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All Journal Healthcare Nursing Journal Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Suryanti Suryanti
Department Of Medical Education, Faculty Of Medicine, Universitas Dian Nuswantoro, Indonesia
Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Nursing Social Sciences

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Pembelajaran Penilaian Kinerja Staf Perawat Klinik Utama Sari Medika Aries Setiawan; Dian Prawitasari; Ngurah Pandji Mertha Agung Durya; Jaka Prasetya; Suryanti Suryanti; Andi Hallang Lewa; Budi Widjajanto; Arditya Dian Andika; Lely Kusumaningrum; Ida Farida
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3255

Abstract

Staf perawat sebagai salah satu profesi yang mulia dan penting maka Klinik Utama Sari Medika setiap periode tertentu perlu melakukan  penilaian dan evaluasi kinerja. Penilaian dilakukan agar dihasilkan layanan yang obyektif serta profesional. Selama ini belum ada penilaian secara terukur yang dilakukan oleh Klinik Utama Sari Medika, sehingga pihak manajemen klinik menginginkan adanya penilaian yang berbasis obyektifitas melalui program kemitraan yang bertujuan memberikan penilaian yang berbasis obyektifitas untuk meningkatkan kinerja para staf perawat yang ada pada klinik untuk selanjutnya tercipta kepuasan layanan bagi para pasien. Metode penilaiaan kinerja menggunakan simple additive weighting, dengan hasil akhir pelatihan diperoleh peningkatan sebesar 94,5% sehingga staf perawat perlu sering mendapatkan kegiatan pelatihan seperti ini
Optimizing Pneumonia Detection on Edge Devices Using YOLOv5lu with Pruning and Quantization Muhammad Satriya Pratama Manggala Kusuma; Cinantya Paramita; Suryanti Chan
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.v11i2.10374

Abstract

Pneumonia continues to be a leading cause of childhood mortality worldwide, with the greatest impact in resource limited regions where access to radiologists is limited. Deep learning has emerged as a promising tool for automated screening, yet conventional object detection models demand high computational resources, restricting their use on low cost edge devices common in rural healthcare settings. To address this challenge, we developed a lightweight pneumonia detection framework designed for realtime inference on standard hardware. Our approach builds on the YOLOv5lu architecture, which incorporates an anchor free decoupled head, and was trained on a balanced dataset of 14,863 chest X-rays from the RSNA Pneumonia Detection Challenge.To enable deployment on edge devices, we applied a sequential compression pipeline. First, 30% of convolutional filters were removed through structured pruning, followed by post training quantization to INT8. These optimizations reduced the model size by nearly half (from 101 MB to 51.72 MB) and improved inference speed to 6.24 ms per image, equivalent to more than 160 frames per second on a standard CPU. Importantly, the quantized model preserved diagnostic performance, achieving a mean Average Precision (mAP@0.4–0.75) of 0.266 compared to the baseline score of 0.287. These findings confirm the practical feasibility of deploying advanced deep learning models in limited resource regions. By effectively balancing efficiency and accuracy, this framework offers a scalable solution for early pneumonia screening and establishes a foundation for extending detection to other diseases, including Tuberculosis and COVID-19.
Pelatihan Lanjutan Siswa SMA Negeri 7 Semarang Tentang Penyusunan Aplikasi Sederhana Pada Pembahasan Coding Manajemen Data Administrasi Kesehatan Aries Setiawan; Imam Nuryanto; Andi Hallang Lewa; Dian Prawitasari; Suryanti Suryanti; Lely Kusumaningrum; Jaka Prasetya; Budi Widjajanto; Karis Widyatmoko; Nur Rokhman; Arditya Dian Andika
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 2 (2026): MEI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i2.3380

Abstract

SMA 7 berupaya mewujudkan lulusan yang memiliki ketrampilan tambahan sebagai bekal setelah lulus. Strategi kepala sekolah dan guru membentuk beberapa kelompok keahlian yang selanjutnya siswa di wajibkan memilih salah satunya. Kelompok keahlian informatika merupakan salah satu yang ditawarkan. Tujuan lain dari kelompok keahlian informatika selain dari mengikuti perkembangan ilmu komputer adalah penguatan logika berfikir melalui pengenalan dasar performa pemrograman aplikasi yang meliputi penguasaan algoritma pemrograman, flowchat diagram alur program dan implementasi ke program dasar. Melalui pelatihan penyusunan aplikasi sederhana untuk peningkatan manajemen kemampuan algoritma terstruktur pada SMA Negeri 7 Semarang diharapkan memudahkan kelompok keahlian informatika dalam mempraktekkan dan manjawab materi-materi yang selama ini diajarkan hanya dengan teori.
Association between Family Support and Elderly Visits at Integrated Elderly Health Post (POSBINDU) in Tasikmalaya, Indonesia Miftahul Falah; Lilis Lismayanti; Nina Pamela Sari; Fitri Nurlina; Heri Budiawan; Faridah Mohd. Said; Salah Khlief Almotairi; Suryanti Chan; Henri Setiawan; Ima Sukmawati
HealthCare Nursing Journal Vol. 8 No. 1 (2026): HealthCare Nursing Journal
Publisher : LP3M Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/healthcare.v8i1.6512

Abstract

The elderly are people who have lost their capacity to restore themselves and operate normally. In Indonesia, an Integrated Elderly Health Post (POSBINDU) is one of the health services for the elderly. The elderly's activeness in going to POSBINDU is a type of aged health behavior in an attempt to maintain and develop their health ideally. One of the variables influencing older trips to POSBINDU is family support. The purpose of this study was to evaluate the association between family support and older participation in POSBINDU. The quantitative research approach was applied in this study. This study's sample consisted of 36 respondents (total sampling) at Sauyunan. This study included a cross-sectional design (univariate and bivariate analysis). The results showed that the elderly received no supported from 28 respondents (77.8%), whereas family supported was obtained by 8 respondents (22.2%). Elderly do not actively participate in POSBINDU, As much as 29 respondents (80.6%), whereas 7 respondents (19.4%) were actively participating in POSBINDU. With a significant p-value of 0.002 for the bivariate test findings, it can be stated that there was a relationship between family support and the activeness of elderly attending POSBINDU. The study hopes that families will have a better understanding of how to give effective family assistance for elderly.
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 Andi Hallang Lewa Arditya Dian Andika Aries Setiawan Budi Widjajanto Budi Widjajanto Cinantya Paramita Dian Prawitasari Faridah Mohd. Said Fitri Nurlina Henri Setiawan Heri Budiawan Ida Farida Ima Sukmawati Imam Nuryanto Jaka Prasetya Lely Kusumaningrum Lilis Lismayanti Miftahul Falah Muhammad Satriya Pratama Manggala Kusuma Ngurah Pandji Mertha Agung Durya Nur Rokhman Rendra Gunawan Salah Khlief Almotairi Sari, Nina Pamela Widyatmoko Karis