Lovastatin plays a role in lowering the cholesterol level in the human blood, especially the bad cholesterol or low density lipoproteins (LDL). Concurrently, lovastatin increase the good cholesterol or high density lipoproteins (HDL), to prevent the formation of plaque inside the blood vessels. The objective of this research was to experimentally optimize the lovastatin compound produced by Monascus purpureus FTC5357 under solid state fermentation (SSF) using oil palm frond (OPF). In order to identify the optimal condition to produce lovastatin, four parameters which were pH, initial moisture content, peptone and potassium, were optimized using Box-Behnken design. Based on the ANOVA analysis performed, initial moisture content, potassium and peptone contributed significantly to the lovastatin production. Meanwhile, pH had the least impact to the lovastatin production.. Peptone pronounced to be the most contributed factor, as the lovastatin production increased with the increasing of peptone in the substrate. Under optimized condition (pH 5.50, moisture content at 60%, 3.40 g of potassium, and 3.30 g of peptone) maximum lovastatin yield was 45.84 μg/g. The lovastatin produced through SSF using OPF as a substrate by Monascus purpureus FTC 5357 has a great potential to be utilized as a source of lovastatin in future.
Purpose
This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.
Design/methodology/approach
The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.
Findings
The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.
Originality/Values
This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.
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