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All Journal Jurnal Kebidanan ILMU KELAUTAN: Indonesian Journal of Marine Sciences BULETIN OSEANOGRAFI MARINA Majalah Keperawatan Unpad Jurnal Pelita Pendidikan Asas: Jurnal Sastra Interest : Jurnal Ilmu Kesehatan Jurnal Bioterdidik: Wahana Ekspresi Ilmiah JKG (Jurnal Keperawatan Global) Panggung Aquasains : Jurnal Ilmu Perikanan dan Sumberdaya Perairan SEIKO : Journal of Management & Business Jurnal Keperawatan Abdurrab Jurnal Dunia Kesmas JKM (Jurnal Kebidanan Malahayati) Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Menara Ilmu Perspektif Pendidikan dan Keguruan Dedikasi: Jurnal Pengabdian Masyarakat Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] JTIEE (Journal of Teaching in Elementary Education) Buletin Pembangunan Berkelanjutan Jurnal Anak Usia Dini Holistik Integratif (AUDHI) JIHBIZ :Global Journal of Islamic Banking and Finance. Jurnal Ilmiah Wahana Pendidikan Kosala : Jurnal Ilmu Kesehatan ELIPS: Jurnal Pendidikan Matematika Jurnal HUMMANSI (Humaniora, Manajemen, Akuntansi) Zona Kedokteran: Program Studi Pendidikan Dokter Universitas Batam Biology and Education Journal (BaEJ) Atmosfer: Jurnal Pendidikan, Bahasa, Sastra, Seni, Budaya, Dan Sosial Humaniora Jurnal Informatika: Jurnal Pengembangan IT International Journal of Health Literacy and Science Green Inflation: International Journal of Management And Strategic Business Leadership Panggung Prosiding Seminar Nasional Ilmu Teknik Jurnal Pendidikan dan Sosial Humaniora Edumaspul: Jurnal Pendidikan
Suryanti Suryanti
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Desain Chatbot Digital Twin Atlet Pencak Silat Wanda Listiani; Sri Rustiyanti; Anrilia E.M Ningdyah; Sriati Dwiatmini; Suryanti Suryanti
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.200

Abstract

This research aims to develop a customized chatbot based on a local large language model (LLM) using Ollama Anything as a form of psychosocial support for Pencak Silat athletes. Mental toughness is a critical factor for Pencak Silat athletes, particularly when coping with competitive failure or sports-related injuries. Injuries sustained in Pencak Silat competitions often involve psychological consequences, including trauma, fear, anxiety, and disturbances in self-identity. To address these challenges, the proposed chatbot functions as a screen-integrated psychosocial support system for athletes. This research used an experimental method combined with Natural Language Processing (NLP) techniques was employed to construct a digital twin chatbot capable of simulating athlete-centered conversations. The Pencak Silat Athlete Chatbot is designed to assist athletes by providing responsive support when they experience defeat or performance setbacks during competitions. The research findings indicate that, although the chatbot is functional, its conversational responses remain relatively rigid, access times are prolonged, and further testing with Pencak Silat athletes in controlled settings is required. Overall, the development of the Pencak Silat Athlete Digital Twin Chatbot represents an ongoing effort to advance digital innovation and strengthen the ecosystem of sports achivements development in Indonesia.
Organizational Commitment Matters More than Supervision in Nursing Documentation Compliance Suryanti Suryanti; Supriyantoro Supriyantoro; Idrus Jus’at
Green Inflation: International Journal of Management and Strategic Business Leadership Vol. 3 No. 1 (2026): February : Green Inflation: International Journal of Management and Strategic B
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/greeninflation.v3i1.681

Abstract

Nursing care documentation is crucial for service quality and patient safety, but incomplete and inconsistent documentation remains a challenge in hospitals. This study focuses on nurses at Medika Lestari Hospital, where documentation compliance is below expectations. The aim is to analyze how organizational commitment and supervision affect nursing care documentation, with work motivation as an intervening variable. A quantitative cross-sectional design with a structural model approach was used, and data were collected via structured questionnaires and analyzed using SEM-PLS. The results show that organizational commitment positively impacts documentation compliance (β = 0.268; p = 0.013), highlighting the importance of nurses’ attachment to organizational goals. Supervision, however, has no significant direct effect on documentation (β = 0.220; p = 0.109). Both organizational commitment (β = 0.285; p = 0.018) and supervision (β = 0.382; p = 0.000) significantly influence work motivation, indicating that managerial control and organizational attachment contribute to motivation. However, work motivation does not significantly affect documentation (β = 0.231; p = 0.053) and does not mediate the effects of commitment or supervision on compliance. In conclusion, improvements in documentation are primarily driven by organizational commitment rather than motivational or supervisory factors. Hospital management should focus on enhancing nurses’ organizational commitment and aligning supervisory practices with institutional values to improve documentation compliance sustainably.
Comparative Evaluation of VGG16, MobileNetV2, and ResNet50 for Pediatric Pneumonia Classification Using Grad-CAM Rendra Gunawan; Cinantya Paramita; Suryanti Suryanti
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10373

Abstract

 Pneumonia remains one of the leading causes of mortality among children worldwide, particularly in developing countries, where early and accurate diagnosis is crucial. This study aims to evaluate and compare the performance of three deep learning architectures, namely VGG16, MobileNetV2, and ResNet50, for pediatric pneumonia classification using chest X-ray images. The dataset consists of pediatric chest radiographs (ages 1-5 years) obtained from Guangzhou Women and Children’s Medical Center, which were preprocessed through normalization and data augmentation techniques to improve model generalization. The classification task involves three categories: normal, bacterial pneumonia, and viral pneumonia. Model performance was evaluated using accuracy, precision, recall, specificity, F1-score, G-Mean, and AUC. The experimental results show that all models achieve competitive performance, with accuracy ranging from 79% to 82%, where VGG16 outperforms the other architectures. Furthermore, Grad-CAM is applied to enhance interpretability by visualizing important regions in X-ray images that influence model decisions. The results demonstrate that Grad-CAM provides meaningful visual explanations, supporting the reliability of deep learning models in assisting clinical diagnosis.
Co-Authors Afnila Fritadini Hariono Andi Nurhartati Anggita Fajerin, Wisnaya Anrilia E.M Ningdyah Anrilia E.M. Ningdyah Ayumiati, Ayumiati Azharsyah Ibrahim Budi Jatmiko Budianto Budianto Cinantya Paramita Devi Lestari Nurul Aulia Dewi Ambarwati Dwi Ariani Dwi Ariani S Dwi Retnani Sinarwati Dwi Retnani Srinarwati Efilona Setri Eka R Nia Elsa Liana Fadhil Ahmad Fitriyeni Fitriyeni Hamzah Hamzah Hikmah Riski Ramadania Ibnu Hajar Idrus Jus’at Iffa Nadiya Hanifah Irfan Arsid Ismail Marzuki Joni Pranata Kamase, Jeni Lia Safira M. Bambang Edi Siswanto Miftah Muna Rosiyah Mochammad Nursalim Musfirah Musfirah Nadia Widodo Nefri T Nian Diani Indrafuri Nurma Nurma Oni My Anjliani Pratiwi Juniar A. G Rahmadhani Nuzul Putri Rendra Gunawan Rica Rian Rosta Minawati Salsabyla Fadheela Sardi Sardi Sepita Ferazona Sepita Ferazona Serlin serang SISKA NUR WAHIDA Siti Lestari Siti Risna Wati Siti Robiah Siti Rudiyanti Soematri Diantha Sri Rustiyanti Sri Widayati Sriati Dwiatmini St. Asma Ul Husna Syah su wignyo Sudarmi Sudarmi Suhartono Suhartono Supriharyono Supriharyono Supriyantoro Supriyantoro Syarifah fazira Tataq Siswono Tengku Idris, Tengku umi faizah Wanda Listiani Willy Indrawan Wiryanto Wiryanto Wiryanto Wiryanto Yandri Yandri