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All Journal Jurnal Pengabdian Ilmu Kesehatan Jurnal Pengabdian Bidang Kesehatan Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Dianna Dianna
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Computer Science & IT Health Professions Nursing Public Health

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Meningkatkan Minat Wanita Usia Subur (WUS) untuk melakukan pemeriksaan IVA melalui pemberian Buku Saku Dini Fitri Damayanti; Dianna Dianna; Taufik Hidayat
Jurnal Pengabdian Ilmu Kesehatan Vol. 3 No. 3 (2023): November: Jurnal Pengabdian Ilmu Kesehatan
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpikes.v3i3.2924

Abstract

Cervical cancer is caused by HPV or Human Papilloma Virus. There are 32,469 cases per year of cervical cancer in Indonesia, with a mortality rate of 18,279 people. Efforts that can be made to increase WUS's interest in conducting IVA examinations are through health promotion activities about IVA examinations. Health promotion is one of the primary prevention that can be done to prevent cervical cancer. The primary prevention strategy that can be done is by providing health education about cervical cancer. The method chosen is to use a pocket book. Pocket books are educational information media with words that are easy to understand, illustration images that are in accordance with attractive designs so as to make WUS understand and understand material about cervical cancer. Pocket books can be taken home and read independently by WUS, so it will cause WUS interest in conducting IVA examinations.
Meningkatkan Minat Wanita Usia Subur (WUS) Untuk Melakukan Pemeriksaan IVA Melalui Pemberian Buku Saku Dini Fitri Damayanti; Dianna Dianna; Taufik Hidayat
Jurnal Pengabdian Bidang Kesehatan Vol. 2 No. 1 (2024): Jurnal Pengabdian Bidang Kesehatan
Publisher : PPNI UNIMMAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57214/jpbidkes.v2i1.49

Abstract

Cervical cancer is caused by HPV or Human Papilloma Virus. There are 32,469 cases per year of cervical cancer in Indonesia, with a mortality rate of 18,279 people. Efforts that can be made to increase WUS's interest in conducting IVA examinations are through health promotion activities about IVA examinations. Health promotion is one of the primary prevention that can be done to prevent cervical cancer. The primary prevention strategy that can be done is by providing health education about cervical cancer. The method chosen is to use a pocket book. Pocket books are educational information media with words that are easy to understand, illustration images that are in accordance with attractive designs so as to make WUS understand and understand material about cervical cancer. Pocket books can be taken home and read independently by WUS, so it will cause WUS interest in conducting IVA examinations.
Evaluasi Efektivitas Model Klasifikasi Sentimen untuk Analisis Opini Publik terhadap Kebijakan Lingkungan Berdasarkan Data Media Sosial Berbahasa Indonesia Dada Suhaida; Adisti Primi Wulan; Rosanti Rosanti; Dianna Dianna
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 2 No. 2 (2024): Maret: Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v2i2.951

Abstract

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.
Co-Authors Adisti Primi Wulan Dada Suhaida Dini Fitri Damayanti Rosanti Rosanti Taufik Hidayat