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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Rekayasa elektrika
Sanggrahita, Diadora
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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PENDEKATAN BERBASIS DECISION TREE DENGAN POLA WILDCARD UNTUK MENGINDENTIFIKASI PLAT NOMOR KENDARAAN PULAU JAWA Aryandra, Chaesa Akmal; Sanggrahita, Diadora; Kusumaningrum, Safira Damayanti; Ahyubi, Iqbal Al; Iksan, Nur
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6728

Abstract

Pengembangan sistem klasifikasi wilayah berdasarkan kode plat nomor kendaraan di Pulau Jawa. Pentingnya penelitian ini terletak pada kebutuhan untuk mengidentifikasi asal wilayah kendaraan secara otomatis guna mendukung sistem informasi dan analisis data transportasi. Metode yang digunakan mencakup pengumpulan data plat nomor, preprocessing data dengan penghapusan karakter khusus dan pemisahan elemen, serta klasifikasi menggunakan pendekatan berbasis aturan wildcard dan algoritma pembelajaran mesin Decision Tree. Karakter wildcard () digunakan untuk membentuk pola deteksi huruf depan atau belakang plat nomor, yang dikonversi ke simbol REGEX untuk mempermudah pencocokan. Evaluasi dilakukan dengan skema train-test split 80:20, menghasilkan akurasi sebesar 58%. Hasil menunjukkan bahwa model lebih akurat pada kota/kabupaten dengan jumlah data lebih besar. Temuan ini menunjukkan bahwa kombinasi pendekatan wildcard dan Decision Tree cukup efektif dalam klasifikasi asal wilayah kendaraan, dan mengindikasikan bahwa keberagaman serta volume data sangat berpengaruh terhadap performa model klasifikasi. Keywords: DecisionTree; Wildcard; Kendaraan; Jawa.
Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning Subiyanto, Subiyanto; Hangga, Arimaz; Bahatmaka, Aldias; Salim, Nur Azis; Sutrisno, Deyndrawan; Yunus, Elfandy; Budi Arif Prabowo, Setya; Hilmi Farras, Muhammad; Sanggrahita, Diadora
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.39281

Abstract

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.
Co-Authors Ahyubi, Iqbal Al Aldias Bahatmaka Arimaz Hangga, Arimaz Aryandra, Chaesa Akmal Budi Arif Prabowo, Setya Hilmi Farras, Muhammad Kusumaningrum, Safira Damayanti Nur Iksan Salim, Nur Azis Sutrisno, Deyndrawan Yunus, Elfandy