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Heca Journal of Applied Sciences
Published by PT. Heca Sentra Analitika
ISSN : -     EISSN : 29879663     DOI : https://doi.org/10.60084/hjas
Core Subject : Health, Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Engineering Health Professions Medicine & Pharmacology
Heca Journal of Applied Sciences is a premier international scientific journal that publishes high quality original research articles, review articles, and case reports in the field of applied sciences. The journal mission is to encourage interdisciplinary research, promote knowledge sharing, and advance the development and application of innovative strategies. Heca Journal of Applied Scien is committed to excellence, relevance, and impact and provides a valuable resource for researchers, practitioners, and academics worldwide. Topics of this journal includes, but not limited to: Mathematics, Physics, Chemistry, Biology, Pharmacy, Informatics, Statistics, Marine Sciences, Fisheries Sciences, Veterinary Sciences, Medical Sciences, Nursing Sciences, Dentistry, Disaster Sciences, Environmental Sciences, Materials Science, Earth Sciences, Enviromental Sciences, Engineering, and Interdisciplinary research in the field of applied sciences.
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): September 2024" : 5 Documents clear
The Emotional Journey: An Exploration of Women's Pre-Birth Anxieties Suryani, Lilis; Kamil, Hajjul; Hasanuddin, Hasanuddin; Yahya, Mustanir; Sulastri, Sulastri
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.182

Abstract

Pregnancy is an important period in a woman's life, but it is often accompanied by worries and fears that cause birth anxiety. Anxiety generally varies in intensity from mild to severe. Anxiety has a negative impact on the health of the mother and fetus, and it causes many women to refuse to give birth naturally. This study aims to explore the psychological and emotional dimensions of pregnant mothers' levels of anxiety toward childbirth, identifying the various concerns and factors that cause anxiety. The research design was non-experimental exploratory descriptive, conducted from May to June 2022. Respondents consist of multigravida women with a gestational age of 36–40 weeks (third trimester). The sample was chosen using purposive sampling. The data collection process uses a questionnaire in the form of semi-structured questions. The level of anxiety for multigravida women is moderate anxiety (36.0%), severe anxiety (32.0%), mild anxiety (24.0%), and 4.0% each with severe anxiety and not anxiety. The aspects found are generally feared to have a cesarean delivery (28.0%) and worry about the baby's condition (20.0%). Factors that cause anxiety are generally the condition of the baby (24.0%), illness suffered by the mother (12.0%), and negative experiences about childbirth (12.0%). Multigravida women generally experience various anxieties during labor, which are caused by various factors that influence them. There are aspects of anxiety that are different from previous studies. Screening for anxiety symptoms and education during the antenatal period are necessary.
Indigenous Knowledge and Herbal Medicine: Exploring the Ethnobotany of the Karo Tiganderket Tribe in Indonesia Singarimbun, Emalia; Elfrida, Elfrida; Indriaty, Indriaty
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.208

Abstract

This ethnobotanical study investigates the traditional use of medicinal plants in Tiganderket Village, Karo Regency, Indonesia, to document local plants' diversity, uses, and preparation methods for medicinal purposes. Utilizing a quantitative descriptive approach, data were gathered from 30 informants, including traditional healers and residents. The study identified 92 plant species from 44 families, with the Zingiberaceae family being the most dominant. Frequently used plants, such as Piper betle (belo) and Zingiber officinale (ginger), were primarily employed to treat common ailments like fever, boils, and joint pain. Boiling (74%) was the most common method of plant preparation, and leaves (50%) were the most frequently used plant parts. The Relative Frequency of Citation (RFC) revealed Acorus calamus and Curcuma longa as the most cited species. At the same time, 40 of 66 recorded diseases showed high Informant Consensus Factor (ICF) values, reflecting shared knowledge of plant use. This study highlights the rich preservation of traditional medicinal plant knowledge in Tiganderket Village, which continues to play a crucial role in local healthcare practices.
Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Emran, Talha Bin; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.216

Abstract

Mpox is a viral zoonotic disease that presents with skin lesions similar to other conditions like chickenpox, measles, and hand-foot-mouth disease, making accurate diagnosis challenging. Early and precise detection of mpox is critical for effective treatment and outbreak control, particularly in resource-limited settings where traditional diagnostic methods are often unavailable. While deep learning models have been applied successfully in medical imaging, their use in mpox detection remains underexplored. To address this gap, we developed a deep learning-based approach using the ResNet50v2 model to classify mpox lesions alongside five other skin conditions. We also incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability. The results show that the ResNet50v2 model achieved an accuracy of 99.33%, precision of 99.34%, sensitivity of 99.33%, and an F1-score of 99.32% on a dataset of 1,594 images. Grad-CAM visualizations confirmed that the model focused on relevant lesion areas for its predictions. While the model performed exceptionally well overall, it struggled with misclassifications between visually similar diseases, such as chickenpox and mpox. These results demonstrate that AI-based diagnostic tools can provide reliable, interpretable support for clinicians, particularly in settings with limited access to specialized diagnostics. However, future work should focus on expanding datasets and improving the model's capacity to distinguish between similar conditions.
Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants Uhanto, Uhanto; Yandri, Erkata; Hilmi, Erik; Saiful, Rifki; Hamja, Nasrullah
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.219

Abstract

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.
Potential for Electrical Energy Savings in AC Systems by Utilizing Exhaust Heat from Outdoor Unit Hamja, Nasrullah; Yandri, Erkata; Hilmi, Erik; Uhanto, Uhanto; Saiful, Rifki
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.223

Abstract

This study explores the potential of utilizing waste heat from air conditioning systems, one of the largest consumers of electrical energy. Currently, most of the waste heat generated by outdoor units is typically released into the environment without being utilized, leading to missed energy-saving opportunities. This study analyzes the potential for improving electrical energy efficiency in air conditioning (AC) systems by harnessing this waste heat. Two primary approaches are evaluated: the first is the use of waste heat for domestic water heating, and the second is the conversion of heat into electrical energy using thermoelectric generators (TEG). The results of this research indicate that both methods have the potential to improve overall energy efficiency significantly. However, challenges related to conversion efficiency and integration of these technologies with AC systems require further, more specific studies. These findings are expected to contribute to more efficient and environmentally friendly cooling systems by optimizing technology and overcoming barriers to wider implementation.

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