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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) PAKDEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Rekam Medis dan Manajemen Informasi Kesehatan PELS (Procedia of Engineering and Life Science) Medika Trada Indonesian Journal of Health Information Management Services (IJHIMS) ENGAGEMENT: Jurnal Pengabdian Masyarakat Jurnal manajemen informasi kesehatan MANGGALI ABDIKES TRADA : JURNAL PENGABDIAN MULTIDISIPLIN DAN PEMBERDAYAAN MASYARAKAT Joong-Ki Jurnal Rekam Medis dan Informasi Kesehatan Indonesia Jurnal Mitra Pengabdian Farmasi
Bajeng Nurul Widyaningrum
Politeknik Bina Trada Semarang
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Journal : Jurnal Inotera

 1 
Analisis Sentimen terhadap Isu Agama Berdasarkan Komentar Netizen di Instagram Presiden Jokowi Dengan Metode Naive Bayes Classifier Lingga Kurnia Ramadhani; Bajeng Nurul Widyaningrum
Jurnal Inotera Vol. 9 No. 1 (2024): January-June 2024
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol9.Iss1.2024.ID296

Abstract

The presence of Instagram is one of the most popular social media platforms. Make people make a habit of posting photo and video content. In this way, Instagram can become a source of public opinion and sentiment data, which data can be used for social studies. Social studies is still a crucial issue for countries today, including Indonesia, is a matter of religion. Apart from problems regarding religion, public sentiment can be researched from Instagram. The sentiment dataset related to religion is taken from public comments from President Joko Widodo's content regarding Merry Christmas Greetings and Visits to Churches. The analysis system will use sentiment analysis using a method from machine learning, namely the naive Bayes classifier to determine positive & negative sentiments. The comment dataset is 2,600 in which the positive and negative sentiment class datasets are determined manually by experts in the field of linguistics or communication totaling 1,309 to be used as training data. the results of testing the Naïve Bayes classifier training data with a total of 1309 obtained an accuracy prediction of 97.63% and test data where positive & negative sentiment classes had not been determined before hand amounted to 1291 in sentiment analysis obtained positive sentiment 70.56% & negative sentiment 29.44%.
Enhancing EMR Programming through a TPACK-Based Interactive Application in Health Informatics Education widiyanto, Wahyu Wijaya; Nugroho, Suryanto; Widyaningrum, Bajeng Nurul
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID547

Abstract

This study explores the potential of an interactive learning application, designed using the Technological Pedagogical Content Knowledge (TPACK) and Self-Regulated Learning (SRL) frameworks, to enhance student competence in basic Electronic Medical Record (EMR) programming within health informatics education. Using a quasi-experimental pretest-posttest design, the research involved 64 third-semester students in a vocational Health Information Management program. Participants were divided into two groups: an experimental group utilizing the interactive application and a control group receiving traditional instruction. Data were collected through achievement tests, motivation surveys, and usage log analysis. Results revealed that the experimental group demonstrated significantly higher learning achievement (M = 78.6 vs. 65.9), improved motivation (Likert mean = 4.35), and stronger self-regulated learning behaviors (87.1% module completion and 81% repeated simulations). These findings suggest that interactive tools grounded in pedagogical frameworks can effectively bridge abstract programming logic with clinical relevance, fostering both engagement and autonomous learning. The study contributes a scalable and replicable model for integrating simulation-based programming instruction in health informatics curricula.
Co-Authors Agistina Rivana, Baiq Riska Bayu Ariwibowo, Bayu Budiyanto Budiyanto Budiyanto Budiyanto Destri Maya Rani Diah Kari Artati Florentina Nadira Saniman Fuad Abdillah Hendro Prabowo Lingga Kurnia Ramadhani Lutfiyah Rizqulloh Maya Rani, Destri Nadiah, Salma Nugroho, Suryanto Nurul Hasanah puja, raafi Puspita, Rina Rani, Destri Maya Ratih Dwi Anggraynie Ratna Ratna Renha Maria Pelera Ojan Rina Puspita Sulchan, Sulchan Wiansah, Rizal Andina Widiyanto, Wahyu Wijaya Yuli Arinta Dewi