Depok, 10 January 2026 — Machrumnizar officially earned a Doctoral degree in Public Health from the Faculty of Public Health (FPH), Universitas Indonesia (UI) after successfully defending his dissertation entitled “A Pediatric Tuberculosis Screening Model with Automated Machine Learning–Based Scoring Systems in Primary Health Care Facilities.” The doctoral promotion session was chaired by Prof. Dr. Dra. Dumilah Ayuningtyas, M.A.R.S.
In his dissertation research, Machrumnizar highlighted the persistently high burden of pediatric tuberculosis (TB), which accounts for approximately 12% of the world’s 10.6 million TB cases. In Indonesia, TB incidence has increased by more than 30%, yet detection rates remain low. Pediatric TB diagnosis still largely relies on clinical symptoms without adequate bacteriological confirmation, resulting in substantial underreporting. This situation is further exacerbated by subjective manual screening processes and fragmented recording and reporting systems in primary health care facilities.
To address these challenges, Machrumnizar developed a machine learning–based pediatric TB screening model capable of automating the TB scoring process. The model integrates clinical and demographic data, nutritional status, contact history, supporting examinations, and variations in the capacity of primary health care facilities across Indonesia. The study employed a quantitative retrospective cohort design conducted in primary health centers (puskesmas) in West Jakarta. Data were obtained from electronic medical records and the Tuberculosis Information System (SITB), followed by verification, cleaning, and transformation processes.
The machine learning model was developed under four scenarios to accommodate health facilities with varying diagnostic capabilities. Despite differences in data availability, the decision tree algorithm demonstrated the most stable performance, achieving an accuracy of up to 89% and an AUC exceeding 94–95% across multiple tests. The most influential predictive features for pediatric TB included lymph node enlargement, positive tuberculin test results, history of contact with TB sources, chest X-ray findings, bacteriological examination results, and nutritional status.
As the primary output of his research, Machrumnizar produced a prototype pediatric TB screening application capable of delivering rapid, objective, and standardized risk assessments. The application provides diagnostic summaries, detailed input data, encoding mechanisms, and data management with access control to ensure patient data security and confidentiality. This technology is expected to support primary health care facilities in accelerating early detection of pediatric TB, reducing manual errors, and improving service efficiency.
The doctoral promotion session involved Prof. Dr. Drs. Tris Eryando, M.A. as Promoter, with Prof. dr. Adang Bachtiar, M.P.H., D.Sc., and Dr. dr. Rina Kurniasri Kusumaratna, M.Sc., serving as Co-Promoters. The examining committee consisted of Prof. Dr. dr. Anhari Achadi, S.K.M., Sc.D.; Dr. dr. Nastiti Kaswandani, Sp.A.; Dr. dr. Maxi Rein Rondonuwu, D.H.S.M., M.A.R.S.; and Dr. Dedy Sugiarto, S.Si., M.M., M.Kom.
Machrumnizar concluded that pediatric TB scoring systems can be significantly improved through the integration of machine learning, with the decision tree algorithm emerging as the most reliable method to support screening processes in primary health care settings. He recommended that the government develop an artificial intelligence implementation roadmap for TB control, integrate the prototype with the national TB system and BPJS Kesehatan, and conduct field trials of the application as follow-up steps.
Based on the successful defense of his dissertation, Machrumnizar was officially awarded the title of Doctor of Public Health. He is recorded as the 5th PhD graduate in Public Health in 2026, the 337th graduate of the Public Health doctoral program, and the 494th doctoral graduate of FPH UI overall. This achievement represents a tangible contribution to digital innovation in public health, particularly in strengthening early detection efforts for pediatric tuberculosis in Indonesia. (AMR)