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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Faktor Exacta Jukung (Jurnal Teknik Lingkungan) CogITo Smart Journal Pendas : Jurnah Ilmiah Pendidikan Dasar KACANEGARA Jurnal Pengabdian pada Masyarakat Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika Jurnal Pengabdian Masyarakat Multidisiplin SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal EduHealth Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Mahasiswa Ilmu Komputer Telematika MKOM Jurnal INFOTEL Jurnal Ticom: Technology of Information and Communication
Painem, Painem
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.
Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier Solichin, Achmad; Zulqan, Helmi; Painem, Painem; Pradiptha, Anindya Putri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5357

Abstract

Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.
Co-Authors Abdullah 'Alim Achmad Solichin Akbar Maulana Putra Andiono, Endro Anidnya Putri Pradiptha Anindya Putri Pradiptha Anna Waris Nainggolan Denny Eka Erlianto Derian Rabbani Destriyani Destriyani Dwi Kristanto Fau, Yentiria Febriana Sari, Febriana Gabriel Yoda Gustiegan Hari Soetanto Hendri Irawan Heri Soetanto Indra Indra Irawan Irawan Ismoyo, Maret Java, Muhammad Arya Joko Christian Chandra Kaishananda Dwi Takanwiedi Kusumaningsih, Dewi Malvi, Afif Mohammad Syafrullah MS Khabibur Rahman Muhammad Sadli Nair, Anju A Pradiptha, Anindya Putri Pramudita, Bagas Prayogo, Aditya Izhar Eka Purba, Edy Marjuang Purwanto Purwanto Rifky Aditia Hamdan Riwurohi, Yan Everhard Riyan Sugiharto Rusdah Rusdah Safitri, Rani SINAGA, ROSMANI situmorang, tetti seriati Syafrullah, Mohammad Tomi Hartanto Umay, SITI Utomo Budiyanto Wahyuawaludin, Nizam Windarto, Windarto Yusup Supriyadi Zahrul Basim Zulqan, Helmi