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Occupational Safety and Health (K3) is an important aspect in protecting workers from potential accidents and health problems arising from work activities, including in the administrative sector, which is often considered low-risk. However, the Human Resources (HR) Department at PT Micro Madani Institute (MMI) Solo Branch still found several potential hazards and work risks that have not been optimally identified. This study aims to apply the Hazard and Operability Study (HAZOPS) and Job Safety Analysis (JSA) methods to identify and mitigate hazards and work accident risks in the HR Department's activities at PT Micro Madani Institute (MMI) Solo Branch. The research method is a qualitative study with a descriptive approach through observation and interviews. Risk analysis used the JSA and HAZOPS methods. The results of the study show that the application of the HAZOPS method in five sections of the HR Department successfully identified 53 potential hazards and risks, consisting of 12 extreme risks, 23 high risks, 15 moderate risks, and 3 low risks with the Employee Training and Development section as the section with the largest number of risks. Meanwhile, the application of the JSA method also identified 53 potential hazards and risks from 19 work activities. The dominant risks stem from non-ergonomic work postures, prolonged sitting, intensive computer use, and high workloads. This study concludes that the JSA and HAZOPS methods are effective in identifying potential occupational hazards and risks and can serve as the basis for control efforts to minimize workplace accidents and improve OHS implementation in the HR Department.

The increase in the number of elderly residents demands the provision of residential facilities that not only meet physical needs, but also support the psychological and social well-being of their users. This study aims to formulate the concept of behavioral architecture-based nursing home design by focusing on the relationship between elderly activity patterns, privacy levels, and spatial relationships of space in the local context of Indonesia. This study uses a qualitative-descriptive approach in the framework of architectural design, with data collection through observation of elderly activities, site analysis, and documentation studies. Activity data was analyzed to identify space needs and usage patterns, then synthesized with site characteristics to formulate the concepts of zoning, circulation, and behavior-based spatial relationships. The results of the study show that the activities of the elderly form a layered behavioral structure, including residential and health activities as primary needs as well as social, productive, and educational activities as support for psychosocial welfare. Hierarchically arranged space zoning based on privacy levels has been proven to improve the readability of the space, sense of security, and comfort of the elderly. The integration of green open spaces as part of the activity system also strengthens support for light physical activity and social interaction. This study confirms that the application of behavioral architecture allows the translation of data on elderly behavior and site conditions into a contextual, humanist, and quality-of-life-oriented design concept. These findings provide practical implications for designers and policymakers in the development of sustainable elderly housing.

Low productivity and high sick leave in the weaving division of PT XYZ in Yogyakarta are caused by excessive physical and mental workload on Shuttle Loom Unit 2 operators. The productivity of the weaving division of PT XYZ was recorded at only 61.96% (target 75%) with sick leave of 4.17%, indicating operator fatigue. This study measured physical workload using the Cardiovascular Load (CVL) method based on heart rate and mental workload using NASA-TLX, and analyzed the correlation with age and length of service. The results showed a moderate physical workload category (40% light CVL <30, 60% moderate 30-60) at a temperature of 30.5°C, and high-very high mental workload (45% high 50-79, 55% very high ≥80, average EF 79.3) due to strict quality targets (0.5% defects). There is a correlation between CVL and NASA-TLX with age. Recommendations include reducing the daily production target from 100 to 85-88 yards, optimizing ergonomics, and training to reduce EF to 65 and defects to 0.3, to increase effective productivity.

The conversion of agricultural land to non-agricultural land continues to increase along with the pressures of urbanization, industrialization, and settlement expansion. This condition poses risks to food security, environmental sustainability, and farmer welfare. This article reviews literature based on 25 abstracts/research results on LP2B in Indonesia to map policy implementation patterns, the relationship between LP2B and regional spatial planning, inhibiting factors, and the direction of policy strengthening. The method used is a narrative review with thematic synthesis of normative legal studies, juridical-empirical, qualitative, mixed methods, and spatial-quantitative approaches. The results of the review indicate: (1) LP2B is highly dependent on the harmonization of spatial planning policies, especially RTRW/RDTR and licensing mechanisms based on KKPR-OSS; (2) many regions are still stuck at the land inventory-identification stage, not yet reaching the determination and operational protection through LP2B Regional Regulations; (3) dominant obstacles include regulatory asynchronous, weak law enforcement, minimal cross-agency coordination, limited data by name by address, suboptimal socialization, and conflicts of interest in non-agricultural development; (4) incentive-disincentive instruments have not been implemented consistently, although socially farmers tend to accept LP2B protection; and (5) quantitative evidence at the national level shows that LP2B policies have a positive effect on the percentage of rice fields, despite being suppressed by population density and real estate sector growth. This article emphasizes the need for an integrated spatial governance approach, strengthening regional institutions, and designing policies that are socially and environmentally just to ensure that LP2B is effective in maintaining regional food security.

Landslides in the Giritengah Catchment Area are influenced by several factors, including geological conditions, rainfall intensity, geomorphology, soil characteristics, and inappropriate land use practices, all of which affect regional spatial planning and environmental stability. This study presents a literature review that analyzes landslide vulnerability, evaluates the impact of land use changes, and proposes integrated Soil and Water Conservation Techniques (SWCT) to support sustainable land management. The analysis applies Geographic Information System methods using thematic map overlays such as rainfall distribution, slope gradient, geological structure, and land use patterns. The results show that areas categorized as having high landslide vulnerability cover 44.02% or approximately 158.69 hectares of the catchment area, while areas with very low vulnerability account for only 0.12% or about 0.79 hectares. Land use conversion, particularly mixed dryland agriculture, has increased landslide risk by reducing slope stability and increasing surface runoff. To address this issue, conservation strategies are recommended, including vegetative measures such as greening 38.51 hectares in settlement areas and implementing agroforestry systems across 218.48 hectares. In addition, structural measures such as three dam retainers and twenty gully plugs are proposed in both protected and cultivation zones to support disaster mitigation and align with regional spatial planning policies.

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.

The R05 Community Service Program (KKN), Subgroup 1 in Kalikatir Village, Gondang District, Mojokerto Regency, aims to implement hydram pump technology as an environmentally friendly and energy-efficient irrigation solution. This technology is designed to meet the water needs of 100 hectares of banana plantations, especially during the dry season. The implementation method includes initial surveys, design, manufacture, installation, and testing of the hydram pump, involving the participation of Kalikatir villagers. The results show that the pump is capable of lifting water to a height of 30 meters with 70% efficiency, although it has not yet reached the daily target. This technology has been proven to reduce operational costs and increase agricultural productivity. It is hoped that in the future, this technology can be further developed to improve its efficiency and sustainability.

Soil strength characteristics are strongly influenced by its physical and mechanical properties, one of which is shear strength. Soil shear strength is affected by cohesion (c), internal friction angle (ϕ), and soil moisture conditions. In open-pit mining conditions, soil moisture content is greatly influenced by rainfall and water seepage, which can increase the degree of saturation within the soil mass. An increase in the degree of saturation generally leads to a rise in pore water pressure, thereby reducing the effective normal stress and resulting in a decrease in soil shear strength. This study aims to determine the effect of the degree of saturation on soil shear strength. This research employs a quantitative method to analyze the influence of the degree of saturation under three conditions (natural, dry, and saturated) on soil shear strength through laboratory testing using the direct shear test. The tests conducted include soil physical properties testing in accordance with SNI 1965-2008, specific gravity testing based on SNI 1964-2008, and soil shear strength testing following SNI 3420-2016. The results indicate that the average degree of saturation under natural conditions is 64.63% with a cohesion value of 7.4 kN/m², under dry conditions is 33.18% with a cohesion value of 8.2 kN/m², and under saturated conditions is 83.08% with a cohesion value of 3 kN/m². It can be concluded that a higher degree of saturation or more saturated soil samples result in lower cohesion values, whereas a lower degree of saturation or drier soil samples lead to higher cohesion values.

This study aims to analyze the characteristics of the soil at PIT North with two highwall and lowwall locations at PT Karya Putra Borneo, Kutai Kartanegara Regency, East Kalimantan Province. The research method was carried out by collecting primary data from the results of field tests using the sondir method with the Cone Penetration Test(CPT) tool and laboratory tests using physical properties, specific gravity, grain size, soil consistency, and consolidation tests, as well as secondary data from the results of literature studies. The parameters analyzed for soil characteristics include the sondir cone resistance (qc) value, grain size fraction distribution, soil plasticity index, and the rate of land subsidence through consolidation. The results showed that at PIT North the highwall area had a qc value of 5.6 Mpa, Fr 0.2%, IP 8.06%, Cc 1, a large rate of land subsidence of 3.3 mm with a settlement period of 4 years. In the lowwall area with a qc value of 10.31 Mpa, Fr 0.16%, IP 6.39%, Cc 0.535, the rate of settlement was 4.4 mm with a settlement period of 3 years. From these results, it is concluded that the soil at the highwall and lowwall locations has different compressibility, seeing the different soil Cc values and the soil plasticity index that affect the compressibility level of the soil and the value of the land settlement rate.

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.

Mine slopes play a crucial role in ensuring the safety and sustainability of mining activities. Continuous mining operations can alter slope conditions, thereby increasing the potential for instability and slope failures. Slope stability is influenced by the balance between resisting forces and driving forces acting on the rock mass. When the resisting forces are smaller than the driving forces, the slope becomes unstable and failure may occur. Therefore, slope stability analysis is essential to prevent landslide hazards, as it directly affects occupational safety, equipment security, other assets, and overall production continuity. These conditions have encouraged PT Insani Bara Perkasa to develop a more accurate and real-time slope monitoring system. The application of radar-based monitoring was initiated using the CHCNAV PS-2000 system. This study aims to analyze the deformation behavior of mine slopes using radar technology. Data acquisition was conducted at Pit K1 Mahakam, PT Insani Bara Perkasa, covering Area 1 South, AR 2 South, AR 3 South, AR 5 South, AR 6 South, AR 7 West, AR 8 West, AR 9 South, AR 13 South, as well as TA-2, TA-3, and TA-6 areas. The study was carried out from November 2025 to December 2026. The analysis results indicate that most monitoring points are in stable conditions, with indications of a progressive phase observed in the AR 6 South area.

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.

Education plays a crucial and essential role in improving the quality of human resource globally, thus a research was conducted with the aim of producing a product, namely a learning module. This module is one of the learning resources that can be used by students independently. Therefore, the module was developed to function as a learniing guide for students. The lerning model used in this research is Creative Problem Solving. In this learning model, the teacher presents problems so that students can find answers innovatively and sharpen  their critical thinking skills. The suitability of this learning module will be evaluated based on three aspect, namely validity, practicality, and effectiveness. Based on the research sample data, this consists of students from class XI Electrical Power Installation Techniques (TITL) 1 at SMKN 1 Driyorejo. This method uses the Research & Development (R&D) approach. The analysis of differences in learning outcomes was carried out using the One Group Pretest-Posttest Method, the treatment in the form of a learning module based on the Creative Problem Solving model was given to student. The research result show that (1) the modules suitability is stated as very valid wth a score of 89,60, (2) aspect, it is stataed as very practical with a total average reaching 90,60, (3) effectiveness is proven from the improvement in learning outcomes in terms of knowledge and domains with an average pretest score of 51,3, while the average posttest score is 85 with a significance of 0.000.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Lifestyle changes, such as smoking habits, low physical activity, and suboptimal sleep patterns, have the potential to affect various health indicators. However, empirical evidence showing a direct link between lifestyle factors and objective health indicators still shows varying results. This study aims to analyze the relationship between lifestyle factors and health indicators in adult respondents, in an effort to provide an empirical picture of behavioral factors related to health conditions. This study used a quantitative approach with a cross-sectional observational analytic design. Data were obtained from 94 respondents who had complete lifestyle data and health examination results. Lifestyle factors analyzed included smoking habits, exercise frequency, and sleep duration, while health indicators included body mass index (BMI), systolic and diastolic blood pressure, blood sugar levels, cholesterol, uric acid, and pulse rate. Data analysis was performed using descriptive statistics, the Shapiro–Wilk normality test, and the Spearman correlation test according to the characteristics of the data distribution. The results showed that smoking habits were significantly associated with diastolic blood pressure (p < 0.05), exercise frequency was significantly associated with BMI and systolic blood pressure (p < 0.05), and sleep duration was significantly associated with uric acid levels (p < 0.05). Meanwhile, the relationship between lifestyle factors and other health indicators did not show statistical significance. These findings indicate that the influence of lifestyle on health is specific to certain indicators and is not evenly distributed across all health parameters. This study concludes that identifying lifestyle factors relevant to certain health indicators is important as a basis for formulating more targeted health promotion strategies.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics

Advances in information technology have changed the way humans obtain and manage information, including in the world of education. School websites have become an important medium for conveying academic, administrative, and school activity information quickly and efficiently. However, not all educational institutions are able to optimize the functions of their websites. This study was conducted to determine user satisfaction with the website of SMP Negeri 5 Kota Jambi using the Webqual 4.0 model and Importance Performance Analysis (IPA). The research method used was a descriptive quantitative approach with data collection through the distribution of questionnaires to 291 respondents from a total population of 1,065 students. The analysis was conducted by measuring the three main dimensions of Webqual 4.0, namely usability quality, information quality, and service interaction quality, then using IPA to map service improvement priorities. The results showed that most users were satisfied with the quality of the website, particularly in  terms of ease of use and service interaction. However, the timeliness of information updates and the responsiveness of the display on mobile devices still needed improvement. Recommendations for improvement focused on the dimensions in the "Concentrate Here" quadrant of the IPA analysis.

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

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.