Abstract:Drug abuse is a problem that can be influenced by various interrelated factors. This study aims to apply Mamdani Fuzzy logic in predicting the level of drug abuse in Padang City based on several factors obtained from documentation…
umentation and interviews. The input variables used consist of the Age of the Perpetrator, Environment, Socializing, Gender, and Family, while the output variable is the Level of Drug Abuse with Low and High categories. The research stages include data collection, determining variables and fuzzy sets, fuzzification, forming if-then rules, inference using the minimum operator, composition using the maximum operator, and defuzzification using the centroid method. A total of 64 fuzzy rules are used to connect the combination of input variables with the output. Testing is done through manual calculations and implementation using MATLAB. The test results show that the output categories from manual calculations and MATLAB are the same in three test data, namely High, High, and Low. These results indicate that the Mamdani Fuzzy method can be used in modeling the level of drug abuse based on the variables used.
Abstract:In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing…
prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Abstract:Artificial intelligence is likely to have a significant impact on marketing strategies and customer behaviors in the years ahead. Research in this field has grown considerably, demonstrating AI's ability to simulate human…
n behavior and perform tasks intelligently. With growing interest among marketing researchers and practitioners, this research aims to provide an overview of the evolution of both marketing and AI research fields. This paper explores the emerging role of artificial intelligence in personalized engagement marketing, which focuses on creating, communicating, and delivering customized offerings to customers. Utilizing the Systematic Literature Review technique, we examined over 300 academic articles to uncover prevalent themes and gain a deeper understanding of current AI utilization in marketing. We then propose a plan for future research that examines potential changes in marketing strategies and customer behaviors while emphasizing critical policy considerations related to privacy, bias, and ethics. The implications for marketing managers are discussed along with predictions about how AI will impact branding and customer management practices going forward. Our research highlighted several benefits of integrating AI into marketing such as improved customer interactions, increased revenue, reduced expenses, and enhanced overall efficiency. However, this research also pointed out areas requiring further investigation including challenges posed by AI integration like shortage of skilled personnel and data privacy concerns.
Abstract:This study aims to analyze the evaluation of public policy and environmental disaster mitigation predictions regarding the cancellation of the strategic glass industry development in Rempang Island. The study employed a…
qualitative approach with a case study design and data collection through systematic literature review. Data analysis was conducted using the interactive model of Miles, Huberman & Saldana (2014). The results show that the Rempang Eco City National Strategic Project (NSP) failed comprehensively due to a procedurally flawed and non-participatory formulation process. Economic valuation research by Trend Asia et al. (2025) found that the average household income of Rempang residents reached IDR 32.77 million per household per month, far exceeding the government’s claim of IDR 3 million, while potential environmental losses reached IDR 109 million per household per month. Based on Dunn’s (2003) six policy evaluation criteria, this policy proved to be ineffective, inefficient, inadequate, inequitable, unresponsive, and inappropriate. President Prabowo Subianto’s decision to exclude Rempang Eco City from the NSP list through Presidential Regulation No. 12 of 2025 was the right step, yet still requires more decisive regulation to provide legal certainty for affected communities.
Abstract:This study discusses the implementation of the Naïve Bayes method to predict catering sales at PT.Negara Rasa Indonesia. The background of this study is based on the problem of suboptimal sales due to the absence of a structured…
tructured sales prediction system. The Naïve Bayes method was chosen because of its simplicity, speed, and ability to classify data with a high degree of accuracy. The data used in this study is historical sales data from the last two years, which has undergone cleaning, labeling, and transformation into four sales categories, namely very popular, popular, fairly popular, and less popular. The testing process was carried out using RapidMiner software by dividing the dataset into training data and test data at various ratios of 80:20. The test results showed a very high level of accuracy, with the highest value reaching 91.41%. These findings prove that the Naïve Bayes method is reliable for predicting catering sales, thereby assisting decision-making in more efficient sales management and planning at PT. Negara Rasa Indonesia.
Abstract:This study investigates the tensile uplift capacity of screw piles in clay soil as an alternative anchoring system for slope stabilization in cohesive ground conditions. The research aims to address the limited availability…
ity of empirical field data on screw-pile behavior under repeated loading and to evaluate the agreement between theoretical predictions and actual in situ responses. The methodology employs an experimental approach through in situ testing with screw-pile diameters of 10 cm, 15 cm, and 20 cm, and embedment depths ranging from 0.6 m to 1.0 m. The tests were conducted under both static and repeated tensile loading using a hydraulic jack system, accompanied by vertical deformation measurements to establish load–displacement curves. Theoretical capacity was calculated using a limit equilibrium approach for comparison with experimental results. The findings reveal a nonlinear load–displacement response, characterized by initial stiffness followed by progressive deformation into the post-yield stage. At a maximum load of 2.858 tons, deformation increased with diameter, from 5.10 cm (10 cm) to 8.49 cm (20 cm). Under repeated loading, failure occurred at a lower load of approximately 1.6 tons with a maximum deformation of 1.648 cm, indicating potential capacity degradation due to cyclic loading. The comparison between theoretical and field results shows significant deviations, with analytical predictions generally underestimating the in situ capacity. This highlights the limitations of simplified models that do not fully account for shaft adhesion, installation disturbance, soil heterogeneity, pore-water pressure effects, and cyclic degradation. Overall, this study contributes valuable field pull-out test data for screw piles in clay under repeated loading, emphasizing the need for design calibration based on full-scale testing for slope stabilization applications. The results also suggest opportunities for further research involving long-term monitoring and advanced modeling of cyclic degradation.
Abstract:Coal mining, handling, and transportation activities are major sources of airborne particulate matter containing respirable crystalline silica, which poses significant risks to respiratory health. Silicosis remains a serious…
ious occupational and environmental disease affecting not only workers but also communities living near coal-handling areas. Physical environmental factors, including air quality, temperature, humidity, and wind speed, play an important role in influencing dust generation, dispersion, and inhalation exposure. This narrative review aims to synthesize current evidence on the influence of physical environmental conditions on silica exposure and silicosis development, integrating findings from environmental monitoring studies, epidemiological research, and experimental Wistar rat models. A literature search was conducted using major scientific databases to identify relevant peer-reviewed articles published between 2010 and 2024. The reviewed evidence indicates that prolonged or high-intensity exposure to silica dust is strongly associated with chronic pulmonary inflammation and progressive fibrosis. Environmental conditions can exacerbate exposure risk by increasing airborne particulate concentrations and respiratory vulnerability. Experimental studies using Wistar rats provide mechanistic insights into silica-induced lung injury, supporting epidemiological observations in human populations. This review highlights the importance of integrating environmental, occupational, and biological perspectives to improve risk prediction, early detection, and preventive strategies for silicosis in coal-handling regions.
Abstract:PDAM Tirta Kualo is a regional company that supplies clean water to the surrounding community. Accurate estimation of water bill amounts is crucial to assist PDAMs in managing resources and finances efficiently. This study…
dy aims to create a prediction model for total water bills using the linear regression method. The data used is historical customer billing data which is analyzed to identify the relationship between air usage volume and total billing.
The findings show that a simple regression model can describe the water bill amount with an impressive accuracy of 0.9926. This precise model allows it to be used effectively in PDAM financial planning and assists customers in estimating their water usage.
Abstract:Graduation marks the completion of a certain level of schooling. This study aims to predict the graduation of students at SDN 016528 BP Mandoge based on their abilities. The goal of this research is to reduce the rate of…
student failure to graduate by making predictions based on examination scores collected by the institution. The method used in this study is Naive Bayes, a technique in Data Mining that utilizes probability and statistics to predict future outcomes based on previous data. This method was chosen due to its advantage in predicting graduation rates from concrete data, ensuring the results are reliable and applicable for future predictions. The dataset used in this study includes graduation data for SDN 016528 BP Mandoge students for the 2019/2020 academic year, comprising 171 students, with 120 students used for training data and 51 students for testing data, achieving a model accuracy of 98%.
Abstract:Bankruptcy risk is a critical issue for publicly listed companies as it may threaten business sustainability and undermine investor confidence. This study aims to examine the effects of asset growth, revenue growth, market…
et valuation, property, plant and equipment (PPE), goodwill, and research and development expenditure on bankruptcy risk, proxied by the Altman Z-score, among publicly listed companies in ASEAN countries. The study employs a quantitative approach using secondary data obtained from 1,354 non-financial firms listed in Indonesia, Malaysia, Thailand, Singapore, and the Philippines over the 2015–2023 period, yielding a total of 7,726 firm-year observations. Data were analyzed using a fixed-effects panel regression model. The findings reveal that asset growth and goodwill exert a positive and significant effect on the Altman Z-score, whereas research and development expenditure has a negative and significant effect. Revenue growth demonstrates a marginally positive influence, while market valuation and PPE do not exhibit a significant effect on bankruptcy risk. These results suggest that corporate financial stability is determined not only by financial factors but also by the quality of strategic resources and firms’ adaptive capabilities. The novelty of this study lies in the integration of the Resource-Based View and Dynamic Capabilities Theory perspectives into a bankruptcy prediction framework for publicly listed companies across ASEAN countries.