CLASSIFICATION OF PRE-MARITAL SEX AMONG ADOLESCENTS USING COST-SENSITIVE WEIGHTED RANDOM FOREST ON IMBALANCED DATA
Abstract
Adolescents are a critical developmental phase characterized by significant physical, cognitive, and psychosocial changes, which increase vulnerability to risky behaviors, including premarital sexual activity. Such behavior may lead to serious consequences, such as unintended pregnancy, unsafe abortion, sexually transmitted infections, and HIV/AIDS. This study aims to classify premarital sexual behavior among adolescents using demographic, knowledge, and behavioral factors. The data were obtained from the 2019 Program Performance and Accountability Survey (SKAP), consisting of approximately 5,300 weighted adolescent respondents in East Java. The dataset exhibits extreme class imbalance, with only 0.3% of adolescents reporting premarital sexual behavior. To address this issue, three classification methods were applied: standard Random Forest (RF), Weighted Logistic Regression (WLR), and Cost-Sensitive Weighted Random Forest (CSWRF). Model performance was evaluated using 5-fold cross-validation and multiple metrics, including accuracy, sensitivity, specificity, precision, F1-score, G-Means, and Area Under the Curve (AUC). The results indicate that although the standard RF achieved very high accuracy (above 99%), it failed to identify the minority class, resulting in undefined specificity and poor discriminative ability (AUC ≈ 0.83). The WLR model improved classification balance, achieving reasonable specificity (0.8667) and AUC (0.9459), but produced more false positives. In contrast, the CSWRF model demonstrated the best overall performance, with high AUC values (above 0.95), improved specificity, and a better balance between sensitivity and specificity, indicating its effectiveness in handling highly imbalanced data. Variable importance analysis using Mean Decrease Accuracy (MDA) identified Knowledge of Adolescent Reproductive Health (ARH), residence type, and behavioral factors such as dating experience as the most influential predictors. Despite these findings, this study is limited by potential underreporting bias and exclusion of socio-cultural and psychological variables. Future research should incorporate additional predictors and explore advanced modeling approaches to improve classification performance.
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References
WHO, Handout for Module A Introduction. 2018.
BKKBN, “SURVEI KINERJA DAN AKUNTABILITAS PROGRAM KKBPK (SKAP) KELUARGA,” Puslitbang KB dan KS Badan Kependud. dan Kel. Berencana Nas., pp. 5–24, 2019, [Online]. Available: https://id.scribd.com/document/492839675/SKAP-KELUARGA-2019
F. Bobyanti, “KeNAKALAN REMAJA,” JERUMI J. Educ. Relig. Humanit. Multidiciplinary, vol. 1, no. 2, pp. 476–481, 2023, doi: https://doi.org/10.57235/jerumi.v1i2.1402.
Y. Y. Yedemie, “PSYCHOSOCIAL PREDICTORS OF ATTITUDE TOWARD PREMARITAL SEXUAL PRACTICE AMONG UNIVERSITY STUDENTS, ETHIOPIA,” Front. Psychol., vol. 15, no. November, pp. 1–7, 2024, doi: https://doi.org/10.3389/fpsyg.2024.1369964.
R. B. Shrestha, “PREMARITAL SEXUAL BEHAVIOUR AND ITS IMPACT ON HEALTH AMONG ADOLESCENTS,” J. Heal. Promot., vol. 7, no. June, pp. 43–52, 2019, doi: https://doi.org/10.3126/jhp.v7i0.25496.
D. L. Sufyan and Y. Nurdiantami, “PEER INFLUENCE AND DATING AS PREDICTORS OF PRE- MARITAL SEXUAL BEHAVIOR AMONG INDONESIA UNMARRIED YOUTH,” vol. 30, no. Ichd, pp. 235–241, 2020, doi: https://doi.org/10.2991/ahsr.k.201125.041.
V. Nuraldila and D. S. Yuhandini, “KETERKAITAN PENGETAHUAN TENTANG KESEHATAN REPRODUKSI REMAJA DENGAN PERILAKU SEKS PRA NIKAH PADA SISWA-SISWI KELAS XI DI SMA PGRI 1 KABUPATEN MAJALENGKA TAHUN 2017,” Care J. Ilm. Ilmu Kesehat., vol. 5, no. 3, p. 431, 2017, doi: https://doi.org/10.33366/cr.v5i3.710.
T. Kee and W. K. O. Ho, “OPTIMIZING MACHINE LEARNING MODELS FOR URBAN SCIENCES: A COMPARATIVE ANALYSIS OF HYPERPARAMETER TUNING METHODS,” Urban Sci., vol. 9, no. 9, 2025, doi: https://doi.org/10.3390/urbansci9090348.
A. Ridwan, U. Muzakir, and S. Nurhidayati, “OPTIMIZING E-COMMERCE INVENTORY TO PREVENT STOCK OUTS USING THE RANDOM FOREST ALGORITHM APPROACH,” Int. J. Softw. Eng. Comput. Sci., vol. 4, no. 1, pp. 107–120, 2024, doi: 10.35870/ijsecs.v4i1.2326.
J. L. Speiser, M. E. Miller, J. Tooze, and L. Edward, “A COMPARISON OF RANDOM FOREST VARIABLE SELECTION METHODS FOR CLASSIFICATION PREDICTION MODELING,” Expert Syst. Appl., vol. 134, pp. 93–101, 2019, doi: https://doi.org/10.1016/j.eswa.2019.05.028.
V. Ignatenko, A. Surkov, and S. Koltcov, “RANDOM FORESTS WITH PARAMETRIC ENTROPYBASED INFORMATION GAINS FOR CLASSIFICATION AND REGRESSION PROBLEMS,” PeerJ Comput. Sci., vol. 10, 2024, doi: https://doi.org/10.7717/peerj-cs.1775.
N. N. Sari, I. Zain, K. Fithriasari, and A. Muhaimin, “BR+ FOR ADDRESSING IMBALANCED MULTILABEL DATA CLASSIFICATION COMBINED WITH RESAMPLING TECHNIQUE,” 2021, doi: https://doi.org/10.4108/eai.19-12-2020.2309179.
I. Zain et al., “IMBALANCED DATA ANALYSIS OF ADOLESCENT RISK BEHAVIOR OF DRUG ABUSE USING RANDOM FOREST,” 2021, doi: https://doi.org/10.4108/eai.19-12-2020.2309145.
W. A. Tegegne, “SELF-ESTEEM, PEER PRESSURE, AND DEMOGRAPHIC PREDICTORS OF ATTITUDE TOWARD PREMARITAL SEXUAL PRACTICE AMONG FIRST-YEAR STUDENTS OF WOLDIA UNIVERSITY: IMPLICATIONS FOR PSYCHOSOCIAL INTERVENTION,” Front. Psychol., vol. 13, no. August, pp. 1–7, 2022, doi: https://doi.org/10.3389/fpsyg.2022.923639.
J. D. Kasingku, A. Hubert, and F. Sanger, “PERAN PENDIDIKAN AGAMA DALAM MEMBENTENGI REMAJA DARI PERGAULAN BEBAS,” Educatio, vol. 9, no. 4, pp. 2114–2122, 2023, doi: https://doi.org/10.31949/educatio.v9i4.6061.
M. S. Santos, J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos, “CROSS-VALIDATION FOR IMBALANCED DATASETS: AVOIDING OVEROPTIMISTIC AND OVERFITTING APPROACHES [RESEARCH FRONTIER],” IEEE Comput. Intell. Mag., vol. 13, no. 4, pp. 59–76, 2018, doi: https://doi.org/10.1109/MCI.2018.2866730.
H. Akbar and W. K. Sanjaya, “KAJIAN PERFORMA METODE CLASS WEIGHT RANDOM FOREST PADA KLASIFIKASI IMBALANCE DATA KELAS CURAH HUJAN,” J. Sains, Nalar, dan Apl. Teknol. Inf., vol. 3, no. 1, 2023, doi: https://doi.org/10.20885/snati.v3i1.30.
C. Chen, A. Liaw, and L. Breiman, “USING RANDOM FOREST TO LEARN IMBALANCED DATA,” Univ. California, Berkeley, no. 1999, p. 24, 2008.
L. Breiman, “RANDOM FORESTS,” Mach. Learining, vol. 45, pp. 5–32, 2001, doi: https://doi.org/10.1201/9780429469275-8
S. Janitza and R. Hornung, ON THE OVERESTIMATION OF RANDOM FOREST’S OUT-OF-BAG ERROR, vol. 13, no. 8. 2018. doi: https://doi.org/10.1371/journal.pone.0201904
F. Cappelli, G. Castronuovo, S. Grimaldi, and V. Telesca, “RANDOM FOREST AND FEATURE IMPORTANCE MEASURES FOR DISCRIMINATING THE MOST INFLUENTIAL ENVIRONMENTAL FACTORS IN PREDICTING CARDIOVASCULAR AND RESPIRATORY DISEASES,” Int. J. Environ. Res. Public Health, vol. 21, no. 7, 2024, doi: https://doi.org/10.3390/ijerph21070867.
D. Krishnan and S. Singh, “COST-SENSITIVE BOOTSTRAPPED WEIGHTED RANDOM FOREST FOR DOS ATTACK DETECTION IN WIRELESS SENSOR NETWORKS,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2021-Decem, no. December 2021, pp. 375–380, 2021, doi: https://doi.org/10.1109/TENCON54134.2021.9707254.
G. Zeng, “A COMPREHENSIVE STUDY OF COEFFICIENT SIGNS IN WEIGHTED LOGISTIC REGRESSION,” Heliyon, vol. 10, no. 15, p. e35040, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e35040.
R. A. Danquah, “HANDLING IMBALANCED DATA: A CASE STUDY FOR BINARY CLASS PROBLEMS,” 2020, [Online]. Available: http://arxiv.org/abs/2010.04326
M. Altalhan, A. Algarni, and M. Turki-Hadj Alouane, “IMBALANCED DATA PROBLEM IN MACHINE LEARNING: A REVIEW,” IEEE Access, vol. 13, pp. 13686–13699, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3531662.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “DEEP SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002.doi: https://doi.org/10.1613/jair.953
Asniar, N. U. Maulidevi, and K. Surendro, “SMOTE-LOF For Noise Identification In Imbalanced Data Classification,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3413–3423, 2022, doi: https://doi.org/10.1016/j.jksuci.2021.01.014.
I. Zain, A. T. Rumiati, E. O. Permatasari, and N. N. Sari, “IMBALANCED DATA ANALYSIS OF ADOLESCENT UNINTENDED PREGNANCY AND PRE-MARITAL SEX USING UNIVARIATE AND BIVARIATE RANDOM FOREST,” Open Public Health J., vol. 16, no. 1, pp. 1–7, 2023, doi: https://doi.org/10.2174/18749445-v16-e230111-2022-65.
A. A. Salih and A. M. Abdulazeez, “EVALUATION OF CLASSIFICATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW,” J. Soft Comput. Data Min., vol. 2, no. 1, pp. 31–40, 2021, doi: https://doi.org/10.30880/jscdm.2021.02.01.004.
Z. Hweju, F. Kopi, and K. Abou-El-Hossein, “PARAMETER IMPORTANCE ANALYSIS: RANDOM FOREST APPROACH,” J. Phys. Conf. Ser., vol. 2256, no. 1, 2022, doi: https://doi.org/10.1088/1742-6596/2256/1/012019.
H. Han, X. Guo, and H. Yu, “VARIABLE SELECTION USING MEAN DECREASE ACCURACY AND MEAN DECREASE GINI BASED ON RANDOM FOREST,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 0, pp. 219–224, 2016, doi: https://doi.org/10.1109/ICSESS.2016.7883053.
F. Martinez-Taboada and J. I. Redondo, “THE SIESTA (SEAAV INTEGRATED EVALUATION SEDATION TOOL FOR ANAESTHESIA) PROJECT: INITIAL DEVELOPMENT OF A MULTIFACTORIAL SEDATION ASSESSMENT TOOL FOR DOGS,” PLoS One, vol. 15, no. 4, pp. 1–10, 2020, doi: https://doi.org/10.1371/journal.pone.0230799.
S. Nur’aini and A. Muhlisin, “THE RELATIONSHIP BETWEEN KNOWLEDGE OF REPRODUCTIVE HEALTH AND ADOLESCENTS ATTITUDES TO PREMARITAL SEXUALITY,” Indones. J. Glob. Heal. Res., vol. 2, no. 4, pp. 799–806, 2019, doi: https://doi.org/10.37287/ijghr.v2i4.250.
A. N. Fauziah and S. Maesaroh, “PENGARUH UMUR DAN TINGKAT PENDIDIKAN TERHADAP PERILAKU SEKS PRANIKAH PADA REMAJA DI RW 03 KALURAHAN MOJOSONGO SURAKARTA INFLUENCE THE AGE AND LEVEL EDUCATION TOWARD PREMARITAL SEX BEHAVIOR OF ADOLESCENT OF RW 3 , MOJOSONGO DISTRICT OF SURAKARTA,” Indones. J. Med. Sci., vol. 4, no. 2, pp. 202–207, 2017.
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