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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Content design: E-learning module for study on cultural tourism information systems based on local wisdom 2

Content design: E-learning module for study on cultural tour...

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2nd International Conference on Vocational Education and technology, IConVET 2019

作者： Pageh, I.M. Permana, A.A.J. Historical Education Faculty of Law and Social Sciences Ganesha University of Education Singaraja Indonesia Information Technology Department Informatics Management Study Program Faculty of Engineering and Vocational Ganesha University of Education Singaraja Indonesia

Facing the 4.0 industrial revolution, it is necessary to carry out reforms and innovations related to national education. Reforms and innovations in the implementation of components of the education system involve changes in curriculum, graduate competencies and assessments, teacher qualifications, funding, facilities and infrastructure, decentralization and educational autonomy. Bali as a world tourist destination must be maintained and passed on to the younger generation. The younger generation can use information technology to help promote Balinese culture. Bali has a lot of local wisdom that can be implemented in the tourism sector. Therefore to promote those local wisdoms, a module that can be accessed through e-learning is developed to help students develop a cultural tourism package based on local wisdom to be promoted online. The modules developed have been tested by content and media experts, which can be used independently by students. Modules can be accessed online at e-learning and they are ready to use instructions for students so they can work on assignments and projects independently. Modules containing this theory and practice are for making brochures, tour packages, videos, and websites, all of which are connected to promote tourism based on local Balinese wisdom. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Students

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Footstep Detection for Indoor Positioning using Accelerometer and Magnetometer Sensor on Smartphone

Footstep Detection for Indoor Positioning using Acceleromete...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Made Liandana Bagus Made Sabda Nirmala Gede Angga Pradipta Dandy Pramana Hostiadi Study Program Information Technology Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Study Program Information System Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia

ISBN: (纸本)9781665453967

Indoor positioning using inertial sensors can be used as an alternative to GPS because indoor GPS signals are weaker than outdoors. Inertial sensors such as accelerometers and magnetometers are already packaged in smartphone devices, so the use of smartphones for indoor positioning is an option. Footstep detection is essential in determining position indoors; in this study, footsteps were detected using the accelerometer sensor. While the heading direction is calculated using the Magnetometer sensor. Previous studies have shown that the position of the smartphone, when held, affects indoor positioning estimated. So in this study, it is proposed to use the angle of inclination. In addition, to identify the initial position, a marker in the form of a QR Code is used. The proposed approach consists of 5 processes, namely: calculating the tilt angle, detection of footsteps, calculating the heading direction, estimated displacement, and the estimation of the position. The error in the position estimation is used as a test parameter. The longer path length results in a smaller error than the shorter path.

关键词： Accelerometers Magnetometers Inertial sensors Estimation QR codes Intelligent systems Cybernetics

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Implementation of K-Medoids Clustering Algorithm for Grouping Palm Oil Exports by Destination Country 3

Implementation of K-Medoids Clustering Algorithm for Groupin...

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3rd International Conference on Informatics, Multimedia, Cyber, and information System, ICIMCIS 2021

作者： Haryadi, Deny Adidrana, Demi Institut Teknologi Telkom Jakarta S1 Information Technology Study Program Jakarta Barat Indonesia Institut Teknologi Telkom Jakarta Prodi S1 Teknologi Informasi Jakarta Barat Indonesia

ISBN: (纸本)9781665427333

Palm oil is one of the plantation products that becomes the export of Indonesia's leading commodity. The volume of palm oil exports tends to increase every year. This is influenced by the large demand for palm oil in palm oil importing countries in the world. The high demand for palm oil is an opportunity that must be developed so that Indonesia can compete with its competitors in the current pandemic. Indonesia as the world's largest palm oil producer must be able to group the countries that are priority as the largest importers of palm oil. For this reason, it is necessary to group the countries that are a priority as the largest importer of palm oil. The purpose of this study is to group palm oil exports based on the destination country using the K-Medoids Clustering algorithm. Based on the results of tests that have been conducted in this study using the K-Medoids Clustering algorithm, Cluster 1 is a category of countries importing low palm oil or Low, namely 7 (Netherlands, USA, Spain, Egypt, Bangladesh, Italy, Singapore) of 10 categories of countries tested, then cluster 2 is a category of countries importing medium palm oil or Medium which is 1 (Pakistan) of 10 categories of countries tested, and lastly cluster 3 is a category of high palm oil importing countries or High which is 2 (India and China) from 10 categories of countries tested. © 2021 IEEE.

关键词： Palm oil

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Japanese character (Kana) pattern recognition application using neural network

Japanese character (Kana) pattern recognition application us...

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International Conference on Electrical Engineering and Informatics, ICEEI

作者： Sari Dewi Budiwati Joko Haryatno Eddy Muntina Dharma Information System Study Program - Telkom Polytechnic Electrical Engineering Department Institute Technology of Telkom

Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters. This is the reason why we need pattern recognition. Handwriting recognition has been the object of intensive research during the last decades, including Japanese characters recognition. In this research, we build an application to recognize Kana's character handwriting. Pattern recognition on Kana is started based on Optical Character Recognition (OCR) consist of scan image, preprocessing, feature extraction and post-processing. Classification process will be done in post-processing using neural network back propagation. The processing of input images, involves training and testing image. Training image uses 3.956 characters done in three phases of training. Testing image uses 552 characters of some images inputted in training phase and image which has not been used before. The test result achieve maximum accuracy of recognition system at 91.88% from train image and 31.03% from test image.

关键词： Training Testing Neurons Character recognition Biological neural networks Feature extraction Writing

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Precision Rice Vending Machine by Using Multiple Load Cell and IoT Based

Precision Rice Vending Machine by Using Multiple Load Cell a...

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Computer Science, information technology and Engineering (ICCoSITE), International Conference on

作者： Herman Yuliandoko Farisqi Panduardi Nuraini Lusi Sapto Wibowo Information Technology Program Study Politeknik Negeri Banyuwangi Banyuwangi Indonesia Mechanical Engineering Program Study Politeknik Negeri Banyuwangi Banyuwangi Indonesia Electrical Engineering Program Study Politeknik Negeri Malang Malang Indonesia

Indonesia has the world's largest Muslim population, so the potential for Zakat, Infaq, and Sadaqah (ZIS) funding is enormous. The existence of ZIS in the current Covid-19 pandemic is of extreme importance, as issues such as economic crisis, social inequality, and the rise of dhuafa (poor) are becoming more and more visible. However, in the presence of coronavirus, ZIS has become more challenging to manage and distribute. Bans on gatherings, distancing rules, and other restrictions to prevent the spread of the coronavirus make ZIS more challenging to manage. Therefore, we need innovations to build bridges between people in need and supporters. The Internet of Things (IoT) is one system that can bridge this. In this system, IoT enables machine-to-machine communication and reduces human interaction. This research uses IoT to support Rice Vending Machine with high accuracy. The accuracy is achieved using five load cells and a good rice way-out valve mechanism. In previous machines using a single load cell or open the valve according to a certain time. Based on its test results analysis and MAPE values, we found the machine's accuracy facing obtained very well, and the website monitoring was also very effective. This research provides innovation in ZIS management and dramatically benefits society.

关键词： COVID-19 Bridges Technological innovation Machine-to-machine communications Pandemics Sociology Valves

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Collaboration model of software development

Collaboration model of software development

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2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011

作者： Kusumasari, Tien Fabrianti Supriana, Iping Surendro, Kridanto Sastramihardja, Husni Information System Program Study STMIK im Bandung Jl.Jakarta No.79 Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Jl. Ganessa No.10 Bandung Indonesia

ISBN: (纸本)9781457707520

Software development is a series of processes activity involving more than one person, especially in medium to large software projects. Each activity of software development is carried out by various combination of different individual. Collaboration occurs in every phase of the process software development. The right team collaboration method is one of the important things in success software development. This research is focused to develop collaboration model of software development to meet time schedule, budget, scope, and software *** collaboration model of software development is generated from survey of collaboration method (about face to face and virtual collaboration) and software development methodology especially agile methodology. The direct collaboration was used in the software specification processes, and combination (with a direct and virtual) collaboration was used during software design, implementation, and validation. Team coordination in every process of software development was done with face to face method. The same artifacts were used in this collaboration model with some collaboration software tools. Combination between face to face and virtual communication was used in this model. The model is implemented in small case software development project. During the software development, some measurement is carried out. The measurement included the measurement of project completion time, cost, software specification, and software quality. The software is developed in time schedule (two months), cost under budged, and meet specification. In addition, the software has built a fairly good quality. Thus, this collaboration model good use in software development to achieved accuracy of schedule, cost, scope, and quality. © 2011 IEEE.

关键词： Software design

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International Conference on information Management and technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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Bibliometric Analysis of Image Segmentation with Deep Learning: An Analytical study 1

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Windarto, Agus Perdana Yuhandri, Y. Bukhori, Saiful Information Technology Study Program Universitas Putra Indonesia YPTK Padang Indonesia STIKOM Tunas Bangsa Pematangsiantar Indonesia Universitas Putra Indonesia YPTK Padang Indonesia Computer Science Department University of Jember Jember Indonesia

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

This study presents a comprehensive bibliometric analysis of research pertaining to the utilization of deep learning techniques for image segmentation using CNN algorithms. A dataset comprising 1078 publications from journals and conference proceedings between 2018 and 2022 was examined through keyword search, co-occurrence network analysis, and keyphrase analysis. The study offers valuable insights into the present research landscape and identifies prominent areas of investigation, encompassing deep learning's application in medical imaging, disease detection, and object detection. Our analysis reveals China as a leading contributor to deep learning research for image segmentation, with 279 publications and 4,380 citations in Scopus. Advances in Biomedical Optics and Imaging—Proceedings of SPIE are pinpointed as the most productive source for deep learning research in image segmentation, with a specific emphasis on biomedical optics and imaging. The co-occurrence network analysis highlights the red cluster primarily focusing on methods and algorithms associated with deep learning in image segmentation. In contrast, the blue cluster demonstrates the application of these methods and algorithms to other objects or methods, with the "human" node standing out in terms of frequency and centrality. Our keyphrase analysis reveals the growing trend of Cellular Artificial Neural Networks over the past five years, indicating a shift in research focus toward this domain. Overall, this study's findings demonstrate the potential of deep learning to deliver precise and efficient segmentation of medical images, thereby enhancing clinical outcomes and patient care. Furthermore, our study contributes to an enhanced understanding of the current research landscape and identifies avenues for future exploration in deep learning techniques for image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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