Indonesia needs energy to meet its needs. Energy sourced from waste is called biomass briquettes. The manufacture of briquettes is still done in a traditional way, so there are product quality problems. A computational approach has been taken to base the quality of the product on certain characteristics so that the types of briquettes that are made can be classified or sorted. The research objective of this work is to determine the quality of briquettes. The approach uses deep learning methods and convolutional neural network (CNN) architecture. Classification is based on good and bad briquette products, and testing is based on the level of performance accuracy. The dataset formed consists of 5,280 images. As training data, 85% of data is used and 15% is used as test data using 300 epoch parameters, 32 batch sizes, and learning speed up to 0.001. The results of testing and evaluating the performance of briquette products with the general CNN architecture have a level of accuracy that is not optimal. The optimization results of testing the MobileNetV2 architecture with a ratio of 70:30 obtains predictions with 0.99 as the highest accuracy value and 0.73 as the lowest. The average predicted value of the model is 0.95. The computational approach is able to provide traditional communities with solutions for the process of making briquettes.

1. [1] N. Ferronato and V. Torretta, “Waste mismanagement in developing countries: A review of global issues,” Int. J. of Environmental Research and Public Health, Vol.16, No.6, Article No.1060, 2019. https://doi.org/10.3390/ijerph16061060
2. [2] A. F. Widiyanto et al., “Knowledge and practice in household waste management,” Kesmas: Jurnal Kesehatan Masyarakat Nasional, Vol.13, No.3, pp. 112-116, 2019. https://doi.org/10.21109/kesmas.v13i3.2705
3. [3] K. Kasmad et al., “Utilization of potential waste resources as the key to success in implementing an ideal waste treatment system,” Abdi Laksana: Jurnal Pengabdian Kepada Masyarakat, Vol.2, No.3, pp. 507-512, 2021 (in Indonesian). https://doi.org/10.32493/al-jpkm.v2i3.13511
4. [4] S. S. Rath et al., “Biomass briquette as an alternative reductant for low grade iron ore resources,” Biomass and Bioenergy, Vol.108, pp. 447-454, 2018. https://doi.org/10.1016/j.biombioe.2017.10.045
5. [5] Musabbikhah et al., “Optimization of biomass briquettes production process using Taguchi method to fulfill the need of environment friendly alternative fuel,” Jurnal Manusia dan Lingkungan, Vol.22, No.1, pp. 121-128, 2015 (in Indonesian). https://doi.org/10.22146/jml.18733
6. [6] V. Wiley and T. Lucas, “Computer vision and image processing: A paper review,” Int. J. of Artificial Intelligence Research, Vol.2, No.1, pp. 28-36, 2018. https://doi.org/10.29099/ijair.v2i1.42
7. [7] N. J. Gajbhiye and L. P. Raut, “Briquettes making machine for industrial and agricultural purpose,” Int. Research J. of Engineering and Technology, Vol.5, No.2, pp. 594-598, 2018.
8. [8] R. T. Haug, “The Practical Handbook of Compost Engineering,” Lewis Publishers, 1993.
9. [9] National Standardization Agency, “The Conformity Assessment Scheme Against Indonesian National Standards for the Chemical Sector Contains Technical Guidelines for the Briquette Product Certification Scheme,” pp. 208-219, 2019.
10. [10] Z. Gao, M. Z. Q. Chen, and D. Zhang, “Special issue on ‘Advances in condition monitoring, optimization and control for complex industrial processes’,” Processes, Vol.9, Article No.664, 2021. https://doi.org/10.3390/pr9040664
11. [11] E. N. Arrofiqoh and H. Harintaka, “The implementation of convolutional neural network method for agricultural plant classification in high resolution imagery,” Geomatika, Vol.24, No.2, pp. 61-68, 2018 (in Indonesian). https://doi.org/10.24895/jig.2018.24-2.810
12. [12] A. Johny et al., “Optimization of CNN model with hyper parameter tuning for enhancing sturdiness in classification of histopathological images,” Proc. of the 2nd Int. Conf. on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), 2020. https://doi.org/10.2139/ssrn.3735831
13. [13] A. D. Arafah and S. S. Harsono, “Analysis the effect of coconut shell charcoal mixed doses and adhesive in characteristics Jamu dregs briquettes,” Berkala Sainstek, Vol.9, No.4, pp. 179-185, 2021 (in Indonesian). https://doi.org/10.19184/bst.v9i4.27326
14. [14] A. Dmitriev, “Mathematical Modeling of the Blast Furnace Process,” Cambridge Scholars Publishing, 2019.
15. [15] N. U. R. Rather, “Introduction to renewable energy technologies in India,” Educreation Publishing, 2018.
16. [16] S. Minaee et al., “Deep learning based text classification: A comprehensive review,” arXiv: 2004.03705, 2020. http://arxiv.org/abs/2004.03705
17. [17] G. Pierce, “Resizing digital images to actual size (I:I) using Adobe® Photoshop®,” J. of the Association for Crime Scene Reconstruction, Vol.15, No.1, pp. 13-16, 2009.
18. [18] S. Zhang et al., “Cucumber leaf disease identification with global pooling dilated convolutional neural network,” Computers and Electronics in Agriculture, Vol.162, pp. 422-430, 2019. https://doi.org/10.1016/j.compag.2019.03.012
19. [19] F. D. Adhinata et al., “Comparative study of VGG16 and MobileNetV2 for masked face recognition,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, Vol.7, No.2, pp. 230-237, 2021. https://doi.org/10.26555/jiteki.v7i2.20758
20. [20] M. G. F. Costa et al., “Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images,” BMC Medical Imaging, Vol.19, No.1, Article No.85, 2019. https://doi.org/10.1186/s12880-019-0389-2
21. [21] T. Okatani, “On deep learning,” J. of the Robotics Society of Japan, Vol.33, No.2, pp. 92-96, 2015 (in Japanese). https://doi.org/10.7210/jrsj.33.92
22. [22] A. Taner, Y. B. Öztekin, and H. P. Duran, “Performance analysis of deep learning CNN models for variety classification in hazelnut,” Sustainability, Vol.13, No.12, Article No.6527, 2021. https://doi.org/10.3390/su13126527
23. [23] T. R. Allen and V. L. Wright, “Idaho National Laboratory Mission Accomplishments, Fiscal Year 2015,” Idaho National Laboratory, 2015. https://www.osti.gov/biblio/1236797 [Accessed August 17, 2022]