Inovasi Alat Pendeteksi Asam Urat dan Kolesterol Non-Invasive Berbasis Model Regresi Terintegrasi IOT Guna Meningkatkan Kesehatan Masyarakat di Indonesia
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Abstract
Health problems caused by high cholestero and uric acid are becoming more common around the world, including in Indonesia. High cholesterol causes over 2.6 million deaths every year and affects about 28% of people in Indonesia. At the same time, diseases related to uric acid—like gout and kidney issues—also affect many people, with around 7.3% of Indonesians experiencing these problems. These facts show that it is very important to take action by raising awareness, encouraging healthy habits, and improving early treatment. Conventional methods for detecting cholesterol and uric acid levels are invasive, requiring blood samples, which can cause discomfort and have a risk of infection. Additionally, a significant portion of the population experiences fear of injections, further complicating early detection. This research aims to develop a non-invasive device integrated with IoT technology for detecting cholesterol and uric acid levels using a regression model. The device integrates optical sensors with near-infrared (NIR) light at 940 nm for cholesterol and 1550 nm for uric acid. The accuracy of the regression model was evaluated, yielding an R² value of 96% for cholesterol and 97.5% for uric acid, which indicates high reliability and consistency in the results. Furthermore, the device is capable of real-time monitoring and data logging, enabling both users and healthcare professionals to continuously track health conditions and support more accurate and timely diagnoses.
Keyword: Non-invasive detector, Cholesterol, Uric Acid, IoT, Regression.
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References
Anupongongarch, P., Kaewgun, T., O'Reilly, J. A., dan Khaomek, P. (2022). A Study On The Relation Between Digital Output And Uric Acid In Artificial Blood Solution By Using A Uric Acid Detector. International Journal Of Applied, 15(2).
Ayodele, B. V., Mustapa, S. I., Mohammad, N., dan Shakeri, M. (2021). Long-term energy demand in Malaysia as a function of energy supply: A comparative analysis of Non-Linear Autoregressive Exogenous Neural Networks and Multiple Non-Linear Regression Models. Energy Strategy Reviews, 38, 100750.
Chakrabortty, M., Singh, R., dan Das, A. (2019). Optimization of dynamic tracking sensors for accurate biomarker detection. Journal of Molecular Sensors, 25(3), 157-163. https://doi.org/10.1002/jms.2793
Darmawan, P. S., Kaligis, S. H., dan Assa, Y. A. (2016). Gambaran kadar asam urat darah pada pekerja kantor. eBiomedik, 4(2).
Darmawan, H. Y., Sanubari, A., Saida, N., Fauzi, R. N., Ristiani, N., Oktivasari, P., dan Wardhani, R. N. (2022, September). Non-invasive iot home medical check- up programming to monitor blood sugar, cholesterol, uric acid, and body temperature. In 2022 5th International Conference of Computer and Informatics Engineering (IC2IE) (pp. 236-240). IEEE.
Dritsas, E., dan Trigka, M. (2022). Machine learning methods for hypercholesterolemia long-term risk prediction. Sensors, 22(14), 5365.
Duncanson, E., Le Leu, R. K., Shanahan, L., Macauley, L., Bennett, P. N.,Weichula, R., ... dan Jesudason, S. (2021). The prevalence and evidence-based management of needle fear in adults with chronic disease: a scoping review. Plos one, 16(6), e0253048.
Fatmala, C. T., Hayati, M., Permatasari, R., Hudori, M., dan Dalimunthe, D. Y. (2024). Pemodelan Jumlah Kasus HIV/AIDS di Provinsi Lampung Menggunakan Regresi Binomial Negatif. Journal of Mathematics: Theory and Applications, 6(2), 168-177.
Fitriani, R., Azzahri, L. M., NURMAN, M., dan Hamidi, M. N. S. (2021). Hubungan Pola Makan Dengan Kadar Asam Urat (Gout Artritis) Pada Usia Dewasa 35-49 Tahun. Jurnal Ners, 5(1), 20-27.
Koessoy, N. A., Purwanto, D. S., dan Kaligis, S. H. (2025). Hubungan Kadar Kolesterol Total dengan Tekanan Darah pada Pasien Hipertensi. e- CliniC, 13(2), 177-182.
Nayan, N. A., Ab Hamid, H., Suboh, M. Z., Aminaldin, N. M., Azizul, N. I. M., dan Nizar, M. N. M. (2021, March). Machine learning application for classifying serum uric acid level with photoplethysmogram. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 522-527). IEEE.
Ndrepepa, G. (2018). Uric acid and cardiovascular disease. Clinica chimica acta, 484, 150-163.
Nugroho, G. W., dan Effendi, R. (2022). Rancang Bangun Sistem Pengukuran Luas Permukaan Kulit Menggunakan Konveyor dan Sensor Optik Berbasis Arduino. Jurnal Teknik ITS, 11(1), F1-F7.
Özdede, M., dan Guven, A. T. (2023). Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification. Cureus, 15(12).
Padmavilochanan, D., Pathinarupothi, R. K., Menon, K. U., dan Chandra, S. (2023, June). Minimal features based non invasive cholesterol computation using machine learning. In 2023 3rd International Conference on Intelligent Technologies (CONIT) (pp. 1-8). IEEE.
Patil, S., dan Patil, S. (2021). Linear with polynomial regression: Overview. International Journal of Applied Research, 7(8), 273-275.
Shubha, B., Anuradha, M. G., Poornima, N., Suprada, H. S., dan Prathiksha, R. V. (2023). An Implementation of blood Glucose and cholesterol monitoring device using non-invasive technique. EMITTER International Journal of Engineering Technology, 76-88.
Susanto, N. (2020). Cholesterol Level as Predicting of Uric Acid Increase in Elderly. Jurnal Ilmu Kesehatan Masyarakat, 11(01), 23-30.
Swastini, I. G. A. A. P. (2021). Gambaran kolesterol total pada lansia di Puskesmas I Denpasar Selatan. Meditory: The Journal of Medical Laboratory, 9(2), 68-77.
Turyadi, I. U. (2021). Analisa Dukungan Internet of Things (IoT) terhadap Peran Intelejen dalam Pengamanan Daerah Maritim Indonesia Wilayah Timur. Jurnal Teknologi dan Manajemen Informatika, 7(1), 29-39.
Umar, U., Hasmah, H., Alyah, R., dan Syam, A. N. (2021). Desain Alat Ukur Kadar Asam Urat Non-Invasive Dengan Sensor Near Infrared. Lontara Journal of Health Science and Technology, 2(2), 69-81.
Umapathi, K., Karthika, V., Mathumitha, M. K., Aakash, J. R., dan Gokul, M. (2023, March). Design and implementation of non-invasive technique blood glucose and cholesterol detection using machine learning. In 2023 9th International Conference on Electrical Energy Systems (ICEES) (pp. 127-131). IEEE.
Umar, U., Syarif, S., dan Ingrid Nurtanio, I. (2022). A Non-Invasive Method Applied to Measure Cholesterol and Glucose Levels. Journal of Hunan University Natural Sciences, 49(10).
Zaslavsky, A., Perera, C., dan Georgakopoulos, D. (2013). Sensing as a service and big data. arXiv preprint arXiv:1301.0159.