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International Journal of Geoheritage and Parks 2025年第2期13卷 239-249页

作者： Adrianda, Ratna Savira Sahara, Rina Gusti, Ugi Kurnia Silokek Geopark Information Center West Sumatra Indonesia Bumi Sriwijaya Research Group Indonesia Geological Engineering Study Program Sriwijaya University Indonesia

Geopark Ranah Minang Silokek holds unique and diverse geodiversity, encompassing geomorphology, sedimentology, stratigraphy, paleontology, tectonic, structural geology, and complex geological history. This area promises potential to be recognized as a UNESCO Global Geopark, with a geological rock spanning Pre-Tertiary and Neogene epochs. The study focused on assessing 14 prominent geosites to quantitatively evaluate their geological significance, potential educational value, touristic value, and degradation risks, and intrinsic values. The study's main goal was to globally promote the area's rich geoheritage among the public and geoscientists. Results from this assessment categorized Geopark Ranah Minang Silokek within the moderate-high value range, with the Silokek Karst Complex and Granit of Sangkiamo identified as high-value geosites. Our study identified that there were 5 geological phases that could be studied and observed in the Geopark Ranah Minang Silokek. The findings underscored the geopark's potential for educational and tourism purposes, highlighting its suitability as a pioneering geo-education site and earth science field laboratory in Sumatra, Indonesia. This study also highlighted future initiatives which would include designing geotourism activities and educational programs within Geopark Ranah Minang Silokek and beyond. © 2025 Elsevier B.V.

关键词： geo-education geodiversity geoheritage Geopark Ranah Minang Silokek geosite geosite assessment geotourism

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The Recommendation System for Increasing the Independence of Micro, Small and Medium Enterprises (MSMEs) Using the Normalized Rating Frequency (NRF) Method 4

The Recommendation System for Increasing the Independence of...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Lestari, Sri Yulmaini Aswin Kurniawan, Rio Sulyono Pratama, Yan Aditiya Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Business Economics Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9781665433945

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving the economy of small communities and the stability of the Indonesian economy. However, they still face various problems such as capital, raw materials, distribution, licensing, and others. This study developed a special treatment recommendation system to increase the independence of MSMEs by adopting a ranking-based method to produce recommendations related to problem-solving steps from MSMEs. The ranking method used Normalized Rating Frequency (NRF). This method performed an aggregation process of the ratings given by MSMEs in looking at the priority level of problem-solving based on previous experience. The result of the experiment showed that an average NDCG value was 0.7916. This explained that the quality of the ranking was good so that it was feasible to be recommended in solving problems in the form of special treatment to acquire to the independence of MSMEs. © 2021 IEEE

关键词： Recommender systems

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Impact of preparedness on safety behavior in dealing with emergencies on container ships: an analysis of the role of safety knowledge, motivation, and awareness

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Australian Journal of Maritime and Ocean Affairs 2024年第1期16卷 59-73页

作者： Kuncowati Sholihah, Qomariyatus Ciptadi, Gatot Koderi Faculty of Shipping Vocation Hang Tuah University Surabaya Indonesia Business Engineering and Industrial Management Compartment Brawijaya University Malang Indonesia Department of Animal Science Brawijaya University Malang Indonesia Environmental Science Doctoral Study Program Brawijaya University Malang Indonesia

The loading and unloading activities of container ships at the Tanjung Perak Port is imperative, as they can be carried out quickly and efficiently. These ships tend to have a high frequency of visits and possess the largest gross tonnage among ships used for the transportation of goods. The occurrence of an accident involving a container ship can have a detrimental effect on the seamless delivery of goods via sea transportation. Furthermore, the crew be adequately prepared in order to mitigate the risk. As evidenced by research on safety behaviour have not taken preparedness as a factor into consideration when addressing emergencies. Therefore, this study aims to investigate the impact of safety knowledge, motivation, and awareness on safety behaviour in dealing with emergencies on container ships in accordance with relevant regulations. A total of 180 ship crew members served as respondents and it analysed using SEM-PLS. The results showed that preparedness, including safety procedures, emergency training, and crew security culture is an important aspect in dealing with emergencies. Furthermore, this study obtained an R square value of 0.525 in the structural model, indicating that the independent variables have an influence on behaviour, and it was classified in the good category. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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Entropy-Based Analysis of Electromyography Signal Complexity During Flexion of the Flexor Carpi Radialis Muscle Under Varied Load Conditions 4th

Entropy-Based Analysis of Electromyography Signal Complexity...

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4th International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2023

作者： Katherine Putra, Alfian Pramudita Kurniawan, Angeline Shane Istiqomah, Dezy Zahrotul Sholihah, Nisa’ul Al-Salehi, Khalid Ali Salem Ain, Khusnul Sapuan, Imam Andarini, Esti Biomedical Engineering Master Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia Biomedical Engineering Study program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia School of Nursing Southern Medical University Guangdong Guangzhou510515 China

ISBN: (纸本)9789819714629

The motor impairments that stroke patients experience as a primary contributor to death and disability also impact their quality of life. Due to its characteristics as complex muscle signals, the Electromyograph (EMG) signal may be one of the markers in rehabilitation and a substitute that may be used to judge the success of rehabilitation. This study aims to analyze the complexity of EMG signals originating from the flexion of the flexor carpi radialis muscle with various weights indicating the rehabilitation state. It is important to monitor the progress of rehabilitation, and it can be done by using complexity analysis. The complexity of the EMG is examined in this work utilizing entropy measurement, namely Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Approximate Entropy (fApEn). We concentrated on the flexor carpi radialis muscle using flexion–extension exercises and varying the weight load. This study’s results indicate differences in the complexity of the EMG signal in the flexor carpi radialis when performing flexion–extension movements. Three differences showed a correlation based on the value of ApEN, SampEn, and fApEn. Complexity Analysis using fApEn showed the best correlation at R = 0.9966, which can be used to evaluate the rehabilitation process. The increase in weight was followed by the increase of the fApEn value, indicating that the value can be used to evaluate the rehabilitation process. The use of complexity analysis of EMG signal from flexion movement in flexor carpi radialis with various weights can be used to indicate the progress in the rehabilitation process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Entropy

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Optimization of the 5G VANET Routing Protocol on AODV Communication with Static Intersection Node 3

Optimization of the 5G VANET Routing Protocol on AODV Commun...

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3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

作者： Sahabuddin Achmad, Andani Syarif, Syafruddin Stmik Profesional Computer Engineering Study Program Makassar Indonesia Hasanuddin University Electrical Engineering Department Makassar Indonesia

ISBN: (纸本)9781665405140

The integration of 5G and VANET will result in an intelligent transportation system. Relay of 5G data from mobile networks to VANET which is greatly underutilized can reduce the burden on mobile networks. Wireless technology has been used in vehicles or better known as the Vehicular Ad hoc Network (VANET). One of the keys to the success of sending data on VANET is the Path Selection Algorithm which is the task of the routing protocol the routing protocol that is widely used in research in the VANET environment is AODV (Adhoc On-Demand Distance Vector), because among other protocols, AODV is the most suitable and suitable for implementation in the VANET environment, and AODV is also one of the reactive routing protocols. This study includes an increase in the performance of a routing protocol, so in this study, the SIN (Static Intersection Node) is added to the RSU (Road-Side Unit) which is located at an intersection. Which functions as a repeater that helps send data packets to other vehicles around it so that it can increase the Packet Delivery Ratio and minimize Packet Loss and End to End Delay the focus of this research is to determine the most ideal Static Intersection Node position as well as the most effective number of Static Intersection Nodes. © 2021 IEEE.

关键词： Vehicular ad hoc networks

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Development of STEM project-based learning student worksheet for Physics learning on renewable energy topic 12

Development of STEM project-based learning student worksheet...

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12th International Physics Seminar, IPS 2023

作者： Muliyati, D. Prastiawan, F. Mutoharoh, M. Department of Physics Education Faculty of Mathematics and Natural Sciences Universitas Negeri Jakarta East Jakarta13220 Indonesia Industry Engineering Study Program Universitas Krisnadwipayana Jatiwaringin Bekasi13077 Indonesia

This research is motivated by the development of education in the 21st-century, which demands students to master the 4C skills (creative thinking, critical thinking, collaboration, and communication), particularly in the field of physics. The nature of physics learning requires active and interactive teaching methods that can be implemented through project-based activities for students. One effective instructional media is the student worksheet designed based on STEM-PjBL (science, technology, engineering, and mathematics project-based learning) principles. This research aims to assess the suitability of STEM-PjBL student worksheets as a learning media, which can serve as a recommendation to assist teachers in teaching renewable energy topics. This study uses the ADDIE model to adopt the Research and Development (R&D) method. This research results in a STEM-PjBL student worksheet as a physics learning media to understand renewable energy topics. The STEM-PjBL student worksheet is expected to assist students in mastering 21st-century skills effectively. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Weed Detection Based on Fine-Tuned YOLOv5

Weed Detection Based on Fine-Tuned YOLOv5

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International Conference on Information Technology and electrical engineering (ICITEE)

作者： Indra Agustian Sunu Wibirama Igi Ardiyanto Ika Novia Anggraini Pariyanto Noviansyah Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Study Program of Electrical Engineering Universitas Bengkulu Bengkulu Indonesia Department of Electrical and Information Engineerig Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

Conventional weed control often ignores weed locations, potentially causing land damage. AI-based weed detection offers a modern solution. Unfortunately, little attention has been paid to how an augmentation technique improves performance of AI-based weed detection. To address this research gap, we proposed a weed detection model based on fine-tuned YOLOv5 to detect weeds in corn fields aged 1 to 3 weeks. YOLOv5 offers computational benefits, includes auto-augmentation capabilities, and presents a more organized visualization of the outcomes from the training data. The primary dataset used in this study comprised 3,437 images with a total of 18,267 annotated objects, including corn plants and weeds. We observed the effect of mosaic augmentation on the dataset images based on auto-augmentation in YOLOv5. We found that mosaic augmentation with a 0.75 probability outperformed 0.5 and 1.0. The best mAP performance was at the level @0.5 with score of 0.776 for all classes. Meanwhile, the mAP@0.5:0.95 was 0.573 for all classes, indicating that the model’s performance requires further improvement. Our study is promising as a guidance for future development of AI-based weed detection.

关键词： YOLO Training electrical engineering Noise Training data Plantations Lighting Robustness Image augmentation Information technology

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Smart Home Security System Using Face Recognition Based on IoT- CNN 2

Smart Home Security System Using Face Recognition Based on I...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Febriantono, M. Aldiki Zuhair, Alvin Khaeruddin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Islam Blitar Electrical Engineering Department Blitar Indonesia University of Muhammadiyah Malang Diploma III Electronic Technology Vocational Study Program Malang Indonesia

ISBN: (纸本)9798350324945

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning Computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning Computer Vision holds tremendous benefits for homeowners. © 2023 IEEE.

关键词： Face recognition

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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Accuracy Results Comparison of Machine Learning Classification for Mangroves on Worldview-2 Satellite Imagery 7

Accuracy Results Comparison of Machine Learning Classificati...

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7th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2024

作者： Alina, Aldea Noor Jaelani, Lalu Muhamad Hayati, Noorlaila Syariz, Muhammad Aldila Institut Teknologi Sepuluh Nopember & Universitas Dr. Soetomo Study Program of Geomatics Engineering Department of Geomatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Geomatics Engineering Surabaya Indonesia

ISBN: (纸本)9798331543105

Mangrove forests are one of the highest carbon stores in tropical forests and the amount is very high compared to the average carbon storage in other types of forests in the world. High-resolution satellite imagery provides the ability to obtain finer details, allowing more accurate identification of different types of vegetation and mangrove structures. Worldview - 2 high resolution image can cover large mapping areas, making it more efficient to monitor changes on a large scale. The location of mangroves with limited access required remote sensing techniques which were relatively more cost and time efficient compared to field survey. Machine learning-based classification methods were compared to determine the most accurate and reliable performance results for mangrove classification. In this study, comparisons were made using accuracy test points to assess the level of accuracy of mangrove and another land cover classification generated from the Iso Cluster, Maximum Likelihood, Random Forest and Support Vector Machine methods. According to this study, the four machine learning displayed overall accuracy above 0.5. For kappa accuracy, only Iso Cluster Classification displayed value slightly below 0.5 (0.49). Four machine learning able to distinguish mangrove remarkably with user and producer accuracy 1.0. Worldview - 2 spatial resolution contribute to this accuracy, which enable classification to perform with 0.5 meter resolution. Random forest outperformed another machine learning classification with kappa accuracy 0.88 even without additional parameter and expected to overcome challenges in mapping mangrove vegetation which often has spectral values similar to other vegetation and located in quite heterogeneous ecosystem environment. © 2024 IEEE.

关键词： Vegetation mapping

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