Comparison of Transfer Learning Architecture Performance for Indonesian Auction Object Classification
Abstract
The Indonesian auction, one of the sources of Indonesia's income for Non-Tax State Revenue (PNBP), faces challenges in accurately classifying auction objects, limiting revenue optimisation. This research aims to compare the performance of several transfer learning architectures on the Indonesian Auction Object Dataset, which includes categories such as Buildings, Cars, Motorbikes, and Salvage Materials. Seven pre-trained transfer learning models—MobileNetV2, NASNetMobile, EfficientNetV2B0, DenseNet121, Xception, InceptionV3, and ResNet50V2—were evaluated against a baseline model, focusing on validation accuracy, model size, and computational efficiency. MobileNetV2, NASNetMobile, DenseNet121, Xception, InceptionV3, and ResNet50V2 all achieved 100% validation accuracy, outperforming the baseline model's 96.5% accuracy. MobileNetV2 stands out for its efficiency, reaching 100% accuracy in just eight epochs with a compact model size of 11.1 MB. In contrast, EfficientNetV2B0 performed poorly on this dataset, achieving only 25% validation accuracy. These findings confirm that transfer learning architectures can significantly improve auction object classification accuracy while reducing the model size and training time, highlighting the benefit of transfer learning for optimising Indonesian auction systems.
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References
Kementerian Keuangan, “Peraturan Menteri Keuangan Nomor 122 Tahun 2023 tentang Petunjuk Pelaksanaan Lelang,” 2023. Accessed: Sep. 02, 2024. [Online]. Available: https://jdih.kemenkeu.go.id/in/dokumen/peraturan/fe1bd404-e081-4ccd-5dd1-08dbf6b83bff
Direktorat Jenderal Kekayaan Negara, Laporan Kinerja Direktorat Lelang Tahun 2023. Direktorat Jenderal Kekayaan Negara, 2024. Accessed: Sep. 02, 2024. [Online]. Available: https://lelang.go.id/assets/lap-capkin-91dcbd20.pdf
A. Simanjuntak, S. B. Siahaan, D. Megaria Elisabeth, Y. Welly, and D. D. Hutagalung, “The Effect of Auction Fee, Administration Fees of State Receivables Management and Management of State-Owned Assets Against Actualization of State Non-Tax Revenue on Kantor Pelayanan Kekayaan Negara dan Lelang Medan Period 2019-2021,” Indonesian Journal of Banking and Financial Technology, vol. 1, no. 4, pp. 315–328, Oct. 2023, doi: 10.55927/fintech.v1i4.6581.
A. S. Sulaeman and L. P. C. D. Mayasari, “PENERIMAAN BEA LELANG: EMPIRICAL FACTOR ANALYSIS PADA PROSES LELANG ASET NEGARA DI INDONESIA,” EKUITAS (Jurnal Ekonomi dan Keuangan), vol. 7, no. 1, pp. 21–41, Mar. 2023, doi: 10.24034/j25485024.y2023.v7.i1.5085.
G. M. M. Sujak and H. N. Rofiq, “Pemanfaatan Artificial Intelligence untuk Optimalisasi PNBP: Studi Kasus Bea Lelang pada Lelang Indonesia,” Indonesian Treasury Review Jurnal Perbendaharaan Keuangan Negara dan Kebijakan Publik, vol. 9, no. 2, pp. 112–124, Jun. 2024, doi: 10.33105/itrev.v9i2.873.
S. Mastura Ajeng, D. K. Sugiharti, and A. Cahyadini, “Perlindungan Hukum Terhadap Pemenang Lelang Eksekusi Hak Tanggungan Atas Ketidaksesuaian Objeknya yang Dilakukan Melalui E-Auction,” INNOVATIVE: Journal Of Social Science Research, vol. 4, no. 4, pp. 475–487, Jul. 2024, doi: 10.31004/innovative.v4i4.12845.
J. Jareño, G. Bárcena-González, J. Castro-Gutiérrez, R. Cabrera-Castro, and P. L. Galindo, “Enhancing Fish Auction with Deep Learning and Computer Vision: Automated Caliber and Species Classification,” Fishes, vol. 9, no. 4, p. 133, Apr. 2024, doi: 10.3390/fishes9040133.
J. Bobulski and S. Szymoniak, “Deep Learning Method for Classifying Items into Categories for Dutch Auctions,” Computer Assisted Methods in Engineering and Science, vol. 31, no. 1, pp. 67–79, Jan. 2024, doi: 10.24423/cames.2024.979.
Z. Ardalan and V. Subbian, “Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review,” Front Artif Intell, vol. 5, Feb. 2022, doi: 10.3389/frai.2022.780405.
G. Kaur, N. Sharma, R. Chauhan, K. Joshi, and R. Gupta, “Rice Leaf Disease Detection Using InceptionV3 Transfer Learning Model,” in 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/SMARTGENCON60755.2023.10442321.
A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, 2022, doi: 10.1016/j.aiia.2021.12.002.
S. Niu, Y. Liu, J. Wang, and H. Song, “A Decade Survey of Transfer Learning (2010–2020),” IEEE Transactions on Artificial Intelligence, vol. 1, no. 2, pp. 151–166, Oct. 2020, doi: 10.1109/TAI.2021.3054609.
B. Sailaja and T. VenuGopal, “Brain MRI Image Classification and Analysis Using Modified ResNet50V2 for Parkinson’s Disease Detection,” SN Comput Sci, vol. 4, no. 6, p. 854, Nov. 2023, doi: 10.1007/s42979-023-02313-y.
J. Chen, D. Zhang, M. Suzauddola, Y. A. Nanehkaran, and Y. Sun, “Identification of plant disease images via a squeeze‐and‐excitation MobileNet model and twice transfer learning,” IET Image Process, vol. 15, no. 5, pp. 1115–1127, Apr. 2021, doi: 10.1049/ipr2.12090.
C. C. Foong, G. K. Meng, and L. L. Tze, “Convolutional Neural Network based Rotten Fruit Detection using ResNet50,” in 2021 IEEE 12th Control and System Graduate Research Colloquium (ICSGRC), IEEE, Aug. 2021, pp. 75–80. doi: 10.1109/ICSGRC53186.2021.9515280.
“Keras Application Benchmark.” Accessed: Sep. 02, 2024. [Online]. Available: https://keras.io/api/applications/
F. J. J. Joseph, S. Nonsiri, and M. Annop, “Keras and TensorFlow: A Hands-On Experience,” in Advanced Deep Learning for Engineers and Scientists: A Practical Approach, R. and A. S. A. and K. G. R. Prakash Kolla Bhanu and Kannan, Ed., Cham: Springer International Publishing, 2021, pp. 85–111. doi: 10.1007/978-3-030-66519-7_4.
N. Nayman, A. Golbert, A. Noy, and L. Zelnik-Manor, “Diverse Imagenet Models Transfer Better,” in 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, Jan. 2024, pp. 1903–1914. doi: 10.1109/WACV57701.2024.00192.
A. Chatterjee, J. Mukherjee, and P. P. Das, “ImageNet Classification Using WordNet Hierarchy,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1718–1727, Apr. 2024, doi: 10.1109/TAI.2023.3297086.
R. D. Bhosale and D. M. Yadav, “Analysis of EfficientNet Family Models by Retraining for Tuberculosis Detection from Chest X-Ray Images,” in 2023 Second International Conference on Informatics (ICI), IEEE, Nov. 2023, pp. 1–6. doi: 10.1109/ICI60088.2023.10420853.
T. Ibtekar, Md. R. Haque, and A. Y. Srizon, “Comparative Study Between ResNet And EfficientNet Family For Classification of Leukemia,” in 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ICCIT60459.2023.10441438.
A. D. Saputra, D. Hindarto, and H. Santoso, “Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201,” Sinkron, vol. 8, no. 1, pp. 48–55, Jan. 2023, doi: 10.33395/sinkron.v8i1.11906.
L. Yong, L. Ma, D. Sun, and L. Du, “Application of MobileNetV2 to waste classification,” PLoS One, vol. 18, no. 3, p. e0282336, Mar. 2023, doi: 10.1371/journal.pone.0282336.
I. Naskinova, “Transfer learning with NASNet-Mobile for Pneumonia X-ray classification,” Asian-European Journal of Mathematics, vol. 16, no. 01, Jan. 2023, doi: 10.1142/S1793557122502400.
M. F. Fayyad and Mustakim, “Application of AlexNet, EfficientNetV2B0, and VGG19 with Explainable AI for Cataract and Glaucoma Image Classification,” in 2024 International Electronics Symposium (IES), IEEE, Aug. 2024, pp. 406–412. doi: 10.1109/IES63037.2024.10665856.
N. Asaad Zebari, A. A. H. Alkurdi, R. B. Marqas, and M. Shamal Salih, “Enhancing Brain Tumor Classification with Data Augmentation and DenseNet121,” Academic Journal of Nawroz University, vol. 12, no. 4, pp. 323–334, Oct. 2023, doi: 10.25007/ajnu.v12n4a1985.
F. Salim, F. Saeed, S. Basurra, S. N. Qasem, and T. Al-Hadhrami, “DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition,” Electronics (Basel), vol. 12, no. 14, p. 3132, Jul. 2023, doi: 10.3390/electronics12143132.
T. H. Jaware, H. Kasturiwale, R. Thakur, M. D. Jakhete, M. Chavan, and M. Rane, “Deep Learning Model for Colon Cancer Classification using InceptionV3,” Journal of Electrical Systems, vol. 20, no. 4s, pp. 132–139, Apr. 2024, doi: 10.52783/jes.1862.
B. Sailaja and T. VenuGopal, “Brain MRI Image Classification and Analysis Using Modified ResNet50V2 for Parkinson’s Disease Detection,” SN Comput Sci, vol. 4, no. 6, p. 854, Nov. 2023, doi: 10.1007/s42979-023-02313-y.
M. Yeung, E. Sala, C.-B. Schönlieb, and L. Rundo, “Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation,” Computerized Medical Imaging and Graphics, vol. 95, p. 102026, Jan. 2022, doi: 10.1016/j.compmedimag.2021.102026.
B. Baker et al., “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., Curran Associates, Inc., 2022, pp. 24639–24654. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/9c7008aff45b5d8f0973b23e1a22ada0-Paper-Conference.pdf
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