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Rusdah Rusdah
Fakultas Teknologi Informasi Universitas Budi Luhur
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Journal : Jurnal Teknik Informatika (JUTIF)

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Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence Rusdah, Rusdah; Painem, Painem; Kusumaningsih, Dewi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4263

Abstract

Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019.
Co-Authors Abdulhakim Madiyoh Achmad Saleh Achmad Solichin Afrianto, Whisnu Febry Ahadti Puspa Sari Alfad Zebua, Vivid Kristiani Andi Andara Andi Rukmana Anidnya Putri Pradiptha Anita Diana Anubhakti, Dian Ary Maulana Pratama Aryabima, Muhammad Iqbal Bregastantyo, Brian Agni Brury Trya Sartana Budiyoko, Budiyoko Deasy Aprilla Wulandari Deni Mahdiana Devit Setiono Dewi Kusumaningsih Diwi Apriana Dwi Achadiani Dwi Kristanto Eka Dewi Satriana Elfy Susanti Ernita Rahayu Fauzan, Muhammad Rafi Hari Soetanto Haris Kurniawan, Haris Hin, Law Li Humisar Hasugian Ilham Akbar Muharrom Ilyas, Aldrin Nur Imam Halim Mursyidin Indah Puspasari Handayani Indra Nugraha Irawati, Riri Izzati, Fildza Joko Christian Chandra Joko Sutrisno Juliasari, Noni Kardena, Sucinda Kirana, Anindya Sasi Kusumaningsih, Dewi Lauw Li Hin Linda Ratna Sari Lis Suryadi, Lis Luhur Bayuaji, Luhur Mahesworo Langgeng Wicaksono Marimin , Mawarni, Ajeng Citra Mehmet Sıtkı ā°lkay Mohammad Syafrullah Muhamad Sobirin Jamil Muhammad Fauzan Hadi Saputra Muhammad Rifqi Mukhtar, Ridha Painem, Painem Painem, Painem Patlisan, Patlisan Pebrianti, Dwi Prayoga, Adistiar Pudoli, Ahmad Purwanto Purwanto Putri, Ine Widyaningrum Mustama Raden Rahmad Rafi Naufal AlBasri Rahmat Fajar Rahmawati Alvira Rahmawati, Fadilla Salsabila Raissa, Benita Hasna Ratna Ujiandari Renaldi Setiawan Putra Rizky Pradana, Rizky Roeswidiah, Ririt Rohmad Atkha Rosyadi, Ibnu Fallah Ruwirohi, Jan Everhard Setyawan Widyarto Shintya Yulianti Sri Hanafi Sri Wahyuningsih Subandi, Nurul Arifin Supardi Supardi Susi Widyawati Tri Annisa Hidayati Triana Anggraini Yulianawati Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Zaqi Kurniawan