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All Journal Computer Science (CO-SCIENCE) Madiun Spoor : Jurnal Pengabdian Masyarakat
Teguh Arifianto
Indonesian Railway Polytechnic
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Materials Science & Nanotechnology Social Sciences

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OPTIMIZING GENERATIVE AI THROUGH HYBRID TRAINING TO ENHANCE RAILWAY VOCATIONAL STUDENTS’ CAREER READINESS Damar Isti Pratiwi; Teguh Arifianto; Kiki Juli Anggoro; Ardian Yosep Yohannes; Riana Eka Budiastuti
Madiun Spoor : Jurnal Pengabdian Masyarakat Vol 6 No 1 (2026): April 2026
Publisher : Politeknik Perkeretaapian Indonesia Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37367/pqnc4q08

Abstract

Transformasi digital dan perkembangan Generative Artificial Intelligence (Generative AI) telah membawa perubahan signifikan dalam praktik rekrutmen kerja, khususnya dalam penyusunan dokumen karier profesional seperti Curriculum Vitae (CV) dan surat lamaran kerja (cover letter). Mahasiswa vokasi sebagai calon lulusan siap kerja dituntut memiliki kemampuan adaptif terhadap perkembangan teknologi digital serta keterampilan dalam menyusun dokumen karier yang kompetitif dan selaras dengan standar industri. Namun demikian, masih banyak mahasiswa yang mengalami kesulitan dalam menghasilkan dokumen yang efektif, terstruktur, dan kompatibel dengan sistem Applicant Tracking System (ATS). Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kesiapan karier mahasiswa vokasi di Kota Madiun melalui model pelatihan hybrid yang mengintegrasikan pemanfaatan generative AI. Program ini melibatkan 120 mahasiswa vokasi yang dibagi ke dalam empat kelas pelatihan. Tahapan pelaksanaan meliputi persiapan, pelatihan interaktif hybrid, serta evaluasi melalui pretest dan posttest. Aplikasi Generative AI yang digunakan meliputi chatgpt, canva, text-to-speech, dan duolingo. Hasil evaluasi menunjukkan adanya peningkatan signifikan pada pemahaman peserta, yang tercermin dari kenaikan skor posttest. Selain itu, CV dan cover letter peserta menunjukkan perbaikan pada aspek struktur, kualitas konten, optimasi ATS, dan tampilan visual. Peserta juga memberikan respons positif terhadap relevansi materi dan manfaat praktis pelatihan. Program ini menunjukkan bahwa pelatihan hybrid berbasis generative AI efektif dalam mendukung peningkatan literasi digital dan kesiapan karier mahasiswa vokasi, khususnya dalam menghadapi tuntutan kompetitif industri perkeretaapian.
Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring Aulya Anggita Putri Selendra; Teguh Arifianto; Fathurrozi Winjaya
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.11674

Abstract

Safety in railway shunting operations requires continuous monitoring of train distance and speed to reduce the risk of operational accidents. In practice, shunting activities are still highly dependent on manual observation and verbal communication, while the performance of vision based safety systems under real operational conditions remains uncertain. In addition, comprehensive performance evaluations of deep learning based object detection models in real shunting environments, particularly under different hardware capabilities and lighting conditions, are still limited. This study aims to evaluate the performance of the YOLOv8 algorithm for real-time distance and speed monitoring during railway shunting operations. The system was tested using a camera-based detection approach under different processor configurations, namely an internal CPU and an RTX GPU, and under morning, daytime, and nighttime lighting conditions. System performance was evaluated based on accuracy, precision, and real-time detection capability across these conditions. The results show that the system achieved an average accuracy of 87.32% when operating on a CPU which increased to 91.30% when using a GPU. Optimal performance was observed under adequate daylight conditions, while reduced lighting led to a decline in performance, particularly on CPU-based processing. These findings indicate that hardware configuration and lighting conditions play a critical role in determining the reliability of YOLOv8-based safety monitoring systems for railway shunting operations.
Co-Authors Ardian Yosep Yohannes Aulya Anggita Putri Selendra Damar Isti Pratiwi Fathurrozi Winjaya Kiki Juli Anggoro Riana Eka Budiastuti