bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship of Demographic Profile to Satisfaction in Interprofessional Collaboration among Medical-Surgical Ward Nurses in Selected Hospitals of Shandong, China: A Cross-Sectional Study
Authors: Li Meng , Agunod Cyrelle D.
Published: July 2024

With the spread of the concept of team medicine, active nurse-doctor collaboration is closely related to nursing quality. Nursing standards recognize that collaborative practice is a necessary condition for high-quality patient care, but many nurses are often reluctant to participate in interprofessional teams, indicating that challenges in nurse-doctor collaboration still exist. This study aimed to investigate the relationship between demographic data and nurse interprofessional satisfaction of medical and surgical nurses in tertiary hospitals in Shandong Province, and to provide a reference direction for improving teamwork. Methods: The research method was a cross-sectional study. Medical and surgical nurses in three tertiary hospitals in Shandong Province were surveyed. Medical and surgical nurses who were officially registered in the hospital and had been engaged in internal medicine or surgery for at least one year were selected. They were aged 18 to 45 years. The researchers used SPSS software to analyze the data, and descriptive statistics (frequency percentage distribution, mean score, and standard deviation) were used to present the satisfaction scores. If the data did not follow a normal distribution, nonparametric tests were used. Results: The average scores of the interprofessional collaboration satisfaction scale for the participating nurses were: "overall expectation" 2.67; "perception dimension of professional collaboration" 3.78; "The overall relationship satisfaction of professional cooperation" 3.83; "Communication and tolerance of interprofessional collaboration" 3.80; "Equality of interprofessional collaboration" 3.81; "Satisfaction with interprofessional collaboration" 2.98. The results of non-parametric tests showed that there was a significant difference between marriage and nurses' satisfaction with interprofessional collaboration (P<0.05). Conclusion: The overall satisfaction of nurses with interprofessional collaboration in three tertiary hospitals in Shandong Province was generally average. The three hospitals should improve the management model and care about the mental health of nurses according to factors such as the marital status of nurses. Nurses should make a good distinction and plan between work and life to promote interprofessional collaboration.
Effect of Audio and Audiovisual Distraction Technique on Anxiety of Pediatric Patients During Pit and Fissure Sealant Application: A Clinical Trial
Authors: Pandya Dr. Parth R , Niharika Dr. Nishma , R Dr. Indhuja , S Dr. Balaji , Nayan Dr. Rajeev , Dwivedi Dr. Viidhii
Published: July 2024

This clinical trial evaluates the impact of audio and audiovisual distraction techniques on reducing anxiety in pediatric patients undergoing pit and fissure sealant application. A total of 60 children aged 6-12 requiring sealant application were randomly assigned into three groups: Control (no distraction), Audio distraction (listening to music), and Audiovisual distraction (watching cartoons). Anxiety levels were measured using the Venhem Anxiety Rating Scale (VARS) before and after the procedure.  The study found a significant reduction in anxiety levels in the audio and audiovisual distraction groups compared to the control group. Specifically, the audiovisual group showed the greatest reduction in VARS scores, followed by the audio group.
Conceptual Model of the Influence of Value Chains and Livelihood Assets on the Performance of Coconut Product Processing Cooperatives in North Maluku Province, Indonesia
Authors: Muhammad Munawir , Koestiono Djoko , * Syafrial , Isaskar Riyanti
Published: July 2024

This study formulates a model of the influence of value chain and livelihood asset variables on the operational performance of coconut product processing cooperatives in North Maluku Province. Sample determination was conducted using a purposive sampling technique. The samples used in this study include cooperative managers and production units. The analytical method employed is structural equation model (SEM) analysis with the SmartPLS approach. The research findings indicate that the model for improving the operational performance of cooperatives through the value chain and livelihood asset approach in North Maluku Province indicates that these two variables have a significant impact on cooperatives’ operational performance. Value chain variables such as outbound logistics, service, infrastructure, and technology development are strongly correlated with cooperatives’ operational performance. Likewise, livelihood asset variables, such as natural, financial, social, and entrepreneurial capital, also have a significant impact on cooperatives’ operational performance. Higher value chains and livelihood assets have a more significant impact on cooperatives’ operational performance. This indicates that both the value chain and livelihood assets play an important role in improving cooperatives’ operational performance. Therefore, enhancing the value chain and livelihood assets can significantly improve cooperatives’ operational performance.
Assessment of River Water Quality as a Raw Water Source by Regional Drinking Water Companies
Authors: Setiyowati Titik , * Salampak , Neneng Liswara , Florante Adji Fengky , Darung Untung , Elvince Rosana , * Ardianor , Sudyana I. Nyoman , Supriyono Lautt Bambang , Maria Rotinsulu Johanna
Published: July 2024

This study examines the water quality of the Sampit River in East Kotawaringin Regency, which serves as a source of raw water for the production of drinking water. Increased human activity results in various problems for the environment, in this case, water quality. Currently, the Sampit River is a source of raw water for the Regional Drinking Water Company (PDAM) Bagendang Unit, where the quality of the raw water also affects the quality of clean water produced and distributed by the PDAM Bagendang Unit to the community, especially in Sampit City. Therefore, the objective of this study is to assess the quality of the Sampit River water over the 3-year period from 2021 to 2023. In addition, the study analyzes activities in the vicinity of the Sampit River based on secondary data obtained from relevant agencies. The sampling method in this research was purposive, considering aspects of the field and season (March) and the presence of other activities around the Sampit River. Water quality test includes measurements of turbidity, color, TDS, temperature, odor, pH, Fe, hardness, Mn, NO3-N, NO2-N, organic substances, total coliform, and E. coli, which are then compared with PP Number 22 of 2021. Based on the results of the analysis carried out on the chemical and physical parameters of the Sampit River water, it was found that the water color, water temperature, Fe, Mn, total coliform, and E. coli exceeded the quality standards according to PP Number 22 of 2021. Meanwhile, the TDS, pH, NO3-, and NO2- are still below the quality standards according to PP Number 22 of 2021. These observations indicate that the condition of the Sampit River, which serves as the raw water source for the PDAM Bagendang Unit, is experiencing fluctuating changes, particularly during the rainy season (March 2021, 2022, and 2023). This will become a concern later in the processing of raw water into clean water conducted by the PDAM Bagendang Unit.
Peran Pentahelix dalam Pengembangan Pariwisata Berbasis Masyarakat di Desa Wisata Bonjeruk, Kabupaten Lombok Tengah
Authors: Pribadi Teguh Iman , Setiawan Muhamad Alam
Published: July 2024

Keberhasilan pengembangan pariwisata di suatu daerah tidak terlepas dari peranan berbagai pemangku kepentingan, khususnya pentahelix, yaitu pemerintah, akademisi, pelaku bisnis, masyarakat, dan media. Berangkat dari hal tersebut, penelitian ini bertujuan untuk mengidentifikasi dan menganalisis peran pentahelix dalam pengembangan pariwisata berbasis masyarakat di Desa Wisata Bonjeruk dari enam sisi pemberdayaan: pemungkinan, penguatan, perlindungan, penyokongan, pemeliharaan, dan pengawasan. Hasil penelitian ini menunjukkan bahwa pemberdayaan masyarakat yang dilakukan pentahelix dalam pengembangan pariwisata berbasis masyarakat di Desa Wisata Bonjeruk telah berjalan dengan baik dari sisi pemungkinan, penguatan, perlindungan, penyokongan. Namun masih belum maksimal dari sisi pemeliharaan, dan pengawasan. Hal ini dapat dilihat dari kurangnya proses pemantauan dan proses peninjauan yang dilakukan oleh pentahelix. Penelitian ini memberikan wawasan tentang bagaimana pemangku kepentingan dapat berkontribusi lebih efektif dalam pengembangan desa wisata, serta pentingnya peningkatan kualitas hidup masyarakat melalui partisipasi aktif dan kolaboratif dalam kegiatan pariwisata.
Participant voice first: The Busara research agenda on ethical research in the Global South
Authors: Wambua Joel , Owsley Nicholas , Wein Tom
Published: July 2024

Busara is dedicated to advancing and applying behavioral science in the Global South. We recognize the power imbalances in research and aim to improve their approach to be more ethical. To achieve this, we propose to organize and formalize our research agenda on ethics, focusing on the voices of research participants and their societies. We plan to conduct empirical studies to understand the preferences and experiences of participants, co-create and disseminate changes to research processes, and prioritize racial, gender, and social justice in our ethical practices. Our ultimate goal is to produce better quality research that respects the dignity of participants and serves the communities we study.
EDITORIAL REVISTA ARTE 22 (2024.1)
Authors: Ecker Elisabeth Cristina do Amaral , Romero Marcelo de Andrade
Published: July 2024

É com satisfação que apresentamos mais um volume da Revista Científica Arte 21, do Centro Universitário Belas Artes de São Paulo, que desta vez foca na fascinante área do design de moda. Este volume é dedicado a explorar as interfaces entre a moda, a sustentabilidade, a identidade e as inovações metodológicas, oferecendo uma reflexão profunda e diversificada sobre o impacto e o futuro deste campo criativo.
METODOLOGIA RIZOMÁTICA: criando redes de compartilhamento de conhecimento e experiências
Authors: Nakad Valeska
Published: July 2024

O artigo apresentado explora a transformação das relações humanas e do conhecimento na era da informação. Inspirado por Gilles Deleuze, o texto contrasta o modelo tradicional da árvore do conhecimento com a abordagem rizomática, que promove uma rede não linear de conexões entre diversos campos do saber. Este modelo reflete a fluidez e dinamicidade da modernidade líquida descrita por Zygmunt Bauman, onde a volatilidade das relações sociais e de trabalho demanda constante adaptabilidade. A Metodologia Rizomática aplicada às empresas visa disseminar conhecimento tácito e experiências através de redes colaborativas, fortalecendo a inovação e a competitividade. A teoria da destruição criativa de Joseph Schumpeter, que destaca a importância da inovação empresarial, e o círculo dourado de Simon Sinek, que enfatiza o propósito nas ações empresariais, são incorporados para promover o crescimento sustentável. Esta metodologia busca transformar dados em conhecimento aplicável, incentivando a cooperação e a criação de uma rede de boas práticas. A abordagem rizomática permite que as empresas compartilhem suas histórias e estratégias, fomentando um ambiente de aprendizado contínuo e adaptável às rápidas mudanças do mercado globalizado.
ESTRATÉGIAS VAREJISTAS: processos comerciais e comportamentais na atualidade
Authors: Dalquano Junior Osmar , Lima Otavio Pereira
Published: July 2024

Este artigo contempla reflexões emergentes no processo decisório de compra dos consumidores na sociedade brasileira, que surgiram durante a pandemia do COVID 19 e reverberam em desdobramentos contemporâneos, referenciando o varejo 4.0 e as estratégias de marcas para o conhecimento, a cativação e a conversão de clientes. Portanto, o entendimento do que se trata o varejo 4.0, as variações e os ajustes durante a jornada do consumidor é objetivo deste estudo. Tais olhares e reflexões estão pautados em movimentos de mercado apresentados nas últimas edições da NRF de Nova Iorque e vivência dos autores em polos comerciais de Minas Gerais, Paraná e Assunção, capital do Paraguai.
POR QUE O SMALL SIZE IMPORTA? Consumo de moda, corpo e identidade
Authors: Mitraud Francisco , Konopka Regiane , Andrade Tatiane
Published: July 2024

O artigo propõe uma reflexão sobre a moda small size, roupas para mulheres de baixa estatura. Revê, com base na literatura, o percurso da moda Plus Size, hoje reconhecida einstitucionalizada, que começou como movimento minoritário nos Estados Unidos e que se transformou nos Fat studies, passando depois a ser uma demanda para a indústria do varejo de moda. A seguir, refletimos questões ligadas à construção identitária da mulher baixinha e de como para o mercado e para a academia ela permanece um “mulher-invisível. Para conceituar o que consideramos para efeitos desse artigo como baixa-estatura, buscamos referência em trabalho da ABNT em conjunto com o Sebrae, chamado de Sizebr, e que culminou com a publicação da norma NBR 16933:2021. Essas informações e demandas são confrontadas com os aspectos de produção industrial da moda e as dificuldades de atendimento. Metodologicamente, além da revisão da literatura especializada, realizou-se uma pesquisa quantitativa com público específico. A pesquisa conclui que, apesar de haver uma demanda latente, ela não é suficiente para que a indústria da moda responda com produção adequada. Para que isso ocorra, é necessário que um movimento social com engajamento das mulheres de baixa estatura dê visibilidade aos seus anseios
Bagaimana Mencapai Tujuan Organisasi Dan Meningkatkan Kinerja Organisasi
Authors: Maula Toriqul , Eka Pratama Ahmad Yudha
Published: July 2024

The success of a business depends on the performance of the organization, which means the ability to implement strategies effectively to achieve organizational goals. This research aims to present a number of studies regarding effective organizational performance to achieve organizational goals. The research method uses a systems literature review. This research was carried out by reviewing several articles or journals related to organizational performance, starting with conducting a study of various articles, then looking for similarities and differences in each journal and then compiling them into a study with various concepts. Organizations that focus on improving organizational performance must be able to meet employee job satisfaction, which in turn helps stimulate better employee performance and improve organizational performance.
Pengembangan Sumber Daya Manusia Dan Perbaikan Etika Kerja Karyawan
Authors: Rasyid Maesyaroh , Wahyudi Dedi
Published: July 2024

In today's globalized and fiercely competitive business landscape, developing human resources (HR) and fostering better work ethics among employees are crucial for organizations to thrive. This means investing in training, education, and career growth to improve employees' skills and knowledge. Effective training not only enhances technical abilities but also soft skills like communication and teamwork. Meanwhile, cultivating strong work ethics, like integrity and responsibility, lays the groundwork for a healthy work environment. Employees with good work ethics tend to be more loyal and productive. This study aims to explore how HR development and improved work ethics impact organizational performance. It will look into strategies companies can use to enhance HR quality and foster an ethical work culture. Ultimately, by understanding and implementing these practices, organizations can better navigate complex business challenges and ensure long-term success.
Literasi Digital : Solusi Tantangan Dan Peluang Komunikasi Sosial Di Era Digital
Authors: Alfiani Alfiani , Ariba Azraf  , Muhammad abdullah kamal  , Arjuna
Published: July 2024

The digital era, marked by a revolution in information and communication technology, has brought significant changes in various aspects of human life, including in the field of social communication. Studies on digital literacy; Solutions to Social Communication Challenges and Opportunities in the Digital Era aims identify opportunities and challenges in social communication, as well as provide strategies related to challenges in social communication in the digital era. This study was processed using the literature study method. Literature study is one of the research methods by collecting, reading, and recording literature studies related to the research, then processing it as a basis to build a title framework with a complete unity.The resultsofthe study show that social communication opportunities are fast and wide accesso information, further communication, learning and self-development, and online communities. The challenges are informationoverload, the spread off akenews, data privacy and security, the digital divide, ethicsand behavior in cyberspace. Therefore, digital literacy is present as a strategy to overcome existing challenges.
Pengembangan Sistem Penasehat Akademik (SIPEKAD) Menggunakan Metode User Centered Design
Authors: Gunaditiok Gunadi , Kudadiri Parlindungan , Agustin  , Hamdani  , Susanti
Published: July 2024

STMIK Amik Riau merupakan perguruan tinggi komputer pertama di Provinsi Riau. Sebagai yang pertama dan berfokus kepada bidang komputerisasi, maka seyogyanyalah semua aktifitas yang dilakukan baik yang berhubungan dengan pekerjaan maupun pelayanan terhadap mahasiswa haruslah berbasiskan komputerisasi dan mobile. Salah satu layanan yang menjadi permasalahan dalam penelitian ini adalah layanan Penasehat Akademik (PA) yang masih dilakukan secara konvensional. Berbagai permasalahan yang terjadi yakni tidak terinputnya Kartu Rencana Studi (KRS) mahasiswa ke sistem feeder oleh bagian Pangkalan Data Perguruan Tinggi (PDPT) karena keterlambatan penyerahan, kesulitan menemui dosen untuk berkonsultasi karena kesibukan dosen, waktu bimbingan yang relatif cepat sehingga tidak dapat menyampaikan keluhan mahasiswa dalam proses perkuliahan serta lembar konsultasi PA yang mudah rusak dan hilang. Banyaknya permasalahan yang ada secara konvensional menimbulkan keinginan untuk mengintegrasikan seluruh permasalahan yang ada tersebut kedalam sebuah sistem yang dapat diakses oleh mahasiswa secara online. Didalam membangun sebuah sistem yang mengintegrasikan seluruh permasalahan yang ada, penulis menggunakan proses pengembangan sistem berupa metode User Centered Design. Penerapan metode ini difokuskan didalam sistem yang dibangun untuk mencapai tujuan penelitian dan sesuai dengan keinginan pengguna. Hasil yang telah dicapai diimplementasikan secara langsung kepada mahasiswa dan memberikan  tanggapan yang positif  karena kemudahan mahasiswa dalam menemui dosen PA untuk berkonsultasi terhadap permasalahan selama proses perkuliahan serta kemudahan dosen  untuk memonitor seluruh histori mahasiswa PA nya.
Analysis and Design of Employee Management Information Systems Using the Web-Based Waterfall Method
Authors: Vitianingsih Anik Vega , Fardhan Maulana Abelardi , Kacung Slamet , Lidya Maukar Anastasia , Wati Seftin Fitri Ana
Published: July 2024

In today's digital era, the need for an employee management information system is increasingly urgent. Organizations must be able to overcome the complex challenges of managing their human resources to remain competitive in the ever-changing market. With the right Personnel Information System, organizations can optimize the management of personal, performance, and administrative information of their employees efficiently. One important aspect of a Personnel Information System is the mapping of workforce needs, which enables organizations to plan appropriate employee recruitment and development strategies. In addition, efficient scheduling is also a key focus, as proper placement and wise resource allocation can improve overall productivity. However, manually managing employee data is no longer sufficient in this digital age. Errors, delays, and loss of information often occur in manual processes, causing losses in terms of both time and finances. Therefore, the implementation of a robust and efficient Personnel Information System is a must. The Waterfall method, with its structured step-by-step approach, was able to provide clear guidance in the development of this system. A comprehensive analysis stage ensures that the needs of the organization are well understood, while the design stage guarantees that the system design meets the right specifications. With the results of this study, it is expected that organizations will be able to develop a Personnel Information System that suits their needs, improve the efficiency of human resource management, and optimize overall performance. Thus, the Personnel Information System is not only an administrative tool, but also one of the key factors in organizational success in this digital era.
Introversion-Extraversion Prediction using Machine Learning
Authors: Fieri Brillian , La'la Joshua , Suhartono Derwin
Published: July 2024

Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance.
Breed Lineage Prediction of Small Ruminants Using Deep Learning
Authors: Kamil Mohammad Farizshah Ismail , Akmal Jamaludin Nor Azliana , Mohd Isa Mohd Rizal
Published: July 2024

Sheep are a significant food source for humans, besides cattle and poultry. Despite its significance to Malaysian Muslims, who make up approximately 60% of the local population, the sheep supply is limited by the high mortality rate caused by fatal diseases such as foot and mouth disease (FMD) and tetanus. Infected sheep can spread food-borne bacteria, such as Escherichia coli, at various preparation phases, contaminating the meat. The objectives of this study are to identify internal and external factors that influence sheep breed lineage continuity, investigate current practices for collecting and managing data knowledge on sheep breed and hereditary diseases, and propose a sheep breed and disease data knowledge model based on the feedforward artificial neural network (FANN) deep learning method. This study utilized qualitative and quantitative data to obtain in-depth answers to the research questions, which involves collecting all the information required for the system development using the FANN deep learning method. This study found that breeding is the leading data group for tracking each sheep's ADG and BCS. Feed type, sanitization, and medication influence sheep’s daily increase and health. Collaboration, worker knowledge, and climate are recognized as external factors that potentially influence sheep's daily increase. The interview analysis also suggested attributes that could contribute to detecting breed lineage, including breed, category, ADG, and BCS. Therefore, it is recommended that future research adopt this method for other farmed animals.
Land Suitability for Mustard Plants Using Multi-Objective Optimization by Ratio Analysis Method
Authors: Hatta Heliza Rahmania , Ariani Riska , Khairina Dyna Marisa , Maharani Septya , Kamila Vina Zahrotun , Wijayanti Arini
Published: July 2024

Sawi dapat dikembangkan atau dikembangkan dari sudut pandang finansial dan bisnis untuk memenuhi permintaan pembeli dan menangkap peluang pasar yang signifikan. Sawi merupakan tanaman hortikultura yang mempunyai daya adaptasi tinggi dan waktu panen yang relatif singkat. Sawi ini menawarkan banyak keuntungan bagi petani. Misalnya saja banyak petani yang menanam sawi di Samarinda, Kalimantan Timur, Indonesia. Meskipun sangat mudah beradaptasi, beberapa spesies sawi tidak tumbuh subur di tanah tertentu. Tanah yang baik sangat penting untuk hasil optimal saat menanam sawi. Sawi yang ditanam dapat diseleksi dengan menggunakan pendukung keputusan berdasarkan kriteria lahan untuk mendapatkan hasil terbaik. Tujuan dari penelitian ini adalah untuk merekomendasikan tanaman sawi yang cocok berdasarkan kebutuhan luas dengan menggunakan pendekatan multi-objective optimize by ratio analysis (MOORA). MOORA merupakan suatu metode pengambilan keputusan yang membantu dalam memilih alternatif terbaik dari beberapa pilihan atau alternatif berdasarkan beberapa kriteria atau tujuan. Pengamatan ini menggunakan lima kriteria yaitu jenis tanah, pH tanah, curah hujan, suhu, ketinggian lokasi, dan enam alternatif sawi. Berdasarkan uji lahan, sawi yang direkomendasikan metode MOORA adalah Sawi Sendok atau Pak Choy dengan nilai Yi sebesar 7,6698. Jadi yang dipilih sebagai sawi yang ditanam di lahan tersebut adalah Sawi Sendok atau Pak Choy. Untuk penelitian selanjutnya perlu dilakukan penambahan atau penyesuaian kriteria dan sensor baru secara real-time yang dapat diterapkan untuk meningkatkan efisiensi sawi menuju smart farming yang fokus pada hasil yang lebih baik dengan tetap menjaga keseimbangan alam.
Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification
Authors: Sasongko Sudi Mariyanto Al , Tsaury Shofian , Ariessaputra Suthami , Ch Syafaruddin
Published: July 2024

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system.
Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network
Authors: Rahman Aviv Yuniar , Istiadi Istiadi , Hananto April Lia , Fauzi Ahmad
Published: July 2024

Indonesia is a country that has a diversity of animal species with the top 10 predicate in the world. The population of animal species, including starlings, is very widely known in the country. Starlings currently in Indonesia are diverse, ranging from standard to rare in Indonesia. This starling has its characteristics based on the type, color, sound, etc. In the first problem, the first accuracy performance when using the GLCM texture feature with Artificial Neural Network is 68%. Furthermore, the second problem is the accuracy performance of typing using the GLCM texture feature with a Decision Tree of 50%. This research aims to improve the starling classification system accuracy using Gabor and Wavelet texture features with artificial Neural Networks. Based on testing in the classification of starlings using the GLCM, Gabor, and Wavelet features, the highest degree of precision can, therefore, be concluded to be at the GLCM and Wavelet feature levels. The GLCM and Wavelet level accuracy results reached 83% at a rate of learning 0.9. In the experiments that have been done, the GLCM and Wavelet levels can increase accuracy using Artificial Neural Networks. In the classification process, the type of starlings also shows that the computational time in testing is much faster in producing accuracy values. In addition, the accurate accuracy while testing the starling category also increases.
Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas
Authors: Nugraha Deny Wiria , Ilham Amil Ahmad , Achmad Andani , Arief Ardiaty
Published: July 2024

This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model
Authors: Putrada Aji Gautama , Alamsyah Nur , Fauzan Mohamad Nurkamal
Published: July 2024

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.
Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms
Authors: Gunawan Wawan , Wiradiputra Raychal Ababil , Puspita Sari Anggi , Prayama Deddy , Rikardo Nainggolan Esron
Published: July 2024

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, developers must have sufficient fundamentals, and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. Using Naïve Bayes and C 4.5 algorithms into a dataset with nine programming languages related to mobile app development. Results obtained in This research show Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native app frameworks is a technology that would have the opportunity in the future with an accuracy level above 90% with Naïve Bayes and C 4.5 algorithms. These results are obtained by testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. This research Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology.
Storychart: A Character Interaction Chart for Visualizing the Activities Flow
Authors: Abidin Zainal , Munir Rinaldi , Akbar Saiful , Mandala Rila , Widyantoro Dwi H.
Published: July 2024

Event-predicate-based storyline extraction results in a chronologically ordered activity journal. The extraction results contain complex human activities, so the activity journal requires a visualization model to describe actor interactions. This paper proposes a chart to visualize the activities' flow to describe the characters' interactions in an activity journal. This chart is called a storychart. Storycharts have an actor channel that can accept single entities or teams. The actor channel allows changing the type from single to a team or vice versa and moving members to other teams. The activity channel serves as a connector to accommodate interactions between actors. The activity channel provides a visual space for the elements of what, where, and when. Event predicates are the core of what. Therefore, the storychart visualizes the event predicate using glyphs to attract the reader’s attention. The main contribution of this paper is to introduce a team channel that can visualize the identity of team members and an activity channel that can visualize the details of events. We invited participants to discover the reader’s perception of the ease of team recognition and the integrity of the meaning of the narrative visualized by the storychart. Participants involved in the evaluation were filtered by literacy score. Evaluation of storychart reading showed that readers could easily distinguish teams from single actors, and storycharts could convey the story in the activity journal with little reduction in meaning.
A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems
Authors: Yadav Jitendra Singh , Yadav Narendra Singh , Sharma Akhilesh Kumar
Published: July 2024

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities.
The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge
Authors: Crysdian Cahyo
Published: July 2024

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.
Literature Reviews of RBV and KBV Theories Reimagined - A Technological Approach Using Text Analysis and Power BI Visualization
Authors: Arief Ikhwan , Hasan Alizar , Putri Nilda Tri , Rahman Hafiz
Published: July 2024

Over the years, the Resource-Based View (RBV) and Knowledge-Based View (KBV) have solidified their roles as pivotal paradigms in strategic management literature. With an emphasis on Small and Medium Enterprises (SMEs), this study uses text analysis and Microsoft Power BI to explore these concepts innovatively. The study implements a systematic literature review, extracting data from Scopus, Web of Science, and DOAJ databases to assemble a comprehensive literature corpus. The methodology incorporates text analysis to draw out key themes, relationships, and trends, and these are subsequently visualized using Power BI to create an engaging, interactive representation of data. Components like word clouds, co-occurrence networks, and trend lines are generated, while Power BI's dynamic filtering and drill-down functionalities facilitate thorough data investigation. The results display significant overlap between RBV and KBV, denoting possible integration junctures for these theories within the domain of strategic management. Additionally, the study underscores the relevance of these insights for SMEs, emphasizing the part played by unique resources, encompassing knowledge assets, in catalyzing innovation and fostering a competitive edge. The study concludes by recognizing the significant theoretical and practical implications of integrating text analysis and Power BI in conducting literature reviews. This methodology bolsters our understanding of RBV and KBV, offering small and medium-sized enterprises a beneficial instrument to traverse these intricate theories. The study suggests that future research could broaden the application of this methodological approach to encompass other strategic management theories.
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal
Authors: Chyan Phie , Achmad Andani , Nurtanio Ingrid , Areni Intan Sari
Published: July 2024

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Project-Based Learning Model Development Using Flipped Classroom for Drawing Learning in College
Authors: Mayar Farida , Putra Febri Wandha , Monia Fenny Ayu , Kosassy Siti Osa , Fadli Rima Pratiwi , Arinalhaq Ririen
Published: July 2024

The learning process was initially carried out online during the pandemic then after the post-pandemic, learning activities began to be carried out face-to-face. This raises a new problem, because lecturers who teach are starting to be required to use various media in the learning process in the new normal era. But in reality, the lecturers still use old methods such as lectures or task-based learning. Therefore, the research objective is to develop a new model that can be used in the learning process in higher education. This type of research is development research using the ADDIE approach. The instrument used was a student learning motivation questionnaire and a student learning satisfaction questionnaire following the lesson. Another instrument used is a Likert model scale with three alternative answers. The operational procedure taken in this research and development goes through three stages, namely: (1) preliminary study, (2) preparation of conceptual models (3) validity test and (4) practicality test. The results of the validity test show that the model book and manual are in the very valid category from the aspects of design, language and content. Furthermore, the practicality test results show that model books and guidebooks for project-based learning models using flipped classrooms for drawing lectures in tertiary institutions are practically used by lecturers. The implications of this research can help lecturers in designing practical learning.
History Students’ Readiness in Using QR Code Based E-Job Sheet
Authors: Aisiah Aisiah , Purwati Sherly , Pernantah Piki Setri , Afriani Rini , Maharani Putri Bintang
Published: July 2024

The research objective is to identify primary history students' readiness to use QR code-based e-job sheets in terms of knowledge, experience, and supporting facilities. The research material consists of digital technology devices, smartphones, internet networks, electronic worksheets, QR Code applications, and an assessment of historical learning. This research applied a quantitative approach. Research respondents included significant history students at the Faculty of Social Sciences Universitas Negeri Padang from 2018 to 2022, including 140 students. Data were collected through a Google Form application questionnaire and shared with the students via WhatsApp. The data were analyzed quantitatively by using percentages and averages. General findings showed that many students were ready to use QR code-based e-job sheets, but others still needed them. The particular findings included: 1) more than half of respondents knew what the e-job sheet was, but two-thirds of them had no experience using the e-job sheet, 2) related to QR codes, it was found that half of the respondents knew what QR code was, but a part of them had no experience using QR codes, and 3) two-thirds of the respondents stated that supporting facilities such campus internet and their smartphone were unable to support the use of QR codes-based e-job sheets. QR code-based e-job sheets have become essential in optimizing digital technology in learning activities. Further research is needed to measure the effectiveness of using QR code-based e-job sheets as an alternative strategy and instrument for assessing learning outcomes in the digital era.