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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jurnal Media Publikasi Terapan Transportasi
Ariyanti, Karin Nur Fitria
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Electrical & Electronics Engineering Transportation

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Identifikasi Pengguna Aplikasi Transportasi Access by KAI dengan Ulasan dan Rating Menggunakan Analisis Sentimen Ariyanti, Karin Nur Fitria; Susanti, Anita
Jurnal Media Publikasi Terapan Transportasi Vol. 2 No. 1 (April) (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mitrans.v2n1.p30-40

Abstract

Pengaruh besar teknologi semakin dirasakan pada zaman ini. Salah satu contoh yaitu penggunaan aplikasi Access by KAI untuk pengguna jasa moda transportasi kereta. Access by KAI merupakan aplikasi dari PT. Kereta Api Indonesia yang menyediakan layanan terhadap pengguna moda transportasi kereta. Analisis sentimen merupakan analisis pendapat atau opini seseorang tentang suatu hal baik berupa layanan dan operasional secara otomatis. Analisis sentimen mempermudah seseorang untuk mengukur tingkat kepuasan pengguna moda transporatsi kereta sekaligus pengguna aplikasi Access by KAI terhadap layanan yang diberikan tanpa harus membaca ribuan sampai ratusan komentar atau ulasan sekaligus. Analisis sentimen menggunakan data ulasan dan rating sebagai indikator penilaian terhadap layanan aplikasi dari PT. Kereta Api Indonesia. Metode pemograman yang dilakukan menggunakan long short term memory (LSTM) dan kuantitatif deskriptif untuk metode penelitiannya. Satu-satunya data yang digunakan yaitu data primer dari ulasan dan rating aplikasi Access by KAI yang didapat dari website google playstore halaman aplikasi Access by KAI. Hasil analisis yang didapat menunjukkan mayoritas pengguna aplikasi Access by KAI memberikan sentimen negatif pada ulasan dan sangat tidak puas pada rating perolehan terbanyak diraih bintang 1 dengan jumlah 1834 terhadap layanan aplikasi Access by KAI . Penelitian ini memberikan solusi bagi pembuat aplikasi (developer) dan pengguna aplikasi untuk mengatasi masalah tersebut.
Motorcycle Parking Availability Monitoring Using YOLOv5 and Mobile-Based Systems Wibisono, R. Endro; Susanti, Anita; Haratama, Kusuma Refa; Aribowo, Widi; Ariyanti, Karin Nur Fitria; Oliva, Diego; Shehadeh, Hisham A.; Umar, Abubakar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16087

Abstract

The increasing number of motorcycles in developing countries has intensified parking management challenges, particularly in high-density environments with irregular vehicle arrangements. This study proposes a motorcycle parking availability detection system using the YOLOv5 object detection algorithm to address limitations of conventional parking methods. The research contribution is the development of a context-aware detection framework using a locally collected dataset and the evaluation of its performance under real-world parking conditions.The dataset consists of 1,200 images collected from campus parking areas and is divided into training, validation, and testing sets. The images were annotated into occupied and vacant classes and trained using YOLOv5 with 100 epochs. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP@0.5) on a held-out test set.The results show that the model achieves an F1-score of 0.57 and mAP@0.5 of 0.566, indicating moderate detection performance in dense and occluded environments. Although a precision of 1.00 is obtained at a confidence threshold of 0.978, this condition significantly reduces recall, highlighting a trade-off between detection accuracy and coverage. The confusion matrix and recall–confidence analysis reveal that errors are primarily caused by occlusion, shadow effects, and background interference. Compared to previous studies focusing on car parking detection, this system demonstrates comparable performance while addressing the unique complexity of motorcycle parking. However, the relatively small dataset size and environmental variability limit generalization.In conclusion, the proposed system provides a feasible initial approach for motorcycle parking detection, but further improvements in dataset diversity, annotation quality, and model robustness are required to achieve reliable large-scale deployment.
Co-Authors Anita Susanti Haratama, Kusuma Refa Oliva, Diego R. Endro Wibisono Shehadeh, Hisham A. Umar, Abubakar Widi Aribowo