A simple and efficient method has been developed for the simultaneous determination of eight flavonoids (orientin, hyperoside, rutin, myricetin, luteolin, quercetin, kaempferol, and apigenin) in Sonchus arvensis by high-performance liquid chromatography diode array detector (HPLC-DAD). This method was utilized to differentiate S. arvensis samples based on the plant parts (leaves, stems, and roots) and the plant’s geographical origin. The chromatographic separation was carried out on a reverse-phase C18 column by eluting at a flow rate of 1 mL/min using a gradient with methanol and 0.2% aqueous formic acid. In the optimum conditions, the developed method’s system suitability has met the criteria of good separation. The calibration curve shows a linear relationship between the peak area and analyte concentration with a correlation coefficient (r²) > 0.9990. The ranges for the analytes’ limits of detection and quantitation were 0.006–0.015 and 0.020–0.052 µg/mL, respectively. Intra-day and inter-day precision expressed in terms of RSD values were <2%, and the accuracy range based on recovery was 97–105%. The stability of all analytes within 48 h was about 2%. By combining HPLC-DAD fingerprint analysis with chemometrics, the developed method can classify S. arvensis samples based on the plant parts and geographical origin.

Rain tree (Samanea saman) is a dominant species commonly planted as a shade tree in urban landscapes in the tropics, including in Indonesia. This species is also targeted for routine inspection and monitoring within tree management systems. Management should particularly focus on tree health through assessment of tree growth conditions as well as tree stability. However, baseline information on the relationships of many key characteristics for the rain tree (e.g., growth, morphometrics, and biomechanics) is lacking. Therefore, an investigation was carried out to determine the relationship of morphometric parameters to tree growth, which included use of an acoustic tool to assess the elasticity of the tree stem. Fifty rain trees were selected for data collection and evaluated using stress wave velocity tools and visual morphometrics assessment. The results demonstrated that, in general, the trees had good vigor and stability. Statistical analysis showed a strong relationship between tree growth parameters and selected morphometric characteristics. The use of a nondestructive acoustic-based instrument enabled determining the modulus of elasticity, which provided equivalent results with the elasticity value obtained through a common method for determining tree stability. Only live crown ratio and tree slenderness parameters had a relationship with the dynamic modulus of elasticity. These results offer a means of measurement that is simpler, less expensive, and faster than current methods, although further testing is needed for different tree species and growth sites.

Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.

Tree stability relies on the characteristics of both root and crown structures. However, studying root systems is challenging due to their underground location, often requiring destructive methods for assessment. Non-destructive approaches offer potential solutions, such as the root detector tool. However, research in this area remains limited and requires further development. This study aims to evaluate the root detector tool by inspecting the radial root distribution in trees with different tree crown shapes, both excurrent (Agathis loranthifolia) and decurrent (Samanea saman), which grow in various soil slopes and soil slope positions. In addition, we establish correlations between tree morphometry, the physical properties of soil, root attributes, sound wave velocity, and their relationship. Based on the results, it was found that the root detector tool is effective in evaluating root distribution, including identifying the main root. The slope position of the tree in a slope class influences the radial distribution of the main roots. This is related to the crown growth as indicated by the direction of its crown. Principal Component Analysis (PCA) findings suggest that parameter morphometric and soil and root properties data clustering align with slope position rather than slope class.

Clustering is a process of grouping data into several groups (clusters) so that data in one cluster has a homogeneous level of similarity and data between clusters has heterogeneous similarity. A common example of a clustering algorithm is K-Means Clustering. Compared with classical clustering algorithms, the biclustering algorithm is a two-dimensional data grouping process. The biclustering algorithm functions to find data submatrices, namely row subgroups and column subgroups that have high correlation. One example of a biclustering algorithm is Cheng and Church Biclustering (CC Biclustering). The aim of this research is to evaluate the performance of the biclustering algorithm against classical clustering algorithms. Analysis applied to CC Biclustering and K-Means Clustering to identify distribution patterns of Indonesian export goods in the period 2013 to 2022. Based on research results, the optimal scenario for the K-Means algorithm is scenario 2, that is the application of the 7 cluster K-Means algorithm with pre- processing data scaling. Meanwhile, the optimal scenario for the CC Biclustering algorithm is scenario 1, that is the application of the CC Biclustering algorithm with a tolerance value of 0.10 with data scaling pre-processing. However, from these two scenarios, based on the MSR/Volume value, it was concluded that the best scenario is scenario 1 in the application of the CC Biclustering algorithm which has an MSR/Volume value of 0.077.