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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.

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.

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.

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.

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

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.

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.

Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas

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.

The digital transformation of health services through the Mobile JKN application was introduced by BPJS Kesehatan to provide easier access for the public in obtaining information, managing membership administration, and receiving health services more quickly and efficiently. This study aims to measure the readiness level of patients at Abdul Manap Regional Hospital, Jambi City, in adopting the Mobile JKN application using the HOT-Fit method, which covers three main components: Human, Organization, and Technology. Data were collected from 360 respondents through questionnaires and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that technology factors—including system quality, information quality, and service quality— along with organizational support have a significant effect on system use and user satisfaction, which in turn positively influence the net benefits. The outer loading values of all indicators exceeded 0.7, with Composite Reliability above 0.8 and AVE above 0.6, confirming that the research instruments are valid and reliable. Overall, patients at Abdul Manap Hospital are categorized as ready to adopt Mobile JKN, although improvements in digital literacy and stronger organizational support are still required to optimize its utilization.

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.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

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.

This research aims to recontextualize the local wisdom embedded in the visual culture of the Muara Jambi Temple through the development of folklore-inspired batik motif designs. As a traditional Indonesian textile craft, batik serves not only functional purposes but alsp embodies profound cultural values and indigenous knowledge systems. Batik as a medium of cultural expression that invites multidimensional interpretaion within both practical and theoretical academic discourses. Its visual strength lies in the richness of colors, ornaments, and symbolic elements, which generate diverse interpretative meanings. These interpretations subsequently shape value systems that function as guiding principles in the everyday lives of Indonesian communities. The research used an experimental design method integrated with computer vision techniques to generate distinctive folklore batik motifs rooted in the cultural heritage of Muara Jambi, Sumatra. The research results are the creation of a Muara Jambi folklore batik motif that represents cultural expression, resilience, and continuity through the preservation of traditional patterns and environmentally conscious practices within the Jambi community. The transformation of motifs derived from artifacts from the Muara Jambi temple complex, serves as a primary source of inspiration for contemporary folklore batik design. The application of computer vision in the batik design process constitutes a form of sustainable digital innovation, facilitating the preservation, reinterpretation, and adaptive transformation of traditional visual heritage.

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.