The Fast Economic Growth Indicator, a newly developed metric leveraging big data, provides policymakers with timely insights crucial for assessing the economic impact of policies or events. Among various open-source datasets, aviation data stands out as a potential indicator of rapid economic growth, given its inherent graph structure with airports as nodes and flight connections as edges. However, global flight data, being dynamic and complex, poses challenges in analysis. To glean comprehensive insights, it's imperative to condense this graph data into representative vector values while preserving node relationships. In this study, we utilize the dynamic graph node embedding method to quantify the influence levels of airports relative to each other. Traditional node embedding methods often prioritize homophily over structural equivalence, challenging directly extracting influence levels. To address this limitation, we introduce anchored dynamic graph node embedding, employing a virtual node as a reference point in embedding space to enable direct calculation of influence levels. These influence metrics are then compared to the GDP of airport regions. Using USA domestic flight data from 1988 to 2021 as a case study, our methodology demonstrates promising results, boasting a 0.94 correlation coefficient with national GDP and a 0.9 correlation coefficient with state Gross State Product (GSP). This research aims to advance dynamic graph node embedding methods towards structural equivalence rather than homophily, enhancing applicability to tasks emphasizing node structure over neighborhood proximity. An example of the benefits of this research is its utility in addressing Influence Maximization Problems within dynamic graphs.

In data analysis, the preprocessing step is crucial, directly impacting the accuracy and reliability of results. While data preprocessing using a programming language offers capacity, speed, and reproducibility advantages, the complexity of learning programming languages and the scarcity of supportive educational tools pose significant challenges. This study introduces the Data Preprocessing Automatic Assessment (DPAA) module, designed to facilitate learning data preprocessing through programming. The DPAA module, developed using Python and Pandas, utilizes a model answer-based assessment method. It features a self-assessment mechanism that simultaneously displays the outputs of the student’s code alongside the model answer, highlighting discrepancies for visual emphasis. Additionally, it includes an automatic evaluation method that compares and evaluates results after transforming them into an array. Furthermore, feedback is provided when the student's answer is incorrect. An example was constructed to validate the DPAA module based on a tutorial from the official Pandas website. The DPAA module, along with examples, was reviewed by informatics teachers at a high school for gifted students and was confirmed for its effectiveness. The DPAA module is expected to support the learning of data preprocessing syntax using Pandas, thereby aiding in the broader application of Python in data analysis. This innovative tool promises to enhance educational outcomes by making the learning process more interactive and supportive, ultimately fostering a deeper understanding of data preprocessing techniques.

MOOCs, or massive open online courses, have become increasingly popular in the last several years to offer employees online training and development opportunities. Traditional methods of training, such as in-house or external trainings, frequently provide several obstacles, including high prices, logistical complications, and time limits. MOOCs have leveled the playing field in online learning by making quality education available to everyone, especially working adults, allowing them to reskill and upskill their work abilities by offering scalable, flexible, and cost-effective alternatives to address issues encountered in traditional methods of training. Even though MOOCs have been the subject of extensive research in educational institutions, there has been relatively little investigation on the acceptance of MOOCs at the organizational level, particularly among employees. The main challenges with MOOCs are their low completion rates and high dropout rates. This research aims to fill a gap in the literature by providing a Corporate MOOCs acceptance model to assess the possibility of employees using MOOCs in corporate settings for reskilling and upskilling. The proposed model was formulated using the Technology Acceptance Model, Social Support Theory, and Theory of Belongingness as the foundations, besides extending the model with four additional constructs: perceived credibility, perceived convenience, digital competence, and perceived learning. Recommendations for conducting empirical research on the model are provided on how the model will be assessed to be successfully used in measuring employees’ acceptance of Corporate MOOCs.

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

Automated decision-making systems are massively engaged in different types of businesses, including customer-oriented sectors, and bring countless achievements in persuading customers with more personalized experiences. However, it was observed that the decisions made by the algorithms could bring unfairness to a person or a group of people, according to recent studies. Thus, algorithmic fairness has become a spotlight research area, and defining a concrete version of fairness notions has also become significant research. In existing literature, there are more than 21 definitions of algorithmic fairness. Many studies have shown that each notion has an incompatibility problem, and it is still necessary to make those notions more adaptable to the legal and social principles of the desired sectors. Yet, the constraints of algorithmic fairness for customer-oriented areas have not been thoroughly studied. This motivates us to work on a systematic literature review to investigate the sectors concerned about algorithmic fairness as a significant matter when using machine-based decision-making systems, what are the well-applied algorithmic fairness notions, and why they can or cannot be directly applicable to the customer-oriented sectors, what are the possible algorithmic fairness constraints for the customer-oriented sectors. By applying the standard guidelines of systematic literature review, we explored 65 prominent articles thoroughly. The findings show 43 different ways of algorithmic fairness notions in the varieties of domains. We also identified the three important perspectives to be considered for enhancing algorithmic fairness notions in the customer-oriented sectors.

The Rawa Kidang Restoration is an area designated to plant tree species that rhinos like. Food is planted at the Rawa Kidang Restoration to maintain the rhino population. Knowledge of the importance of tree health is necessary because this can be a guide for rejuvenating or breeding trees attacked by disease or pests. To know the quality of rhino food, you need information about the health of rhino food trees. This research aims to investigate the health of rhino food trees in the Rawa Kidang Restoration. This research uses the Cluster Plot Level Index (CLI) formula to observe and examine canopy conditions and tree damage. The canopy condition parameters consist of five parameters: live crown ratio, density, transparency, diameter, and dieback. The parameters for the tree damage condition are the damage's location, the type I feel, and the level of tree damage. The research results show that the tree health value in cluster plot 1 is 2.35; in plot cluster 2, 2.50; in plot cluster 3, 3.70; and in plot cluster 4, 2.92. So, it can be concluded that the average tree health value in the four plot clusters is in the medium category. The severity of canopy conditions and tree damage can be influenced by surrounding conditions such as sunshine, growing conditions, water, and growing space.

Rice continues to be the staple food for most Indonesians. Because Indonesians have such ingrained consumption patterns, switching to other food sources besides rice can be difficult, contributing to the country’s high rice consumption. Indonesia has plenty of local food resources that can be used as a carbohydrate source and staple food. However, a lack of understanding about the foods' potential use and nutritional content has caused many of them to grow wildly in nature, neglected, and underutilized. Most recently, there has been an increase in studies examining the potential of underutilized carbohydrate sources, including their nutritionally rich compounds, following the 2018 agriculture policy in promoting agriculture and food diversification. Taro beneng (Xanthosoma undipes K. Koch), sago (from the genus Metroxylon), sweet potato (Ipomea batatas L.), and sorghum (Sorghum bicolor L.) are the local sources of carbohydrates and have the potential to be utilized as a healthy alternative to white rice, gluten-free product, or as an ingredient of supplementary or complementary foods. To find out if underutilized foods in Indonesia have the potential to substitute rice and gluten-free products and also to assist with food diversification initiatives, a comparison of their nutritional compositions was conducted using a literature review. Taro beneng, sago, sweet potatoes, and sorghum can be used as healthy alternatives for substituting rice and gluten-free products. This underutilized food offers diverse nutrients and many possibilities for product diversification, such as analog rice, symbiotic yogurt, gluten-free biscuits, and noodles.

Increased population growth has implications for increased industrial and transportation activities. This activity increased gas emissions in the form of Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2), resulting in acid rain. The measuring instrument, a rain gauge, is used to measure acid rain in Greater Bandung at Telkom University, Indonesia, but it is currently unable to measure in real-time. Therefore, this study aims to measure acid rain in real-time by measuring parameters such as pH, temperature, conductivity, and precipitation to test the acidity contained in rainwater and evaluate the tool's performance with data for March 2022. The analysis uses various statistical methods: anomaly detection, outlier detection, central tendency, person correlation analysis, Mean Absolute Percentage Error, T-test, and Analysis of Variance (ANOVA). Each parameter can be worked out in real-time based on the central tendency and rain dispersion results. If there is only daily rain, then the correlation for each parameter is the highest, namely pH if there is acid rain. The real-time average pH is 6.45; in the laboratory, it is 6.56, so the MAPE value is 5.37 (good category). Even though this tool can work well, it needs to improve the quality of the temperature control in it. Since temperature significantly affects pH, the results show a negative correlation of -0.80 between pH and temperature. In the ANOVA test, the resulting p-value, when compared with data in the laboratory, is >0.05, meaning that the average daily pH does not have a significant difference from the average laboratory test.

As a social media platform, X (formerly Twitter) has become a massive source of real-time, unstructured data, providing valuable insights into people's opinions on various issues. One crucial social phenomenon that has attracted attention on social media is the discourse around abstention (commonly known as “golput” in Indonesia) in the context of the presidential election. Abstention refers to the deliberate act of refusing to vote. Understanding the patterns, preferences, and topics associated with abstention discourse can provide valuable knowledge for political analysis. This study aims to discover knowledge based on patterns, sentiment polarization, and issues from unstructured X data to understand the discourse surrounding abstention in the 2024 Indonesian presidential election. The methodology involves collecting data from the X API, conducting Social Network Analysis (SNA) to analyze the social structure, preprocessing the data, and searching for the best sentiment analysis model through hyperparameter tuning on six Machine Learning (ML) models. Then, Latent Dirichlet Allocation (LDA) is employed with coherence score evaluation to identify topics related to the issue. The results indicate that 2,489 tweets discussing abstention were collected during the study period, exhibiting varied daily trends. SNA analysis reveals the formation of clusters within the dataset, alongside the identification of influential actors through three different centrality calculations. The sentiment analysis results show that the Logistic Regression (LR) model with count vectorizer is the best-performing model, with a predominance of positive sentiment polarity over negative sentiment. Evaluation of LDA using coherence scores indicates the presence of five topics related to abstention. This research contributes to knowledge discovery on the X platform by providing valuable insights into the discourse surrounding abstention in the Indonesian presidential election. These findings offer a deeper understanding of public opinion, political engagement, and election dynamics.