International Journal of Health, Engineering and Technology
International Journal of Health, Engineering and Technology (IJHET) is to provide research media and an important reference for the progress and dissemination of research results that support high-level research in the field of Health, Engineering and technology. Original theoretical work and application-based studies, which contribute to a better understanding of all areas of Health, Engineering and Technology , the journal publishes articles six times a year in May, July, September, November, January and March. Scope: International Journal of Health, Engineering and Technology (IJHET) is to provide a research medium and an important reference for the advancement and dissemination of research results that support high-level research in the fields of Health, Engineering and Technology Research. Original theoretical work and application-based studies, which contributes to a better understanding all fields of Health, Engineering and Technology Research. Healt : Clinical Nutrition, Community Nutrition, Institutional Nutrition, Food Technology, Food Security, Pediatric Physiotherapy, Geriatric Physiotherapy, Cardiovascular and Pulmonary Physiotherapy, Musculoskeletal Physiotherapy, Sports Physiotherapy, Public Health, Community Sanitation, Environmental Health, Nursing, Biology, Medicine, Pharmacy. Engineering : The field of mechanical Engineering include expertise in energy conversion, construction machinery, manufacturing and materials. The field of Electrical Engineering which includes skills power engineering, telecommunications engineering and information, as well as control and instrumentation. The field of Chemical Engineering which includes expertise in the field of new and renewable energy, the environment field. The field of Civil Engineering which includes expertise in the fields of structural, geotechnical, transportation and water. The field of Metallurgical Engineering which includes expertise in extraction, manufacturing and characterization of materials. The field of Industrial Engineering which includes enterprise management system, working system and the ergonomics and manufacturing systems. Technology: Open Source Application, Information Management, Information System, IT & Social Impact, Geographical Information System, Web Engineering, Database Design & Technology, Data Warehouse, Network Security, Data Mining, Computer Architecture Design, Mobile Programming.
Articles
272 Documents
<![CDATA[Analysis of Antibiotic Use In Ispa Patients At Uptd. Tanah Luas Community Health Center, North Aceh Regency ]]>
<![CDATA[Sri Wastuti]]>;
<![CDATA[Nurhasanah Rumanda]]>;
<![CDATA[Tasya Humaira]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 6 (2025): IJHET MARCH 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i6.256
<![CDATA[Upper respiratory tract infection (URTI) is an acute infection that attacks the nose, paranasal sinuses, pharynx, trachea, bronchi, lungs, and epiglottis. Bacteria, viruses, and microbes are the causes of URTI. Antibiotics are the most widely used class of drugs to treat infections. This study aims to determine the rationality of the use of antibiotics used by patients with upper URTI at the Tanah Luas Health Center, North Aceh Regency based on the right indication, right drug selection, right dose, and right patient. This type of research is a non-experimental study with a descriptive analytical research type. Data collection was carried out retrospectively on patient medical records. Data were obtained by analyzing data on the distribution of age, gender, antibiotic therapy, right indication, right drug, right dose and right patient. The results of this study indicate the rationality of antibiotic use in outpatient URTI patients at the UPTD Tanah Luas Health Center, North Aceh Regency, seen based on the criteria of right indication of 100%, right dose of 100%, right drug of 100%, and right patient of 100%.]]>
<![CDATA[Pattern of Drug Use For Dyspepsia In Outpatient Patients at dr. Zubir Mahmud Hospital ]]>
<![CDATA[Sri Wastuti]]>;
<![CDATA[Alfi Sulthan]]>;
<![CDATA[Hepni Hepni]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 6 (2025): IJHET MARCH 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i6.257
<![CDATA[Dyspepsia is one of the most common digestive problems found in the community. Symptoms felt such as heartburn, nausea, vomiting, decreased appetite, belching and feeling full quickly. This study aims to determine the pattern of drug use in outpatient dyspepsia patients at the dr. Zubir Mahmud Regional General Hospital. This study used a retrospective descriptive method conducted in January-December, data obtained from prescriptions for inpatient dyspepsia patients at the dr. Zubir Mahmud Regional General Hospital. The results showed that there were 2030 patients diagnosed with dyspepsia. The majority of dyspepsia disease occurred in women 54 people (55.67%) and at the age of 46-65 years consisting of 38 people (39.17%). The most commonly used type of drug is omeprazole 48 (26.22%), the most common drug preparation is capsule preparation 85 R/ (46.44%) and the most common drug class is the Proton Pump Inhibitor class as much as 85 R/ (46.44%).]]>
<![CDATA[IoT-Based Driver Health Monitoring System with Location Based Service Feature for Fast Treatment at Nearest Health Facilities ]]>
<![CDATA[Jaya, Dery Yuswanto]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 6 (2025): IJHET MARCH 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i6.263
<![CDATA[This research aims to develop and test an Internet of Things (IoT)-based driver health monitoring system with Location Based Service (LBS) features for quick response to the nearest health facility. The system uses health sensors (heart rate, body temperature, SpO2, and fatigue), GPS module, and Wi-Fi/GSM-based communication to monitor the driver's condition in real-time. The research method includes system design, sensor testing, field testing with 25 drivers, and system performance analysis. The results show that the health sensor has an accuracy above 90%, with detection of critical conditions within 3-5 seconds and sending emergency notifications within 10 seconds. The LBS feature successfully provided health facility recommendations with 98% accuracy. Field tests detected several cases of critical conditions and proved the effectiveness of the system in real conditions. Ninety percent of participants expressed satisfaction with the system. The research conclusion confirms that the system can improve driver safety, reduce the risk of accidents due to health problems, and has the potential for wide application in the transportation and logistics sectors. However, there are limitations such as dependence on the internet and the accuracy of fatigue sensors that need to be improved in future research.]]>
<![CDATA[Nutrition Education Video Can Improve Knowledge And Hemoglobin Levels In Students Of Sman 1 Singaraja Bali Province ]]>
<![CDATA[Ida Ayu Eka Padmiari]]>;
<![CDATA[Desak Made Ardhya Pramesti Suci Sanjiwani]]>;
<![CDATA[Desak Putu Sukraniti]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 6 (2025): IJHET MARCH 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i6.264
<![CDATA[Based on the 2013 and 2018 Riskesdas, the prevalence of anemia in adolescents in Indonesia has increased from 18,4% to 32%. And there are 21,4% of young women forget to take iron tablets. The purpose of this study was to determine differences in knowledge and hemoglobin levels before and after giving nutrition education videos to female students of SMAN 1 Singaraja. The type of research used is quantitative research with a quasi-experimental design using the one-group pretest-posttest design method. The number of samples in this study were 61 female students. Using a nonparametric test, namely the Wilcoxon Test. The characteristics of the sample were 32 samples (51,6%) aged 16-20 years, while 30 samples (48,4%) aged 10-15 years. The results of data collection show that the average age of the sample is 16 years, where the average sample has a BMI of 20,18 kg/m2 which is a normal nutritional status. There was an increase in the average value of the sample's Hb level of 0,3 from 14,2 to 14,5. The knowledge of the sample experienced an increase in the average value of 6,7 from 71,2 to 77,9. Wilcoxon Test Results On the Hb level variable, the sig. 0,159 (sig. >α ; α = 0,05) so that there is no significant difference in the results of Hb levels between before and after the nutrition education intervention is given.In the knowledge variable, the sig value is obtained. 0,000 (sig. <α ; α = 0,05), where there is a difference in the results of knowledge between before and after the nutrition education intervention is given. Therefore nutrition education is needed in any media to maintain health and prevent anemia in female students]]>
<![CDATA[Community Participation In The Exclusive Breastfeeding Program In Temanggung ]]>
<![CDATA[C. Ermayani Putriyanti]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 6 (2025): IJHET MARCH 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i6.265
<![CDATA[Exclusive breastfeeding not only increases immunity, reduces infant and toddler mortality rates due to diarrhea and respiratory tract infections. There is a relationship between exclusive breastfeeding and high levels of child intelligence, increased income when aged 30 years, and prevents cancer later in life. The impact of breastfeeding reduces postpartum bleeding, prevents breast cancer and ovarian cancer. This study aims to determine community participation in the exclusive breastfeeding program in the Parakan Health Center work area. Data were obtained by conducting in-depth interviews, observation and documentation. Community participation in supporting exclusive breastfeeding has not been maximized, there has been no development of new strategies related to the exclusive breastfeeding program, the community has not been involved in program planning, there has been no special allocation of funds for exclusive breastfeeding program activities. The cause of breastfeeding failure is highest in working mothers and families, especially grandmothers. Community participation needs to be increased through cooperation with community leaders, religious leaders, youth organizations in the socialization of exclusive breastfeeding. There needs to be a policy for allocating village funds for the socialization of exclusive breastfeeding, the knowledge of cadres regarding exclusive breastfeeding is increased, so that cadres can provide counseling and not just remind. There needs to be cooperation with companies or factories regarding the policy of providing rooms for expressing and breastfeeding babies.]]>
<![CDATA[The Influence of Social and Environmental Factors on the Behavior of Using Personal Protective Equipment (PPE) When Driving on Students of SMK Muammadiyah Aek Kanopan in North Labuhan Batu Regency: Cross Sectional Study ]]>
<![CDATA[Fauziah Gusvita Syarah]]>;
<![CDATA[Nayodi Permayasa]]>;
<![CDATA[Asdi Lastari]]>;
<![CDATA[Dwiana Kartika Putri]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 4 No. 1 (2025): IJHET May 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v4i1.270
<![CDATA[Traffic safety is one of the important aspects in community life that is often ignored, especially by the adolescent age group. Students are a productive age group who often use motorbikes as their main means of transportation to school or other activities. This makes them one of the groups that are vulnerable to traffic accidents, especially when they do not use Personal Protective Equipment (PPE) when driving. To analyze the Influence of Social and Environmental Factors on the Behavior of Using PPE for Driving on Students of SMK Muammadiyah Aek Kanopan in North Labuhanbatu Regency, the type of observational study with a cross-sectional design, the population of all students in grades X and XI, as many as 330 people. From the population, a sample of 142 respondents was determined in 2025, the sampling technique used a non-probability sampling technique with a purposive sampling method, Peer Influence = p (0.001), Traffic Safety Education Received at School = p (0.001), Distance from Home to School = p (0.001), Access to Proper PPE = p (0.001) have an effect on the use of PPE while driving. While the most influential variable is traffic safety education received at school (Exp (b) = 7.984. This study concludes that peers, traffic safety education, distance from home to school, and access to PPE have a significant effect on the use of PPE while driving, with traffic safety education as the most dominant factor.]]>
<![CDATA[Implementation of the Support Vector Machine (SVM) Algorithm to Improve the Accuracy of Computer Network Performance Predictions ]]>
<![CDATA[Desi Irfan]]>;
<![CDATA[Fahruzi Sirait]]>;
<![CDATA[Rahadatul, Aisy Riadi]]>;
<![CDATA[Aldi Indrawan]]>;
<![CDATA[Juni Purwanto]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 4 No. 1 (2025): IJHET May 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v4i1.271
<![CDATA[Computer network performance is very important in supporting various digital activities, but systems often cannot accurately predict changes in performance, which can cause service disruptions and economic losses. This research aims to implement the Support Vector Machine (SVM) algorithm to increase the accuracy of network performance predictions based on parameters such as latency, packet loss, throughput and jitter. Data is collected through network simulation and real data monitoring, then processed with normalization and selection of relevant features. The SVM model is tested with various kernels, including linear, RBF, and polynomial, to find the best configuration. Performance evaluation uses accuracy, precision, recall, F1-score, and ROC-AUC metrics, with cross-validation to increase the reliability of the results. The results show that the RBF kernel provides a prediction accuracy of 92%, higher than baseline methods such as Decision Tree and Logistic Regression. This model shows its potential to be applied in computer network monitoring systems to predict network performance in real-time, with the possibility of wider implementation in artificial intelligence-based network applications. Therefore, this research not only contributes to machine learning theory in the field of computer networks, but also provides practical solutions that can improve the management and optimization of network performance in various environments that require fast and accurate data processing]]>
<![CDATA[Implementation of the Neural Network Algorithm in Monitoring Child Development to Screen for Developmental Disorders at an Early Age ]]>
<![CDATA[Santosa Pohan]]>;
<![CDATA[Rani Darma Sakti Tanjung]]>;
<![CDATA[Riyan Agus Faisal]]>;
<![CDATA[Nur Indah Nasution]]>;
<![CDATA[Nadya Fitriani]]>;
<![CDATA[Juni Purwanto]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 4 No. 1 (2025): IJHET May 2025 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v4i1.272
<![CDATA[This research aims to implement a Neural Network (NN) in monitoring children's development, especially to detect developmental disorders from an early age. The data used includes variables such as Age, Height, and Weight, which have been normalized to have a uniform scale. The modeling process begins with the use of Convolutional Layers to extract important features from numerical data, which are then passed to the ReLU activation layer to introduce non-linearity to the model, enabling the detection of more complex patterns. After that, Max Pooling is carried out to reduce data dimensions and increase computing efficiency. This model was trained using 100 normalized data, and continued with the use of fully connected layers to process further information. In the output layer, a sigmoid activation function is used to generate probability predictions, allowing binary classification (whether a developmental disorder is present or not). Evaluation results show that this model has an accuracy of 85%, which indicates its effectiveness in detecting child developmental disorders based on available data. Although the results are promising, there is still room for improvement, especially in improving the model's accuracy and ability to handle more complex data. Overall, this research shows that Neural Networks can be a useful tool in the early detection of childhood developmental disorders, with potential for broad applications in the fields of children's health and education.]]>
<![CDATA[Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital ]]>
<![CDATA[Quratih Adawiyah]]>;
<![CDATA[Riyan Agus Faisal]]>;
<![CDATA[Nailatun Nadrah]]>;
<![CDATA[Juni Purwanto]]>;
<![CDATA[Baginda Restu Al Ghazali]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 3 No. 4 (2024): IJHESS NOVEMBER 2024 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v3i4.273
<![CDATA[Heart disease is one of the leading causes of death in Indonesia, so it is important to identify risk factors that contribute to the increasing incidence of heart disease. This study aims to classify risk factors for heart disease using the Decision Tree method with the CART (Classification and Regression Tree) algorithm at Rantauprapat Regional Hospital. The data used includes factors such as Age, High Blood Pressure, High Cholesterol Levels, Body Mass Index (BMI), Family History, Smoking, Unhealthy Diet, and Low Physical Activity. The results of the analysis show that the factors Age, High Blood Pressure, and High Cholesterol Levels have a significant effect on the increased risk of heart disease, with a model accuracy of 80%. Although this model successfully classifies high risk well, there are some errors in identifying low risk, as reflected in the Recall value (0.67).]]>
<![CDATA[Classification of Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm ]]>
<![CDATA[Fahruzi Sirait]]>;
<![CDATA[Halimah Tusakdiyah Harahap]]>;
<![CDATA[Nadya Fitriani]]>;
<![CDATA[Rika Handayani]]>;
<![CDATA[Baginda Restu Al Ghazali]]>
<![CDATA[ International Journal of Health Engineering and Technology Vol. 2 No. 4 (2023): IJHET NOVEMBER 2023 ]]>
Publisher : <![CDATA[CV. AFDIFAL MAJU BERKAH ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.55227/ijhet.v2i4.274
<![CDATA[Infertility is a reproductive health problem that has a significant impact globally, especially in developing countries such as Indonesia. This study aims to classify the risk of infertility in female patients at Rantauprapat Regional Hospital by utilizing the Naive Bayes algorithm based on electronic medical record data. The data used consisted of 500 medical records of female patients of childbearing age during the period 2019–2022, which had been processed and divided into training data (70%) and testing data (30%). The analysis and modeling process was carried out using the RapidMiner application without requiring programming skills. The results showed that the Naive Bayes model was able to classify the risk of infertility with an accuracy level of 86.7%, precision of 91.0%, recall of 93.2%, and F1-score of 92.1%. The main factors that most influence the classification of infertility include a history of reproductive disease, patient age, hormonal examination results, body mass index, and history of sexually transmitted infections. These findings indicate that the integration of the Naive Bayes algorithm into medical record data can be an effective solution for early detection of infertility clinically and support data-based decision making. This study also recommends increasing data and attribute coverage, as well as comparison with other algorithms for more optimal results in the future]]>
