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All Journal International Journal of Electrical and Computer Engineering Jurnal Simetris Mediator Jurnal Teknologi Informasi dan Ilmu Komputer KOMUNIKA Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROSIDING KOMUNIKASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Ilmu Teknik dan Komputer Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) International Journal of Educational Research and Social Sciences (IJERSC) International Journal of Community Service International Journal of Qualitative Research Electronic Journal of Education, Social Economics and Technology International Journal of Health, Engineering and Technology West Science Business and Management Bulletin of Social Studies and Community Development Indonesian Journal of Community Services Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Siber International Journal of Education Technology
Ida Nurhaida
Universitas Lampung
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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
Analisis Sentimen berbasis Deep Learning Terhadap Kesetaraan Gender di Bidang STEM: Perspektif dan Implikasinya Mariam, Siti; Nurhaida, Ida
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29071

Abstract

Women's participation in Science, Technology, Engineering, and Mathematics (STEM) is still low due to discrimination, gender stereotypes, and lack of access to equal career opportunities. This research analyzes public sentiment about gender equality in STEM fields using the Knowledge Discovery in Database (KDD) approach with the Long Short-Term Memory (LSTM) algorithm. The data consists of 1,200 tweets (2018-2024) collected through web crawling and processed using KDD techniques such as preprocessing, transformation, data mining and evaluation. The resulting LSTM model showed 86.25% accuracy, 88.18% precision, 82.20% recall, and 85.00% F1-score. Sentiment analysis showed support and appreciation for women in STEM (positive sentiment) and criticism of gender discrimination and stereotypes (negative sentiment). This study faced challenges in the form of data imbalance and the model's difficulty in understanding the Indonesian context. Our findings confirm the importance of policies that support gender equality and inclusive work environments. This research is expected to improve people's perception of gender equality and increase the representation of women in STEM fields, especially in Indonesia.
Aplikasi Artificial intelligence untuk Klasifikasi Lengkungan Kaki: Solusi berbasis Radiografi Haris, Abdul; Nurhaida, Ida
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29098

Abstract

Identifying foot arch types is crucial for maintaining health and comfort. Flat foot arches can cause pain and discomfort, potentially interfering with activities such as sports. This research aims to develop an Artificial intelligence (AI)-based application to detect normal and flat foot arch types through X-ray images. The YOLOv8 model with bounding box is converted to TensorFlow Lite format to be integrated into a mobile platform through Android Studio. The application uses a waterfall model without maintenance, starting from the analysis of x-ray dataset needs, development and testing of the YOLOv8 model, conversion to TensorFlow Lite, design, black box testing, and application on Android devices. This application can only identify x-ray photos of the soles of the feet looking right and left. Confusion matrix application testing with 150 epochs shows performance with recall 86.2%, precision 77.1%, accuracy 83.3%, mAP50 94.9%, and mAP50-95 76.2%. Black box testing on mobile devices using datasets augmented with 45° horizontal shear and 90° rotation resulted in maximum identification accuracy compared to traditional methods such as the wet foot test. Traditional methods print the soles of the feet with an identification process that requires precision of the patient's standing position. This app detects flatfoot early, improving comfort in daily activities and sports.
Co-Authors ., Karomani Abdul Haris Ahmad Riza Faizal, Ahmad Riza Akhmad Riza Faizal Akmal Junaidi Ali, Husain Andi Windah Andi Windah* Arif Sugiono Arnila Purnamayanti Aryanti Nina Yudha Ayumi, Vina Azis, Diky Luqman Darojat, Agus Dhamanti, Inge Djauharie, Arlyandi S Djuadi, Noverman Faisal, Fairo Mahaputranda Fikri, Muhammad Huri Fitri Yakub Giovanni, Netaniel Halim, Yeru Dwi Pratama Hapzi Ali Hazni, Elvira Hestin Oktiani Hidayat, Januar Putra Ichsan Ichsan Khansa, Devanna Alandra Kurnia Muludi Maryani, Eri Nandi Haerudin Negara, Komang Putra Satria Nina Yudha Aryanti Pairulsyah Pairulsyah Pratiwi, Chelomitha Arsy Putra, Purwanto Renti Oktaria, Renti Reza Gustifa, Adinda Saputra, Muhammad Azi Sirat, Abdul Hadi SITI MARIAM Slamet Riyadi Sugeng P Hariyanto Tina, Kartika Tjahjono, Benny