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Ida Bagus Adisimakrisna Peling
Politeknik Negeri Bali
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Hyperparameter Tuning of YOLOv8n for Real-Time Material Truck Detection I Gede Angga Saputra; I Nyoman Eddy Indrayana; Ida Bagus Adisimakrisna Peling
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1138

Abstract

The increasing number of material trucks on arterial roads has posed challenges for traffic surveillance and regulatory compliance. Traditional monitoring techniques that rely on manual observation are often ineffective and susceptible to irregularities, highlighting the need for automated real-time monitoring systems. This study proposes a lightweight object detection approach using YOLOv8n to improve real-time truck detection performance in traffic monitoring applications. A quantitative experimental methodology was employed by performing hyperparameter tuning through adjustments to the number of epochs, batch size, optimizer, and learning rate. The dataset was collected from real traffic environments using smartphone cameras and CCTV (TP-Link Tapo C320WS). A total of 36 experimental configurations were evaluated using Precision, Recall, F1-score, mAP@50, and mAP@50–95 metrics. Experimental results showed that the optimal configuration, consisting of 100 epochs, a batch size of 16, the Adam optimizer, and a learning rate of 0.001, achieved a mean Average Precision (mAP)@50 of 0.9302 and mAP@50–95 of 0.7226. Although the performance improvement over the baseline YOLOv8n model was relatively modest, repeated experiments demonstrated improved model stability and consistency after hyperparameter optimization. Real-time deployment on a local GPU achieved a stable processing speed of 14–23 Frames Per Second, with an average of 19 FPS, enabling real-time monitoring performance aligned with the camera input rate. The integrated system successfully combines object detection, tracking, and license plate recognition for practical traffic monitoring applications. However, smaller objects such as license plates remained more challenging to detect due to localization limitations under occlusion and low-light conditions.
PERFORMANCE EVALUATION OF AUTOMATED MEETING SUMMARIZATION BASED ON OPEN AI WHISPER AND INDOT5 FINE-TUNING I Gusti Lanang Oka Wiyana; Putu Indah Ciptayani; Ida Bagus Adisimakrisna Peling
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4581

Abstract

Abstract: Manual meeting documentation risks losing important information due to cognitive fatigue. Although automated summarization models have evolved, integrated end-to-end systems for Indonesian spoken language remain highly limited. This study aims to design and evaluate an end-to-end automated meeting summarization architecture that directly integrates Automatic Speech Recognition (ASR) via OpenAI Whisper for transcription and the IndoT5 language model for abstractive summarization. IndoT5 was fine-tuned using a dataset of 486 Indonesian spoken language transcript pairs. Testing was conducted on a CPU infrastructure using MP4, MP3, and WAV formats. Results show the optimal fine-tuning configuration significantly improved accuracy, achieving ROUGE-1 (0.4167), ROUGE-2 (0.1973), and ROUGE-L (0.2701) scores. Computationally, the system achieved a Real-Time Factor below 1, processing data faster than the actual recording duration. Conclusively, integrating Whisper and IndoT5 shows potential in producing coherent meeting summaries with lightweight computational overhead, making it viable for local infrastructure implementation to ensure data privacy. Keywords: abstractive summarization; ASR; end-to-end pipeline; IndoT5; real-time factor Abstrak: Dokumentasi rapat manual rentan menghilangkan informasi penting akibat keterbatasan kognitif. Meskipun model peringkas otomatis telah berkembang, implementasi sistem terintegrasi (end-to-end) khusus percakapan lisan berbahasa Indonesia masih sangat terbatas. Penelitian ini bertujuan merancang dan mengevaluasi arsitektur peringkas rapat otomatis end-to-end yang mengintegrasikan langsung Automatic Speech Recognition (ASR) melalui OpenAI Whisper untuk transkripsi dan model bahasa IndoT5 untuk peringkasan abstraktif. Adaptasi domain dilakukan melalui fine-tuning IndoT5 menggunakan 486 pasang dataset transkrip lisan berbahasa Indonesia. Pengujian pada infrastruktur CPU menggunakan format MP4, MP3, dan WAV. Hasil pengujian menunjukkan konfigurasi fine-tuning optimal berhasil meningkatkan akurasi, dengan skor ROUGE-1 (0,4167), ROUGE-2 (0,1973), dan ROUGE-L (0,2701). Sistem mendemonstrasikan efisiensi komputasi dengan nilai Real-Time Factor di bawah 1, mengindikasikan waktu pemrosesan lebih cepat dari durasi rekaman asli. Kesimpulannya, integrasi Whisper dan IndoT5 menunjukkan potensi dalam menghasilkan ringkasan yang koheren dengan beban komputasi ringan, sehingga layak diimplementasikan pada infrastruktur lokal organisasi untuk menjaga privasi data. Kata kunci: ASR; end-to-end pipeline; IndoT5; peringkasan abstraktif; real-time factor
Co-Authors Alfariza, Muhammad Jaka Ananta Wijaya Astya Prayudha, I Putu Bhaskara, I Made Adi Gde Brahupadhya Subiksa I Gede Angga Saputra I Gede Angga Saputra I Gusti Lanang Oka Wiyana I Nyoman Arnawan I Nyoman Eddy Indrayana I Nyoman Harini Puspita I Putu Arich Arthawan I Putu Astya Prayudha I Putu Astya Prayudha I Putu Oka Wisnawa Ida Bagus A. Swamardika l Gusti Ngurah Janardana Luh Gede Putri Suardani Made Pasek Agus Ariawan Made Pasek Ariawan Made Pradnyana Ambara, Made Pradnyana Ni Ketut Pradani Gayatri Sarja Ni Nyoman Harini Puspita Nyoman Putra Sastra Prayudha, I Putu Astya Putu Indah Ciptayani Wiguna, I Komang Arya Ganda