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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Biogenerasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Multidisciplinary Applied Natural Science Bulletin of Information Technology (BIT) EXPLORER Jurnal Pengabdian Masyarakat Gemilang (JPMG) Journal of Scientific Research, Education, and Technology Buletin Ilmiah Informatika Teknologi Prosiding Seminar Nasional Unimus Management of Information System Journal International Conferences On Early Childhood Education Proceedings
Andi Ernawati
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Journal : Management of Information System Journal

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Estimasi Sudut Kedatangan yang Ditingkatkan dengan CNN pada Array Antena MIMO Menggunakan Data Sinyal IoT Dunia Nyata Karim, Abdul; Ernawati, Andi
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2564

Abstract

− This study proposes the application of a Convolutional Neural Network (CNN)–based approach to analyze signals in Internet of Things (IoT)–based MIMO antenna systems, with the aim of enhancing the understanding of system performance characteristics, particularly in predicting latency parameters. The CNN model is trained using real-world IoT signal data that have undergone comprehensive preprocessing stages, including data normalization, missing value handling, and feature engineering to ensure compatibility with the model input format. Experimental results on previously unseen test data demonstrate that the proposed model achieves a test loss of 1.4410, represented by the Mean Squared Error (MSE), and a Mean Absolute Error (MAE) of 0.9395. These results indicate that the model attains a relatively low prediction error and effectively captures the nonlinear relationships between signal features and system responses. Visualization of the testing results reveals a strong correlation between actual and predicted latency values, although some dispersion remains due to channel complexity and the inherent variability of IoT signals. The distribution of prediction errors is centered around zero, indicating the absence of significant systematic bias in the model. Overall, the findings confirm the potential of CNN as a reliable approach for modeling and performance analysis of IoT-based MIMO antenna systems, while also highlighting opportunities for further development in spatial parameter estimation and intelligent wireless communication system optimization.
Co-Authors - Idris Abdul Karim Ahmad Johari B. Sihes Alam, Sitti Nur Aris Tri Wahyudi Arridha Zikra Syah Aulia, Ananda Ayu Ofta Buton Buton Cucum Sumiati Darmeli Nasution Durrotul Muniroh Ahdaniyah Farta wijaya, Rian Idris Idris Iman Rusmana Iwan Purnama Khairul Mia sumiani Madi Misnarliah Mohd. Anuar B. Abdul Rahman Muhammad Arif Mansur Muhammad Iqbal Nurhaliza Sofyan, Siti Ofta Sari, Ayu Oftasari, Ayu Parini Puridawaty , Brigita Rolly Yesputra Salki Sasmita Saputra, Maulian Sari, Ayu Ofta Siregar, Andree Risky Yuliansyah Sitorus, Zulham Sofyan, Siti Nurhaliza Sri Budiarti Poerwanto sri murtini . Sri Wahyuni Sugeng Santoso Susilawati Syahruddin Lasadike Wijaya, Rian Farta Wijaya, Rian Farta Wijaya