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This research aims to analyze the role and impact of PT Batujaya Bersama Sejahtera (PT BBS) on the socio-economic conditions of the community in Walandano Village, Balaesang Tanjung District. The background of this study is driven by the massive expansion of the mining industry in Central Sulawesi, which triggers a structural shift from traditional agriculture to an industrial economy. This study employs a qualitative method with data collection techniques including in-depth interviews, observation, and documentation. The findings indicate that PT BBS plays a significant role in local economic development by providing employment opportunities, increasing household income, and improving public infrastructure such as roads and jetties. However, the study also identifies social disruptions, including public protests regarding land issues and environmental concerns like dust and noise pollution. The implications of this research suggest that the company must strengthen its Corporate Social Responsibility (CSR) programs by focusing on sustainable community empowerment and more transparent communication to mitigate social risks. These findings contribute to the literature on regional economic development and social change in coastal mining areas.

Chrysanthemum is an ornamental plant widely cultivated in Indonesia, with various varieties that have unique and attractive characteristics. Each variety responds differently to changes in light intensity. Providing shade can help regulate the light intensity received by chrysanthemum seedlings during the acclimatization stage. This study aimed to determine the best shade percentage for the acclimatization growth of three chrysanthemum varieties. The research was conducted in the screenhouse of the Faculty of Agriculture, Tanjungpura University, for three months, from October to December 2024. The experiment used a Split Plot Design (Split Plot) with a Randomized Block Design (RBD), consisting of two factors: shade percentage and variety. There were three levels of shade and three chrysanthemum varieties, resulting in nine treatment combinations. Each treatment was repeated three times, with each replication consisting of three sample plants, resulting in 81 experimental units. The shade percentages used were 25% (n1), 50% (n2), and 75% (n3), and the varieties tested were Xanne (v1), Suciyono (v2), and Pinka Pinky (v3). Observed variables included plant survival rate, plant height, stem diameter, internode length, number of internodes, number of flower primordia, leaf color changes, along with supporting data such as temperature, humidity, and light intensity. The results showed that 50% shade was effective in promoting plant height and the number of flower primordia in the three varieties: Xanne, Suciyono, and Pinka Pinky. The Suciyono variety exhibited good vegetative and generative growth under all shade percentages.

The agricultural sector plays a strategic role in meeting food needs while maintaining environmental sustainability and community welfare. However, conventional farming practices that rely on chemical inputs have caused various problems, such as soil degradation, loss of biodiversity, and environmental pollution. This condition has encouraged the adoption of the Sustainable Agriculture concept, which emphasizes a balance between ecological, economic, and social aspects. Its successful implementation is strongly influenced by farmers’ perceptions as the main actors. This study aims to analyze rice farmers’ perceptions of the implementation of Sustainable Agriculture in swampy areas of Tebas District, Sambas Regency. The research was conducted from October to November 2025 using a survey method involving 95 farmers selected through the Slovin formula and proportional random sampling technique. Data were collected through observation, interviews, and Likert-scale questionnaires, and analyzed using descriptive analysis and binary logistic regression. The results show that farmers’ perceptions are generally positive, particularly regarding water management, variety selection, and cropping patterns. However, the use of organic fertilizers, environmentally friendly pest control, and post-harvest management are still considered difficult. Education level and non-farming occupations significantly influence farmers’ perceptions. Therefore, enhancing extension services, training, and support for business diversification is necessary to promote the adoption of Sustainable Agriculture.

This study aims to analyze the level of financial literacy and examine its influence on the household welfare of vegetable farmers. Agricultural households, particularly smallholder vegetable farmers, frequently experience economic vulnerability due to several structural challenges such as unstable agricultural income, seasonal production patterns, fluctuating market prices, limited access to formal financial services, and inadequate financial management skills. These conditions often make farm households more susceptible to economic shocks, including crop failure, input price increases, or sudden market price declines. In this context, financial literacy becomes an essential capability that enables farmers to manage their financial resources more effectively.This research employed a quantitative research design using a survey approach. The study involved 120 vegetable farmers selected as respondents from major vegetable-producing areas. Data were collected through structured questionnaires designed to measure farmers’ financial literacy levels and household welfare conditions. Financial literacy was assessed through indicators such as financial knowledge, financial behavior, and financial attitudes, while household welfare was evaluated based on indicators including consumption stability, education and health expenditures, savings capacity, and overall economic resilience. The collected data were analyzed using descriptive statistics to describe the characteristics and financial literacy levels of respondents, and multiple linear regression analysis to examine the relationship between financial literacy and household welfare.The results of this study highlight the importance of strengthening financial education programs targeted at agricultural communities. Improving financial literacy among vegetable farmers can contribute not only to better household financial management but also to broader rural economic development. Therefore, financial education initiatives should be integrated into agricultural extension programs, farmer group activities, and local government development strategies. Such initiatives may include training in household financial planning, simple bookkeeping for farm businesses, savings management, and responsible credit use. By enhancing farmers’ financial capabilities, these programs can help improve household welfare, strengthen rural economic resilience, and support the long-term sustainability of the agricultural sector.

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 study explores the shift from traditional to modernist educational paradigms in Islamic boarding schools (pesantren), which have been in existence since before Indonesia’s independence. The shift involves adapting learning methods while retaining traditionalist approaches, ensuring that they meet modern needs without eliminating their core values. The study uses a qualitative-phenomenological approach to examine three main areas: (1) the strategy of educational values and spiritual practices employed by pesantren leaders, with an emphasis on the TAQWA method, which aims to improve student understanding quickly; (2) the integration of Qur'an literacy, religious traditions, and environmental empowerment in the educational process; and (3) the impact of this model on student character development. Qur'an literacy in this context extends beyond reading and memorizing verses, focusing on understanding and actualizing its values in daily life. Religious practices such as book study, worship routines, and etiquette coaching promote moral development. Environmental activities, such as agriculture and natural resource management, encourage independence and ecological awareness. This holistic approach can serve as a model for character education, blending spiritual, social, and environmental aspects, while strengthening pesantren's role in fostering moral and ecological awareness.

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

Regional economic development requires understanding the structure and performance of economic sectors to create effective policies. PadangiCity, the capital of West SumatraiProvince, plays a strategic role in the regional economy. However, differences in sector contributions and growth indicate structural imbalances that need attention. This studyiaims to identify leading and potential economic sectors in Padang City to support sustainable development planning. The study uses Location Quotient (LQ), iDynamic Location Quotient (DLQ), and the Growth Ratio Model (GRM) to analyze secondary data on GrossiRegional Domestic Product (GRDP) at constant 2010 prices from 2020 to 2024, sourced from the CentraliBureau of Statistics of Padang City and West Sumatra Province. LQ results show that most sectors in Padang City are base sectors, especially business services, transportation and warehousing, ifinancial and insurance services, real estate, and wholesale and retail trade. DLQ analysis indicates that mining and quarrying, trade, transportation and warehousing, iinformation and communication, and health and social services have higher growth prospects than the reference region. GRM results show that trade, information andicommunication, real estate, health services, andiother services are leading sectors with good performance and growth potential. In contrast, agriculture, manufacturing, and construction are still lagging sectors. These findings highlight a structural shift in Padang City’s economy toward service-sector dominance and underline the need for sustainable, inclusive, and adaptive development policies to support long-term economic growth.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

Brebes Regency is one of the main national centers of shallot production in Indonesia, where agricultural land dynamics play a crucial role in sustaining production performance. Rapid infrastructure development and land-use change have raised concerns regarding the sustainability of shallot cultivation areas. This study aims to analyze spatial–temporal changes in shallot cultivated area and production in Brebes Regency during 2023–2024 and to examine the relationship between land area changes and production levels at the sub-district scale. A descriptive quantitative approach based on spatial analysis was employed using Geographic Information Systems (GIS). Spatial data consisted of administrative boundary maps, while non-spatial data included shallot cultivated area and production data obtained from the Brebes Regency Office of Agriculture and Food Security. Data integration and analysis were conducted using QGIS through attribute joining and inter-annual comparison. The results indicate that total shallot cultivated area increased from 26,331 ha in 2023 to 28,628 ha in 2024, accompanied by a significant rise in production from 289,942.05 tons to 409,106.90 tons. Spatial analysis reveals that major production centers remain concentrated in the central and northern sub-districts, particularly Wanasari, Larangan, Bulakamba, and Tanjung. Although a positive relationship between land expansion and production increase is evident, variations among sub-districts suggest that productivity and local conditions also play important roles. The findings highlight the effectiveness of GIS-based analysis in supporting spatially informed agricultural land management and policy formulation.

The shift in economic structure from a reliance on traditional agriculture to modern industrialization has yielded various competitive advantages for a region. The presence of leading sectors at the local level is a strong indicator of increased added value and high productivity, fueled by a healthy competitive climate among business actors. This phenomenon is closely related to the characteristics of the industrial sector, which possesses very strong forward and backward linkages, making it a primary driver of national economic growth. Progress in the manufacturing sector is not merely a measure of economic success but also serves as a driver of fiscal stability through massive employment absorption and increased foreign exchange earnings. Industrial growth is considered highly strategic because it holds significant potential for future expansion through technological innovation and production efficiency. This condition makes industrial cluster development an instrument for driving sustainable economic growth in Indonesia, while simultaneously strengthening the competitiveness of local products in the global market through the integration of a more organized production system.

Industrialization is an economic development process characterized by the growth of the industrial sector as a key driver of economic progress in Indonesia. Beyond its role in creating added value and enhancing national competitiveness, industrialization is closely interconnected with other strategic sectors, including agriculture, services, infrastructure, and employment. Strong linkages between industry and these sectors are essential to ensure that economic growth is inclusive and sustainable. This study aims to analyze industrialization strategies that integrate and strengthen relationships with other sectors in the development process. The research employs a literature review method using a qualitative descriptive approach. The data are derived from secondary sources, including scientific journals, research articles, policy reports, and official publications relevant to industrial and sectoral development. The findings indicate that industrialization strategies aligned with agriculture can increase productivity and value-added processing, while linkages with the service sector and infrastructure development can improve efficiency, distribution networks, and market access. Furthermore, integrated industrialization contributes significantly to employment creation and regional development, reducing economic disparities between areas. Therefore, the formulation of an industrialization strategy that is well-coordinated with other sectors is crucial to achieving balanced economic growth, strengthening structural transformation, and supporting sustainable development in Indonesia.

Regional development faces dynamic challenges amid rapid economic growth driven by natural resource extraction. This study aims to identify leading economic sectors, analyze structural economic transformation, and evaluate the role of these sectors in regional development. The research employs a quantitative method with a descriptive approach. Secondary data consist of Gross Regional Domestic Product (GRDP) at constant prices over the past five years. The analytical techniques applied include Location Quotient analysis to identify base sectors, Shift-Share analysis to assess structural changes as well as comparative and competitive advantages, and Klassen Typology to classify sectoral growth patterns. The results reveal a structural shift from primary sectors, such as agriculture and fisheries, toward secondary sectors, including mining and manufacturing. Despite challenges related to development equity, these leading sectors serve as key drivers of regional economic growth. To maximize the contribution of leading sectors to broader regional development, this study recommends that government policies prioritize the strengthening of intersectoral linkages.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

The development of digital technology has encouraged the government to utilize social media as an effective means of public communication. The Ministry of Agriculture is one institution that actively uses Instagram to convey information, build a positive image, and interact with the public. This study aims to determine the Public Relations strategy in managing the Instagram social media account @kementerianpertanian. This study uses the Public Relations strategy theory from Cutlip and Center, which consists of four stages: problem identification, planning and programming, communication and action, and evaluation. The research method used is a descriptive qualitative approach, with data collection techniques through in-depth interviews and documentation of the Ministry of Agriculture's Public Relations team. The results show that the Ministry of Agriculture's Public Relations has implemented a planned communication strategy, starting from planning informative and educational content, utilizing Instagram features such as reels, stories, and live to increase interaction, to routine evaluation based on engagement data (likes, shares, comments, and saves). This strategy also adapts to social media trends and audience needs. Public Relations actively responds to user questions and comments to maintain two-way communication. However, challenges such as negative comments, limited resources, and changes in the Instagram algorithm are obstacles that still need to be overcome. Overall, the implemented strategy has successfully supported the improvement of the image and transparency of public information within the Ministry of Agriculture.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

This study aims to analyze production levels and identify leading vegetable commodities in Rejang Lebong Regency during the period of 2023–2024. The data used is secondary data sourced from the Rejang Lebong Regency Agriculture Office and empirical calculations based on Location Quotient (LQ) analysis. The results show that the total vegetable production in Rejang Lebong Regency in 2023 reached 3,451,596 quintals and decreased to 3,198,925 quintals in 2024. The commodities with the highest average production were cabbage (647,021 quintals), eggplant (646,994.5 quintals), and carrots (339,041.5 quintals), while the lowest production was found in kale and long beans. LQ analysis shows that there are eight leading commodities (LQ > 1), namely spring onions, green beans, cauliflower, potatoes, cabbage, mustard greens, carrots, and large chilies, most of which are highly competitive highland crops. The results of this study confirm that Rejang Lebong Regency has strong potential as a horticultural center in Bengkulu Province. Therefore, a commodity-based agricultural development strategy is needed that is oriented towards increasing productivity, added value, and the region's competitiveness in a sustainable manner.

This study aims to assess the growth rate of harvested area and rice production in Bengkulu Province during the 2019–2024 period and to examine spatial variation across districts as a basis for regional agricultural policy planning. The data used are secondary, consisting of a series of spatial and temporal data on harvested area and rice production; the analysis method includes calculating the compound annual growth rate (CAGR) for each indicator and examining temporal and spatial patterns across districts. The analysis results indicate a negative growth rate in the rice sector, with a relatively larger contraction in harvested area compared to production, accompanied by fluctuations and differences between years, indicating heterogeneity in agricultural performance at the district level. These findings have clear policy implications: the need for agricultural land protection measures, improvement and maintenance of irrigation infrastructure, increased farmer access to technology and markets, and the implementation of indicator-based growth rate monitoring to determine intervention priorities. To strengthen the policy base, recommended further research includes field verification and combined (quantitative-qualitative) studies to identify local factors driving the observed trends and fluctuations.