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This study aims to improve subcontractor safety performance through strengthening governance systems, leading safety indicators, capability building, and performance monitoring at PT Putra Perkasa Abadi (PPA) Jobsite Borneo Indobara (BIB). The research was motivated by the increasing trend of subcontractor incidents during Quarter 1 to Quarter 3 of 2025, where seven incidents occurred and exceeded the company threshold. In addition, subcontractor performance evaluation scores remained below the target threshold of 70, while customer satisfaction scores in subcontractor management decreased from 8.45 to 7.58. The study applied a continuous improvement approach using Quality Control Circle (QCC), Pareto analysis, Nominal Group Technique (NGT), and root cause analysis with the Ishikawa method. Improvement initiatives were implemented through four strategic pillars: strengthening governance, reinforcing leading indicators, capability building, and integrated dashboard monitoring. The implementation results showed significant improvements in subcontractor safety management. The subcontractor incident frequency rate was reduced to zero incidents in Quarter 4 of 2025, subcontractor performance evaluation increased above the threshold to 70.81, and customer satisfaction scores improved to 8.55. The findings indicate that structured governance strengthening combined with real-time monitoring and subcontractor engagement can significantly improve mining safety performance and stakeholder trust.

Blast hole drilling is an initial stage that plays a critical role in determining the success of blasting activities in mining and construction operations. Drilling quality directly affects rock fragmentation, drilling equipment efficiency, drilling equipment productivity, as well as blasting safety and costs. This study aims to analyze the influence of drilling parameters on blast hole quality, including hole diameter, drilling depth, hole inclination, and the suitability of drilling patterns with respect to the planned blasting design. The research methodology involves field observations, actual measurements of drill holes, and analysis of data obtained from drilling and blasting productivity. The results show that deviations in hole depth and direction can lead to irregular distribution of blasting energy, resulting in suboptimal rock fragmentation and an increased potential for flyrock and overbreak. In addition, the selection of appropriate drilling methods and equipment based on rock mass characteristics has been proven to improve productivity and blast hole quality. Therefore, controlling drilling parameters and implementing proper operational procedures are essential to enhance the overall effectiveness and productivity of blasting activities. This study is expected to serve as a reference for the planning and evaluation of blast hole drilling activities to achieve more efficient, safe, and economical operations.

The use of building materials plays a crucial role in determining the quality of the built environment, particularly in the context of row housing in densely populated urban areas. This study aims to evaluate the types of materials used in the construction of row housing in Cemara Asri, Medan, with a focus on sustainability and environmental friendliness criteria. The methods employed include direct site surveys and literature studies related to the characteristics of both interior and exterior materials applied to housing units. The research findings indicate that most of the materials used—such as red bricks, ceramic tiles, clay roof tiles, and concrete ventilation blocks—possess good energy efficiency and durability potential. However, they do not fully meet sustainability standards in terms of production processes and waste management. The study also found that residents' awareness of environmentally friendly material selection remains limited, and the procurement of materials tends to follow local availability and economic considerations. These findings are expected to serve as a foundation for improving environmentally friendly material planning in row housing developments in other urban areas.

Blasting is a critical method for overburden removal in open-pit coal mines, where fragmentation quality directly impacts loading efficiency and operational costs. This study aims to analyze the actual fragmentation resulting from overburden blasting at Pit 4 Middle of PT. Victor Dua Tiga Mega, Central Kalimantan, to predict fragmentation using the Kuz-Ram model, and to evaluate the conformity of both results against the company standard (boulder size ≤144.6 cm or ≥50 cm for analysis). The research employed a quantitative comparative method. Primary data included blasting geometry and photographs of muck piles from 10 blasting events, which were analyzed using WipFrag software to obtain actual fragmentation distribution. Secondary data comprised rock characteristics and explosive properties for Kuz-Ram prediction input. The results showed significant variation in actual boulder percentage (≥50 cm), ranging from 6.19% to 32.91% with an average of 16.05% (medium category). Statistical analysis revealed a very weak negative correlation (r = -0.21) between powder factor (PF) and boulder percentage, indicating that PF is not the dominant factor within the consistent application range (0.21-0.23 kg/bcm). Comparison with Kuz-Ram predictions showed that the model consistently over-predicted coarse material, with an average difference of +25.21%, suggesting the need for rock factor (A) recalibration. It is concluded that the blasting results are inconsistent, strongly influenced by uncontrollable factors such as geological conditions. Recommendations include geometri evaluation, particularly burden and spacing, and calibration of the Kuz-Ram model for more accurate future predictions.

The evaluation of haul road geometry at PT Trubaindo Coal Mining was conducted to ensure that the haul road from the stockroom to the jetty meets applicable technical standards, thereby supporting the smoothness, safety, and efficiency of coal transportation activities. This study aims to analyze the conformity of haul road geometric parameters, including straight road width, curve width, curve radius, cross slope, longitudinal grade, and superelevation, based on the standards of AASHTO 7th Edition and Kepmen 1827 of 2018. The research method employed field measurements using a Terrestrial Laser Scanner (TLS), followed by data processing using Maptek and Surpac software. The obtained data were then compared with the applicable standards to determine the level of conformity of the haul road geometry. Based on field measurements, the average values obtained were 14.99 m for straight road width, 18.45 m for curve width, 0.22 m for cross slope, 6.97% for grade, 223.51 m for curve radius, and 1.40 m for superelevation. Furthermore, based on calculations and analysis referring to AASHTO 7th Edition and Kepmen 1827 of 2018, and considering the largest haulage equipment used (Volvo FMX 480), the ideal haul road geometry values were determined as follows: 13 m for straight road width, 17 m for curve width, 124 m for curve radius, 0.26 m for cross slope, 1 m for superelevation, and 6% for grade based on PT Trubaindo Coal Mining standards.

Coal stockpile volume measurement is an essential part of mining production control. This study aims to evaluate the calculation of coal stockpile volume using a Total Station (TS) at PT. Bukit Baiduri Energi, Kutai Kartanegara Regency, East Kalimantan Province, and to assess the accuracy and effectiveness of this method in operational activities. Data acquisition was carried out through direct field measurements using a Total Station with a detailed surface point survey method. The collected data were processed using Minescape 5.7 software to generate a triangulated surface model, and the volume was calculated using the Cut and Fill method. The results show that the Total Station method produced a stockpile volume of 21,069.15 m³ with a high level of accuracy due to direct field measurement. This method provides advantages in elevation data accuracy; however, it requires relatively more time and manpower during the data acquisition process. Based on these findings, the use of Total Station is recommended for stockpile volume calculations that require high accuracy, particularly for production evaluation and coal reserve reporting.

This research aims to analyze the quality of electronic services (E-Service Quality) on user satisfaction of the DANA digital wallet application in the West Jakarta area. The evolution of financial technology (fintech) in Indonesia has encouraged rapid growth in the use of digital wallets, including DANA which is recorded to have more than 200 million users by 2024. However, this rapid growth is still marred by a number of user complaints regarding system reliability, service response speed and transaction security. Therefore, this study is important to evaluate how much e-service quality dimensions influence user satisfaction levels. The research method used is a quantitative approach with an associative type of research. Data was collected through questionnaires from 100 respondents who were active users of the DANA application in the West Jakarta area using a purposive sampling technique. Data analysis was carried out using SPSS software through validity tests, reliability tests, and simple linear regression analysis. The research results show that E-Service Quality has a positive and significant effect on user satisfaction of the DANA application with a coefficient of determination (R Square) of 0.907, which means that 90.7% of the variation in user satisfaction can be explained by the E-Service Quality variable, while the remaining 9.3% is influenced by other factors outside the research model. The results of the significance test (t test) show a significance value (Sig. < 0.05) which indicates that the influence of E-Service Quality on user satisfaction has been proven to be statistically significant. The results of this study are expected to serve as evaluation material for DANA application managers in improving the quality of digital services, particularly in terms of efficiency, system reliability, security, and user responsiveness. Furthermore, this research is expected to provide theoretical contributions to the development of studies in the field of information systems and digital service management.

Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.

Occupational Health and Safety (OHS) is an important element in creating a safe, healthy, and sustainable work environment. PT. X as a logistics and port operations company has potential occupational hazards originating from physical, chemical, and biological factors that need to be managed optimally. This study aims to evaluate the implementation of OHS at PT. X based on the results of measurements of physical, chemical, and biological factors of the work environment and their compliance with the provisions of the Minister of Manpower Regulation No. 5 of 2018. This study uses a descriptive method with an evaluative approach to work environment monitoring data in 2025 in the generator and office areas. The parameters analyzed include noise, lighting, hot work climate (ISBB), inhalable and respirable dust exposure, and microbiological air quality in the form of total bacteria and fungi. The results show that most parameters meet the specified standards, with the exception of the generator area which exceeds the noise limit and the hot work climate which exceeds the Action Level (AL). The implementation of OHS at PT. X has been running quite well, indicated by most of the work environment parameters that meet the standards. However, strengthening risk controls, particularly regarding noise and hot working conditions in operational areas, is still necessary. This evaluation is expected to serve as a basis for continuous improvement in the implementation of Occupational Health and Safety (OHS) to protect workers from potential occupational hazards and support the productivity and sustainability of company operations.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

The stability of disposal slopes is a critical aspect of open-pit mining operations because it directly affects operational safety and the continuity of overburden dumping activities. Disposal areas composed of overburden materials generally exhibit heterogeneous characteristics, particularly when soft materials such as mud are present, which can significantly reduce slope stability. Therefore, a comprehensive slope stability evaluation is required prior to further disposal development. This study aims to assess the stability condition of a disposal slope under initial conditions, evaluate the influence of material conditions, and analyze the effectiveness of counterweight application in improving both the safety factor and disposal capacity. The research methodology involved the collection of primary and secondary data, including slope geometry, lithological conditions, and the physical and mechanical properties of disposal materials obtained from laboratory testing and company technical data. Slope stability analysis was performed using the limit equilibrium method with the assistance of geotechnical software, taking into account groundwater conditions and operational loading. The analysis results indicate that the initial disposal condition yielded a safety factor of 0.718, indicating an unstable slope condition. After simulating the removal of mud material, the safety factor increased to 0.907 but remained below acceptable stability criteria. The application of a counterweight significantly improved slope stability, resulting in a safety factor of 1.498. Further optimization through slope geometry redesign produced a final safety factor of 1.101, which satisfies the requirements stipulated in KEPMEN ESDM No. 1827 K/30/MEM/2018. Additionally, the redesign increased the disposal capacity from 119,507,864.23 LCM to 119,682,378.22 LCM, representing an increase of 174,513.99 LCM. These results demonstrate that counterweight application combined with geometric optimization is effective in enhancing both slope stability and disposal capacity.

This research looks into how site managers in construction projects in Malang City manage and improve their use of human resources. The research is driven by the significance of effective human resource management in ensuring project success and the lack of studies focusing on the operational strategies of site managers within a local context. This study uses a quantitative method that is both descriptive and analytical. It involves giving out a survey to 40 people, including site managers, contractors, and foremen. The independent variables include leadership and supervisory style, human resource planning and workforce allocation, training and competency development, team communication and coordination, performance monitoring and evaluation, as well as motivation and welfare. The dependent variable is the degree to which human resource management is optimized. The results of validity and reliability tests indicate that all research instruments are valid and reliable. Multiple linear regression analysis shows that leadership and supervisory style, along with human resource planning and workforce allocation, have a significant partial effect on improving HRM practices. Other variables have a positive but statistically insignificant partial effect on the estate. Overall, the management of human resources in construction projects in Malang City is considered to be fairly good. This study confirms that leadership and workforce planning are the main strategic factors for site managers in improving the effectiveness of human resource management in construction projects.

Cilacap Regency in Central Java Province is recognized as an important contributor to national food production due to its extensive agricultural land and reliance on technical irrigation systems. The performance of irrigation networks, particularly secondary channels in the Serayu Irrigation Area (DI), is essential in regulating water levels to support agricultural activities in Sampang and Karangasem Villages. Variations in rainfall and field conditions often create water level differences that disrupt irrigation distribution, reducing water flow efficiency to rice fields and lowering productivity, especially during the dry season. This study aims to design secondary irrigation channels capable of maintaining stable water levels based on irrigation requirements. The research applies a descriptive quantitative method using rainfall data from BMKG, along with land area and elevation data interpreted from Google Earth imagery in 2025. The data were analyzed using probability and Thiessen polygon methods to estimate design rainfall, determine water availability, calculate irrigation demand, and plan channel dimensions. The analysis shows that Secondary Channel BGS 4.B serves an irrigation area of 103.57 hectares with a planned discharge capacity of 0.0343 m³/s. The channel design includes a base width of 0.65 meters and a water depth of 0.65 meters, with water surface elevations of 14.27 meters upstream and 13.15 meters downstream. Evaluation using a 10-year rainfall return period of 151.677 mm shows the channel can convey discharge effectively while maintaining water levels below the embankment, ensuring reliable irrigation distribution.

The construction and infrastructure sectors are shifting toward lighter, low-emission, and sustainable materials in response to the high carbon footprint and excessive weight of common materials such as concrete and steel. One promising alternative widely developed is natural fiber–based composites. However, studies comparing mechanical properties of variations of natural fibers within a single framework remain limited. This study aims to evaluate and compare composite mechanical properties reinforced by sisal fiber, bamboo fiber, and pineapple leaf fiber to determine the optimal fiber type for sustainable infrastructure applications. The research methodology involved fabrication of composite specimens using a unidirectional fiber configuration with a resin matrix, molded following ASTM D638 Type I dimensional and geometrical requirements. Tensile testing was conducted to evaluate mechanical responses, including ultimate tensile behavior, deformation characteristics, and elastic properties, which were presented in tabular and graphical forms. The results show that incorporation of all natural fiber types significantly enhanced composite mechanical properties, exhibiting an average tensile strength of approximately 26 MPa. Pineapple leaf fiber demonstrated balanced mechanical behavior combining strength and ductility, while sisal fiber showed superior tensile resistance and rigidity. Bamboo fiber provided moderate mechanical improvement. Overall, natural fiber–reinforced composites demonstrate strong potential as environmentally friendly alternative materials for infrastructure applications, with mechanical characteristics adjustable based on reinforcing fiber type.

The growing pressure on urban drainage systems caused by blockages and environmental pollution highlights the importance of developing sustainable filtration materials. This study explores the use of coffee waste as an environmentally friendly filler integrated into polyester fabric (PE 24S) to create a green filtration medium for drainage applications. The research focuses on developing and characterizing the composite material through macro-photographic morphology analysis, tensile strength testing, and antibacterial evaluation. The findings demonstrate that polyester fabric modified with coffee waste shows a tensile strength of 54.024 ± 5.498 MPa, elongation of 111.128 ± 6.915%, and a Young’s modulus of 0.486 ± 0.543 MPa, indicating improved flexibility and sufficient mechanical durability for drainage system use. Additionally, antibacterial testing reveals that the composite material can inhibit microbial growth due to the presence of natural bioactive compounds found in coffee waste. Overall, the results suggest that coffee waste–enhanced polyester fabric offers a sustainable and mechanically reliable alternative for environmentally friendly drainage infrastructure while supporting circular economy practices.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Sanitation is an important aspect in the fish feed industry to ensure product quality, product safety, and employee health. PT. CentralPertiwi Bahari is a fish feed industry that has implemented sanitation as part of its product quality assurance system. This study aims to examine the implementation of sanitation at PT. CentralPertiwi Bahari to obtain an overview of the implementation of sanitation based on Permenkes No. 70 of 2016. This study uses a qualitative descriptive method carried out in January-February 2025. Data collection was carried out through observation, interviews, document reviews and scientific literature. Aspects studied include external environmental sanitation, factory environmental sanitation, building sanitation, production process sanitation, raw material sanitation, and employee sanitation. The results of the study indicate that PT. CentralPertiwi Bahari has implemented sanitation in accordance with the provisions of Permenkes No. 70 of 2016 as indicated by clean environmental and building conditions, the availability of good sanitation facilities, proper management of raw materials, and employee sanitation that is consistently implemented. The implementation of sanitation at PT. CentralPertiwi Bahari has supported efforts to maintain quality and occupational health. However, conditions requiring attention, such as water pooling outside the factory during rainy seasons, require remedial measures to optimize sanitation implementation. The results of this study are expected to serve as a basis for company evaluation and a reference for similar industries in improving sustainable sanitation practices.