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All Journal Seminar Nasional Produk Terapan Unggulan Vokasi Frontier Advances in Applied Science and Engineering
Motulo, Firmansyah Reskal
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Pengembangan Asesmen Kinerja Berbasis STEM Untuk Menilai Kemampuan Pemecahan Masalah Matematika Mahasiswa Pada Pembelajaran Matematika Teknik Lambonan, Oldi Malfri; Motulo, Firmansyah Reskal; Runtuwene, Steven Johny
Prosiding Seminar Nasional Produk Terapan Unggulan Vokasi Vol 2 No 1 (2023): Prosiding Seminar Nasional Produk Terapan Unggulan Vokasi Politeknik Negeri Manad
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat Politeknik Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sistem pembelajaran sangat penting untuk direvitalisasi karena melalui sistem pembelajaran yang efektif dan efisien, akan diperoleh lulusan perguruan tinggi atau calon tenaga kerja yang berkualitas, produktif, dan mampu bersaing. Dalam penelitian ini merujuk pada salah satu bagian dalam sistem pembelajaran berupa sebuah perangkat pembelajaran yaitu asesmen yang dapat menilai secara utuh pemecahan masalah dalam pembelajaran matematika teknik. Penelitian ini bertujuan untuk mengembangkan instrumen yang digunakan untuk menilai kinerja kemampuan pemecahan masalah mahasiswa pada pembelajaran matematika teknik yang memenuhi kriteria valid dan reliabel dan menerapkan asesmen kinerja berbasis STEM (Science, Technology, Engineering, and Mathematics) untuk menilai kemampuan pemecahan masalah matematika mahasiswa. Penelitian ini termasuk penelitian dan pengembangan yang mengacu pada dua model yakni 4D: define, design , develop, dan disseminate dan Oriondo & Antoni. Desain pengembangan dikelompokkan atas enam prosedur yakni: (a) eksplorasi, (b) pengembangan, (c) pengembangan prototype, (d) uji coba produk dan revisi, (e) uji validitas teoritis dan empirik, dan (f) penerapan produk. Hasil penelitian menunjukkan asesmen kinerja yang dikembangkan valid dan reliabel sebagai instrument penilaian kinerja berbasis STEM. Asesmen yang dikembangkan adalah asesmen untuk pemecahan masalah. Berdasarkan hasil penelitian diketahui kemampuan pemecahan masalah mahasiswa tergolong rendah
Multi-Stage Computer Vision Framework with Ensemble Learning for Real-Time Glass Packaging Defect Detection in Industrial Applications Jonah Alfred Mekel; Rick Resa Wahani; Motulo, Firmansyah Reskal; Alfred Noufie Mekel; Tineke Saroinsong; Tammy Tinny V. Pangow; Jerry Heisye Purnama; Jedithjah Naapia Tamedi Papia
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.572

Abstract

Transparent glass packaging inspection presents significant challenges for automated quality control systems due to optical complexities including reflections, refractions, and low-contrast defect patterns. This research develops a comprehensive multi-stage computer vision framework integrating specialized algorithmic modules with ensemble machine learning for real-time defect detection in industrial glass packaging lines. The framework implements four specialized detection stages: (1) meniscus-corrected liquid level measurement using dual-camera validation and polynomial surface fitting, (2) seal integrity assessment through Circular Hough Transform combined with geometric, texture, and color feature extraction, (3) lid positioning evaluation via calibrated geometric centroid analysis with tolerance-based classification, and (4) multi-method contamination detection integrating color aberration analysis, histogram-based particle detection, and morphological operations. The system employs an ensemble classification architecture combining modified MobileNetV2 convolutional neural network with Random Forest classifier, optimized for edge computing deployment. Industrial validation at PT AQUWAR Bintang Semesta demonstrated 91.6% overall detection accuracy with 347 milliseconds average processing time per container across 2,847 test samples spanning multiple defect categories. The modular framework architecture enables independent optimization of detection stages while maintaining real-time processing capabilities, providing a robust foundation for transparent packaging quality control in high-volume manufacturing environments.
Co-Authors Alfred Noufie Mekel Jedithjah Naapia Tamedi Papia Jerry Heisye Purnama Jonah Alfred Mekel Oldi Malfri Lambonan Rick Resa Wahani Runtuwene, Steven Johny Tammy Tinny V. Pangow Tineke Saroinsong