TINJAUAN SISTEMATIS PERAN JARINGAN SYARAF TIRUAN DAN DEEP LEARNING DALAM DIAGNOSA DEMAM BERDARAH DAN TIFUS

Nurhadi Nurhadi, Billy Hendrik

Abstract


Demam berdarah dengue (DBD) dan tifus merupakan penyakit endemik dengan tingkat morbiditas yang signifikan di negara tropis, sehingga memerlukan diagnosa yang cepat dan akurat untuk mencegah komplikasi serius. Teknologi kecerdasan buatan, seperti Jaringan Syaraf Tiruan (JST) dan Deep Learning (DL), telah banyak diterapkan untuk meningkatkan akurasi dan efisiensi diagnosa medis. Penelitian ini bertujuan untuk mengevaluasi peran JST dan DL dalam diagnosa DBD dan tifus dengan menggunakan kerangka PRISMA. Dari hasil pencarian literatur pada database Google Scholar, Springer, IEEE Xplore, dan ACM Digital Library, sebanyak 40 studi terpilih dari total 388 artikel yang diidentifikasi. Analisis menunjukkan bahwa DL mampu mencapai akurasi hingga 95% dalam diagnosa DBD berbasis citra medis, sementara JST mencatat sensitivitas lebih dari 90% untuk diagnosa tifus berdasarkan data klinis. Meski memiliki potensi besar, hambatan seperti keterbatasan dataset dan kebutuhan komputasi tinggi perlu diselesaikan untuk optimalisasi penerapan dalam dunia medis.

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References


Al Bataineh, A., & Manacek, S. (2022). MLP-PSO Hybrid Algorithm for Heart Disease Prediction. Journal of Personalized Medicine, 12(8). https://doi.org/10.3390/jpm12081208

Ali, R., Hussain, J., & Lee, S. W. (2023). Multilayer perceptron-based self-care early prediction of children with disabilities. Digital Health, 9. https://doi.org/10.1177/20552076231184054

Balamurugan, S. A. alias, Mallick, M. S. M., & Chinthana, G. (2020). Improved prediction of dengue outbreak using combinatorial feature selector and classifier based on entropy weighted score based optimal ranking. Informatics in Medicine Unlocked, 20(July), 100400. https://doi.org/10.1016/j.imu.2020.100400

Banumathy, D., Khalaf, O. I., Romero, C. A. T., Indra, J., & Sharma, D. K. (2022). CAD of BCD from Thermal Mammogram Images Using Machine Learning. Intelligent Automation and Soft Computing, 34(1), 667–685. https://doi.org/10.32604/iasc.2022.025609

Bohm, B. C., Borges, F. E. de M., Silva, S. C. M., Soares, A. T., Ferreira, D. D., Belo, V. S., … Bruhn, F. R. P. (2024). Utilization of machine learning for dengue case screening. BMC Public Health, 24(1), 1–9. https://doi.org/10.1186/s12889-024-19083-8

Carreras, J., Hiraiwa, S., Kikuti, Y. Y., Miyaoka, M., Tomita, S., Ikoma, H., … Nakamura, N. (2021). Artificial neural networks predicted the overall survival and molecular subtypes of diffuse large B-cell lymphoma using a pancancer immune oncology panel. Cancers, 13(24). https://doi.org/10.3390/cancers13246384

Chaw, J. K., Chaw, S. H., Quah, C. H., Sahrani, S., Ang, M. C., Zhao, Y., & Ting, T. T. (2024). A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients. Healthcare Analytics, 5(November 2023), 100290. https://doi.org/10.1016/j.health.2023.100290

Dimu, N., Rangga, A. A., & Sanga, F. E. O. (2024). Sistem Pakar Diagnosa Penyakit Demam Berdarah Menggunakan Metode Naive Bayes Berbasis Web Puskesmas Waimagura. Jurnal Ilmu Komputer Dan Bisnis, 15(1), 202–211. https://doi.org/10.47927/jikb.v15i1.740

Fahmi Limas, A., Rosnelly, R., & Nursie, A. (2023). A Comparative Analysis on the Evaluation of KNN and SVM Algorithms in the Classification of Diabetes. Scientific Journal of Informatics, 10(3), 251. https://doi.org/10.15294/sji.v10i3.44269

Gupta, G., Khan, S., Guleria, V., Almjally, A., Alabduallah, B. I., Siddiqui, T., … AL-subaie, M. (2023). DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms. Diagnostics, 13(6). https://doi.org/10.3390/diagnostics13061093

Hamdani, H., Arifin, Z., & Septiarini, A. (2022). Expert System of Dengue Disease Using Artificial Neural Network Classifier. JUITA: Jurnal Informatika, 10(1), 59. https://doi.org/10.30595/juita.v10i1.12476

Joelianto, E., Mandasari, M. I., Marpaung, D. B., Hafizhan, N. D., Heryono, T., Prasetyo, M. E., … Ahmad, I. (2024). Convolutional neural network-based real-time mosquito genus identification using wingbeat frequency: A binary and multiclass classification approach. Ecological Informatics, 80(January), 102495. https://doi.org/10.1016/j.ecoinf.2024.102495

Joo, Y., Namgung, E., Jeong, H., Kang, I., Kim, J., Oh, S., … Hwang, J. (2023). Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Scientific Reports, 13(1), 1–15. https://doi.org/10.1038/s41598-023-49514-2

Karolcik, S., Manginas, V., Chanh, H. Q., Daniels, J., Giang, N. T., Huyen, V. N. T., … Georgiou, P. (2024). Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study. EBioMedicine, 104, 105164. https://doi.org/10.1016/j.ebiom.2024.105164

Khan, M. A. R., Akter, J., Ahammad, I., Ejaz, S., & Jaman Khan, T. (2022). Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree. Health Information Science and Systems, 10(1), 1–18. https://doi.org/10.1007/s13755-022-00202-x

Likmi, S. (2022). Comparative Analysis of Naive Bayes , K-Nearest Neighbors ( KNN ), and Support Vector Machine ( SVM ) Algorithms for Classification of Heart Disease Patients. JOIN (Jurnal Online Informatika), 7(2), 219–225. https://doi.org/10.15575/join.v7i2.919

LUTFAN ANAS ZAHIR, & SULIANA MAFIROH. (2024). Optimasi Kuat Tekan Beton Dengan Jaringan Syaraf Tiruan Metode Multi Layer Perceptron. Jurnal Daktilitas, 4(01), 45–55. https://doi.org/10.36563/daktilitas.v4i01.1161

Madhavan, M. V., Thanh, D. N. H., Khamparia, A., Pande, S., Malik, R., & Gupta, D. (2021). Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques. Computers, Materials and Continua, 66(3), 2939–2955. https://doi.org/10.32604/cmc.2021.012466

Mayrose, H., Bairy, G. M., Sampathila, N., Belurkar, S., & Saravu, K. (2023). Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics. Diagnostics, 13(2). https://doi.org/10.3390/diagnostics13020220

Mayrose, H., Sampathila, N., Muralidhar Bairy, G., Nayak, T., Belurkar, S., & Saravu, K. (2023). Deep learning approach for detection of Dengue fever from the microscopic images of blood smear. Journal of Physics: Conference Series, 2571(1). https://doi.org/10.1088/1742-6596/2571/1/012005

Mishra, S., Tripathy, H. K., Mallick, P. K., Bhoi, A. K., & Barsocchi, P. (2020). Eaga-mlp—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors (Switzerland), 20(14), 1–34. https://doi.org/10.3390/s20144036

Mulia, M. R., Kaswar, A. B., Andayani, D. D., Sadri, A., Makassar, U. N., & Korespondensi, P. (2024). Classification of the Nutritional Content of Bananas Based on Texture and Color Features in the Lab and Using Artificial. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 11(3), 507–518. https://doi.org/10.25126/jtiik.2024118332

Mulya, S., Nurcahyo, G. W., & Hendrik, B. (2024). Perbandingan Tingkat Optimalisasi Metode K-Nearest Neighbor Dan Naïve Bayes Dalam Klasifikasi Kelayakan Alat Laboratorium Kimia. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 5(2), 483–495.

Mumtaz, Z., Rashid, Z., Saif, R., & Yousaf, M. Z. (2024). Deep learning guided prediction modeling of dengue virus evolving serotype. Heliyon, 10(11), e32061. https://doi.org/10.1016/j.heliyon.2024.e32061

Paluang, P., Thavorntam, W., & Phairuang, W. (2024). Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand. International Journal of Geoinformatics, 20(7), 28–42. https://doi.org/10.52939/ijg.v20i7.3401

Phillips, M. T., Meiring, J. E., Voysey, M., Warren, J. L., Baker, S., Basnyat, B., … Pitzer, V. E. (2021). A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data. Statistics in Medicine, 40(26), 5853–5870. https://doi.org/10.1002/sim.9159

Press, B. (2024). IMPLEMENTASI JARINGAN SYARAF TIRUAN ( JST ) MELALUI IMAGE PROCESSING UNTUK MENDETEKSI KOLESTEROL DARAH DENGAN TEKNIK NON- INVASIVE. Bravo Press.

Putri, A., Hardiana, C. S., Novfuja, E., Try, F., & Siregar, P. (2023). Comparison of K-NN , Naive Bayes and SVM Algorithms for Final-Year Student Graduation Prediction Komparasi Algoritma K-NN , Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir. Indonesian Journal of Machine Learning and Computer Science, 3(April), 20–26.

Rahman, M. S., Pientong, C., Zafar, S., Ekalaksananan, T., Paul, R. E., Haque, U., … Overgaard, H. J. (2021). Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One Health, 13(December). https://doi.org/10.1016/j.onehlt.2021.100358

Rani, N. S., Chandrajith, M., Pushpa, B. R., & Nair, B. J. B. (2020). A deep convolutional architectural framework for radiograph image processing at bit plane level for gender & age assessment. Computers, Materials and Continua, 62(2), 679–694. https://doi.org/10.32604/cmc.2020.08552

Rita, S. (2022). Prediksi Luas Lahan Sawah Dengan Program Matlab Menggunakan Jaringan Syaraf Tiruan. JRIS : Jurnal Rekayasa Informasi Swadharma, 3(1), 1–7. https://doi.org/10.56486/jris.vol3no1.255

Rohana, T., Nurlaelasari, E., Awal, E. E., & Novita, H. Y. (2024). Kajian Model Jaringan Syaraf Tiruan Untuk Memprediksi Secara Dini Tingkat Kelulusan Mahasiswa. Technologia : Jurnal Ilmiah, 15(4), 629–640.

Rustam, F., Reshi, A. A., Aljedaani, W., Alhossan, A., Ishaq, A., Shafi, S., … Rupapara, V. (2022). Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology. Saudi Journal of Biological Sciences, 29(1), 583–594. https://doi.org/10.1016/j.sjbs.2021.09.021

Sebastianelli, A., Spiller, D., Carmo, R., Wheeler, J., Nowakowski, A., Jacobson, L. V., … Schneider, R. (2024). A reproducible ensemble machine learning approach to forecast dengue outbreaks. Scientific Reports, 14(1), 1–17. https://doi.org/10.1038/s41598-024-52796-9

Tunay, M., Pashaei, E., & Pashaei, E. (2022). Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/1612468

Wahyu Redhani, A., & Hidayat, N. (2021). Implementasi Metode Naïve Bayes untuk Diagnosa Pengidap Demam Berdarah pada Kelurahan Antasan Besar berbasis Web. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(12), 5320–5328. Retrieved from http://j-ptiik.ub.ac.id

Wang, M., Zhang, J., Wang, X., Zhang, B., & Yang, Z. (2023). Source Discrimination of Mine Water by Applying the Multilayer Perceptron Neural Network (MLP) Method—A Case Study in the Pingdingshan Coalfield. Water (Switzerland), 15(19). https://doi.org/10.3390/w15193398

Wu, J. (2021). A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/2861292

Zhang, J., Li, C., Yin, Y., Zhang, J., & Grzegorzek, M. (2023). Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. In Artificial Intelligence Review (Vol. 56). https://doi.org/10.1007/s10462-022-10192-7

Zhou, S., Liu, X., Sun, Y., Chang, X., Jia, Y., Guo, J., & Sun, H. (2024). Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network. International Journal of Digital Earth, 17(1), 1–16. https://doi.org/10.1080/17538947.2024.2393255




DOI: http://dx.doi.org/10.36723/juri.v16i2.719

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