Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on a Large Indonesian Product Review Dataset

Authors:
raw_author_name: Ade Romadhony
raw_affiliation_string: School of Computing, Telkom University, Bandung, Indonesia
raw_author_name: Said Al Faraby
raw_affiliation_string: School of Computing, Telkom University, Bandung, Indonesia
raw_author_name: Rita Rismala
raw_affiliation_string: School of Computing, Telkom University, Bandung, Indonesia
raw_author_name: Untari Novia Wisesty
raw_affiliation_string: School of Computing, Telkom University, Bandung, Indonesia
raw_author_name: Anditya Arifianto
raw_affiliation_string: School of Computing, Telkom University, Bandung, Indonesia

Abstract: Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches. Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis
Cash Sales Information System Multiuser Based On Human Computer Facilities Semarang

Authors:
raw_author_name: None Fujiama Diapoldo Silalahi
raw_affiliation_string: Universitas Sains dan Teknologi Komputer
raw_author_name: None Toni Wijanarko Adi Putra
raw_affiliation_string: Universitas Sains dan Teknologi Komputer

Abstract: Sarana Insan Computer (SIK), a sales company operating in the computer sector in Central Java, has utilized Information Systems even though they are not yet well integrated between one system and another. Therefore, in this thesis the author will develop a cash sales system model by choosing SIK as the research object. The cash sales information system created includes a system for ordering goods, sending goods, sales returns and cash sales reports. This research aims to make it easier to present reports directly, accurately, quickly and to enable systems that do not yet exist to be integrated into an integrated system. So that the cash sales information system can be implemented well, a software development process is carried out which is based on correct software engineering. The software development model used in this research is a multiuser development model. Information technology which is developing rapidly today really provides support for the development of a cash sales information system for a computer sales company such as SIK, especially with the use of computer-based information technology or better known as a Computer Based Information System, because of the use of computer technology in a system. information will be able to process data more quickly with minimal error rates, save labor and save costs. The Cash Sales Information System is designed using direct data processing methods. The use of the direct data processing method is intended so that every incident of cash sales information can be processed directly. The procedures that will be processed consist of the administration section which handles administration, the purchasing section which handles incoming goods, the sales section which handles customer service and the computer goods sales section, the personnel section and the leadership itself.&#x0D;
Catch Production Data Information System and Fish Auction at UPT PPP Pondokdadap

Authors:
raw_author_name: Andik Isdianto
raw_affiliation_string: UB

Abstract: Sendang Biru beach is facing directly to the Indian Ocean, an area with abundant fishing potential. People working as fisherman make Sendang Biru is a fishing ground. Fishing ports play an important role in the development of every activity related to fisheries, especially capture fisheries. The fishing port is a place for government activities in the field of fisheries, such as loading and unloading of fish. Unit Pelaksana Teknis Pelabuhan Perikanan Pantai (UPT PPP) Pondokdadap is one of the coastal fishing ports with the largest landing of fish, especially tuna, tuna, and skipjack in Malang. Fishery production results are obtained from fishing by fishermen in the sea. The types of fish mostly found in Sendang Biru are dominated by large pelagic fish, namely tuna. Fishing production is obtained from fishermen's catches with fishing gear in the form of purse seines and handlines. A lifeboat with handline fishing gear is the fleet used to catch large pelagic fish. Total fishery production from production volume and production value on January 1 to June 30, 2022 obtained results on fishery production volume reaching 5,596,271 kg with fishery production value reaching Rp. 137,691,524,000. There are several obstacles during the fishing process regarding bad weather or rainfall with high waves, which can affect fishermen's income in fishing activities classified as low.
Concepts of Money Laundering Resulting Destroy the Structure of Financial Policy

Authors:
raw_author_name: Mohammad Shafiqul Islam

Abstract: Money laundering is the technique used to make significant sums of money earned illegally through drug trafficking, terrorism, or other major crimes that appear to have come from legitimate sources. Money laundering has a negative influence on the nation's economic and political stability, hence it must be strictly prohibited. As one of the most significant components of the financial system, it serves as the backbone of the money market and plays a vital role in raising funds for beneficial investments that advance economic growth in a nation. An effective and stable financial system is a requirement for the nation's overall growth.
Hubungan antara minat baca dengan kemampuan berpikir kritis dalam pembelajaran IPS di sekolah dasar

Authors:
raw_author_name: Jenny Indrastoeti Siti Poerwanti
raw_affiliation_string: Sebelas Maret University
raw_author_name: Idam Ragil Widianto Atmojo
raw_affiliation_string: Sebelas Maret University

Abstract: The research purpose was determined the correlation of reading interest and critical thinking ability in social studies learning at &lt;span&gt;elementary school&lt;/span&gt;. This research type is a quantitative approach with correlational research design. The research sample was fifth grade students at SDN Laban 01, SDN Wirun 04, SDN Laban 02, and SDN Plumbon 02, Mojolaban District, Sukoharjo Regency. The sampling technique applies the probability sampling technique of cluster random sampling type. Data collection techniques for reading interest and critical thinking ability are in the form of tests and questionnaires. Analysis prerequisite test using normality and linearity tests. Hypothesis testing using simple correlation analysis by applying Pearson Product Moment correlation and multiple correlation analysis with the help of the SPSS 21 program. The results showed that reading interest affect critical thinking ability in fifth grade students of SD in Mojolaban District.
Faktor kesulitan membaca pemahaman teks cerita bahasa jawa peserta didik kelas V Sekolah Dasar

Authors:
raw_author_name: Siti Aminah Nur hidayah
raw_author_name: Joko Daryanto
raw_author_name: Idam Ragil Widianto Atmojo

Abstract: &lt;p&gt;This research is motivated by observation and interview data which indicate there are problems at school regarding the low reading comprehension skills of fifth grade students at SDN Tegalayu Surakarta in the 2022/2023 academic year. To overcome these problems, the researcher conducted a qualitative study with the aims of: (1) To describe the difficulties in understanding Javanese texts (2)to explain the factors that cause difficulties in understanding Javanese texts.This research was conducted on teachers and students of class V SDN Tegalayu Surakarta. In this research, a qualitative descriptive approach was used, and observation, interviews, and documentation studies were used to collect data.Source triangulation and triangulation techniques are used to test the validity of the data.From the research, data were obtained in the form of difficulties experienced by students, namely difficulties in interpreting words, difficulties in making new sentences using words in the text of the story and difficulty answering questions according to the contents of the story. While the factors that affect the difficulty of reading comprehension skills are intellectual factors, psychological factors, environmental factors and physiological factors.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Kendala implementasi P5 dengan tema bhinneka tunggal ika di sekolah dasar

Authors:
raw_author_name: Sri Marmoah
raw_affiliation_string: Universitas Sebelas Maret
raw_author_name: Fadhil Purnama Adi
raw_affiliation_string: Universitas Sebelas Maret

Abstract: &lt;p class="Abstract"&gt;&lt;em&gt;This research was conducted to examine the obstacles to implementing P5 with the topic Bhinneka Tunggal Ika at SDN Bumi 1 No. 67 Surakarta starting from the planning stage, implementation, to assessment and efforts made to overcome it. This research uses a qualitative descriptive method approach. The obstacles encountered in the implementation of P5 are due to internal factors and external factors. The obstacles caused by internal factors are the absence of training regarding P5 for teachers, P5 is not well documented due to lack of teacher understanding, and differences in perception between teachers and school principals. Meanwhile, obstacles are caused by external factors such as limited learning media, the lack of role of teachers and school principals in establishing collaboration, and the lack of involvement of committees and supervisors. The solution to overcome these obstacles are to encourage the strengthening of teacher capacity through training to be able to implement P5 in accordance with the guidelines, to equalize perceptions and build communication between teachers and the school principals, to work with the parents of pupils in providing adequate learning media and to optimize the role of the head, teacher, committee, and supervisor to support a collaborative school ecosystem.&lt;/em&gt;&lt;/p&gt;
Pengaruh media audiobook cerita keberagaman sosial budaya di indonesia terhadap keterampilan menyimak peserta didik kelas V SD se-kecamatan banjarsari

Authors:
raw_author_name: Siti Istiyati
raw_affiliation_string: Sebelas Maret University
raw_author_name: Septi Yulisetiani
raw_affiliation_string: Sebelas Maret University

Abstract: &lt;p class="Abstract"&gt;&lt;em&gt;The aim of this research is to examine how audiobook media stories about social diversity in Indonesia influence the listening skills of grade V students in elementary schools in Surakarta's Banjarsari District. The design of this research is experimental. Methods of data collection include tests and observations. According to the research's findings, the use of audiobook media stories of socio-cultural diversity in Indonesia impacts the listening skills of students in Banjarsari District, Surakarta's grade V elementary schools. The paired sample t-test on the hypothesis test, which examines the post-test values of the experimental and control groups. The hypothesis test findings show significant values of 0.009 &amp;lt; 0.05. The average post-test score in the experimental group was 81.3 &amp;gt; 72.2, which was higher than the pre-test score. It was also discovered that the experimental group's average post-test score was higher than the control group's, with 81.3 &amp;gt; 70.7. Students with this attitude are those who quickly focus on listening to the teacher's explanation and are able to reply to the story.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms Regarding The Popularity of Presidential Candidates In The Upcoming 2024 Presidential Election

Authors:
raw_author_name: Saruni Dwiasnati

Abstract: This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine (SVM), in analyzing the popularity of presidential candidates for the 2024 Presidential Election (Pilpres). The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decision-makers and campaign teams in preparation for the upcoming 2024 Pilpres.
Inaugural Editorial: Editorial about the Founding of Medinformatics

Authors:
raw_author_name: Hao Lin
raw_affiliation_string: School of Life Science and Technology, University of Electronic Science and Technology of China, China

Abstract: The convergence of biology and artificial intelligence represents a paradigm shift in scientific exploration. The integration of AI into the study of omics—genomics, proteomics, transcriptomics, metabolomics, and beyond—has revolutionized our ability to decipher the complexities of biological systems. From predictive modeling of gene structures to the elucidation of protein interactions, from the analysis of transcriptomic profiles to the exploration of epigenetic landscapes, AI has become an indispensable tool in the biologist's toolkit. In this journal that focuses on the integration of information and life medicine, we embark on countless journeys of artificial intelligence applications in biomedical fields, focusing on the application of omics analysis in analyzing biological processes or disease occurrence and development. In addition, this journal also focuses on the forefront of interdisciplinary research, with a particular focus on the knowledge and methods of artificial intelligence, mathematics, physics, and other disciplines to solve biomedical problems, enhancing our rational understanding of life and disease. As we founded this magazine, we invited researchers, scholars, and practitioners from different backgrounds to publish cutting-edge biomedical informatics research or join our editorial board. Let us explore the frontiers of knowledge, push the boundaries of innovation, and together, forge a brighter future for biomedical science.
Patient Admission Projection Using Facebook Prophet

Authors:
raw_author_name: SK Althaf Rahaman

Abstract: Healthcare facilities face the continual challenge of managing patient admissions efficiently while maintaining high standards of care. Accurate forecasting of patient admissions is crucial for optimizing resource allocation, ensuring adequate staffing, and enhancing overall operational efficiency. To address this challenge, this research paper explores the application of Facebook Prophet, an advanced time series forecasting tool, in predicting patient admissions within healthcare facilities. Facebook Prophet, a specialized time series forecasting model developed by Facebook's Core Data Science team, is then employed to model and predict patient admissions. Facebook Prophet is particularly well-suited for handling time series data characterized by seasonality, trends, and special events. The model's flexibility and ease of use make it an ideal choice for healthcare providers seeking to leverage advanced analytics techniques for predictive purposes. In the implementation of Facebook Prophet, the model is meticulously trained and validated using the pre-processed historical admission data. The model's ability to capture complex temporal patterns, including seasonality and special events such as holidays or outbreaks, is thoroughly evaluated. By incorporating these patterns into the forecasting process, the model can provide accurate predictions of future patient admissions. By accurately predicting patient admissions, healthcare facilities can optimize resource allocation, streamline operations, and ensure adequate staffing levels to meet patient demand. Additionally, the model enables healthcare providers to proactively plan for special events or seasonal fluctuations in admission rates, further enhancing operational efficiency. The application of Facebook Prophet holds significant promise for improving patient care delivery and operational outcomes in healthcare facilities.
Cyber Security from a Business Perspective

Authors:
raw_author_name: Vikas Verma
raw_affiliation_string: iNurture,  Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
raw_author_name: Saurabh Singhal
raw_affiliation_string: Department of Computer Science and Engineering, Apex Institute of Technology, Chandigarh
University, Chandigarh, India

Abstract: In today’s era of Information Technology, we have encountered drastic changes in computing methodologies due to a tremendous increase in ONLINE communication traffic both in terms of the number of users and data communication. The COVID–19 pandemic has brought the entire world online. Cyber Security plays an important role in the field of information technology. In order to secure information, one can face many challenges. Nowadays, governments and other organisations are following various measures to prevent vivid cybercrimes. In this chapter, we have raised concern over the drastic increment in an ONLINE communication system, which urges the need for the development and deployment of Cyber Security in a business environment.
Region-based convolutional neural networks for occluded person re-identification

Authors:
raw_author_name: Atiqul Islam
raw_affiliation_string: Swinburne University of Technology Sarawak Campus
raw_author_name: Mark Tee Kit Tsun
raw_affiliation_string: Swinburne University of Technology Sarawak Campus
raw_author_name: Lau Bee Theng
raw_affiliation_string: Swinburne University of Technology Sarawak Campus
raw_author_name: Caslon Chua
raw_affiliation_string: Swinburne University of Technology, Melbourne

Abstract: In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.
Development of the support strategy for Videoconferencing Discussion Class Using Facial Expression Analysis Data

Authors:
raw_author_name: Minji Kim

Abstract: Objectives In order to develop the design principles for online discussion in videoconferencing instruction, this study develops the design principles by using learning analysis data analyzed with facial expression analysis tool and confirms the user's reaction.&#x0D; Methods Using the design and development research method, the initial design principles was developed by analyzing previous research and problems of videoconferencing classroom field. They were modified and developed through expert validation tests for three times. A prototype of facial expression analysis tool was developed to evaluate usability, and the final design principles were developed.&#x0D; Results The final support strategy consists of 9 support strategies and 26 detailed guidelines, tailored to the pre-debate, during the debate, and post-debate stages. In this regard, it has been confirmed by users that the strategy enables to simultaneously perceive non-verbal elements and to provide timely feedback. However, it was also observed that the intervention of instructors can function both as a strength and as a disruptive factor to discussions.&#x0D; Conclusions The above design principles are significant in that it suggested the prescription using learning analysis data what is facial expressions. It showed a new possibility to apply artificial intelligence technology at learning analysis by using facial expression analysis tool that automatically analyzes present a result value.&#x0D;