Pengembangan Otomasi Inventaris Farmasi Rumah Sakit Gigi dan Mulut dengan Metode Convolutional Neural Network

Khairul Abdi; Amalia; Mohammad Andri Budiman

doi:10.30865/json.v7i4.9832

Authors

Khairul Abdi Universitas Sumatera Utara
Amalia Universitas Sumatera Utara
Mohammad Andri Budiman Universitas Sumatera Utara

DOI:

https://doi.org/10.30865/json.v7i4.9832

Keywords:

inventaris farmasi, Rumah Sakit Gigi dan Mulut, Convolutional Neural Networks, YOLO11s, deteksi objek, otomasi inventaris

Abstract

Pengelolaan inventaris farmasi di Rumah Sakit Gigi dan Mulut memerlukan proses yang cepat, teliti, dan terdokumentasi karena ketersediaan obat serta bahan kedokteran gigi berpengaruh langsung terhadap kelancaran pelayanan klinis. Penelitian ini mengembangkan prototipe otomasi inventaris berbasis citra menggunakan model deteksi objek YOLO11s yang dibangun di atas prinsip Convolutional Neural Network. Dataset terdiri atas 2.562 citra, 11 kelas produk, dan 5.486 anotasi bounding box yang dibagi menjadi data latih, validasi, dan uji dengan rasio 70:15:15. Tahapan penelitian meliputi akuisisi citra, anotasi, quality control dataset, praproses, augmentasi, pelatihan, evaluasi, serta perancangan integrasi hasil deteksi ke rekap inventaris. Pada data uji terbatas, model menghasilkan precision 99,37%, recall 98,86%, mAP@0.5 99,41%, dan mAP@0.5:0.95 90,03%. Hasil ini menunjukkan bahwa YOLO11s berpotensi membantu pengenalan jenis produk, lokalisasi objek, dan perhitungan jumlah item pada skenario inventaris farmasi yang tercakup dalam dataset. Namun, klaim kinerja masih perlu divalidasi lebih lanjut pada jumlah kelas yang lebih besar dan kondisi rak farmasi nyata yang lebih beragam.

References

[1] A. Almeman, “The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift,” J. Heal. Popul. Nutr., vol. 43, no. 1, pp. 1–22, 2024, doi: 10.1186/s41043-024-00550-2.

[2] E. P. B. Abimanyu, S. Satibi, and D. Endarti, "Digital Technologies in Hospital Pharmacy: A Systematic Review of Their Impact on Efficiency, Safety, and Inventory Management," J. Prev. Med. Public Health, pp. 1-11, 2025, doi: 10.3961/jpmph.25.495.

[3] M. D. Simpson and H. S. Qasim, “Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications,” Pharmacy, vol. 13, no. 2, p. 41, 2025, doi: 10.3390/pharmacy13020041.

[4] S. Al-Hourani and D. Weraikat, “A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions,” Sustain., vol. 17, no. 14, pp. 1–27, 2025, doi: 10.3390/su17146591.

[5] S. S. Dhurwey, P. Kumar, U. Sendur, and A. Verma, AI-Driven Inventory Management System for Pharmacy Stores, no. Icaaai. Atlantis Press International BV, 2025. doi: 10.2991/978-94-6463-738-0_83.

[6] R. K. Sridharan, A. Xhafa, S. Chaodhari, and S. Putsala, "Automated Vial and Pre-Filled Syringe Counting in the Pharmaceutical Industry Using YOLO and SAHI Techniques," Int. J. Innov. Sci. Res. Technol., vol. 9, no. 9, pp. 3127–3134, 2024, doi: 10.38124/ijisrt/IJISRT24SEP831.

[7] S. Kang, Z. Hu, L. Liu, K. Zhang, and Z. Cao, “Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis,” Electron., vol. 14, no. 6, pp. 1–36, 2025, doi: 10.3390/electronics14061104.

[8] N. A. Megantara and E. Utami, “Object Detection using YOLOv8 : A Systematic Review,” Sistemasi, vol. 14, no. 3, p. 1186, 2025, doi: 10.32520/stmsi.v14i3.5081.

[9] J. Lungido and E. H. Rachmawanto, “Real-Time Drug Classification Using YOLOv11 for Reducing Medication Errors,” J. Appl. Informatics Comput., vol. 9, no. 4, pp. 1191–1200, 2025, doi: 10.30871/jaic.v9i4.10117.

[10] M. G. Ragab et al., “A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023),” IEEE Access, vol. 12, no. April, pp. 57815–57836, 2024, doi: 10.1109/ACCESS.2024.3386826.

[11] K. Alomar, H. I. Aysel, and X. Cai, “Data Augmentation in Classification and Segmentation: A Survey and New Strategies,” J. Imaging, vol. 9, no. 2, 2023, doi: 10.3390/jimaging9020046.

[12] J.-H. Oh, H.-G. Kim, and K. M. Lee, "Developing and evaluating deep learning algorithms for object detection: Key points for achieving superior model performance," Korean Journal of Radiology, vol. 24, no. 7, pp. 698-714, 2023, doi: 10.3348/kjr.2022.0765.

[13] R. Padilla, S. L. Netto, and E. A. B. da Silva, "A comparative analysis of object detection metrics with a companion open-source toolkit," Electronics, vol. 10, no. 3, Art. no. 279, 2021, doi: 10.3390/electronics10030279.

[14] M. L. Ali and Z. Zhang, "The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection," Computers, vol. 13, no. 12, Art. no. 336, 2024, doi: 10.3390/computers13120336.

[15] J. Sobek et al., "MedYOLO: A medical image object detection framework," Journal of Imaging Informatics in Medicine, vol. 37, no. 6, pp. 3208-3216, 2024, doi: 10.1007/s10278-024-01138-2.

[16] C. Albuquerque, R. Henriques, and M. Castelli, "Deep learning-based object detection algorithms in medical imaging: Systematic review," Heliyon, vol. 11, no. 1, Art. no. e41137, 2025, doi: 10.1016/j.heliyon.2024.e41137.

[17] C. Chen, N. A. Mat Isa, and X. Liu, "A review of convolutional neural network based methods for medical image classification," Computers in Biology and Medicine, vol. 185, Art. no. 109507, 2025, doi: 10.1016/j.compbiomed.2024.109507.

[18] D. M. W. Powers, "Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.