eprintid: 136667 rev_number: 41 eprint_status: archive userid: 216759 dir: disk0/00/13/66/67 datestamp: 2025-09-10 08:12:25 lastmod: 2025-09-10 08:12:25 status_changed: 2025-09-10 08:12:25 type: thesis metadata_visibility: show creators_name: Muhammad Gelvan Alfiandi, - creators_name: Galura Muhammad Suranegara, - creators_name: Endah Setyowati, - creators_nim: NIM2104425 creators_nim: NIDN0011019201 creators_nim: NIDN0408099202 creators_id: gelvanalviandi@upi.edu creators_id: galurams@upi.edu creators_id: endahsetyowati@upi.edu contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_name: Galura Muhammad Suranegara, - contributors_name: Endah Setyowati, - contributors_nidn: NIDN0011019201 contributors_nidn: NIDN0408099202 contributors_id: galurams@upi.edu contributors_id: endahsetyowati@upi.edu title: PENGEMBANGAN MODEL HYBRID CONVOLUTIONAL CAPSULE NETWORK UNTUK DETEKSI MULTI-OBJEK PADA CITRA TERMAL ispublished: pub subjects: T1 divisions: Pend.Multi_S1_PWT full_text_status: restricted keywords: Citra Thermal, Deteksi Objek, Deteksi Multi-Objek, Machine Learning, Deep LearnIing note: https://scholar.google.com/citations?view_op=list_works&hl=id&authuser=4&user=r7W1CDkAAAAJ ID SINTA Pembimbing Galura Muhammad Suranegara: 6703764 Endah Setyowati: 6681149 abstract: Pencitraan termal memainkan peran penting dalam berbagai aplikasi, termasuk pengawasan, navigasi otonom, dan pemantauan keselamatan pejalan kaki. Studi ini menyajikan implementasi Hybrid Convolutional Capsule Network (HCCN) untuk deteksi multi-objek dalam citra termal, dengan fokus pada klasifikasi objek human dan cyclist. Model dievaluasi menggunakan metode 6-Fold Cross-Validation, yang memastikan distribusi kelas objek yang seimbang di seluruh subset validasi. Pendekatan Stratified K-Fold digunakan untuk menjaga distribusi ini, sehingga penilaian model tetap adil dan tidak bias. Temuan ini menegaskan bahwa HCCN mampu melakukan generalisasi dengan baik dalam berbagai skenario pencitraan termal, menjadikannya solusi yang andal untuk tugas deteksi multi-objek di dunia nyata. Penelitian di masa depan dapat berfokus pada penanganan ketidakseimbangan kelas melalui teknik augmentasi data atau class weighting, guna lebih meningkatkan kinerja deteksi, terutama untuk kelas dengan jumlah data yang lebih sedikit. Kata Kunci— Citra Thermal, Deteksi Objek, Deteksi Multi-Objek, Machine Learning, Deep LearnIing ----- Thermal imaging plays a crucial role in various applications, including surveillance, autonomous navigation, and pedestrian safety monitoring. This study presents the implementation of a Hybrid Convolutional Capsule Network (HCCN) for multi-object detection in thermal images, focusing on the classification of human and cyclist objects. The model was evaluated using 6-Fold Cross-Validation, ensuring a balanced distribution of object classes across all validation subsets. A Stratified K-Fold approach preserved this distribution, ensuring fair and unbiased model assessment. These findings highlight HCCN’s capability to generalize well across different thermal imaging scenarios, making it a robust solution for real-world multi-object detection tasks. Future research could focus on addressing class imbalance through data augmentation or class weighting strategies to further enhance detection performance, particularly for minority classes. Keywords— Thermal Imaging, Object Detection, Multi-Object Detection, Machine Learning, Deep Learning date: 2025-07-24 date_type: published institution: Universitas Pendidikan Indonesia department: KODEPRODI20202#Sistem Telekomunikasi_S1 thesis_type: other thesis_name: other official_url: https://repository.upi.edu/ related_url_url: https://perpustakaan.upi.edu/ related_url_type: org citation: Muhammad Gelvan Alfiandi, - and Galura Muhammad Suranegara, - and Endah Setyowati, - (2025) PENGEMBANGAN MODEL HYBRID CONVOLUTIONAL CAPSULE NETWORK UNTUK DETEKSI MULTI-OBJEK PADA CITRA TERMAL. S1 thesis, Universitas Pendidikan Indonesia. document_url: http://repository.upi.edu/136667/2/S_SISTEL_2104425_Title.pdf document_url: http://repository.upi.edu/136667/3/S_SISTEL_2104425_Chapter1.pdf document_url: http://repository.upi.edu/136667/4/S_SISTEL_2104425_Chapter2.pdf document_url: http://repository.upi.edu/136667/5/S_SISTEL_2104425_Chapter3.pdf document_url: http://repository.upi.edu/136667/6/S_SISTEL_2104425_Chapter4.pdf document_url: http://repository.upi.edu/136667/7/S_SISTEL_2104425_Chapter5.pdf document_url: http://repository.upi.edu/136667/8/S_SISTEL_2104425_Appendix.pdf