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Nusantara Science and Technology Proceedings
Published by Future Science
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Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 27 Documents
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Search results for , issue "Multi-Conference Proceeding Series E" : 27 Documents clear
Leprosy Early Detection Through Binary Segmentation Using ResU-Net Andrew Jonathan Brahms Simangunsong
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3721

Abstract

Leprosy is a chronic infectious disease caused by Mycobacterium leprae that can lead to physical deformity if left untreated. Indonesia currently ranks third in the world for leprosy prevalence, with the highest concentration of cases found in the provinces of West Papua, North Maluku, and Papua. These provinces, located in the eastern region of Indonesia, face numerous challenges in terms of healthcare accessibility for early leprosy detection due to various factors and novel, more accessible method to detect leprosy is urgently needed. In this study, we introduce an innovative approach to early leprosy detection by leveraging the ResU-Net model. The ResU-Net, a hybrid architecture, combines the robust U-Net framework, renowned for its efficacy in medical image segmentation, with the powerful ResNet-50 and ResNet-101 backbones. The incorporation of ResNet-50 and ResNet-101 enhances the model's capability to extract intricate features from the target image, allowing for a more comprehensive analysis and ultimately, a more accurate and early detection of leprosy. To train and validate our model, we employ the CO2Wounds Leprosy dataset, a comprehensive collection of medical images showcasing images of leprosy taken using smartphone. The research results demonstrate the promising potential of ResU-Net in accurately identifying leprosy-affected areas within these images with highest IoU scores of around 80% with the ResNet101 backbone and around 79% with the ResNet50 backbone. This method holds great potential for improving the management of leprosy in regions with high prevalence by enabling accessible and timely interventions.
Collaboration Comics in Teaching Practitioner Program Rizki Taufik Rakhman; Rizky Wardhani; Iwan Gunawan
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3722

Abstract

Practical teaching is a program initiated by the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia so that college graduates are better prepared to enter the world of work. The Fine Arts Education study program responds to the practitioner teaching program in illustration courses. The research entitled, "Gatot Kaca Pusaka Multiverse: Collaborative Comics for Teaching Practitioner Programs" aims to evaluate teaching practitioner programs in illustration courses through outputs in the form of artistic work products resulting from collaboration between students, teaching lecturers and practitioner lecturers. Qualitative method in the learning process by recording all events and obstacles at each meeting for 6 times or 1.5 months. Apart from that, the design thinking method is used when students design the collaborative comic, where the researcher will invite students to carry out the five steps in the form of: Empathize, Define, Ideate, Prototype, and Test/Evaluate. Publications in the form of comics that have an ISBN are the target output in research, in addition to scientific articles in national/international journals and proceedings. The results of the research are expected to become initial recommendations for teaching practitioner programs in the Fine Arts Education study program, Jakarta State University.
Empowering a Village Community Through Training on Healthy Lifestyle and Appropriate Medicine-Related Behaviour Sylvi Irawati; Aguslina Kirtishanti; Vendra Setiawan; Astridani Risky Putranti; Reine Risa Risthanti; Finna Setiawan; Nikmatul Ikhrom Ekajayani
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3723

Abstract

Cultural identity may indirectly determine how a person within a particular cultural context takes action to prevent and treat a condition or a disease. Knowledge is one of predisposing factors that can influence both cultural identity and health behavior. Rural communities in Indonesia have their own cultural identity that may influence the health-related behavior of the residents. The Village of Duyung is one of example of this case. The lifestyle and medicine-related behavior of the residents might be influenced by the nature of its collective or communal culture. This report aims to present the level of awareness of the principles of healthy lifestyle and appropriate medicine-related behaviour in the Village of Duyung. A community empowerment program was designed and conducted in July and August 2023. All 23 participants were women with age ranged from. 26 to 47 years. The proportion of participants who were unaware of the principles of healthy lifestyles and medicine-related behavior was as high as 78.3 and 87.0%, respectively. There is a statistically significant difference in the final score of knowledge before and immediately after the training on the principles of healthy lifestyle and medicine-related behavior. The program demonstrated a significant change of the awareness and knowledge of some participants in the Village of Duyung on healthy lifestyle and the appropriateness of medicine-related behaviour. The impact on how this method of education contributes to cultural identity and consistent health-related behavior over longer time needs further study.
Analysis of Arts and Culture Development Model for Increase Visitors in Bangkalan Halal Tourism Suryo Tri Saksono; Ulvia Ika Surya
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3724

Abstract

Tourist attraction management has several problems that must be considered. The lack of information about halal tourism in Bangkalan for tourists outside the region causes tourists to only visit famous destinations. Tourism management has not paid attention to the development of art and culture typical of the region. In addition, the managers do not yet have a suitable model as a foundation for developing art and culture to increase visitors to Bangkalan halal tourism. This is the main problem in this study. The main objective of this study is to find the best formula related to the art and culture development model to increase visitors to Bangkalan halal tourism initiated by researchers by getting input from stakeholders, namely tourism managers, art / cultural actors, surrounding communities, and tourism visitors, and district governments throughout Madura. The model to be used is the Structural Equation Model. Data in this study were collected through observation (questionnaire), interviews, and documentation. Latent variables considered in this model include Accessibility, Attraction, Amenity, Ancillary, and Tourist Visits. This strategy is handed over to the management of Bangkalan halal tour-ism to be implemented to Increase Visitors.
Development of Phonetique Du Français Teaching Materials for Microlearning in The UNJ French Language Education Study Program Subur Ismail; Ratna; Wahyu Tri Widiasuti; Yunilis Andika
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3725

Abstract

This research is based on observations from Phonétique du français lectures which show that students still have difficulty understanding the content of the lecture material. This is due to the fact that most of the sources of lecture material are reference books and journal articles with a high level of French at a minimum level of B1, especially since the lecture language is in French. Meanwhile, students' French level is still low, equivalent to A2 level. For this reason, this research was conducted to overcome problems in Phonétique du français lectures by developing Phonetique du francais teaching materials for micro learning. To develop phonétique du français teaching materials, the EDDIE method is used which includes 5 stages, namely; analysis stage, design stage, development stage, implementation stage, and evaluation stage. In this research, only the analysis stage to the design stage was carried out. At the analysis stage, an analysis is carried out which includes: 1) an Analysis of the number of lecture hours, 2) Analysis of learning objectives, 3) an Analysis of the coverage of Phonétique du français lecture material, 4) an Analysis of the micro learning model used. The design stage includes learning objectives, lecture material content, selected assessment methods and frequency. Thus, this research is only at the design stage, namely the development of phonetique du francais teaching materials by CECRL which uses micro learning (infographics, explanatory videos, short videos, and podcasts). The results of this research have implications for the Phonétique du français lecture process which is designed according to CECRL and according to students' French language skills and microlearning methods that can motivate students to understand the content of Phonétique du français lectures.
Feature Reduction of Lung Cancer Microarray Data Using Mutual Information Selection and PyCaret-Supported Recursive Feature Elimination Andrew Jonathan Brahms Simangunsong; Valha Tsabita Hidayat
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3701

Abstract

Lung cancer remains a leading cause of cancer-related mortality worldwide, and Indonesia's ever-increasing amount of pollution signals an urgency for improvement in lung cancer early detection. One of the methods to detect lung cancer is molecular diagnosis using DNA microarray, which has been proven to be effective. However, the complexity of microarray data with a vast number of features hinders the timely and accurate detection of lung cancer. This study seeks to optimize the features of the data to improve classification performance. Our approach combines Mutual Information Feature Selection with Recursive Feature Elimination, leveraging the PyCaret library to train and evaluate machine learning models. The process involves initial feature reduction using Mutual Information to enhance computational efficiency, followed by training machine learning models with PyCaret. The two best-performing models for each dataset are used to perform recursive feature elimination to search for the most optimal feature. A support vector machine is also used for comparison. The final output will be three subsets of features and another subset that consists of combined features of the rest of other subsets. Finally, PyCaret will be utilized again to train machine learning models with all feature subsets. The study shows that other models can select fewer features compared to the Support Vector Machine and still maintain a powerful predictive power with high accuracy (95% - 98%). In conclusion, our research offers a new approach to selecting optimal features for microarray analysis, with implications for more effective and timely cancer diagnosis.
Arrhythmia Classification Using the Deep Learning Visual Geometry Group (VGG) Model Rudolf Bob Martua B.; Alhadi Bustamam; Hermawan
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3702

Abstract

Cardiovascular disease (CVD) is one of the non-communicable diseases (NCDs) and 32% of the world's people die prematurely due to cardiovascular disease (WHO, 2022). The development of computing technology and artificial intelligence (AI), especially Deep Learning (DL), has contributed significantly to helping medical personnel carry out initial pre-diagnosis and classification of heart disease. In this study, we limit heart rhythm detection research into two categories, namely, Normal (N) and Abnormal (An) which are visualized in a standardized amplitude vs time diagram on the PTBDB dataset. The classification model in this research uses the 1-dimensional Deep Neural Network (1D-DNN) Visual Geometry Group, namely, VGG11, VGG13, VGG16, and VGG19. The denoising technique presented in this study on each ECG data sample thereby improving the quality of training data for the AI detection model. The performance of the VGG16 model shows the best training and validation accuracy with the lowest loss, which is 97.85% accuracy; 97.99% precision; 99.75% recall; and 98.52% f1-score. In this way, medical personnel will be helped more quickly in efforts to prevent and control heart disease that occurs in society, especially in the lower middle class. Further research needs to be done to use VGG with more blocks if the structure of the dataset to be classified is much more complex.

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