CNN merupakan salah satu metode neural network pada deep learning dan machine learning yang baik dalam hal akurasi pada pengenalan citra, sedangkan U-Net biasa Digunakan pada segmentasi citra yang berjenis semantik. Segmentasi semantik, adalah citra yang dibagi menjadi kategori objek dan bukan objek. Proses segmentasi pemain bola melibatkan tahap encoder dan decoder citra sebelum dimanfaatkan dalam proses pelatihan model, tahap pengujian melibatkan penerapan model CNN-U-Net untuk melakukan klasifikasi citra, menghasilkan. yang terdiri dari 11 kelas yaitu Bilah Gawang, Wasit, Iklan, Lapangan, Bola, Pelatih & Ofisial, Penonton, Kiper A, Kiper B, Tim A, dan Tim B Output tersebut akan dievaluasi dengan menghitung akurasi untuk memastikan performa model.
Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters Munsarif, Muhammad; Saman, Muhammad
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p38-45

Abstract

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%. 
Hyperparameter optimization of convolutional neural network using grey wolf optimization for facial emotion recognition Munsarif, Muhammad; Saman, Muhammad; Ernawati, Ernawati; Santosa, Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp898-906

Abstract

Facial emotion recognition (FER) is a challenging task in computer vision with wide applications in areas such as human-computer interaction, security, and healthcare. To improve the performance of convolutional neural networks (CNN) in FER, a novel approach combining CNN with grey wolf optimization (GWO) was proposed to optimize key hyperparameters. The CNN-GWO model was fine-tuned by adjusting hyperparameters such as the number of convolutional layers, kernel size, number of filters, and learning rate. This model was evaluated using the CK+ dataset and achieved an accuracy of 90.97%, demonstrating its competitive performance compared to existing methods. The optimized hyperparameters included three convolutional layers, 35 filters, a kernel size of 5, a learning rate of 0.045990, a dropout rate of 0.4988, and a max pooling size of 3. These results confirm that GWO is effective in optimizing CNN for FER tasks, providing an efficient solution to enhance model accuracy. This approach shows promising potential for future FER applications, highlighting GWO as a valuable optimization technique for CNN architectures.
PENGEMBANGAN AGROPREUNERSHIP DAN DIGITALISASI DALAM UPAYA BRANDING DESA WISATA WONOLOPO Amelia Hanifah, Meike; Mirza, Shabrina; Afwah, Kholifatul; Rahma, Salsabila; Widianto, Alfi; Ratna, Galih; Hardika, Alif; Janata, Jian; Muzdalifah, Siti; Miftakhul, Ahmad; Ramadhani, Tirta; Rayhan, Javier; Munsarif, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 4 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i4.1286-1290

Abstract

Usaha Mikro Kecil dan Menengah (UMKM) dalam perekonomian Indonesia mempunyai peran dan potensi yang besar dalam membangun perekonomian sektoral maupun nasional. Pelaku UMKM di Desa Wonolopo, Kecamatan Mijen, Kota Semarang masih menghadapi permasalahan yang terkait kurangnya pengetahuan masyarakat terhadap Digital Marketing strategi. Keterbatasan ini merupakan permasalahan yang penting bagi UMKM dalam mengembangkan usahanya. Sehingga dalam penelitian ini kami akan memaparkan upaya pengembangan branding yang dilakukan kepada masyarakat Desa Wonolopo. Beberapa kegiatan akan dilaksanakan dalam bentuk kegiatan sosialisasi yang rutin setiap minggu. Dalam pelaksanaanya hanya 20 peserta yang dapat mengikuti, sehubungan masih dalam kondisi Pemberlakuan Pembatasan Kegiatan Masyarakat(PPKM) yang terus menerus berkelanjutan. Hasil dari kegiatan ini, diharapkan para peserta yang mengikuti dapat lebih terbuka secara pengetahuan tentang bagaimana cara mempromosikan potensi Desa Wonolopo dengan mengikuti perkembangan zaman.
Analysis of the Integrated E-Mesp 4CS Mobile Instrument Winaryati, Eny; Munsarif, Muhammad; Junaedi, Iwan; Utomo; Arianty, Alya Dwi; Khoirunisa, Asiva; Ibrahim, Kafitra Marna
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12980

Abstract

The need for valid and contextually relevant supervision instruments remains a key challenge in realizing 21st-century education that integrates the 4Cs skills (critical thinking, creativity, collaboration, and communication) with the Profil Pelajar Pancasila. This study aimed to develop and evaluate the feasibility of the Integrated E-MESp 4Cs Mobile as a digital instrument for academic supervision. Using a research and development (R&D) approach with qualitative data collection, this study conducted Focus Group Discussions, in-depth interviews, and document analyses involving teachers, principals, supervisors, and education experts. The data was analyzed through interactive qualitative procedures, including data reduction, presentation, and verification. The results show that the E-MESp 4Cs Mobile instrument is feasible and relevant to be used as an integrated supervision tool that combines 4Cs indicators with the dimensions of the Pancasila Student Profile. Stakeholders also emphasized the need for standardization aligned with national guidelines and the potential use of artificial intelligence to support data-based supervision and policy recommendations. Overall, the E-MESp 4Cs Mobile instrument demonstrates strong potential to enhance teacher supervision practices, support deep learning, and strengthen the development of humanistic, inclusive, and character-oriented graduates.
Co-Authors Abul Walid Adi Nugroho Afwah, Kholifatul Ahyana, Afan Arga Akhmad Fathurohman Amelia Hanifah, Meike Anggana, Muhammad Wahyu Arianty, Alya Dwi Budi Santosa Budi Santosa Eny Winaryati Ernawati Ernawati Fajar Bima Laksono Firmasyah, Teguh Hannan Isnaen, Muhammad Hardika, Alif Ibrahim, Kafitra Marna Ilham, Ahmad Iwan junaedi Janata, Jian Khoirunisa, Asiva Kindarto, Asdani Ku-Mahamud, Ku Ruhana Kurnia, Janu Yogi Lukman Assaffat Miftakhul, Ahmad Mirza, Shabrina Muzdalifah, Siti Okta Wijaya, Risky Rahma, Salsabila Ramadhani, Rima Dias Ramadhani, Tirta Ratna, Galih Rayhan, Javier Safuan Safuan Sam'an, Muhammad Saman, Muhammad SAMSUDI RAHARJO Sam’an, Muhammad Sari, Nila Kartika Syaifani, M. Amin Utomo Widianto, Alfi