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PERUM BUMI BULU INDAH Gg. Rambutan No.1A RT:01/RW: 06, Desa BULU Kecamatan KRAKSAAN Probolinggo, JAWA TIMUR
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INDONESIA
Jurnal Kesehatan dan Sains
Published by CV. Akira Java Bulu
ISSN : -     EISSN : 30898730     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health
The Journal of Health and Science aims to disseminate research results in the fields of science and health to academics, practitioners, students, and individuals who have competence in their fields including: Nursing Medicine Nutrition Midwifery Environmental Health Public Health Reproductive Health Health Education and Promotion Electromedical
Arjuna Subject : Profesi Kesehatan - Profesi Kesehatan (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 1 (2026)" : 5 Documents clear
Pengaruh Pendidikan Gizi Dengan Media Video Terhadap Pengetahuan Siswa Dalam Pemilihan Jajanan Di SMK Karya Nasional Kabupaten Kuningan Mardhiyah Lestari; Al Rivan Marsyah Dzikri; Shela Dwi Amaliyanti; Akbar Rizkyan Affandi; Andini Hikmatu Diniah; Dwi Putri Andini
Diagnosa Vol. 2 No. 1 (2026)
Publisher : CV, Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/2crf4413

Abstract

Snacks are an inseparable part of adolescents’ dietary patterns, including vocational high school (SMK) students. However, not all snacks consumed meet safety and nutritional standards, thus potentially posing health risks. This study aimed to determine the effect of nutrition education using video media on improving students’ knowledge of choosing healthy snacks. The method used was a pre-experimental design with a one-group pre-test and post-test. The sample consisted of 50 students from SMK Karya Nasional. Data were collected using questionnaires distributed before and after the counseling session. The results showed an increase in students’ knowledge in most indicators, with an average post-test score of 87%. The highest improvement occurred in the indicators of preservatives and food additives. Although some items still showed low understanding, overall, nutrition education using video media was effective in enhancing students’ knowledge about choosing healthy and safe snacks. It is expected that this educational approach can be applied sustainably to encourage wiser and healthier snack consumption behavior among adolescents.
Design of a Portable Non-Contact Body Temperature Measurement Device Based on Arduino Rizka Yuliana Rizka; Desriyanti; Francisco Ade Fristanto; Muhammad Zainuri; Yunan Sepda Kurnyanda
Diagnosa Vol. 2 No. 1 (2026)
Publisher : CV, Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/9br0ev19

Abstract

Advances in electronic instrumentation have enabled the development of digital health monitoring systems. During the COVID-19 pandemic, non-contact body temperature measurement became crucial to reduce physical contact and minimize the risk of virus transmission, as conventional contact-based thermometers present hygiene limitations and are inefficient for mass screening. This study designs and develops a portable non-contact body temperature measurement device based on Arduino Uno. The system utilizes an MLX90614 infrared temperature sensor to measure body temperature without direct contact. Temperature data are processed by the microcontroller and displayed in real time on an OLED screen, with a push button for measurement control and a battery as a portable power source. The experimental results show that the developed system is able to measure body temperature accurately and consistently within an acceptable error range. The MLX90614 sensor exhibits a fast response time, and the OLED display provides clear real-time temperature readings. All system components operate reliably and are well integrated. The proposed Arduino-based portable non-contact body temperature measurement device functions effectively and is suitable for health screening applications. The system offers a practical, safe, and hygienic solution for body temperature monitoring, with potential for further improvements through calibration enhancement and IoT-based integration.
Design and Development of an IoT-Based Blood Oxygen Level DetectionDevice for Real-Time Monitoring Desriyanti Desriyanti; silvya berlian; Thoha Ilyasa
Diagnosa Vol. 2 No. 1 (2026)
Publisher : CV, Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/wtxyrt39

Abstract

Pemantauan kadar oksigen darah (SpO₂) merupakan indikator penting dalam mengetahui kondisikesehatan, khususnya sistem pernapasan. Penelitian ini bertujuan untuk merancang dan membangunalat pendeteksi kadar oksigen darah berbasis Internet of Things (IoT) yang mampu melakukanpengukuran secara real-time. Sistem menggunakan sensor MAX30102 sebagai pendeteksi SpO₂,mikrokontroler NodeMCU ESP32 sebagai pengolah dan pengirim data, serta layar OLED 0,96 inchsebagai tampilan lokal. Metode penelitian meliputi perancangan perangkat keras, pemrogramanperangkat lunak, dan pengujian fungsional sistem. Hasil pengujian menunjukkan bahwa alat mampumendeteksi dan menampilkan nilai SpO₂ secara real-time dengan baik pada kondisi pengukuran yangsesuai. Dengan demikian, alat ini berpotensi digunakan sebagai perangkat monitoring kadar oksigendarah berbasis IoT yang sederhana dan mudah digunakan.
Efektivitas Edukasi Kesehatan Berbasis Media Digital terhadap Pengetahuan Kesehatan Remaja Juniawan mandala Putra; Almadea Faldisa Oktan; Meinorizah Meinorizah
Diagnosa Vol. 2 No. 1 (2026)
Publisher : CV, Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/0ts42x53

Abstract

The rapid development of digital technology has transformed the way adolescents access health information. Young people increasingly utilise digital media such as social media platforms, health applications, and online learning platforms as primary sources of health-related information. This study aims to analyse the effectiveness of digital media–based health education in improving adolescents’ health knowledge. The research employed a mixed methods approach using a sequential explanatory design, integrating both quantitative and qualitative methods to provide a comprehensive understanding of the phenomenon. Quantitative data were collected through pre-test and post-test questionnaires administered to 120 senior high school students in Jakarta who participated in a four-week digital health education programme delivered through social media platforms and educational videos. The quantitative data were analysed using a paired sample t-test to measure differences in health knowledge before and after the intervention. Subsequently, qualitative data were obtained through in-depth interviews with 15 participants to explore their experiences and perceptions regarding the use of digital media in health education. The qualitative data were analysed using thematic analysis. The results indicate a significant improvement in adolescents’ health knowledge after participating in the digital health education programme (p < 0.05). Qualitative findings reveal that digital media were perceived as engaging, easily accessible, and effective in helping adolescents understand health information in a more interactive manner. However, several participants emphasised the importance of verifying the credibility of health information sources in order to prevent misinformation. This study concludes that digital media–based health education is an effective strategy for improving adolescents’ health knowledge and can serve as an innovative approach for health promotion by nursing professionals in the digital era.
Multiclass Classification of Brain Tumors in MRI Images Based on Deep Learning Fachri Ayudi; Felisberto Pereira
Diagnosa Vol. 2 No. 1 (2026)
Publisher : CV, Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/rctfm087

Abstract

Brain tumors are one of the most deadly neurological diseases that require early and accurate diagnosis to determine the right treatment plan. The use of Magnetic Resonance Imaging (MRI) images is the standard in detecting brain tumors, but manual classification by radiologists is time-consuming and has a high risk of subjectivity. This study focuses on the classification of four main categories: glioma, meningioma, pituitary tumor, and healthy brain tumor (not a tumor). This study aims to build an automatic multi-class classification system for brain tumors using a Deep Learning approach with MobileNetV2 and EfficientNetB0 architectures. The training process is carried out using transfer learning techniques and learning rate optimization through system callbacks to ensure the model reaches the best convergence point. The results show that the proposed model is capable of classification with very high performance, achieving an accuracy of 96.88%. The evaluation results using a confusion matrix indicate that the model has a consistent ability to distinguish between tumor classes with an average F1 score of 0.97.

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