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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Aksioma Education Journal
Munsarif, Muhammad
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Computer Science & IT Electrical & Electronics Engineering Mathematics Other

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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.
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

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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.
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.
Co-Authors Abul Walid Budi Santosa Budi Santosa Ernawati Ernawati Fajar Bima Laksono Hannan Isnaen, Muhammad Ku-Mahamud, Ku Ruhana Nila Kartika Sari Okta Wijaya, Risky Saman, Muhammad