Optimization of Blood Clam Supply Control Using the Artificial Neural Network (ANN) Method
DOI:
https://doi.org/10.22441/ijiem.v7i1.33669Keywords:
Optimization, Control, Inventory, Blood shells, ANNAbstract
Mr. Badul MSME faces problems in managing blood clam inventory, namely excess and shortage of stock. To overcome this, research was conducted to design an inventory prediction system using the Artificial Neural Network (ANN) method with the Backpropagation algorithm. The ANN model used has an architecture with 10 input neurons, 10 hidden neurons, and 1 output neuron. The inventory data is normalized before the training process, then the results are denormalized to get the actual prediction. The developed model shows good performance with a very low Mean Squared Error (MSE) value of 2.7359e-06, as well as a correlation coefficient of 0.91478, which shows a strong relationship between predictions and actual data. The prediction results cover the period from January 2023 to December 2024. In January 2023, the inventory was predicted to be 96,050 kg, declining in February to 89,205 kg, and dropping sharply to 68,670 kg in March and April. Inventory increases again in May to August with fluctuations from 75,515 kg to 89,205 kg. A similar pattern occurs in 2024, starting with 96,050 kg in January, decreasing in March and April, then increasing again in the middle of the year, and decreasing again towards the end of the year, with the lowest inventory of 65,933 kg in November and December.Downloads
References
Ayuni, N. W. D., Utthavi, W. H., and Lasmini, N. N. (2025). Artificial Neural Networks: A Deep Learning Approach in Financial Distress Prediction. Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2024 (ICoSTAS-EAS 2024). http://dx.doi.org/10.2991/978-94-6463-587-4_12.
Chauhan, D., Shivani, S., Jung, D., and Yadav, A. (2025). Advancements in Multimodal Differential Evolution: A Comprehensive Review and Future Perspectives. Artificial Intelligence Review, (2025). http://doi.org/abs/2504.00717.
Cholodowicz, E., and Oriowski, P. (2024). Neural Network Control of Perishable Inventory with Fixed. Energies (2024), 17(4), 849; https://doi.org/10.3390/en17040849
Dalm, S., Offergeld, J., Ahmad, N., and van Gerven, M. (2024). Efficient Deep Learning with Decorrelated Backpropagation. CoRR abs/2405.02385 (2024). http://doi.org/abs/2405.02385.
Devita, R. N., Herwanto, H. W., and Wibawa, A. P. (2020). Perbandingan Kinerja Metode Naive Bayes dan K-Nearestneighbor untuk Klasifikasi Artikel Berbahasa Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 5(4), 427-434. https://doi.org/10.25126/jtiik.201854773
Handayani, P., Kurniawan, K., and Adibrata, S. (2020). Kandungan Logam Berat Pb Pada Air Laut, Sedimen Dan Kerang Darah (Anadara Granosa) Di Pantai Sampur Kabupaten Bangka Tengah. PELAGICUS, 1(2): 97.
https://doi.org/10.15578/plgc.v1i2.8910
Hartono, H., and Andaresta, I. (2021). Pengaruh Pengelolaan Persediaan Bahan Baku Terhadap Efisiensi Biaya Persediaan di PT Harmoni Makmur Sejahtera. Jurnal Logistik Indonesia, 5(1): 45–54.
https://doi.org/10.31334/logistik.v5i1.1184
Hidayat, R., Wahyuda, W., and Sitania, F. D. 2024. Soybean Inventory Management at Gesit Tahu Factory Using the Economic Order Quantity (EOQ) Method. IJIEM (Indonesian Journal of Industrial Engineering & Management), 5(2): 477–86.
https://doi.org/10.22441/ijiem.v5i2.22938
Iqbal, I., Rosdi, S. M., Muhtadin, M., Erdiwansyah, E., and Faisal, M. (2025). Optimisation of combustion parameters in turbocharged engines using computational fluid dynamics modelling. International Journal of Simulation, Optimization & Modelling, 1: 63–69. https://e-journal.scholar-publishing.org/index.php/ijsom/article/view/58
Islam, A., Bouzerdoum, A., and Belhaouari, S. B. (2024). Bio-Inspired Adaptive Neurons for Dynamic Weighting in Artificial Neural Networks. Computer Science AI Open (2026). https://doi.org/abs/2412.01454.
Kabangnga, A., Heriansah, H., and Nursida, N. F. (2024). Analisis Laju Filtrasi Dan Morfometrik Kerang Darah (Anadara Granosa) Pada Budidaya Sistem Kokultur Dengan Berbagai Kombinasi Biota. Journal of Marine Research, 13(2): 185–94.
https://doi.org/10.14710/jmr.v13i2.39977
Kapucu, C., and Akpolat, O. (2024). Artificial Neural Network Parameter Optimization: Improving Meteorological Data Predictions through Machine Learning. Adv. Artif. Intell. Res., 4(1), 53–61, 4(1): 53–61.
https://doi.org/10.54569/aair.1535217
Karamoy, W. Y. R., Jan, A. B. H., and Karuntu, M. M. (2022). Analisis Persediaan Bahan Baku Pada Moy Restaurant Tonsaru Di Era Pandemi Covid-19. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 10(1): 510–17.
Larasati, W., Yateno, Y., and Japlani, A. (2022). Analisis Pengendalian Persediaan Tepung Terigu pada UMKM dengan Pendekatan Economic Order Quantity pada Toko Kue Sahara Cake di Gantimulyo Pekalongan Lampung Timur. SNPPM (Seminar Nasional Penelitian dan Pengabdian kepada Masyarakat) Tahun 2022, 79–89. https://prosiding.ummetro.ac.id/index.php/snppm/issue/view/8.
Lasarudin, A., and Maku, R. (2022). Prediksi Pertumbuhan Jumlah Penduduk Menggunakan Algoritma Neural Network. Jurnal Ilmu Komputer (JUIK), 2(2): 37-40.
https://doi.org/10.31314/juik.v2i2.1715
Nand, A. (2025). Next-generation inventory optimization: advanced inventory management harnessing demand variability integrating fuzzy logic and granular differentiability. RAIRO-Oper. Res., 59 (2025) 335–353. https://doi.org/10.1051/ro/2024226
Ping, L. Y., Wong, P., and Han, T. C. (2025). A Study of Data-Driven Methods for Inventory Optimization. Computer Science. http://arxiv.org/abs/2505.08673.
Purba, A. A., Rohmatin, N. Y., and Karim, A. A. 2024. Analysis of Fabrication Material Inventory Control Strategy Using AHP, MUSIC-3D and MIN-MAX STOCK Approaches at PT . Bangun Teknik. IJIEM - Indonesian Journal of Industrial Engineering & Management, 5(3): 916–27.
https://doi.org/10.22441/ijiem.v5i3.26020
Purnamasari, D. A., Nafisyah, A. L., and Sari, L. A. (2024). Inovasi Pemanfaatan Limbah Cangkang Kerang Darah (Anadara Granosa) Sebagai Media Pertumbuhan Nitzschia Sp. JMCS (Journal of Marine and Coastal Science), 13 (3), 136-143.
https://doi.org/10.20473/jmcs.v13i3.60239
Rahmawati, F. H., and Adityarini, E. (2021). Sistem Informasi Persediaan Barang Pada CV. Anak Teladan. Jurnal Sistem Informasi, 10(1): 1–7.
https://doi.org/10.51998/jsi.v10i1.351
Rahmiyanti, R., Defit, S., and Yunus, Y. (2021). Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation. Jurnal Informasi dan Teknologi, 3(3), 109–14.
https://doi.org/10.37034/jidt.v3i3.116
Ramadhan, A., and Saifuddin, J. A. (2024). Analysis of Raw Material Inventory Control Using the Min-Max Stock Method to Control Inventory Costs at PT . Artha King Indonesia. IJIEM (Indonesian Journal of Industrial Engineering & Management), 5(2), 529-544.
https://doi.org/10.22441/ijiem.v5i2.22293
Soori, M., Arezoo, B., and Dastres, R. (2023). Artificial Neural Networks in Supply Chain Management, a Review. Journal of Economy and Technology, 1, 179–96.
https://doi.org/10.1016/j.ject.2023.11.002
Stoilov, Todor, and Krasimira Stoilova. 2025. “Bi-Level Optimization of Inventory and Production.” Cybernetics and Information Technologies 25(1): 126–41.
Downloads
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to UMB. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the UMB copyright notice are indicated, the copies are not used in any way that implies UMB endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
