Sari Atika Parinduri
Sekolah Tinggi Agama Islam Tebing Tinggi Deli

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Experiences and Risk Factors Associated with Transactional Sex Among Men Who Have Sex with Men: A Qualitative Study in Medan, Indonesia Implications for HIV Prevention and Sexual Health Interventions Risydah Fadilah; Sari Atika Parinduri; Nisfi Balqish Rusli
HUMANISMA : Journal of Gender Studies Vol. 9 No. 2 (2025): December 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/humanisma.v9i2.9297

Abstract

Transactional sex between men carries a high risk of transmitting sexually transmitted infections. This study employed a qualitative phenomenological approach through in-depth interviews with homosexual men engaged in transactional sex. Inclusion criteria included men aged 19–40 who were sexually active with heterosexual, homosexual, or bisexual partners. Participants were adults at the time of recruitment but some reported having hinitiated sexual intercourse during adolescence (as early as age 13), reflecting on their earlier experiences. Data were obtained through observation and semi-structured interviews lasting 60–100 minutes per session. Data analysis was conducted in three stages: data reduction, data presentation, and verification. The results showed that multiple partners, a large number of partners, and the lack of personal protective measures, particularly condom use, were dominant factors that increased the risk of transmission and spread of sexually transmitted infections. Furthermore, transactional sex tended to occur without long-term commitment, thus reinforcing risky behavior patterns. These findings emphasize the importance of condom availability and use as primary prevention measures to reduce the risk of infection. This study highlights the urgency of education-based prevention strategies and health promotion for men who engage in same-sex sex. Therefore, increasing awareness about safe sex behavior is a crucial step in reducing vulnerability to sexually transmitted infections among this high-risk group.
Testing the Effect of the Smart Counseling System AI Support Vector Machine Intervention on Reducing Teen Misbehavior Risydah Fadilah; Sari Atika Parinduri; Nuraini Kemalasari Istiqomah
Journal of General Education and Humanities Vol. 5 No. 4 (2026): August
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/gehu.v5i4.1933

Abstract

The study aimed to evaluate the Artificial Intelligence-assisted Smart Counseling System in supporting the detection and classification of adolescent behavioral difficulties and to identify changes in students' behavioral conditions following the intervention. The study used a quantitative pretest–posttest control group design with 666 junior high school students in Medan City: 333 in the experimental group and 333 in the control group. Data were collected using the Strengths and Difficulties Questionnaire and analyzed by comparing pretest and posttest scores, as well as classifying them using the Support Vector Machine method. The results showed that the experimental group's mean Strengths and Difficulties Questionnaire score decreased from 21.00 (standard deviation = 12.469) at the pretest to 18.05 (standard deviation = 8.975) at the posttest. However, the analysis indicated that the difference between the groups at the posttest was not statistically significant, F(2,663) = 1.578, p = 0.207. Therefore, the evidence was insufficient to conclude that the Smart Counseling System was significantly more effective than the control condition. In contrast, the Support Vector Machine analysis showed strong classification performance, with accuracy increasing from 98.50% using the original features to 99.25% after feature engineering for classifying students into the Normal, Borderline, and Abnormal categories. These findings indicate that the Smart Counseling System has potential as a supporting system for the early detection and classification of adolescent behavioral difficulties; however, its effectiveness as an intervention requires further investigation through analyses of time-by-group interactions, effect sizes, and more comprehensive model validation. Therefore, the Smart Counseling System is better positioned as a decision-support tool for counselors rather than a replacement for counselors or a diagnostic instrument.