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This research focuses on optimizing the control system to improve voltage stability in a 10 kW Solar Power Plant (PLTS) located in a tropical region. The main issue addressed is voltage fluctuation caused by the intermittent nature of solar radiation (200–1200 W/m²) and temperature variations (20–50°C), which result in up to 12% overshoot in the inverter. The proposed method implements a Proportional-Integral-Derivative (PID) controller optimized using the Particle Swarm Optimization (PSO) algorithm with real-time irradiation input data. The research integrates a 100 Hz digital low-pass filter to mitigate sensor noise under low irradiation conditions. Simulation results show that the PID-PSO system successfully reduces overshoot from 12.1% to 4.2% under high irradiation, and decreases settling time from 0.62 seconds to 0.31 seconds. The digital filter effectively reduces measurement deviation from 7.2% to 2.8% at 200 W/m² irradiation. The PSO optimization achieved optimal convergence within 37 iterations with an Integral of Time-weighted Absolute Error (ITAE) value of 0.18. This study concludes that the implementation of PID-PSO with a digital filter significantly enhances the voltage stability of the PLTS by 20.3% compared to conventional PID control and is ready to be applied in tropical-region smart grid systems.

This research aims to implement a Multi-Attribute Decision Making (MADM) approach using the AHP–TOPSIS method to assist PT. XYZ in selecting the most suitable intern candidate for the Social Media Specialist position. The increasing number of applicants each year makes the selection process more complex, requiring a systematic and data-driven decision support system. AHP was used to determine the priority weights of five main criteria—Interview, Experience, Portfolio, Skill, and Achievement—along with their subcriteria. All pairwise comparison matrices met the consistency requirements, indicating valid weights. The TOPSIS method was then applied to calculate the preference scores of ten candidate alternatives based on the weighted normalized decision matrix, ideal solutions, and distance measures. The results show that candidate A3 achieved the highest preference score (0.9422), followed by A7 and A8, making them the top recommended candidates. This study demonstrates that integrating AHP and TOPSIS effectively supports companies in conducting objective, efficient, and accurate recruitment decision-making processes.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

This paper was made to explain the results of research on how to obtain the most appropriate citric acid adhesive composition in the manufacture of randu wood fiber composites in order to obtain a strong and suitable composite material. The research was carried out by experimental methods in the laboratory through a series of mechanical tests, namely the bending strength test and the screw grip strength test. The sample specimen is 5 cm × 20 cm × 1 cm for flexural strength testing and 5 cm × 10 cm × 1 cm for screw grip strength test. Composite specimens were made with variations in the composition of citric acid adhesives of 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, and 20% by weight of randu wood. The results showed that the composite of randu wood particles with a citric acid matrix had optimal strength at a certain ratio, which was 7.5%. At the same ratio, the test results of the screw grip strength test also provide the highest value. These findings confirm that the exact composition of the adhesive has a significant impact on the final performance of the resulting composite.

One of the various salted egg products with smoking methods is smoked salted eggs which have a distinctive aroma and taste. This study aims to determine the effect of smoking and storage time  and its  interaction on  smoked salted  eggs  with on  physical  and  chemical  properties, total microbes as well. This research is experimental by using ducks eggs and a mixture of coconut shells and fibers, and rice husks as smoke fuel. The experimental design used is Randomized Complete Block Design (RCBD) with a 3x5 factorial pattern. As the first factor (P) is the smoking time which consists of P0= 0 hours, P1= 12 hours, P2= 15 hours while the second factor (H) is the storage time which consists of H0= 0 days, H1=7 days, H2= 14 days, H3= 21 days, and H4= 28 days. The variables observed were egg weight, albumen and yolk pH, moisture content, protein, and total microbial colony of smoked salted eggs. The results of the study were that the smoking time of 15 hours resulted in the lowest weight of smoked salted eggs (56.13g), while the storage time decreased the pH of albumen. Smoked salted duck eggs have a moisture content of 58.435-67.149%. The length of smoking increases the protein level. Salted duck eggs with a smoking time of 15 hours have the highest protein content, which is 15.39%. however, the duration of smoking and storage did not affect the total microbes of smoked salted eggs and there was no interaction between the duration of smoking and the duration of storage on the physical, chemical and total microbial properties.

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

The rapid development of information technology in today's digital era has significantly impacted organizational performance, particularly in data management and resource planning. One organization that heavily relies on accurate data availability is the Indonesian Red Cross (PMI), especially its Blood Donor Unit (UDD). UDD PMI of Asahan Regency faces challenges in determining monthly blood donor targets to maintain stable blood stock. A shortage of blood supply can be fatal for patients requiring transfusions. Therefore, a system is needed to forecast the number of blood donors, allowing for more accurate decision-making. This study utilizes the Weighted Moving Average (WMA) method to predict the number of blood donors for the following month based on historical data from March 2024 to March 2025. The WMA method is chosen for its ability to assign greater weight to recent data, making the forecast more relevant and accurate. The results of this research are expected to assist UDD PMI Asahan Regency in anticipating blood needs and maintaining optimal stock availability.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

This study aims to evaluate the effectiveness of the green water system in the nursery phase of Nile tilapia Oreochromis niloticus by observing growth performance, water quality, and survival rate. The experiment was conducted for 21 days using 450-liter circular tanks with two treatments: green water and clear water systems. The green water system was established by adding plankton starter to stimulate algal growth, while the clear water system used clean water with routine siphoning. Observations included absolute length, absolute weight, water quality parameters pH, temperature, dissolved oxygen, nitrite, and phosphate, and survival rate. The results indicate that the green water system provided superior nursery performance compared to clear water. Tilapia seeds reared in green water exhibited higher growth in length and weight, more stable water quality, and a greater survival rate 90% than those in the clear water system 80%. These improvements are attributed to the presence of microalgae, which serve as natural feed as well as bioremediation agents that reduce ammonia, nitrite, and phosphate toxicity. Therefore, the green water system proves to be more effective, economical, and environmentally friendly for tilapia nursery culture compared to the clear water system.

The quality of complementary feeding (MP-ASI) for infants aged 6–23 months is a key factor in supporting growth and preventing early nutritional problems. However, MP-ASI practices that do not comply with recommendations are still common, especially in areas with limited access to nutrition information and education. One of the promotive-preventive efforts developed in primary health care is the implementation of toddler classes. This study aims to analyze the effectiveness of toddler classes in improving the nutritional quality of infants receiving MP-ASI in the working area of the Popayato Timur Community Health Center. This study used a quasi-experimental design with a one-group pretest–posttest approach. The study sample consisted of 33 infants aged 6–23 months selected using a total sampling technique. Data were collected through infant anthropometric measurements to assess nutritional status based on indicators of weight for age (BW/A) and weight for length/height (BW/H), as well as assessing the quality of MP-ASI using quality scores before and after the intervention. Data analysis was carried out descriptively and inferentially using paired statistical tests. The results showed an increase in the quality of complementary feeding (MP-ASI) after the implementation of toddler classes, accompanied by improvements in infant nutritional status based on indicators of weight for age and weight for height, with a statistically significant difference between conditions before and after the intervention. In conclusion, toddler classes are effective in improving the quality of complementary feeding and infant nutritional status, thus potentially being an applicable educational strategy in efforts to improve infant nutrition in primary health care.

Work volume calculations are essential for project cost estimation. Many projects, such as the Laboratory Building of the Faculty Engineering at Tanjungpura University, calculate work volumes conventionally. Conventional calculation considered less efficient and prone to errors. Building Information Modeling (BIM) provides a solution that produces more accurate and efficient calculations than conventional methods. This research aims to compare structural work volume results produced by BIM using Autodesk Revit against conventional methods and project’s BOQ. This research also describes the benefits and challenges of BIM implementation based on the researcher’s experience applying BIM with Autodesk Revit in work volume calculation. The comparison between BIM and conventional method shows a maximum difference of 2% across all work items. Meanwhile, the comparison between BIM and the BOQ shows significant differences: 81% in column formwork area, 24% in grade beam/beam concrete volume, 25% in column reinforcement weight, 25% in steel beam weight, and 10% in the steel plate weight. This research proves that BIM implementation produces more accurate and efficient calculations and serves as an effective BOQ cross-check tool. Based on the researcher’s experience in implementing BIM with Autodesk Revit, challenges found in procurement aspects, modeling aspects, and model dependency on reference drawings.    

In the context of Indonesia's post-pandemic financial market dynamics, investment and financing decisions often face challenges of cash flow uncertainty and capital cost volatility, requiring a Profitability Index (PI) and Weighted Average Cost of Capital (WACC) perspective to ensure optimal resource allocation to maximize company value. This study aims to analyze the effectiveness of investment and financing decisions through the integration of PI and WACC based on a synthesis of the latest literature. A descriptive qualitative approach was used through a literature study with secondary data from financial journals and textbooks from 2021-2025, collected from Google Scholar and university repositories, then analyzed thematically with data reduction, presentation, and literature triangulation to interpret the PI, IRR, and WACC indicators. The results show that PI is consistently >1 (ratio of 1.15-1.45) and IRR > WACC (average of 10-12%), confirming the feasibility of 70% of manufacturing projects, while WACC of 9.8% from the optimal capital structure (debt ratio of 40-50%) supports an effective tax shield, despite being constrained by multiple IRRs, conflicting metric rankings, and BI interest rate fluctuations that increase implicit costs by up to 15%. It can be concluded that PI-WACC integration increases theoretical profitability by 12% through precise allocation, but is limited by the generalization of secondary data; a hybrid model with mixed-method validation is recommended for the non-manufacturing sector in emerging markets.

Halal tourism has continued to grow as a highly attractive segment for Muslim travelers, particularly in the provision of Sharia-compliant accommodation such as Islamic hotels. This study aims to examine consumer perceptions of halal food-and-beverage facilities and Sharia-based operational standards in Islamic hotels across the Greater Jakarta area (Jabodetabek). A descriptive quantitative approach was employed, involving 150 respondents who had stayed in Sharia hotels. Data were collected through closed-ended Likert-scale questionnaires and analyzed using the Weighted Mean Score (WMS) technique to evaluate respondents’ assessments of each indicator. The results indicate that consumer perception of halal food-and-beverage facilities falls into the very high category, with average scores exceeding 4.838. Consumers acknowledged that Sharia hotels maintain halal assurance, hygiene, and food safety through proper processing and the availability of halal certification. Regarding operational aspects, consumer perception is also classified as very good, with an average score of 4.606, particularly for policies prohibiting unmarried couples from sharing a room and banning entertainment deemed inappropriate or immoral. However, the use of Sharia-compliant financial institutions still requires improvement. Overall, the findings affirm that Sharia hotels in Jabodetabek have successfully implemented most Sharia principles, although further enhancement of internal operational practices is needed to achieve more comprehensive Sharia compliance.

Improving service quality is a primary priority in hospital management. Service quality can be improved by reducing the percentage of non-value added activities through the Lean Hospital approach. This study aims to identify activities and analyze the root causes of critical waste in the drug distribution and utilization processes at the Outpatient Pharmacy Installation of Karanganyar Regency Hospital. This study is a non-experimental research with a qualitative descriptive design. Critical waste was obtained through the distribution of a waste weighting questionnaire assessed by all personnel involved in the drug distribution and utilization processes. The results show that the Value Stream Mapping calculation for the drug distribution process obtained a lead time of 147.41 minutes and a VAR value of 36%, while in the drug utilization process, compounded prescription service obtained a lead time of 128.53 minutes and a VAR of 24%, and non-compounded prescription service obtained a lead time of 75.8 minutes and a VAR of 26%. The critical waste questionnaire calculation using the Borda method in the drug distribution process showed overproduction 60%, inventory 53.33%, and waiting 43.33%, while in the drug utilization process, waiting 43.75%, overproduction 42.85%, and defect 39.70%. The 5S method, a Lean method, was used to eliminate waste in the service process at the Outpatient Pharmacy Installation of Karanganyar Regency Hospital.

Supplier selection is a strategic aspect of supply chain management that directly impacts product quality and operational efficiency within a company. PT. XYZ, operating in the fashion industry, is currently facing the challenge of selecting the optimal t-shirt supplier and lacks an appropriate decision-making method. The objective of this research is to provide insights into the best decision-making approach for PT. XYZ by applying the Analytical Hierarchy Process (AHP) method. The determination of suppliers is carried out by considering four main criteria, namely product quality, product price, delivery time, and service. The data obtained were generated from interviews and questionnaires given to the owner of PT. XYZ. The results of the analysis have shown that product quality is the most influential criterion with a weight of 0.716, followed by service (0.113), delivery time (0.093), and product price (0.078). The final result states that supplier C has the highest priority weight, which is 0.763. Therefore, PT. XYZ is recommended to choose supplier C as the best alternative supplier. The results of this study indicate that the AHP method can be used as a systematic and objective decision-making tool for supplier selection.

Weight gain can be a concern for some women, with some considering a body conforming to specific beauty standards as an ideal or desired goal. The use of progesterone hormone injections, which affect the appetite control center in the hypothalamus, can increase appetite and potentially lead to weight gain. This research employs a descriptive-analytical method with a quantitative approach conducted at the Independent Midwife Practice in the working area of the Kragilan Serang Community Health Center, Banten, using secondary data from medical records. Data collection involved 96 respondents divided into 48 samples of 1-month injectable contraceptive (KB Suntik) users and 48 samples of 3-month injectable contraceptive users. The total research sample size is 96 respondents, with the majority falling in the 20–40 age group and the remainder aged >40 years. The results indicate that among the 48 respondents using the 1-month injectable contraceptive, the average weight gain is 0. 938 kg, while for the 3-month injectable contraceptive, the average weight gain is 4. 251 kg.

Stunting and wasting are nutritional problems in toddlers that remain a double burden of malnutrition in Indonesia and have an impact on the quality of health and future human resource development. Monitoring the nutritional status of toddlers is generally carried out using anthropometric indicators, but the use of this data is still limited to descriptive analysis. This study aims to apply the K-Means algorithm in clustering infants vulnerable to stunting and wasting based on anthropometric indicators, so that groups of infants with different levels of nutritional vulnerability can be identified. The dataset used consists of infant data with variables of gender, age (months), height (cm), and weight (kg). The research stages included data preprocessing, encoding categorical variables, data normalization, determining the optimal number of clusters using the Elbow and Silhouette Score methods, and analyzing the characteristics of each cluster. The evaluation results showed that the optimal number of clusters was four. Each cluster has different anthropometric characteristics and distributions of stunting and wasting status, ranging from groups with relatively normal nutritional conditions, groups with a tendency toward overnutrition, to groups that are vulnerable to acute and chronic malnutrition. These clustering results provide a more comprehensive and segmented mapping of toddlers, which can be used as a basis for formulating more targeted and data-driven nutrition policies and interventions.

The scarcity of real-world data in Air-Conditioning (AC) fault diagnosis necessitates the use of synthetic data; however, rule-based synthetic datasets often suffer from a significant sim-to-real domain gap. To address this, we propose a Model-Data Coevolution (MDC) framework that employs a Simulated Annealing (SA) controller to optimize augmentation parameters. We introduce a novel technique, Stochastic Feature Decoupling (SFD), which applies independent noise to raw and derived features, contrasting it with traditional Logically-Consistent Augmentation (LCA). Empirical results show that SFD significantly outperforms LCA, achieving a weighted F1-score of 0.93 and increasing NORMAL class recall to 82%. We demonstrate that by breaking deterministic feature links, SFD acts as a robust regularizer, utilizing "physically impossible" data to enhance generalization in complex real-world environments.

This study aims to analyze the effect of using cassava (Manihot esculenta crantz) as a carbohydrate-based feed ingredient on body weight gain in beef cattle. The review was conducted using a descriptive literature study approach based on sixteen scientific articles discussing the nutritional composition, processing methods, and performance responses of beef cattle fed cassava-based diets. The analysis shows that cassava contains 17.45–88.6% dry matter, 2.4–21.45% crude protein, and 11.35–92.2% nitrogen-free extract, with variations influenced by plant part, processing method, and hydrocyanic acid (HCN) content. Processing techniques such as fermentation and ensiling can reduce HCN levels by more than 70% while increasing crude protein content up to 25%, thereby improving digestibility and feed efficiency. The inclusion of cassava in the form of flour, dried chips, pulp, or fermented peel consistently enhances dry matter intake and average daily gain (ADG) of beef cattle at inclusion levels of 20–50% in the diet. Overall, cassava has strong potential as a locally available, economical, and sustainable feed ingredient to improve beef cattle productivity.