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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Media Jurnal Informatika
Elfada, Berliana
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Real-time Convolutional Neural Networks untuk Klasifikasi Emosi Wajah yang Terintegrasi dengan Rekomendasi Konten YouTube Berdasarkan Emosi Secara Rule-Based Gantini, Annisa Dinda; Malika, Adinda Faayza; Elfada, Berliana; Hidayatullah, Priyanto; Sholahuddin, Muhammad Rizqi
Media Jurnal Informatika Vol 17, No 1 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i1.5100

Abstract

Kemampuan sistem untuk mendeteksi emosi pada citra wajah manusia dan memberikan rekomendasi konten YouTube berdasarkan hasil deteksi tersebut dapat meningkatkan interaksi manusia dengan teknologi. Penelitian ini mengusulkan integrasi baru antara deteksi emosi wajah secara real-time dengan sistem rekomendasi multimedia berbasis emosi, menggunakan kerangka kerja ringan berbasis Convolutional Neural Networks (CNN). Dua arsitektur CNN yang efisien—Sequential Fully-CNN dan Mini-Xception—dibandingkan untuk mengklasifikasikan tujuh emosi pada dataset FER-2013. Kontribusi utama dari penelitian ini adalah penggabungan klasifikasi emosi secara real-time dengan sistem pemetaan berbasis aturan untuk merekomendasikan konten YouTube yang relevan secara emosional, menciptakan pengalaman pengguna yang lebih personal dan adaptif. Hasil pengujian menunjukkan bahwa model CNN yang dikembangkan mampu mendeteksi emosi dengan akurasi tinggi sekaligus menjaga efisiensi komputasi untuk aplikasi waktu nyata. Pendekatan ini diharapkan dapat meningkatkan kualitas interaksi manusia-komputer melalui umpan balik multimedia yang responsif dan relevan secara emosional.
Adaptive EKF-Based Ship Trajectory Estimation with Earth Curvature Modeling and Dynamic Noise Tuning Elfada, Berliana; Gardara, Suci Awalia; Soewono, Eddy Bambang; Widhiyasana, Yudi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2397

Abstract

Accurate position estimation is critical for the effectiveness of automatic weapon and navigation systems. Standard Extended Kalman Filter (EKF) models typically adopt flat-Earth assumptions and static noise covariances, which limit their accuracy in operational environments. This study proposes an optimized EKF framework that integrates two complementary approaches. First, ship trajectories are represented in Earth-Centered Earth-Fixed (ECEF) coordinates with a WGS-84 reference to account for Earth’s curvature. Second, process (Q) and measurement (R) covariances are adaptively determined using Joint Likelihood Maximization (JLM) with logarithmic scale exploration, allowing the filter to automatically identify the most accurate configuration. Each Q/R setting is evaluated within the EKF framework using root mean square error (RMSE) derived from radar data logs. The method was tested under short-history scenarios (5 and 10 data points) within an operational range of ±15 km, reflecting conditions commonly encountered in Combat Management Systems (CMS). Results show that while coordinate transformation alone provides only marginal improvements at short ranges, the combination of curvature modelling and adaptive Q/R tuning significantly reduces RMSE, achieving average errors approaching zero with high repeatability as measured by standard deviation. This research demonstrates a novel integration of geometric and statistical optimization in EKF design and highlights its applicability to ship trajectory estimation and defence systems.
Co-Authors Eddy Bambang Soewono Gantini, Annisa Dinda Gardara, Suci Awalia Malika, Adinda Faayza Muhammad Rizqi Sholahuddin Priyanto Hidayatullah, Priyanto Widhiyasana, Yudi