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MENINGKATKAN MINAT DAN KESADARAN MAHASISWA DALAM INTERNASIONALISASI KEGIATAN MELALUI STUDENT MOBILITY Vinza Hedi Satria; Muhammad Reza Pahlawan; Olive Khoirul Lukluil Maknun Al Faishol; Faisal Muttaqin; Angga Lisdiyanto; Addien Haniefardy; Imroatul Ajizah
Sinergi Aksi Nyata Cendekia Vol 1, No 2 (2025): November
Publisher : Lembaga Penelitian, Pengembangan, Pemberdayaan Potensi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6131/sancaka.v1i2.193

Abstract

Internasionalisasi pendidikan tinggi merupakan strategi penting dalam meningkatkan daya saing lulusan di kancah global. Namun, minat mahasiswa untuk terlibat dalam program internasionalisasi masih tergolong rendah, sehingga diperlukan upaya peningkatan pemahaman dan kesadaran mengenai manfaat pengalaman akademik internasional. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan minat serta kesiapan global mahasiswa melalui program student mobility bekerja sama dengan Universiti Sultan Zainal Abidin (UniSZA) Malaysia. Metode pelaksanaan meliputi koordinasi mitra internasional, sosialisasi program, pendampingan administratif dan teknis, pelaksanaan perkuliahan internasional inbound–outbound, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan peningkatan minat mahasiswa dalam mengikuti program internasional, kesiapan administrasi yang lebih baik, serta meningkatnya wawasan global dan kepercayaan diri mahasiswa dalam berinteraksi di lingkungan akademik internasional. Dengan demikian, program ini berhasil mendukung peningkatan internasionalisasi di lingkungan Fakultas Ilmu Komputer UPNVJT sekaligus berkontribusi terhadap pencapaian kompetensi global mahasiswa.
Enhancing Guest Security in Smart Hospitality: Face Recognition-Based Hotel Room Verification Using Haar Cascade Algorithm Adzanil Rachmadhi Putra; Aji Prayoga; Zacky Yaser Malik Gumiwang; Mohammad Daniel Sulthonul Karim; Muhammad Galang Satrio Wicaksono; Olive Khoirul Lukluil Maknun Al Faishol; Affifiana Prisyanti
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.163

Abstract

This study aims to design and implement a hotel room verification system based on facial recognition using the Haar Cascade algorithm. The research was motivated by the growing need to enhance both security and service efficiency in the modern hospitality industry. The study was conducted through several stages, including facial image data collection using a webcam, preprocessing (RGB to grayscale conversion, image resizing, and cropping), model training, and real-time face recognition testing. The Haar Cascade algorithm was employed to detect facial features by utilizing Haar-like features combined with the Adaboost method to accelerate classification. The experimental results showed a recognition accuracy of 55% under varying lighting conditions and viewing angles. These findings indicate that the Haar Cascade algorithm performs adequately in detecting faces under ideal conditions, although further optimization is required to handle lighting variations and facial stability. This research contributes to the application of artificial intelligence technology in hotel security systems, with potential future improvement through the integration of deep learning methods to enhance accuracy and reliability in face verification. Keywords: face recognition, Haar Cascade, hotel room verification, facial detection, digital security.
Predicting Career Transition of Indonesian Badminton Players Using Random Forest and XGBoost Olive Khoirul Lukluil Maknun Al Faishol; Sischa Wahyuning Tyas; Mohammad Al Hafidz
Journal of Research in Mathematics Trends and Technology Vol. 8 No. 1 (2026): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v8i1.25383

Abstract

Research on junior-to-senior career transitions in badminton remains limited, particularly in quantifying conversion rates and applying machine learning for predicting transition success. This study analyzes medal records of 147 Indonesian players across four junior tournaments (BWF World Junior Championships, Asian Junior Championships, Suhandinata Cup, Youth Olympic Games) and two senior tournaments (BWF World Championships, Olympic Games) spanning 1992–2024. Descriptive analysis reveals an overall junior-to-senior conversion rate of 29.3%, with significant variation across medal count groups (χ² = 36.84, p < 0.001). Players with four or more junior medals achieve 51.7% conversion rate compared to 0% for single-medal winners. Random Forest and XGBoost classifiers, trained with SMOTE oversampling and evaluated via repeated stratified cross-validation (5-fold, 10 repeats), achieve 90.8% and 88.8% accuracy respectively, substantially outperforming a logistic regression baseline (83.6%). Junior gold medal count and BWF World Junior participation emerge as the strongest predictors. A sensitivity analysis excluding recent junior medalists confirms the robustness of these findings. These results provide empirical evidence for optimizing talent identification in Indonesian badminton development programs. Keyword: Badminton, Career Transition, Conversion Rate, Random Forest, Talent Identification, XGBoost