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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Media Journal of General Computer Science (MJGCS)
APRILIANI, AULIA
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Detecting the Number of Students Using YOLOv11 to Prevent Proxy Attendance at Universitas Dinamika Bangsa Saputri, Rhadis Steffani; Apriliani, Aulia; Mukminin, Amirul
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.38

Abstract

Attendance is an important criterion for passing courses at Universitas Dinamika Bangsa Jambi. According to the academic regulations of Universitas Dinamika Bangsa Jambi, the minimum attendance requirement for course completion is 75%. The attendance process at the university utilizes an academic information system (SIAKAD) where students log in using a username and password, then scan an attendance barcode or input a unique code. Students often engage in proxy attendance practices, where they are marked present in the system despite being absent in reality. This study discusses the prevention of proxy attendance by employing a human detection system based on YOLOv11, capable of counting the number of students present in the classroom at Universitas Dinamika Bangsa Jambi. The research method involves the design, implementation, and evaluation of the system. This study adopts a deep learning approach using supervised learning methods for model training. The model is trained on a labeled dataset from Roboflow and implemented using the YOLOv11 algorithm. Based on the research results, the authors conclude that the human detection system is effective in counting the number of students in the classroom. However, the system still requires further development to detect criteria or features that can distinguish the detected individuals' status, specifically between students and lecturers.
ANALISIS SENTIMEN MASYARAKAT TERHADAP VIRUS CORONA BERDASARKAN OPINI DARI TWITTER BERBASIS WEB SCRAPER KURNIAWAN, ROBI; APRILIANI, AULIA
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 5 No 1 (2020): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.309 KB) | DOI: 10.24252/instek.v5i1.13686

Abstract

Indonesia menjadi salah satu Negara yang pengguna aktif harian twitternya cukup tinggi, berdasarkan hal tersebut twitter dapat dijadikan sebagai media untuk melakukan analisis sentimen terhadap topik corona. Analisis sentimen merupakan salah satu cabang dari text mining yang melakukan proses klasifikasi pada dokumen atau teks. Penelitian ini bertujuan untuk mengetahui bagaimana dampak virus corona di Indonesia sesuai opini masyarakat melalui twitter. Pengumpulan data dilakukan dengan teknik web scraper yang menghasilkan 1000 record sejak tanggal 20 Januari sampai 1 Februari 2020, data yang telah di scraping kemudian dianalisis mengikuti tahapan text mining yaitu case folding, tokenizing dan filtering. Hasil dari penelitian ini menunjukan persentase opini masyarakat terhadap virus corona yaitu 79% negatif, 11% Netral dan 10% Positif. Kata kunci : corona, analisis sentimen, twitter;
Co-Authors Amirul Mukminin Robi Kurniawan Saputri, Rhadis Steffani