Kohesi: Jurnal Sains dan Teknologi

Articles

Vol. 11 No. 5 (2026): Kohesi: Jurnal Sains dan Teknologi, ISSN 3025-1311

BIOOPTIMA INTELLIGENCE: KERANGKA KONSEPTUAL INTEGRASI AI, MACHINE LEARNING, DAN IOT UNTUK PEMETAAN DAN OPTIMASI PEMANFAATAN BIOMASSA

Submitted
October 12, 2026
Published
2026-10-12

Abstract

Indonesia memiliki potensi biomassa yang besar dari sektor pertanian, perkebunan, kehutanan, peternakan, perikanan, dan limbah perkotaan. Namun, keragaman karakteristik bahan, persebaran geografis, kondisi logistik, serta perbedaan kebutuhan teknologi konversi menyulitkan penentuan alternatif pemanfaatan dan lokasi fasilitas pengolahan yang sesuai. Penelitian ini bertujuan merumuskan BioOptima Intelligence sebagai kerangka konseptual yang mengintegrasikan pemetaan biomassa, Internet of Things (IoT), Machine Learning (ML), dan Artificial Intelligence (AI) untuk mendukung perencanaan pemanfaatan biomassa berbasis data. Metode penelitian menggunakan structured literature review dan analisis konseptual terhadap literatur terkait biomassa, bioenergi, AI/ML, IoT, rantai pasok, dan sistem pendukung keputusan. Hasil sintesis dirumuskan menjadi kerangka yang menghubungkan pemetaan dan karakterisasi biomassa, akuisisi serta validasi data, analitik ML, rekomendasi berbasis AI, dan mekanisme pemantauan serta umpan balik. Secara konseptual, BioOptima Intelligence dirancang menghasilkan peta dan basis data biomassa, analisis kesesuaian wilayah, estimasi potensi energi berdasarkan ketersediaan data, serta rekomendasi alternatif teknologi konversi dan kandidat lokasi fasilitas pengolahan. Kontribusi konseptual penelitian terletak pada integrasi komponen tersebut dalam satu alur pengambilan keputusan adaptif. Namun, kerangka ini belum diuji secara empiris. Penelitian lanjutan diperlukan untuk membangun dataset, mengembangkan prototipe, memvalidasi model, dan menguji kelayakan rekomendasi pada kondisi operasional.

References

  1. Abdollahfard, Y., Sedighi, M., & Ghasemi, M. (2023). A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence. Sustainability, 15(2), 1312. https://doi.org/10.3390/su15021312
  2. De Oliveira Cavalcanti, G., & Pimenta, H. C. D. (2023). Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review. Energies, 16(15), 5753. https://doi.org/10.3390/en16155753
  3. Ejiyi, C. J., Cai, D., Thomas, D., Obiora, S., Osei-Mensah, E., Acen, C., Eze, F. O., Sam, F., Zhang, Q., & Bamisile, O. O. (2025). Comprehensive review of artificial intelligence applications in renewable energy systems: Current implementations and emerging trends. Journal of Big Data, 12, Article 169. https://doi.org/10.1186/s40537-025-01178-7
  4. Helal, M. A., Anderson, N., Wei, Y., & Thompson, M. (2023). A Review of Biomass-to-Bioenergy Supply Chain Research Using Bibliometric Analysis and Visualization. Energies, 16(3), 1187. https://doi.org/10.3390/en16031187
  5. Hess-Dunlop, A., Kakani, H., & Josephson, C. (2024). Towards deep learning for predicting microbial fuel cell energy output. arXiv, 1–12. https://doi.org/10.48550/arXiv.2406.16939
  6. International Energy Agency. (2024). Renewables 2023: Analysis and forecasts to 2028. International Energy Agency. https://www.iea.org/reports/renewables-2023
  7. International Renewable Energy Agency. (2022). Scaling up biomass for the energy transition: Untapped opportunities in Southeast Asia. International Renewable Energy Agency. https://www.irena.org/Publications/2022/Feb/Scaling-up-biomass-for-the-energy-transition-Untapped-opportunities-in-Southeast-Asia
  8. Kementerian Energi dan Sumber Daya Mineral. (2023). Potensi biomassa menjanjikan, Indonesia prediksi hasilkan listrik setara 56, 97 GW. https://www.esdm.go.id/id/media-center/arsip-berita/potensi-biomassa-menjanjikan-indonesia-prediksi-hasilkan-listrik-setara-5697-gw
  9. Khan, M., Naqvi, S. R., Ullah, Z., Taqvi, S. A. A., Khan, M. N. A., Farooq, W., Mehran, M. T., Juchelková, D., & Štěpanec, L. (2023). Applications of machine learning in thermochemical conversion of biomass - A review. Fuel, 332, Article 126055. https://doi.org/10.1016/j.fuel.2022.126055
  10. Khanaum, M. M., Rahman, S., & Borhan, M. S. (2026). Optimizing the Performance of Microbial Fuel Cells: Linking Laboratory Analysis and Multivariate Machine Learning Approach to Enhance Bioelectricity Generation from Wastewater. Fuels, 7(1), 6. https://doi.org/10.3390/fuels7010006
  11. Mondal, P. P., Galodha, A., Verma, V. K., Singh, V., Show, P. L., Awasthi, M. K., Lall, B., Anees, S., Pollmann, K., & Jain, R. (2023). Review on machine learning-based bioprocess optimization, monitoring, and control systems. Bioresource Technology, 370, 128523. https://doi.org/10.1016/j.biortech.2022.128523
  12. Morkūnas, M., Wang, Y., & Wei, J. (2024). Role of AI and IoT in Advancing Renewable Energy Use in Agriculture. Energies, 17(23), 5984. https://doi.org/10.3390/en17235984
  13. Mundu, M. M., Islam, M. R., Habib, M. A., Hasan, M. K., & Islam, S. (2026). Harnessing AI in renewable energy systems: Driving environmental and socio-economic transformation. Energy Informatics, 9, Article 24. https://doi.org/10.1186/s42162-026-00624-x
  14. Poornima, S., Manikandan, S., Prakash, R., Deena, S. R., Subbaiya, R., Karmegam, N., Kim, W., & Govarthanan, M. (2024). Biofuel and biochemical production through biomass transformation using advanced thermochemical and biochemical processes: A review. Fuel, 372, 132204. https://doi.org/10.1016/j.fuel.2024.132204
  15. Rajagopalan, A., Swaminathan, D., Bajaj, M., Damaj, I., Rathore, R. S., Singh, A. R., Blažek, V., & Prokop, L. (2024). Empowering power distribution: Unleashing the synergy of IoT and cloud computing for sustainable and efficient energy systems. Results in Engineering, 21, 101949. https://doi.org/10.1016/j.rineng.2024.101949
  16. Savvidou, M. G., Pandis, P. K., Mamma, D., Sourkouni, G., & Argirusis, C. (2022). Organic Waste Substrates for Bioenergy Production via Microbial Fuel Cells: A Key Point Review. Energies, 15(15), 5616. https://doi.org/10.3390/en15155616
  17. Suhartini, S., Pangestuti, M. B., Elviliana., Rohma, N. A., Junaidi, M. A., Paul, R., Nurika, I., Rahmah, N. L., & Melville, L. (2024). Valorisation of macroalgae for biofuels in Indonesia: an integrated biorefinery approach. Environmental Technology Reviews, 13(1), 269–304. https://doi.org/10.1080/21622515.2024.2336894
  18. Suwarjono, S., & Afriza, A. (2025). Utilization of Smart Technologies Based on Internet of Things and Machine Learning in Energy Management: A Systematic Literature Review. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 5(1), 1494–1498. https://doi.org/10.59934/jaiea.v5i1.1657
  19. Trnka, M., Abdelfattah, A. S., Shrestha, A., Coffey, M., & Cerny, T. (2022). Systematic Review of Authentication and Authorization Advancements for the Internet of Things. Sensors, 22(4), 1361. https://doi.org/10.3390/s22041361
  20. Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T.-C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), 3833–3879. https://doi.org/10.1177/0958305X241256293

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