Sample Selection Method Research Articles

The Influence of Blue Accounting, Corporate Social Responsibility, and Stakeholders Influence Capacity on Financial Performance

This study aims to determine the effect of green accounting, blue accounting, corporate social responsibility (CSR), and stakeholders influence capacity on financial performance. The sample in this study was the Maritime sector companies listed on the Indonesia Stock Exchange in 2021-2022. In drawing samples, the author used a purposive sampling technique, namely a sample selection method based on certain criteria. The data used were obtained from the Indonesia Stock Exchange publication and the official website of each company. This study used 24 samples with 2 years of observation so that the total was 48 observations with multiple linear regression estimation of panel data. The results showed that the green accounting variable was negative and insignificant on financial performance, blue accounting had a positive and insignificant effect on financial performance, corporate social responsibility had a positive and insignificant effect on financial performance, stakeholders influence capacity had a positive and significant effect on financial performance.

Vulnerability Analysis: A Tool for SMEs’ Business Resilience

This study is aimed at examining the vulnerability levels of businesses and assessing the influence of vulnerability on business resilience. This study was undertaken in Surakarta, Central Java, Indonesia. The sample selection method utilizes purposive random sampling. Data is gathered through structured interviews. The vulnerability level of businesses is gauged using an index. The vulnerability index was formulated based on three pivotal variables: exposure, sensitivity, and adaptive capacity. The vulnerability index was ascertained through the aggregation of values derived from all vulnerability indicators. The impact of vulnerability on changes in the external business environment resilience is analyzed through multinomial logistic regression. The research outcomes indicate that SMEs in the food and beverage sector fall within the bracket of moderate vulnerability. Sensitivity emerges as the most pivotal factor influencing the vulnerability index of business units, closely followed by adaptive capacity and exposure. These variables demonstrate a robust impact on SMEs resilience. The critical need for both government and business manager to prioritize ensuring access to suppliers and raw materials, manage raw material pricing, optimize product pricing strategies, and maintain market reach after macro-environmental uncertainty (disaster). This study emphasizes the importance of the durability of SMEs the nimble external environment, where access to suppliers and the price of raw materials, product prices, and market reach often change. It also reveals that sensitivity is a vital variable that affects the durability of SMEs.

The Role of Financial Rations on Financial Distress Conditions in Transportation and Logistics Sector Companies at Indonesian Sharia Stock Index

Financial distress as an early warning signal of conditions that can lead to bankruptcy of a company. This study aims to examine the effect of financial ratios, namely liquidity, profitability, solvency, and activity on the financial distress of a company. The type of data used in this study is secondary data sourced from financial reports available at www.idx.com. The sample selection method is carried out by purposive sampling. The sample in this study was 44 Transportation and Logistics companies listed on the Indonesian Sharia Stock Index during the 2020-2023 period. The data testing tool used is multiple linear regression. The results of the F test analysis show that simultaneously, the variables of liquidity, profitability, solvency and activity have a significant effect on financial distress. Meanwhile, the results of the T test show that the variables of liquidity, profitability, solvency and activity have a significant effect on financial distress.

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals.

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals.

The Impact of Training Distances (20 m, 50 m, and 70 m) on Concentration and Anaerobic Endurance in Archery Sub-Elite Athletes

This study aims to investigate the effects of a new training method for archery athletes with different distances, namely 20 meters, 50 meters, and 70 meters, in improving concentration and anaerobic endurance. The pre- and post-control group design was used in this study with the sample selection method using the purposive sampling technique. The criteria for participants in this study include: actively practicing archery, aged 14-20 years, having at least 5 years of archery competition experience, domiciled in East Java Province, not injured, and willing to participate in the study until the end. 40 sub-elite archery athletes (20 males, 20 females) became participants in this study and were divided into 4 groups: conventional training (CON), training with a shooting distance of 20 m (NEAR), training with a shooting distance of 50 m (MID), and training with a shooting distance of 70 m (LONG) training groups. Each group underwent a 12-week training program specific to their assigned distance. The instrument used to measure concentration is the standardised archery mirror drawing test and to measure anaerobic endurance using the RAST test instrument. The results of the paired sample t-test analysis showed that in the anaerobic endurance variable, the NEAR, MID, and LONG groups had a p-value <0.05, while the CON group had a p-value > 0.05. In the concentration variable (time), all groups had a p-value <0.05, while in the concentration variable (error), there were only two groups (NEAR and CON) that had a p-value <0.05. The Anova Test results showed a significant difference between the CON and MID groups and CON and LONG (p <0.05) in the anaerobic endurance, while in the concentration (time) there was a significant difference between the CON and LONG groups and NEAR and LONG (p<0.05). In conclusion, the MID and LONG groups significantly improve anaerobic endurance and concentration, two important factors for archery success. Longer training distances appear to have a more pronounced effect on concentration.

Pengaruh Solvabilitas, Profitabilitas, Kepemilikan Institusional, Dewan Komisaris Independen, dan Kepemilikan Publik terhadap Pengungkapan Manajemen Risiko: Studi pada Perusahaan Sub-sektor Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2018-2022

The object of this research is a banking sub-sector company listed on the Indonesia Stock Exchange for the period 2018-2022. This study aims to determine the effect of leevrage, profitability, institutional ownership, independent board of commissioners, and public ownership as independent variables and risk management disclosure as the dependent variable. This study used 35 companies, so there were a total of 175 samples used. The study used purposive sampling as a sample selection method used for the selection of certain criteria. This study applied descriptive statistical analysis method and panel data regression analysis. The results of this study show that all variables simultaneously influence risk management disclosure. Meanwhile, leverage, profitability, and independent board of commissioners each have no effect. Meanwhile, institutional ownership and public ownership positively affect risk management disclosure.

Education and women in high-skill occupations in India

Purpose This paper aims to examine women’s likelihood of being employed in a high-skill occupation compared to men in India. It also analyses the impact of education on women’s likelihood of entering a high-skill occupation. Design/methodology/approach This study uses data from Periodic Labour Force Survey 2018–19. National Classification of Occupations 2004 is used to classify the occupations in three categories, i.e. high-skill, medium-skill and low-skill. The paper uses ordered probit with Heckman’s sample selection method to estimate the impact of gender identity and education on women’s likelihood of entering high-skill occupations. Findings The results show that female workers are more likely to be employed in high-skill occupations than male workers. Formal years of schooling and technical education further improve women’s probability of working in high-skill occupations compared to men. However, an increase in monthly per capita expenditure of the households improves men’s chances of being employed in high-skill occupations in comparison to women. It suggests that gender norms are still relevant. Originality/value Studies have found occupational segregation in India with a large share of women being employed in low-wage work. This study examines the issue by focusing on the skill level of occupations. It found that women have a higher likelihood of entering high-skill occupations in India and education is playing an important role in it.

Pengaruh Return On Asset, Free Cash Flow, Firm Size, Terhadap Dividend Payout Ratio Dengan Corporate Governance Sebagai Variabel Intervening Pada Perusahaan Manufaktur

This research aims to determine Return on Asset, Free Cash Flow, and Firm Size on Dividend Payout Ratio with Corporate Governance as an intervening variable in manufacturing companies listed on the Indonesia Stock Exchange for the period 2017 - 2021. The sample selection method used is purposive sampling. The total observations in this research were 155 observations consisting of 31 companies. The data analysis technique used in this research is path analysis with the SPSS version 26 analysis tool. The test results of sub structure I show that Return on Asset and Free Cash Flow have a positive effect on Corporate Governance. While Firm Size has no effect on Corporate Governance. The results of sub-structure II testing show that Return on Asset and Free Cash Flow have a positive effect on Dividend Payout Ratio. Meanwhile, Corporate Governance have a negative effect on Dividend Payout Ratio and Firm Size has no effect on Dividend Payout Ratio. The sobel test results show that Corporate Governance is able to mediate the effect of Return on Asset and Free Cash Flow on Dividend Payout Ratio. Meanwhile, Corporate Governance is unable to mediate the effect of Firm Size on the Dividend Payout Ratio.

Pengaruh Partisipasi Anggaran dan Kejelasan Anggaran terhadap Kesenjangan Anggaran dengan Job Relevant Information sebagai variable moderating

This study aims to test and analyze the effect of budget participation, clarity of budget targets on budgetary slack with Job Relevant Information as a moderating variable. This study was conducted at PT. Indomobil Prima Niaga Sumut Aceh (consisting of 5 branches). The sample selection method of saturated sampling is a sampling technique when all members of the population are used as samples, namely 38 samples. The sample in this study was the Head of the department consisting of Finance and Accounting Head, Aftersales Head, Branch Manager, Regional Manager and Hoyu Manager who are involved in budget preparation, budget implementation and budget accountability. The research approach used in this study is associative research. This type of research is quantitative research. Data collection was carried out using a survey method by distributing questionnaires to respondents. The analysis technique used is the Quantitative Approach Using Statistical Analysis, namely Partial Least Square - Structural Equestion Model (PLS SEM). The results of the study indicate that budget participation has an effect on budgetary slack. Furthermore, clarity of budget targets has an effect on budgetary slack. Job relevant information moderates the effect of budget participation and clarity of budget targets on budgetary slack

Label-noise learning via uncertainty-aware neighborhood sample selection

Existing deep learning methods often require a large amount of high-quality labeled data. Yet, the presence of noisy labels in the real-world training data seriously affects the generalization ability of the model. Sample selection techniques, the current dominant approach to mitigating the effects of noisy labels on models, use the consistency of sample predictions and observed labels to make clean selections. However, these methods rely heavily on the accuracy of the sample predictions and inevitably suffer when the model predictions are unstable. To address these issues, we propose an uncertainty-aware neighborhood sample selection method. Especially, it calibrates for sample prediction by neighbor prediction and reassigns model attention to the selected samples based on sample uncertainty. By alleviating the influence of prediction bias on sample selection and avoiding the occurrence of prediction bias, our proposed method achieves excellent performance in extensive experiments. In particular, we achieved an average of 5% improvement in asymmetric noise scenarios.

Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. However, such clean log data requires expensive and tedious human labeling. In this paper, we thus propose a robust log anomaly detection framework, PlutoNOSPACE, that automatically selects a clean representative sample subset of the polluted log sequence data to train a Transformer-based anomaly detection model. Pluto features three innovations. First, due to localized concentrations of anomalies inherent in the embedding space of log data, Pluto partitions the sequence embedding space generated by the model into regions that then allow it to identify and discard regions that are highly polluted by our pollution level estimation scheme, based on our pollution quantification via Gaussian mixture modeling. Second, for the remaining more slightly polluted regions, we select samples that maximally purify the eigenvector spectrum, which can be transformed into the NP-hard facility location problem; allowing us to leverage its greedy solution with a (1-(1/e)) approximation guarantee in optimality. Third, by iteratively alternating between the above subset selection, a model re-training on the latest subset, and a subset filtering using dynamic training artifacts generated by the latest model, the data selected is progressively refined. The final sample set is used to retrain the final anomaly detection model. Our experiments on four real-world log benchmark datasets demonstrate that by retaining 77.7% (BGL) to 96.6% (ThunderBird) of the normal sequences while effectively removing 90.3% (BGL) to 100.0% (ThunderBird, HDFS) of the anomalies, Pluto provides a significant absolute F-1 improvement up to 68.86% (2.16% → 71.02%) compared to the state-of-the-art sample selection methods. The implementation of this work is available at https://github.com/LeiMa0324/Pluto-SIGMOD25.

Boosting medical diagnostics with a novel gradient-based sample selection method

In the rapidly expanding landscape of medical data, the need for innovative approaches to maximize classification performance has become increasingly critical. As data volumes grow, ensuring that diagnostic systems work with accurate and relevant data is paramount for effective and generalizable classification. This study introduces a novel gradient-based sample selection method, the first of its kind in the literature, specifically designed to enhance classification accuracy by removing redundant and non-informative data. Unlike traditional methods that focus solely on feature selection, this approach integrates an advanced sample selection technique to optimize the input data, leading to more accurate and efficient diagnostics. The method is validated on multiple disease datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset and the Cleveland Coronary Artery Disease (CAD) dataset, demonstrating its broad applicability and effectiveness. To address dataset imbalance, the Adaptive Synthetic Sampling (ADASYN) method is employed, followed by Particle Swarm Optimization (PSO) for feature selection. The refined datasets are then classified using a Support Vector Machine (SVM), showing that even traditional classifiers can achieve substantial improvements when enhanced with advanced sample selection. The results underscore the critical importance of precise sample selection in boosting classification performance, setting a new standard for computer-aided diagnostics and paving the way for future innovations in handling large and complex medical datasets.

Pengaruh Ukuran Perusahaan, Intensitas Modal dan Intensitas Persediaan terhadap Agresivitas Pajak

This research aims to analyze the influence of Company Size, Capital Intensity, Inventory Intensity on Tax Aggressiveness. This type of research is quantitative with secondary data sources. The population in this research is industrial sector companies listed on the Indonesia Stock Exchange (IDX) in 2018-2022. The sample selection method used in this research was purposive sampling so that the final sample obtained was 12 companies. The analysis technique used in this research uses multiple linear regression analysis techniques. The research results show that simultaneously the variables Company Size, Capital Intensity, Inventory Intensity influence Tax Aggressiveness. Then partially it shows that Company Size, Capital Intensity, Inventory Intensity do not have a significant effect on Tax Aggressiveness

Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.

Analysing The Interest In Buying Electric Motorbikes In The Community In Jakarta

This study aims to analyse the Technology Acceptance Model (TAM) on the interest in buying electric motorbikes in DKI Jakarta. TAM is a model used to understand how users accept and use new technology. This model involves several main dimensions, such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Attitude (A). This research uses purposive sampling method for sample selection. The analysis techniques applied include descriptive analysis and average score calculation. The results showed that the Attitude Towards dimension received an average score of 3.37 which was included in the "strongly agree" category and was in the very highlevel interval. The Perceived Ease of Use dimension obtained a score of 3.37 which is in the high interval scale range. Meanwhile, Perceived Usefulness received a score of 3.30 which is also in the very highlevel interval. Of the three dimensions, Attitude Towards has the highest effectiveness value, followed by Perceived Ease of Use, and then Perceived Usefulness. This suggests that a positive attitude towards electric motorbikes is the most powerful factor influencing purchase intention, followed by ease of use and perceived usefulness. The implications show that the factors that influence the purchase interest of electric motorbikes can be measured using TAM, which plays an important role in measuring purchase interest in the Jakarta community. In addition, the results of this study can also serve as a basis for the government in designing policies that support the use of electric motors, such as tax incentives or subsidies for the purchase of electric motors.

The influence of leadership and entrepreneurial orientation on organizational agility mediated by motivation

The hotel sector has started to recover after the post pandemic. The sudden change has forced hotel management to respond quickly to market changes. Since the hotel sector interacts directly with consumers, it needs to be extremely flexible in order to enhance product management and improve productivity. Organizational agility enables it to respond to the constantly shifting integrated environment and adapt to new knowledge. The research aims the study of motivation, entrepreneurial orientation, and leadership as factors to increase organizational agility are significant in the hotel industry. The population in this study was hotel employees in Batam, Indonesia. The data collection method used in the research was to create a questionnaire as a medium for collecting data obtained from respondents. In total, 268 responses were collected. The sample selection method used in this research was purposive sampling. This research was tested using the partial least squares with structural equation model. Result of the research shows that both entrepreneurial orientation and leadership has positive effect on motivation and organizational agility. However, motivation has no significant effect on organizational agility. The relationship between entrepreneurial orientation and leadership which motivation as the mediator, doesn’t has a significant effect on organizational agility.

Sample selection of adversarial attacks against traffic signs

In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlightenment for improving the recognition algorithm. However, with the development of transportation infrastructure, new traffic signs may be introduced. The adversarial attack model for traffic signs needs to adapt to the addition of new types. Based on this, class incremental learning for traffic sign adversarial attacks has become an interesting research field. We propose a class incremental learning method for adversarial attacks on traffic signs. First, this method uses Pinpoint Region Probability Estimation Network (PRPEN) to predict the probability of each pixel being attacked in old samples. It helps to identify the high attack probability regions of the samples. Subsequently, based on the size of high probability pixel concentration area, the replay sample set is constructed. Old samples with smaller concentration areas receive higher priority and are prioritized for incremental learning. The experimental results show that compared with other sample selection methods, our method selects more representative samples and can train PRPEN more effectively to generate probability maps, thereby better generating adversarial attacks on traffic signs.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Pengaruh Persepsi Tarif Pajak, Sanksi Perpajakan, Dan Penerapan Peraturan Pemerintah Nomor 23 Tahun 2018 Terhadap Kepatuhan Wajib Pajak

This research aims to determine the influence of perceptions of tax rates, tax sanctions, and the implementation of government regulation number 23 of 2018 on taxpayer compliance. This type of research is quantitative research with primary data sources. The population in this research are UMKM business actors registered with the South Tangerang City Cooperatives and SMEs Service. The sample selection method used in this research was convenience sampling so that the final sample obtained using this method was 100 respondents. The data technique uses multiple regression analysis methods with the help of Microsoft Excel and SPSS Version 28 data processing. The research results simultaneously show that the variables of perception of tax rates, tax sanctions, and the implementation of government regulation number 23 of 2018 have a simultaneous effect on taxpayer compliance. Partially, it shows that the tax rate perception variable has no effect on taxpayer compliance. Furthermore, the tax sanctions variable influences taxpayer compliance. Meanwhile, the variable implementation of government regulation number 23 of 2018 has no effect on taxpayer compliance.

The Influence of Profit Persistence, Capital Structure, and Dividend Policy on Earnings Quality

This research is motivated by the fact that currently profit is very important for investors and other users of financial reports to understand. Quality profits can be used to make the right decisions, therefore profits are still very important to discuss. This study aims to analyze the effect of profit persistence, capital structure, and dividend policy on earnings quality. This type of research is quantitative research with secondary data sources. The population in this study is 75 energy sector companies listed on the Indonesia Stock Exchange in 2018-2022. The sample selection method used in this study is purposive sampling so that the final sample obtained by this method is 14 companies with 5 years of research obtained 70 data. The research results show that simultaneously the variables of profit persistence, capital structure and dividend policy have an effect on earnings quality. Then, partially, the earnings persistence and capital structure variables have no effect on earnings quality. Meanwhile, the dividend policy variable influences earnings quality. Based on the research results, it can be concluded that companies that have increasingly consistent profits and companies that use funds originating from debt will not influence the high or low quality of the profits produced by the company, while companies that have a higher dividend policy will have a higher quality of profits produced by the company.