Interdisciplinary Research Integration in Higher Education: A Case Study on the Development of an AI-Based Non-Invasive Hemoglobin Detection System
DOI:
10.29303/jppipa.v11i11.12961Published:
2025-11-25Downloads
Abstract
This study examines the effectiveness of Project-Based Learning (PBL) in the development of a non-invasive hemoglobin detection system using photoplethysmography (PPG) integrated with artificial intelligence. A quasi-experimental design was applied to 26 Electrical Engineering students enrolled in Big Data Analysis, Sensor Design, and Embedded Systems courses. Students worked in groups to design, implement, and test prototypes. Data were collected through project rubrics, questionnaires, observations, and reflections, and were analyzed to determine the effectiveness index. The results revealed an overall effectiveness index of 3.8, categorized as good. The highest score was achieved in the affective aspect (4.2), reflecting strong motivation, proactive attitudes, and teamwork. Cognitive (3.9) and reflection (3.8) aspects also showed positive outcomes, while psychomotor (3.6) and product quality (3.5) remained weaker due to technical issues, including prototype accuracy and troubleshooting difficulties. The study demonstrates that PBL effectively integrates theory and practice, enhances 21st-century skills, and fosters meaningful learning experiences. Additionally, the findings highlight the potential of incorporating advanced technologies into engineering education while contributing to innovative health technology solutions for addressing malnutrition.
Keywords:
Hemoglobin detection Photoplethysmography Project based learningReferences
Abuzairi, T., Vinia, E., Yudhistira, M. A., Rizkinia, M., & Eriska, W. (2024). A dataset of hemoglobin blood value and photoplethysmography signal for machine learning-based non-invasive hemoglobin measurement. Data in Brief, 52, 109823. https://doi.org/10.1016/j.dib.2023.109823
Acharya, S., Swaminathan, D., Das, S., Kansara, K., Chakraborty, S., Kumar R, D., Francis, T., & Aatre, K. R. (2020). Non-Invasive Estimation of Hemoglobin Using a Multi-Model Stacking Regressor. IEEE Journal of Biomedical and Health Informatics, 24(6), 1717–1726. https://doi.org/10.1109/JBHI.2019.2954553
Ankalaki, S., G Biradar, V., Naik P, K. K., & S. Hukkeri, G. (2024). A Deep Learning Approach for Malnutrition Detection. International Journal of Online and Biomedical Engineering (IJOE), 20(06), 116–138. https://doi.org/10.3991/ijoe.v20i06.46919
Ariyani, A., Nazurty, N., & Sukendro, S. (2025). The Implementation of a Problem-Based Learning (PBL) Model Assisted by Wordwall Media in the IPAS Subject to Enhance Students’ Learning Outcomes. Jurnal Penelitian Pendidikan IPA, 11(2), 602–606. https://doi.org/10.29303/jppipa.v11i2.9373
Azmi, N., Richasdy, D., & Hasmawati. (2023). Recommendation System in the Form of an Ontology-Based Chatbot for Healthy Food Recommendations for Teenagers. Jurnal Penelitian Pendidikan IPA, 9(7), 5085–5091. https://doi.org/10.29303/jppipa.v9i7.4401
Bitew, F. H., Sparks, C. S., & Nyarko, S. H. (2022). Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public Health Nutrition, 25(2), 269–280. https://doi.org/10.1017/S1368980021004262
Cholidah, R., Danianto, A., Ayunda, R. D., & Rahmadhona, D. (2023). History of Anemia in Pregnancy with Stunting Incidents in Toddlers at Nipah Community Health Center, Malaka, North Lombok Regency. Jurnal Penelitian Pendidikan IPA, 9(12), 12226–12231. https://doi.org/10.29303/jppipa.v9i12.4946
Darmoutomo, E. (2023). Apa itu Malnutrisi? Kenali Penyebab beserta Gejalanya. SiloamHospital.
De Palma, L., Andria, G., Attivissimo, F., Lanzolla, A. M. L., & Di Nisio, A. (2025). Enhancing ABP estimation through comprehensive PPG signal analysis and advanced loss function optimization. Measurement: Journal of the International Measurement Confederation, 256(PB), 118210. https://doi.org/10.1016/j.measurement.2025.118210
Feli, M., Kazemi, K., Azimi, I., Liljeberg, P., & Rahmani, A. M. (2025). Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation. Computers in Biology and Medicine, 188(February), 109798. https://doi.org/10.1016/j.compbiomed.2025.109798
Filho, I. J. S., Rahman, M. M. U., Laleg-Kirati, T.-M., & Al-Naffouri, T. (2024). Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals. IFAC-PapersOnLine, 58(24), 579–583. https://doi.org/10.1016/j.ifacol.2024.11.101
Gong, A., Liu, J., Lu, L., Wu, G., Jiang, C., & Fu, Y. (2019). Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm. Biomedical Signal Processing and Control, 51, 128–137. https://doi.org/10.1016/j.bspc.2019.02.009
Hartana, R. D., & Sela, E. I. (2024). Nutritional Status Classification Of Stunting In Toddlers Using Naive Bayes Classifier Method. Journal of Technology Informatics and Engineering, 3(1), 01–10. https://doi.org/10.51903/jtie.v3i1.154
Hemo, S. A., & Rayhan, M. I. (2021). Classification tree and random forest model to predict under-five malnutrition in Bangladesh. Biometrics & Biostatistics International Journal, 10(3), 116–123. https://doi.org/10.15406/bbij.2021.10.00337
Jeon, Y. J., & Kang, S. J. (2023). Multi-slice Nested Recurrence Plot (MsNRP): A robust approach for person identification using daily ECG or PPG signals. Engineering Applications of Artificial Intelligence, 126, 106799. https://doi.org/10.1016/j.engappai.2023.106799
Kavsaoğlu, A. R., Polat, K., & Hariharan, M. (2015). Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features. Applied Soft Computing, 37, 983–991. https://doi.org/10.1016/j.asoc.2015.04.008
Lakshmi, M., Manimegalai, P., & Bhavani, S. (2020). Non-invasive haemoglobin measurement among pregnant women using photoplethysmography and machine learning. Journal of Physics: Conference Series, 1432(1), 012089. https://doi.org/10.1088/1742-6596/1432/1/012089
Lara-Pompa, N. E., Hill, S., Williams, J., Macdonald, S., Fawbert, K., Valente, J., Kennedy, K., Shaw, V., Wells, J. C., & Fewtrell, M. (2020). Use of standardized body composition measurements and malnutrition screening tools to detect malnutrition risk and predict clinical outcomes in children with chronic conditions. The American Journal of Clinical Nutrition, 112(6), 1456–1467. https://doi.org/10.1093/ajcn/nqaa142
Lestari, H. D., Rahmawati, Y., & Usman, H. (2024). STEM-PjBL Learning Model To Enhance Critical Thinking Skills of Students on Magnets, Electricity, and Technology. Jurnal Penelitian Pendidikan IPA, 10(8), 6027–6037. https://doi.org/10.29303/jppipa.v10i8.8153
Manikam, N. R. M. (2021). Known facts: iron deficiency in Indonesia. World Nutrition Journal, 5(S1), 1–9. https://doi.org/10.25220/WNJ.V05.S1.0001
Maricris, M. (2023). Sistem Informasi Pemantauan Malnutrisi pada Balita Berbasis IoT. Retrieved from https://repository.its.ac.id/104064/
Mayrohmah, S. H., Supriyanto, A., & Nugroho, T. R. (2024). Timbangan Pintar Sebagai Alternatif Pencegahan Stunting Berbasis Internet of Things Dan Artificial Intelligence. Prosiding Seminar Nasional Amikom Surakarta, 2(November), 1294–1306. Retrieved from https://ojs.amikomsolo.ac.id/index.php/semnasa/article/view/542
Muttaqin, R. Z., & Sudiana, D. (2025). Design of Realtime Web Application Firewall on Deep Learning-Based to Improve Web Application Security. Jurnal Penelitian Pendidikan IPA, 10(12), 11121–11129. https://doi.org/10.29303/jppipa.v10i12.8346
Ngoc-Thang, B., Tien Nguyen, T. M., Truong, T. T., Nguyen, B. L.-H., & Nguyen, T. T. (2022). A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system. Biosensors and Bioelectronics: X, 12(June), 100223. https://doi.org/10.1016/j.biosx.2022.100223
Nidianti, E., Nugraha, G., Aulia, I. A. N., Syadzila, S. K., Suciati, S. S., & Utami, N. D. (2019). Pemeriksaan Kadar Hemoglobin dengan Metode POCT (Point of Care Testing) sebagai Deteksi Dini Penyakit Anemia Bagi Masyarakat Desa Sumbersono, Mojokerto. Jurnal Surya Masyarakat, 2(1), 29. https://doi.org/10.26714/jsm.2.1.2019.29-34
Nuri, N., Atiq, M., Proborini, E., Alrina, A., Stiawan, D., & Sulhadi, S. (2023). Profil Kemampuan Berpikir Kritis Mahasiswa Teknik Elektro pada Tugas Project-Based Learning Pompa Air Tanah Tanpa Listrik. Variabel, 6(2), 117. https://doi.org/10.26737/var.v6i2.4885
Pinto, C. F., Parab, J. S., Sequeira, M. D., & Naik, G. M. (2021). Development of Altera NIOS II Soft-core system to predict total Hemoglobin using Multivariate Analysis. Journal of Physics: Conference Series, 1921(1), 012039. https://doi.org/10.1088/1742-6596/1921/1/012039
Rao, N. (2024). Global Hunger Index (GHI) - peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels (p. 1). Globla Hunger Index. Retrieved from https://www.globalhungerindex.org/
Salari, N., Darvishi, N., Bartina, Y., Keshavarzi, F., Hosseinian-Far, M., & Mohammadi, M. (2025). Global prevalence of malnutrition in older adults: A comprehensive systematic review and meta-analysis. Public Health in Practice, 9(December 2024), 100583. https://doi.org/10.1016/j.puhip.2025.100583
Samann, F., & Schanze, T. (2024). Denoising by spectral selections of SVD representations of Hankel matricificated data with application to PPG signals. IFAC-PapersOnLine, 58(24), 175–180. https://doi.org/10.1016/j.ifacol.2024.11.032
Santoso, H. A., Dewi, N. S., Haw, S.-C., Pambudi, A. D., & Wulandari, S. A. (2025). Enhancing nutritional status prediction through attention-based deep learning and explainable AI. Intelligence-Based Medicine, 11(April), 100255. https://doi.org/10.1016/j.ibmed.2025.100255
Sulosaari, V., Beurskens, J., Laviano, A., & Erickson, N. (2025). Malnutrition Diagnosed via Global Leadership Initiative on Malnutrition (GLIM) Criteria – Association with Clinical Outcomes and Predictive Value: A Systematic Review of Systematic Reviews. Seminars in Oncology Nursing, 41(1), 151798. https://doi.org/10.1016/j.soncn.2024.151798
Tang, X., Ding, X., Ma, X., Zhang, S., & Diao, J. (2024). An Exploration of Project-Based Learning Supported by Artificial Intelligence (Issue Icbdie). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-417-4_20
Viruel, S. R., Sánchez Rivas, E., & Ruiz Palmero, J. (2025). The Role of Artificial Intelligence in Project-Based Learning: Teacher Perceptions and Pedagogical Implications. Education Sciences, 15(2), 150. https://doi.org/10.3390/educsci15020150
WHO. (2020). Stunting prevalence among children under 5 years of age (%) (model-based estimates). Global Health Observatory Data Repository.
Widanti, N., Handini, W., Yanto, N. W., & Alamsyah, A. (2023). Development Edge Device Monitoring System Stunting and Malnutrition in Golden age 0–5 years Integrated with AI. Jurnal Penelitian Pendidikan IPA, 9(SpecialIssue), 247–253. https://doi.org/10.29303/jppipa.v9ispecialissue.6397
Yu, L., Yang, X., Wei, H., Liu, J., & Li, B. (2024). Driver fatigue detection using PPG signal, facial features, head postures with an LSTM model. Heliyon, 10(21), e39479. https://doi.org/10.1016/j.heliyon.2024.e39479
Yunidar, Y., Yusni, Y., Nasaruddin, N., & Arnia, F. (2025). CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 9(1), 62–68. https://doi.org/10.29207/resti.v9i1.6068
License
Copyright (c) 2025 Sri Wiji Lestari, Nurdina Widanti, Wike Handini, Ahmad Raafi Haqq, Aditya Alamsyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
