Yeni Herdiyeni

Background: Paddy is a staple food that is essential for global food security. Paddy land suitability evaluation is key to optimizing land resource management and productivity. Existing land evaluation techniques are manual and rigid, making them less representative of land physical data uncertainty and expert judgment reluctance. Most recent spatial decision support system and fuzzy logic-based techniques use one-dimensional models (membership) without considering expert hesitation. Objective: This study aimed to develop a conceptual model for an Intelligent Spatial Decision Support System (ISDSS) for evaluating the suitability of paddy by integrating contextual user requirements with Soft System Methodology (SSM) and numerical examples. Methods: This research methodology integrates SSM to identify problems and numerical examples based on Spherical Fuzzy Set (SFS) for land suitability assessment. The criteria weights are determined using the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP), which represents each criterion through membership (μ), non-membership (ν), and hesitancy (π) degrees to capture hesitation in expert judgment. The findings of the weighing and evaluation were combined using Weighted Overlay Analysis (WOA) to produce a land suitability index. Results: The SSM approach organizes complex and unstructured problems into a systematic conceptual framework. An example of an SFS calculation demonstrates computational feasibility based on expert validation, yielding an overall accuracy of 90% and a Cohen’s kappa coefficient of 0.861, indicating promising conceptual agreement. Conclusion: The integration of SSM and numerical examples provides a conceptual foundation for developing ISDSS for evaluating paddy land suitability. Further research will focus on implementing a prototype to test spatial performance and the validity of results at the operational level. Keywords: ISDSS, Numerical Example, Paddy Land Suitability, Spherical Fuzzy Set, SSM

This study proposes a time sliding-window and interquartile range (IQR) based time-series model to detect upwelling events in the Southern Java waters, Indonesia. The methodology encompasses data acquisition, pre-processing, seasonal analysis, upwelling detection, measurement of upwelling intensity, and lag time assessment. The results show significant seasonal variations in sea surface temperature (SST) and chlorophyll-a concentrations, with pronounced SST decreases between May and September, indicating active upwelling during this period. Spatial analysis identifies high-intensity upwelling centers concentrated between 105°E–120°E longitude and 7.5°S–9.5°S latitude, with prominent clusters around 110°E–115°E. Lag time analysis indicates shorter biological response times in upwelling centers during boreal spring and summer, with a median of 7 days and a mean of 4.2 days, respectively, reflecting a rapid onset of upwelling conditions in these seasons. These findings provide insights that may support the optimization of fishing operation by identifying periods and locations with enhanced marine productivity. Additionally, understanding upwelling dynamics also supports sustainable fisheries management, as nutrient-rich zones provide continuous food supply for marine organism to reproduction and growth. This research presents a quantitative framework for detecting and characterizing upwelling intensity, contributing to improved monitoring and management of coastal marine resources.

Cassava, an essential staple food with diverse applications, has been relatively underexplored in terms of health analysis using vegetation indices. Conventional field surveys face challenges in covering large areas due to resource constraints. Recent advancements in remote monitoring techniques, such as satellite imagery and Unmanned Aerial Vehicles (UAVs), offer a promising alternative. While satellite imagery enables broad-scale surveys, its limited spatial resolution restricts detailed analyses of individual plants or smaller ecosystems. UAV-based vegetation surveys commonly utilize Vegetation Indices (VI) to assess unique spectral information. This study investigated UAV-based methods for mapping cassava distribution in the Telaga Kahuripan smallholder plantation in Bogor, Indonesia, focusing on UAV imagery, segmentation, and vegetation indices to evaluate cassava plant health at 2, 5, and 8 months of age. The results revealed significant variations in vegetation indices across different cassava plant ages. Particularly, the highest values observed at 5 months of age indicated substantial growth, with NDVI and GNDVI values exhibiting R2 ranging from 0.95 to 0.98, indicating a strong correlation. The robust correlation between NDVI and GNDVI implies that both indices can effectively predict plant health using UAV-based monitoring. Comparisons with existing studies suggest potential variations attributable to factors such as geographical location, environmental conditions, and cultivation practices. Understanding these variations is crucial for refining monitoring techniques and informing agricultural practices. Consequently, the findings have implications for enhancing cassava health monitoring and optimizing agricultural practices to ensure sustainable crop production.

Fisheries and Aquatic Sciences

Assessing digital capabilities, including literacy, software, and infrastructure, is crucial in the micro, small, and medium enterprises (MSMEs) digital transformation. This study uses machine learning to assess and predict digital transformation performance for MSMEs in Indonesia. The research stages consist of cluster analysis using the Gaussian Mixture Model, latent variables analysis using Factor Analysis of Mixed Data, assessment analysis, prediction model development using Random Forest, and analysis of digital transformation performance using Shapley Additive exPlanations (SHAP). Findings reveal that MSMEs’ digital transformation relies on key variables: Quick Response Code Indonesian Standard (QRIS) adoption, e-commerce engagement, e-payment use, and online community participation. Machine learning categorizes MSMEs into four readiness levels: intention/beginner, adopter, performer, and transformer. The experimental results show a high classification accuracy with an average accuracy of 97%. The study outcomes can assist policymakers in evaluating and crafting strategic development plans for the digital transformation of MSMEs in Indonesia.

The distribution of carbohydrates across different plant organs, or carbohydrate partitioning, is a vital indicator of nitrogen use efficiency and resource allocation in plants. This study aimed to evaluate the impact of varying nitrogen fertilization rates on carbohydrate partitioning in cassava (Manihot esculenta Crantz). A randomized block design was used with six nitrogen dose treatments (0, 45, 90, 135, 180, 225 kg.ha-1) and four replications. Parameters observed included dry weight and carbohydrate distribution in roots, stems, leaves, root tubers, and petioles over eight months after planting (MAP). Results indicated that nitrogen fertilization significantly influenced cassava plants’ dry weight and carbohydrate partitioning, with optimal nitrogen doses varying by plant organ and growth stage. Nitrogen application enhanced carbohydrate allocation to root tubers, particularly from 3 to 8 MAP.

Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order. Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector