Gultom, Cristin
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Gambaran status vaksinasi human papillomavirus (HPV) dan faktor penghambat pada mahasiswi Fakultas Kedokteran Universitas Sam Ratulangi Gultom, Cristin; Monintja, Tyrsa Christine Natalia; Ottay, Ronald Imanuel
Jurnal Kedokteran Komunitas dan Tropik Vol 13 No 2 (2025)
Publisher : UNIVERSITAS SAM RATULANGI

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Abstract

Background: Human Papillomavirus (HPV) is the leading cause of cervical cancer, which is a global health problem for women, especially in Indonesia. Cervical cancer ranks fourth globally and second in Indonesia as the most common cancer in women in 2022. HPV vaccination is an effective primary prevention and is part of the WHO's global cervical cancer elimination strategy. HPV immunisation in Indonesia was integrated into BIAS in 2023. Medical students are among the high-risk age groups for HPV infection and play a role as future healthcare professionals in raising public awareness about cervical cancer, HPV, and cervical cancer prevention. Aim: To determine the status of human papillomavirus (HPV) vaccination and the inhibiting factors among female students at the Faculty of Medicine, Sam Ratulangi University. Methods: Descriptive research with a quantitative approach and a cross-sectional design through the distribution of a Google Forms questionnaire to 276 female students from the 2025 cohort of the Faculty of Medicine at Unsrat. The analysis used is univariate analysis. Results: A total of 114 respondents (87.8%) had not yet received the HPV vaccine, while 20 respondents (12.2%) had. The dominant barriers were not knowing where to get the HPV vaccine (67.4%), not having time for the HPV vaccine (65.3%), and not receiving a recommendation from healthcare professionals (64.6%). Conclusion: The majority of female students in the 2025 class of the Faculty of Medicine at Unsrat had not yet been vaccinated, with the dominant barrier being not knowing where to get the HPV vaccine.
RISIKO EROSI PENALARAN MATEMATIS DALAM PENGGUNAAN ARTIFICIAL INTELLIGENCE: SEBUAH KAJIAN LLITERATUR Gultom, Cristin; Nababan, Sisilia; Samosir, Angel; Sitanggang, Nova Marcelina; Andriani, Ade
SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA Vol. 6 No. 2 (2026)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/science.v6i2.10174

Abstract

The development of Artificial Intelligence (AI) in mathematics education provides convenience in understanding concepts quickly and adaptively, but it also raises potential risks to students’ mathematical reasoning processes. This study aims to examine the risk of erosion of mathematical reasoning due to the use of AI in mathematics learning. The method used is a Systematic Literature Review (SLR) through the stages of identification, screening, eligibility, and inclusion of relevant articles. Data sources were obtained from scientific articles indexed in Google Scholar published between 2021 and 2026 with active DOIs. The data were analyzed using content analysis and thematic synthesis to identify patterns of research findings. The results indicate that AI is effective in supporting initial conceptual understanding and improving learning outcomes, although the effect tends to be moderate. However, the use of AI also has the potential to create cognitive dependency, reduce the exploration of problem-solving strategies, and weaken students’ confidence in their own mathematical reasoning. These findings highlight an indication of the erosion of mathematical reasoning as an indirect impact of uncontrolled AI use. Therefore, the use of AI needs to be balanced with pedagogical strategies that emphasize active student engagement in the thinking process. ABSTRAK Perkembangan Artificial Intelligence (AI) dalam pembelajaran matematika memberikan kemudahan dalam memahami konsep secara cepat dan adaptif, namun juga memunculkan potensi risiko terhadap proses penalaran matematis siswa. Penelitian ini bertujuan untuk mengkaji risiko erosi penalaran matematis akibat penggunaan AI dalam pembelajaran matematika. Metode yang digunakan adalah Systematic Literature Review (SLR) melalui tahapan identifikasi, penyaringan, penentuan kelayakan, dan inklusi artikel. Sumber data berasal dari artikel ilmiah terindeks Google Scholar dengan rentang tahun 2021–2026 dan memiliki DOI aktif. Data dianalisis menggunakan teknik content analysis dan thematic synthesis untuk mengidentifikasi pola temuan penelitian. Hasil kajian menunjukkan bahwa AI efektif dalam mendukung pemahaman awal konsep dan meningkatkan hasil belajar, meskipun pengaruhnya cenderung moderat. Namun, penggunaan AI juga berpotensi menimbulkan ketergantungan kognitif, mengurangi eksplorasi strategi pemecahan masalah, serta menurunkan kepercayaan siswa terhadap penalaran matematisnya sendiri. Temuan ini menegaskan adanya indikasi erosi penalaran matematis sebagai dampak tidak langsung dari penggunaan AI yang tidak terkontrol. Oleh karena itu, pemanfaatan AI perlu diimbangi dengan strategi pedagogis yang menekankan keterlibatan aktif siswa dalam proses berpikir.    
ANALISIS PELURUHAN KONSENTRASI POLUTAN UDARA PM2.5 DI JAKARTA MENGGUNAKAN MODEL PERSAMAAN DIFERENSIAL EKSAK Gultom, Cristin; Sitanggang, Nova Marcelina
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p92-98

Abstract

Penelitian ini bertujuan untuk menganalisis peluruhan konsentrasi polutan udara PM2.5 di Jakarta menggunakan model Persamaan Diferensial Eksak. Data PM2.5 dianalisis selama 24 jam dengan fokus pada fase penurunan konsentrasi dari pukul 15.00 hingga 23.00. Melalui penerapan persamaan diferensial orde satu dan teknik faktor integrasi, diperoleh model peluruhan eksponensial yang mampu menggambarkan dinamika penurunan PM2.5 menuju nilai ambien sebesar . Estimasi parameter dilakukan menggunakan dua titik data representatif sehingga diperoleh konstanta peluruhan dan konstanta integrasi . Model akhir menunjukkan kecocokan yang konsisten dengan data observasi dan mampu merepresentasikan proses peluruhan alami polutan di udara perkotaan. Hasil perhitungan menunjukkan waktu paruh sebesar 2,66 jam, yang mengindikasikan bahwa selisih konsentrasi PM2.5 di atas nilai ambien berkurang separuhnya setiap sekitar 2 jam 39 menit. Temuan ini memberikan informasi kuantitatif yang relevan bagi pemahaman dinamika kualitas udara Jakarta serta dapat mendukung pengambilan kebijakan mitigasi polusi udara berbasis data dan pemodelan matematis.