DOI: https://doi.org/10.14710/transmisi.27.2.92-100.

NEBULA: WEB-BASED INTERACTIVE DASHBOARD FOR MONITORING TUBERCULOSIS CASES IN SEMARANG CITY

Titis Rifamuthia  -  Program Studi Sistem Informasi, Universitas Dian Nuswantoro, Indonesia
*Ika Novita Dewi orcid scopus  -  Program Studi Sistem Informasi, Universitas Dian Nuswantoro, Indonesia
Slamet Isworo  -  Fakultas Kesehatan, Universitas Dian Nuswantoro, Indonesia
Sri Handayani  -  Fakultas Kesehatan, Universitas Dian Nuswantoro, Indonesia
Sholikun Sholikun  -  Dinas Kesehatan Kota Semarang, Indonesia
Dikirim: 10 Des 2024; Diterbitkan: 30 Apr 2025.
Akses Terbuka Copyright (c) 2025 Transmisi: Jurnal Ilmiah Teknik Elektro under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Sari

Tuberculosis (TB) remains a critical public health concern, particularly in developing nations like Indonesia, where it remains one of the leading causes of mortality. In Semarang City, as of July 2024, 1,624 TB cases were reported, emphasizing the need for robust monitoring systems. While various efforts have been made to reduce TB transmission, challenges such as fragmented data sources and limited access to real-time information hinder effective intervention. This study presents the development of NEBULA (New Breath for Lungs), a web-based interactive dashboard designed to visualize and monitor the spread of TB in Semarang City. Built using Tableau, the dashboard incorporates real-time data filtering and drill-down capabilities, allowing users to analyse TB cases based on parameters such as time, gender, and location. The system aims to assist local health authorities and the general public in tracking TB cases, identifying high-risk areas, and enabling more informed decision-making. By providing clear and actionable data visualization, NEBULA enhances public health surveillance and can serve as a model for managing infectious diseases in other regions. Future enhancements include incorporating predictive analytics to further improve TB management and prevention strategies.

Fulltext View|Download
Kata Kunci: tuberculosis monitoring; interactive dashboard; data visualization; public health informatics; web-based application;

Article Metrics:

Article Info
Bagian: Artikel - Teknologi Informasi dan Komputer
Bahasa : EN
Recent articles
PENGENDALIAN AGV MENGGUNAKAN RASPBERRY PI DAN MOTOR DC DENGAN SISTEM PENGENALAN LINTASAN MENGGUNAKAN KAMERA DAN SENSOR INFRAMERAH INOVASI SIMULATOR DAN ALAT UJI PERFORMA HYBRID STORAGE SYSTEM BATERAI DAN SUPERKAPASITOR IDENTIFIKASI DAN KLASIFIKASI PENYAKIT PADA TANAMAN KOPI ARABICA DENGAN METODE CNN DAN TRANSFER LEARNING DENSENET-201 NEBULA: WEB-BASED INTERACTIVE DASHBOARD FOR MONITORING TUBERCULOSIS CASES IN SEMARANG CITY RANCANG BANGUN MESIN COLD STORAGE DENGAN SISTEM KOMPRESI UAP BERBASIS IOT DESIGN OF A CASCODE BIPOLAR JUNCTION TRANSISTOR (BJT) RADIO FREQUENCY (RF) AMPLIFIER CIRCUIT PEMANFAATAN ENERGI PANEL SURYA UNTUK SMART GARDEN BERBASIS INTERNET OF THINGS MENGGUNAKAN APLIKASI BLYNK More recent articles
Most cited articles
Aplikasi Kendali PID Menggunakan Skema Gain Scheduling Untuk Pengendalian Suhu Cairan pada Plant Electric Water Heater STRATEGI KENDALI KECEPATAN MOTOR INDUKSI MENGGUNAKAN PWM INVERTER BERBASIS JARINGAN SARAF TIRUAN ANALISA PENGGUNAAN LAMPU LED PADA PENERANGAN DALAM RUMAH Studi Arus Bocor Permukaan Bahan Isolasi Resin Epoksi Silane Dengan Variasi Pengisi Pasir Silika (Dengan Polutan Pantai) Alat Pelacak Lokasi Berbasis GPS Via Komunikasi Seluler More cited articles
  1. . The Ministry of Health Republic of Indonesia, “Revised National Strategy of Tuberculosis Care and Prevention in Indonesia 2020-2024 and Interim Plan for 2025-2026,” 2023
  2. . Kementerian Kesehatan RI, “Laporan Kinerja Direktorat Pencegahan Dan Pengendalian Penyakit Menular Tahun 2022,” Kementeri. Kesehat. Republik Indones., pp. 1–119, 2022
  3. . Francisca Chibugo Udegbe, Ejike Innocent Nwankwo, Geneva Tamunobarafiri Igwama, and Janet Aderonke Olaboye, “Real-Time data integration in diagnostic devices for predictive modeling of infectious disease outbreaks,” Comput. Sci. IT Res. J., vol. 4, no. 3, pp. 525–545, 2023, doi: 10.51594/csitrj.v4i3.1502
  4. . R. Saadatzadeh, “The Future of Public Health : Integrating Artificial Intelligence in Disease Surveillance and Prevention,” Foresight Public Heal., vol. 1, no. 2, pp. 1–15, 2024
  5. . S. Ulfah, S. K. Nasution, and F. A. Siregar, “Performance of Tuberculosis Program Officers in the Implementation of Contact Investigations in Medan City,” J. Soc. Res., vol. 2, no. 4, pp. 1232–1245, 2023, doi: 10.55324/josr.v2i4.792
  6. . H. Helmy, M. T. Kamaluddin, I. Iskandar, and Suheryanto, “Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression,” Trop. Med. Infect. Dis., vol. 7, no. 9, 2022, doi: 10.3390/tropicalmed7090212
  7. . Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen, “Data-driven decision-making in healthcare: Improving patient outcomes through predictive modeling,” Int. J. Sch. Res. Multidiscip. Stud., vol. 5, no. 1, pp. 059–067, 2024, doi: 10.56781/ijsrms.2024.5.1.0040
  8. . R. Rabiei and S. Almasi, “Requirements and challenges of hospital dashboards: a systematic literature review,” BMC Med. Inform. Decis. Mak., vol. 22, no. 1, pp. 1–11, 2022, doi: 10.1186/s12911-022-02037-8
  9. . H. Shao, R. Martinez-Maldonado, V. Echeverria, L. Yan, and D. Gašević, “Data Storytelling in Data Visualisation: Does it Enhance the Eficiency and Efectiveness of Information Retrieval and Insights Comprehension?,” Conf. Hum. Factors Comput. Syst. - Proc., 2024, doi: 10.1145/3613904.3643022
  10. . Q. Hasanah, I. Y. R. Pratiwi, and Z. Amalia, “Analysis distribution and segmentation of micro, small, and medium enterprises (MSMEs) in Kediri Residency Area: implications for local economic development,” MATRIX J. Manaj. Teknol. dan Inform., vol. 13, no. 3, pp. 130–141, 2023, doi: 10.31940/matrix.v13i3.130-141
  11. . F. Yusuf Alfiansyah and D. Arisandi, “Perancangan Dashboard Monitoring Status Gizi Balita di Puskesmas Sukanagalih,” J. Ilm. Tek. Inform. dan Sist. Inf., vol. 23, no. 3, pp. 1579–1589, 2023
  12. . J. B. Angela and A. Irsyad, “Implementasi Visualisasi Data Berbasis Web Pada Exploratory Data Analysis Profil Kesehatan Kota Samarinda,” Kreat. Teknol. dan Sist. Inf., vol. 1, no. 1, pp. 9–16, 2023
  13. . I. N. Rizki, M. L. Puspita, D. Prayoga, M. Q. Huda, and T. Selatan, “Implementasi Exploratory Data Analysis untuk Analisis dan Visualisasi Data Penderita Stroke Kalimantan Selatan Menggunakan Platform Tableau,” J. Inform. dan Tek. elektro Terap., vol. 12, no. 1, 2024
  14. . M. Zhuang, D. Concannon, and E. Manley, “A Framework for Evaluating Dashboards in Healthcare,” IEEE Trans. Vis. Comput. Graph., vol. 28, no. 4, pp. 1715–1731, 2022, doi: 10.1109/TVCG.2022.3147154
  15. . L. Beard and N. Aghassibakes, “Resource review,” Journal of the Medical Library Association, vol. 109, no. 1. pp. 159–161, 2021, doi: 10.5195/jmla.2021.1135
  16. . A. Mishra, “The Role of Data Visualization Tools in Real- Time Reporting : Comparing Tableau , Power BI , and Qlik Sense,” Int. J. Sci. Technol., vol. 11, no. 3, pp. 1–8, 2020
  17. . B. M. S. Hasan and A. M. Abdulazeez, “A Review of Principal Component Analysis Algorithm for Dimensionality Reduction,” J. Soft Comput. Data Min., vol. 2, no. 1, pp. 20–30, 2021, doi: 10.30880/jscdm.2021.02.01.003
  18. . H. W. Gichuhi, M. Magumba, M. Kumar, and R. W. Mayega, “A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district,” PLOS Glob. Public Heal., vol. 3, no. 7, pp. 1–20, 2023, doi: 10.1371/journal.pgph.0001466
  19. . E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, “Comprehensive Review of K-Means Clustering Algorithms,” Int. J. Adv. Sci. Res. Eng., vol. 07, no. 08, pp. 64–69, 2021, doi: 10.31695/ijasre.2021.34050
  20. . K. P. Sinaga, I. Hussain, and M. S. Yang, “Entropy K-Means Clustering with Feature Reduction under Unknown Number of Clusters,” IEEE Access, vol. 9, pp. 67736–67751, 2021, doi: 10.1109/ACCESS.2021.3077622
  21. . A. Larasati, R. Maren, and R. Wulandari, “Utilizing Elbow Method for Text Clustering Optimization in Analyzing Social Media Marketing Content of Indonesian e-Commerce,” J. Tek. Ind., vol. 23, no. 2, pp. 111–120, 2021, doi: 10.9744/jti.23.2.111-120

Last update:

No citation recorded.

Last update: 2025-05-27 10:14:52

No citation recorded.