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Self-monitoring of Glucose With A Non-invasive Method Using Near Infrared Sensor Rinda Nur Hidayati; Nur Hasanah Ahniar; Gita Rindang Lestari; Atika Hendryani; Faris Al Hakim
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 11 No 2 (2020): SANITAS Volume 11 Nomor 2 Tahun 2020
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2020.10

Diabetes mellitus or commonly called diabetes is a worldwide epidemic caused by chronic hyperglycemia. Simplify the blood glucose monitoring and easy to use is an essential part of diabetes management. Currently, the use of blood glucose meters conventional in clinical practice needs sufficient reliability. Therefore, self-monitoring of blood glucose with a non-invasive method was presented. A non-invasive blood glucose monitoring device was initially for information on glucose level measurements. A non-invasive method to determine the level of glucose by applying the physical properties of the absorption of the laser sensor that can produce a voltage change at various glucose levels. In this paper, a glucose monitoring module was fabricated with dimensions of 25x27x15 cm which has a minimum system, sensor, and LCD as a display of glucose levels. A minimum system to control the output of data digital value using microcontroller Android nano v.3. Experimentally, testing this module is by comparing the glucose monitoring modules that have been made with a gold standard. The result showed that non-invasive glucose monitoring is the potential for glucose level measurement a sensitivity, resolution, and accuracy of 0.86 mg/dL, 0.01 mg/dL, and 98.96%, respectively. The purposed module of glucose level monitoring offered simple testing for the rapid measurement of glucose levels.

Design Of A Vitiligo Home Phototherapy Using Narrow Band Ultraviolet-B (NB-UVB) Based On Arduino Uno Atika Hendryani; Hazzie Zati Bayani; Vita Nurdinawati
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 13 No 2 (2022): SANITAS Volume 13 Nomor 2 Tahun 2022
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2022.12

Vitiligo is one of the skin diseases where there is a disorder in skin pigmentation (skin color disorder), which is characterized by the presence of macula hypopigmentation (a condition that causes part of the skin color to become lighter than the surrounding skin color) caused by the loss of melanocyte function (a special cell found under the skin epidermis that functions to produce skin pigments/melanin) chronically and progressively from the epidermis. This disease if left untreated can spread to other parts of the body. Phototherapy is a method to help reduce the spread of spotting on the body by using NB-UVB rays. This therapy should be carried out on an ongoing basis. To reduce contact with others during the Covid-19 pandemic and there is a vitiligo disease therapy protocol, the author designed the Home Phototherapy Vitiligo device. The author made this device by using NB-UVB lamps and HC-SR04 sensors to detect the exposure distance of 3 cm. The radiation dose starts from 150 mJ/cm2 – 3000 mJ/cm2. The author measured the irradiation of the device obtained 2.6 mW/cm2 and then converted to a radiation dose formula to get how long the therapy time was. The therapy time is obtained level 1 (58 seconds) – level 22 (1085 seconds). In this device obtained the accuracy of the therapy time at the 58th second, the 540th second and the 1085th second is 99.99%. For the value of the accuracy of the distance at 1 cm is 99.82%, at a distance of 2 cm is 99.96%, at a distance of 3 cm is 99.33%, at a distance of 5 cm is 99.944%.

The Effect Of Content Development On Android Applications On Knowledge Of Dietary Prescriptions On Metabolic Syndrome Risk Tetes Wahyu W; Rinda Nur Hidayati; Jumiyati Jumiyati; Atika Hendryani
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 13 No 2 (2022): SANITAS Volume 13 Nomor 2 Tahun 2022
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2022.13

Metabolic syndrome is generally defined as meeting 3 (three) of 5 (five) criteria including abdominal obesity, hypertriglyceridemia, low HDL (high density lipoprotein) cholesterol, hypertension and hyperglycemia. About 20-25% of the world's adult population has the metabolic syndrome. The purpose of this study was to determine the effect of the android application on knowledge of dietary prescriptions in the community at risk for metabolic syndrome. The research design is a quasi-experimental study (quasi-experimental study), with a non-randomized control group pre-test - post-test test design that aims to determine the possibility of a causal relationship by intervening or giving treatment to one or more experimental groups, then the results (effect) of intervention was compared with a group that was subjected to different treatments (control group). Characteristics of age (p=0.172), gender (p=0.25) and education (p=0.055). Knowledge before intervention p=0,306 and after intervention p=0,009 in both groups. Knowledge before and after intervention in the case group p=0.0005 and knowledge before and after intervention in the control group p=0.0005. The types of outputs produced in this study are application systems for early detection of metabolic syndrome disease and dietary prescriptions, intellectual property rights and research journal articles. Introducing this android application so that it can be better known to the public in general and developing this android application so that it becomes more interesting and easy to understand.

Real-time stress detection and monitoring system using IoT-based physiological signals Atika Hendryani; Dadang Gunawan; Mia Rizkinia; Rinda Nur Hidayati; Frisa Yugi Hermawan
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5132

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

A Portable Vortex Mixer With Object Detection Sensors Using TFT ILI9341 Control Display Hidayati, Rinda Nur; Gunawan, Indra; Hendryani, Atika; Lestari, Gita Rindang; Salsabila, Natasya H
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 15 No 1 (2024): SANITAS Volume 15 Nomor 1 Tahun 2024
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2024.438

A vortex mixer can be utilized for repetitive sample-mixing tasks that need execution over a predetermined period. The subject of this study is a device known as a vortex mixer, which is used to rapidly homogenize liquids in a compact container. The module's design aims to produce a portable vortex mixer with an object detector for use in the solution-mixing process. The module comprises a circuit controlled by ESP32, an infrared sensor for object detection, and a TFT LCD ILI9341 for display. A charging battery can also charge this device, making it easier to transport. This instrument has three degrees of speed: the low-level ranges from 300 to 500 RPM, the medium level ranges from 600 to 1500 RPM, and the high level extends from 1600 to 2500 RPM. The results reveal that the motor speed accuracy of the vortex mixers is low at 95.78%, medium at 97.49%, and high at 99.19%. Moreover, the battery life is lengthy, with an average charging time of 2.62 hours and a discharging time of 9.7 hours for 300 RPM; 7.34 hours for 600 RPM; and 3.03 hours for 1600 RPM, respectively.

Enhancing Adolescent Girls Anemia Prevention Knowledge and Attitudes through Nutritional Consultations Jumiyati, Jumiyati; Hendryani, Atika; Hidayati, Rinda Nur; W, Tetes Wahyu
Media Gizi Indonesia Vol. 19 No. 1SP (2024): MEDIA GIZI INDONESIA (NATIONAL NUTRITION JOURNAL) Special Issue: The 3rd Ben
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/mgi.v19i1SP.20-28

Anemia is a public health problem in Indonesia that can affect all age groups, from toddlers to the elderly, and primarily affects adolescents. One way to prevent anemia among them is by increasing knowledge and attitudes through nutritional consultation to achieve promotive and preventive efforts. In 2018, anemia in women was higher (27.2%) than in men (20.3%). This study aimed to determine the effect of nutritional consultations on the knowledge and attitudes of young women in preventing anemia in Bengkulu City. A quasi-experimental study with a non-randomized control group design, pre-test, and post-test design was conducted from October to November 2021 at SMP N 8 Bengkulu City. The intervention group subjects are 30 respondents, and at control group are 30 respondents; all were purposively selected. A paired T-test was used to analyse the effect of the intervention. The results showed a significant difference in the pre-test and post-test knowledge in the treatment group (p=0.002). At the same time, there was no significant difference between the pre-test and post-test knowledge of the control group (p=0.095). In comparison, the mean attitude before treatment did not differ between groups (p=0.048); each group showed differences after treatment. Intervention and control groups influence knowledge and attitudes before and after treatment (p=0.013).

Implementation of Thermal Camera for Human Stress Detection: A Review Hendryani, Atika; Nurdinawati, Vita; Sambiono, Andy
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.28

Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras. Thermal cameras have several advantages: being non-contact and non-invasive, quick, easy to use, and cost-effective. In general, the architecture of the stress detection system using a thermal camera consists of several stages, including image acquisition, pre-processing, ROI tracking and selection, feature extraction, and statistical analysis or classification. This paper aims to review the use of thermal cameras in detecting stress in humans. This paper also seeks to answer the research question of what analysis can be done to improve stress detection accuracy using thermal camera images. Research shows that ROI selection must be carefully considered to obtain good accuracy. Combining thermal images with other data can improve accuracy in stress detection. Machine learning in classification provides many benefits in recognizing patterns but is highly influenced by the number of datasets used.

Education intervention model on clean and healthy living habits among mothers having children less than 2 years of age border in remote areas, Indonesia Gustina, Mely; Ali, Haidina; Bathari, Rosalia Rina; Hendryani, Atika; Rahmawati, Ullya
Malahayati International Journal of Nursing and Health Science Vol 6, No 6 (2023)
Publisher : Program Studi Ilmu Keperawata Fakultas Kedokteran Universitas Malahayati Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/minh.v6i6.13272

Background: Stunting in toddlers reflects a failure to grow due to nutritional deficiencies. Repeated exposure to fecal pathogens, especially prevalent in areas where open defecation practices exist, can lead to diarrhea, hindered growth, health issues, and irreversible developmental impairments.Purpose: To determine the effectiveness of Agamis Sistematis Interaktif Kreatif/ASIK (Religious, Systematic, Interactive, Creative) education model (focusing on water, sanitation, and hygiene) on clean and healthy living habit (CHLH) among toddlers at risk of stunting in the remote border areas of Enggano Island, Bengkulu Province in 2023.Method: The researcher employed a non-equivalent control group design, also known as a pre-test post-test control group design, for the experimental and control groups. This research involved a sample of 60 mothers having children less than 2 years of age, comprising 30 in the intervention group and 30 in the control group. Data collection utilized a questionnaire. Univariate and bivariate data analysis was conducted using the paired t-test statistical analysis. The results of this study revealed a difference in the mean values between the experimental and control groups concerning WAZ (weight-for-age z-scores), with values in the experimental group pre=72.7000, post=75.2667, and the control group pre=76.8167, post=78.4167.Results: The research findings indicated a difference in CHLH between the treatment and control groups (ρ=0.000, ρ<0.05). The ASIK education model on CHLH among toddlers at risk of stunting is effectively applicable in the remote border areas of Enggano Island, Bengkulu Province in 2023.Conclusion: Government support in the form of budget allocation is crucial for the sustainability of continuous education aimed at stunting prevention in village communities.

Deteksi Pneumonia Menggunakan Explainable AI: Model Hibrid CNN–ViT dan Grad-CAM Atika Hendryani; Vita Nurdinawati; Agus Komarudin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.19822

Pneumonia detection through medical imaging presents a significant challenge, particularly in regions with limited access to healthcare professionals. This study presents an explainable artificial intelligence (XAI) model that integrates convolutional neural network (CNN) and vision transformer (ViT) to enhance the accuracy of pneumonia diagnosis using chest X-ray images. The proposed research aims to enhance diagnostic accuracy by providing explanations through gradient-weighted class activation mapping (Grad-CAM) visualization. The methodology includes image preprocessing, local feature extraction via CNN, and global spatial relationship modelling using ViT. The model was trained on a preprocessed chest X-ray dataset and evaluated using standard performance metrics such as accuracy, precision, recall, and F1 score. The proposed CNN-ViT model was assessed using chest X-ray datasets for pneumonia detection. The experimental results demonstrated that the model achieved an accuracy of 96.5%, precision of 96%, recall of 96%, and F1 score of 94%, These results indicate that the integration of CNN and ViT effectively enhances classification performance and provides a reliable tool for medical image analysis. Furthermore, Grad-CAM visualizations highlight the critical regions in the images that influence the model’s predictions, thereby enhancing interpretability. Compared to conventional models, this approach offers improved transparency in AI-driven diagnostics. Consequently, the proposed model represents a promising and reliable diagnostic tool, particularly beneficial in underserved or remote areas with limited medical infrastructure. Additionally, this research opens opportunities for the development of transparent and XAI-based diagnostic systems.

Deteksi Pneumonia Menggunakan Explainable AI: Model Hibrid CNN–ViT dan Grad-CAM Atika Hendryani; Vita Nurdinawati; Agus Komarudin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.19822

Pneumonia detection through medical imaging presents a significant challenge, particularly in regions with limited access to healthcare professionals. This study presents an explainable artificial intelligence (XAI) model that integrates convolutional neural network (CNN) and vision transformer (ViT) to enhance the accuracy of pneumonia diagnosis using chest X-ray images. The proposed research aims to enhance diagnostic accuracy by providing explanations through gradient-weighted class activation mapping (Grad-CAM) visualization. The methodology includes image preprocessing, local feature extraction via CNN, and global spatial relationship modelling using ViT. The model was trained on a preprocessed chest X-ray dataset and evaluated using standard performance metrics such as accuracy, precision, recall, and F1 score. The proposed CNN-ViT model was assessed using chest X-ray datasets for pneumonia detection. The experimental results demonstrated that the model achieved an accuracy of 96.5%, precision of 96%, recall of 96%, and F1 score of 94%, These results indicate that the integration of CNN and ViT effectively enhances classification performance and provides a reliable tool for medical image analysis. Furthermore, Grad-CAM visualizations highlight the critical regions in the images that influence the model’s predictions, thereby enhancing interpretability. Compared to conventional models, this approach offers improved transparency in AI-driven diagnostics. Consequently, the proposed model represents a promising and reliable diagnostic tool, particularly beneficial in underserved or remote areas with limited medical infrastructure. Additionally, this research opens opportunities for the development of transparent and XAI-based diagnostic systems.

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