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Analysis of protease activity of melinjo (Gnetum gnemon L.) skin on the tenderness of buffalo meat
Full text linkTo enhance the tenderness of meat, it can be accomplished physically or chemically. In chemical way, the process can be accomplished using protease enzymes that can be obtained from fruit, the higher the protease enzyme activity, the softer the meat. In this experiment, the extract and juice of melinjo skin (Gnetum gnemon L) was used to examine the protease enzyme activity. Papaya (Carica papaya) and pineapple (Ananas comosus) were used as a comparison. The research was conducted to understand the influence of incubation time (t) and incubation temperature (To) towards the optimum activity characteristic of protease enzyme on the juice and the extract of melinjo skin, papaya and pineapple. The incubation time of juice and extract was from 0 to 90 minutes with incubation temperature of 37 C and incubation time at 20 minutes with incubation temperature range from 20 to 80C. The protease enzyme activity of juice and extract expand significantly (p0.05) in conjunction with the increase of incubation time and temperature. The research results revealed that the maximum activity of protease enzyme detected at incubation time of 90 minutes for melinjo juice and pineapple skin, and 10 minutes for papaya. The optimum incubation temperature of melinjo skin at 70 C, papaya at 60C and pineapple juice at 60C. Meanwhile, the maximum activity of protease enzyme from melinjo extract identified in the incubation time at 80 minutes, papaya at 90 minutes and pineapple 60 minutes, whereas the incubation temperature of melinjo skin juice at 60 C, papaya at 80 C and pineapple at 30C
Thermal and physical properties of CNF/glutaraldehyde-gelatin-based hydrogel
Full text linkThe use of hydrogel as a wound dressing material is currently being massively developed. In addition to functioning to protect wounds, the use of hydrogel can also provide moisture in a measured manner and can be used as a drug delivery medium. In this study, hydrogel based on CNF and glutaraldehyde crosslinking agent and addition of gelatin were developed with various compositions 0.25; 0.5; 0.75 g to increase the ability of CNF and hydrogel to absorb water so that it is good to be applied as a wound dressing. The composting of the three materials aims to obtain a hydrogel with good thermal and physical properties. Based on physical character for a good ratio of swelling (666,62%) and degree of cross-linking (94%) on the hydrogel with a composition variation of 0.5 g of CNF addition. For the thermal stability of hydrogel var 2, TMax 591oC provides better thermal stability than var 1 and var 3. The morphology of hydrogel shows very small and evenly distributed pores on the surface which can absorb more water
Empirical model for the estimation of global solar radiation in the Aceh Besar Regency, Aceh
Full text linkSolar energy plays an important role in the temperature distribution on the Earth's surface and essential energy that sustains life for human. In the calculation of solar energy, the limitation of solar radiation data is a major obstacle. Solar radiation data is very limited at some observation stations due to the costly procurement of measuring instruments and complicated techniques. Estimation of global solar radiation using empirical models is one way to overcome the limitations of global solar radiation data at various locations. This study built the Angstrom-Prescott model to estimate global solar radiation based on the sunshine durations parameter in Aceh Besar Regency. Two years (2019-2020) global solar radiation and sunshine duration data from Aceh Climatological Station were used to estimate global solar radiation in 2021. Data for 2021 was using to evaluated the equation H/H0=0.28+0.22 (n/N), the results showed good model accuracy with an RMSE value of 0.052 and a MAPE of 11% (good forecasting). The model equation has been reliable to calculate solar radiation in four locations in Aceh Besar Regency
Anatomical and micromorphological characteristics of Pogostemon heyneanus Benth. (Lamiaceae)
Full text linkFoliar micromorphological and anatomical characteristics of Pogostemon heyneanus Benth. (Lamiaceae) was investigated in order to describe its comprehensive characterization and its association with the presence of essential oils. The methods used in this study involved several methods such as cross-section using a sliding microtome and epidermal peeling that were observed under light microscope, and foliar micromorphological characteristics that were observed under scanning electron microscope (SEM). Results showed leaf anatomical and micromorphological characteristics that could be useful in species identification and the localization of chemical properties. The leaf epidermal surfaces were characterized by curved to sinuous anticlinal walls (adaxial side) and sinuous anticlinal walls (abaxial side). The diacytic and anisocytic types of stomata were present only on the abaxial surface. The features of the stem is quadrangular and the well-developed collenchyma function. The sclerenchyma cells are present as clusters at the outer layer of vascular bundles and continuously surround the vascular tissue. Then, there were three forms of crystals found, namely star shaped crystals, prismatic crystals and raphides in the pith area. Eight types of trichomes were observed: simple unicellular, simple multicellular, peltate, short-stalked capitate (unicellular head), short-stalked capitate (bicellular head), short-swollen multistalked (unicellular acute head), long-stalked capitate, and long-swollen stalked capitate (disk head) trichomes. The presence of various glandular trichomes on the leaf surfaces may serve as secretory sites where secondary metabolites or essential oils are produced. The findings on the foliar and stem anatomical and micromorphological characteristics are very useful for the medicinal herbs industry as well as being of taxonomic value
A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation
Full text link. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets
Relationship between seismic acoustic impedance (AI) and total organic carbon (TOC) content: a case study from Australia
Full text link. Shale gas has become of interest of geoscientists globally because of its potentials to expand our energy supply. This research used well logs data and total organic carbon (TOC) data from Perth and Canning Basins, Australia. The objective of this research is to investigate source rock potential of the study area by examining the relationship between TOC content and seismic acoustic impedance (AI) derived from well log data, using regression analysis. The outcomes of this research show that for claystone/siltstone, the relationship between AI and TOC is nonlinear, while for shale the correlation is linear. However, there is no fixed equation that can be used as a standard for this linear/nonlinear relationship. Results show that for a certain type of lithology, the relationship between TOC and AI is different for different formations. This is interpreted to be caused by different depositional environment, diagenesis, mineralogical composition and different depth of burial. Findings of this study are expected to provide some new insights into the relationship between AI and TOC for various types of lithology and contribute to shale gas exploration studies
Predicting life expectancy of lung cancer patients after thoracic surgery using SMOTE and machine learning approaches
Full text link. Lung cancer is a life-threatening condition characterized by the uncontrolled growth and spread of abnormal cells in the lungs. Thoracic surgery is a commonly employed diagnostic and treatment procedure for lung cancer. The objective of this study is to utilize machine learning techniques to predict the life expectancy of lung cancer patients one year after thoraric surgery. The study utilizes the Thoraric Surgery Data Set, consisting of 454 data, with 385 data representing surviving patients and 69 data representing patients who passed away. Due to an imbalance in the data, the Synthetic Minority Oversampling Technique (SMOTE) process is applied to balance the dataset. Multiple machine learning algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), are employed for prediction. Validation is performed using 5-fold cross validation, repeated three times. The results indicate that the KNN model achieves the highest mean accuracy of 84.80% before the SMOTE process, although all models exhibit a low mean F1-score. Following the SMOTE process, the RF model attains the highest mean accuracy of 79.52%, while the KNN model demonstrates the highest mean F1-score of 26.54%. This research contributes valuable insights to clinicians in making informed decisions and improving patient outcomes
Daily behavior and interaction of cats (Felis catus) with humans at a canteen in IPB University
Full text linkUnderstanding feral cat behavior can be one of the aspects that is necessary for proper management and taking policy action in controlling the cat population. This study aimed to observe the daily activities of feral cats and see the correlation between the number of visitors on cat-to-human interaction at a canteen in IPB University. Observation of cats daily behavior was conducted by scan sampling method and cats interaction with humans was conducted by ad libitum sampling. Our results showed that self-care (66.27%) dominated the daily behavior of cats followed by negative behavior (20.83%) and affiliative behavior (12.9%). Correlation test showed there was a significant negative correlation between the number of visitors and frequency of affiliative behavior (p = 0.024). The number of visitors also shows a positive correlation to self-care (p = 0.034). No significant correlation between the number of visitors and negative encounters behavior (p = 0.27). The highest frequency value of cat-human interaction was approaching human (A) 28.15%, followed by vocalization (MV) 28.15% and eye contact (KM) 22.71%. Cat-human interaction behavior did not significantly correlate with the number of visitors (p 0.05). Our result implies that the feral cats use the canteen as a place to rest, not a place to find food and daily activities and human-cat interactions were specific for each study site. Therefore, in the management of feral cats, it was necessary to pay attention to the conditions of their respective habitats
Assessing soil bacterial community response to organophosphate pesticides in agricultural field of Yogyakarta, Java, Indonesia
Full text linkSoil contamination by pesticides is one of the worlds most pressing environmental issues. The widespread use of Organophosphate pesticides (OPPs) in agriculture has led to biological diversity changes. The indigenous bacterial community played significant roles in the remediation of soil contaminated with OPPs. This study examines the overall bacterial community composition of three agricultural fields in Yogyakarta, Java, Indonesia, that were exposed to OPPs. The agricultural field was divided into zones near the beach, residential, and mountainous. Sequencing 16S rRNA amplicon fragments used to analyze the soil bacterial community. It was discovered that Proteobacteria, Actinobacteria, and Firmicutes comprised the majority of the bacterial community. In addition, the samples contain a high relative abundance of Bacillus, Bradyrhizobium, Chryseobacterium, Cystobacter, Microvirga, and Burkholderia. The high alpha diversity indexes suggest that the agricultural soil microbiome provides important ecological services and may harbor a wide variety of bacteria and genes with biotechnological applications. The physicochemical soil characteristics are also correlated with the bacterial community structure. The findings can be used to develop bioremediation strategies that employ native microbes to clean and restore agricultural soil contaminated with OPPs
The Classification of Program Sembako recipients in Payobasung West Sumatra based on the K-nearest neighbors classifier
Full text linkThe "Sembako Program" is a program carried out by the Indonesian government to improve the welfare of low-income communities. The purposes of this study are: (a) to determine the classification of households that deserve to receive basic-food assistance in Koto Panjang Payobasung, West Sumatra, using the KNN classifier and (b) to determine the optimal number of nearest neighbors used in the classification process. The measure of proximity between objects used is the Gower dissimilarity coefficient. This research used primary data consisting of 175 households collected purposively in a survey conducted on all households in Payobasung. The optimal K value is determined by implementing a 5-fold cross-validation procedure.The result showed that the best classification process is when K = 3 nearest neighbors are used since it produces the highest accuracy coefficient and Mattews correlation coefficient (MCC). Therefore, for further work, in deciding the eligibility of a household to receive the Sembako Program in Payobasung, KNN can be used by considering its 3 nearest neighbor