The widespread presence of Fusarium mycotoxins in animal feed is a global issue, not only for the health of livestock but also for ensure the safety of food as an end product. High concentrations of zearalenone (ZEN) and deoxynivalenol (DON) have been detected in the diets of Japanese Black (JB) and Holstein Friesian (HF) breeding herds. Consequently, we monitored serum biochemical parameters over a long time in both herds, focusing on anti-Müllerian hormone (AMH) levels and acute-phase inflammation. Additionally, urinary 8-hydroxy-2′-deoxyguanosine (8-OHdG) and progesterone levels were measured in the HF herd. The JB herd, a ZEN-dominant model with low DON contamination, demonstrated ZEN levels that exceeded the Japanese limit in the purchased total mixed rations (TMR). Conversely, the HF herd, which primary consumes DON-dominant feed with low ZEN contamination, had high DON levels in the dent corn silage. Specifically, the JB herd’s TMR contained 1.79 mg/kg ZEN and 0.58 mg/kg DON, whereas the HF herd’s silage had 15.3 mg/kg DON (dried sample) and 0.1 mg/kg ZEN. Enzyme-linked immunoassay were used to measure urinary ZEN-DON levels following confirmation through liquid chromatography-tandem mass spectrometry. Urinary ZEN-DON levels measured were significantly correlated (p < 0.05, r > 0.6) in both herds. In the HF herd, AMH levels increased (p = 0.01) and serum amyloid A (SAA) levels decreased (p = 0.02) when contaminated and at the end of the monitoring period. Additionally, urinary ZEN and DON levels were significantly correlated with SAA levels (ZEN: p = 0.00, r = 0.46; DON: p = 0.03, r = 0.33), with an increase in ZEN and DON levels resulting in higher SAA levels. The JB herd showed no significant differences. Additionally, in the HF herd, 8-OHdG/Cre levels increased significantly during major contamination periods (p < 0.05). Clinical data from the HF herd indicated an increase in mastitis cases and treatment rates during periods of major contamination. Abortion rates in the HF herd decreased from 22.9% (before monitoring) to 8.9% (during the high contamination period) and finally to 1% (at the end of the monitoring period), with corresponding increases in progesterone levels. ZEN-DON contamination adversely affects breeding cattle’s productivity, reproductive performance, and health. Therefore, monitoring urinary ZEN-DON is valuable for detecting contaminants and ensuring the safety of food products.

The global food crisis is perceived to have a significant impact on the national food sector. Time series clustering, a potent data mining technique, is employed to decipher and interpret intricate temporal patterns. Dynamic Time Warping (DTW), a measure that currently appears to be the most relevant, is predicated on the distance between sequences of elements. This paper explores the application of DTW in data mining algorithms to cluster commodity prices in Indonesia, aiming for enhanced accuracy based on time series movement. The clustering algorithm employs the K-Means method, necessitating a comprehensive description of the groups it forms. The analysis results reveal time series clustering for commodity prices using K-Means. Optimal results are achieved with five clusters, based on the commodity price trend. Influencing factors include seasonal variations and government policies related to consumer demand. It is imperative for the government to establish a robust market monitoring system to track commodity price fluctuations in real-time, thereby facilitating the design of effective price stabilization policies. The insights gleaned from this study can guide decision-makers in implementing targeted interventions to stabilize prices, bolster food security, and ensure sustainable economic growth. Doi: 10.28991/HEF-2024-05-03-02 Full Text: PDF

Prediction of the Number of Foreign Tourist Arrival in Indonesia Halal Tourism Entrance using Simultaneously Fourier Series Estimator

Increasing economic activity is part of the Sustainable Development Goals (SDGs) in encouraging economic growth. This encourages the transformation of the digital economy that gave birth to a payment system with electronic money. The use of electronic money has many benefits such as accelerating the circulation of money and providing convenience in every economic and trading activity. In Indonesia, the volume of electronic money transactions has increased significantly in recent years. For this reason, it is very important to forecast the volume of electronic money transactions in Indonesia as a material for evaluating policies to increase economic growth through the role of the digital economy. This is the aim of this research. In this study, the forecasting of the volume of electronic money transactions is carried out by considering several exogenous variables such as electronic money infrastructure and the conditions of the COVID-19 pandemic. The study was conducted by comparing the accuracy of the model obtained from the ARIMAX-GARCH model and Support Vector Regression (SVR) to predict the volume of electronic money transactions in Indonesia. The results showed that the two exogenous variables had a significant effect on the volume of electronic money transactions. In addition, the SVR model shows better accuracy than the ARIMAX-GARCH model which is indicated by the Mean Absolute Percentage Error (MAPE) value of the testing data of 7.01% and 3.35%, respectively. Thus, this research is expected to be used as a basis for formulating policies related to the development of the digital economy, especially the use of electronic money to support economic growth in Indonesia.

Investors should consider the Indonesia Composite Index (ICI) as a key indicator before making investment decisions, as it reflects the performance of industries and the broader economic growth. In Indonesia, the ICI exhibits fluctuating movements, making accurate forecasting essential for understanding the country's economic conditions, which are closely tied to capital flows, growth, and tax revenues. This study aims to forecast the ICI using the SARIMAX-GARCH model, incorporating macroeconomic factors such as the inflation rate and exchange rate. The findings reveal that both variables significantly impact the ICI, with the model achieving a Mean Absolute Percentage Error (MAPE) of 0.952% for training data and 5.233% for test data. The model's performance is supported by an R² value of 0.9782 and a Mean Squared Error (MSE) of 0.0003. This research not only improves the accuracy of ICI forecasts but also supports Indonesia's 8th Sustainable Development Goal (SDG) for decent work and economic growth. Doi: 10.28991/HIJ-2024-05-03-014 Full Text: PDF