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Jurnal Natural
Published by Universitas Syiah Kuala
ISSN : 14118513     EISSN : 25414062     DOI : https://doi.org/10.24815/jn
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Energy Immunology & microbiology Neuroscience Physics
Jurnal Natural (JN) aims to publish original research results and reviews on sciences and mathematics. Jurnal Natural (JN) encompasses a broad range of research topics in chemistry, pharmacy, biology, physics, mathematics, statistics, informatic and electronic.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "Volume 23 Number 3, October 2023" : 9 Documents clear
Relationship between seismic acoustic impedance (AI) and total organic carbon (TOC) content: a case study from Australia MUHAMMAD RIDHA ADHARI; MUHAMMAD YUSUF KARDAWI
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.30980

Abstract

. 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.
Assessing soil bacterial community response to organophosphate pesticides in agricultural field of Yogyakarta, Java, Indonesia ANNA RAKHMAWATI; BERNADETTA OCTAVIA; SUHARTINI SUHARTINI; TIEN AMINATUN
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.31263

Abstract

Soil contamination by pesticides is one of the world’s 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.
A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation IRVANIZAM IRVANIZAM; ZAKIAL VIKKI; SUTARMAN SUTARMAN; OPIM SALIM SITOMPUL
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.32467

Abstract

. 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.
Predicting life expectancy of lung cancer patients after thoracic surgery using SMOTE and machine learning approaches SELLY ANASTASSIA AMELLIA KHARIS; ARMAN HAQQI ANNA ZILI
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.29144

Abstract

. 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.
Classification of household poverty in West Java using the generalized mixed-effects trees model FARDILLA RAHMAWATI; KHAIRIL ANWAR NOTODIPUTRO; KUSMAN SADIK
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.33079

Abstract

Dealing with fixed effects and random effects can be accomplished by combining statistical modeling and machine learning techniques. This paper discusses the modeling of fixed effects and random effects using a statistical machine-learning approach. We used the generalized mixed-effects trees (GMET), a tree-based mixed-effect model for dealing with response variables that belong to the exponential family of distributions. In this study, both simulation and actual/empirical data utilized the GMET method to discover data conditions that were appropriate for employing this approach. The simulation data was generated using different response variable generations, as well as different values of the variance of random effect and fixed effect coefficients. The findings indicated that the GMET performs similarly for different response variable generation scenarios. However, it performed better when the fixed effect value and the variance of random effects were large. When applied to the empirical data, the GMET method describes fixed effects and random effects and classifies household poverty status quite well based on the area under curve (AUC) value. It has also revealed that important variables for poverty classification are the number of household members, owning land, the type of main fuel used for cooking, and the main source of water used for drinking. In order to address the socioeconomic disparity that leads to poverty, the government may become concerned about these factors. In addition to that information, the use of regional typology as a random effect in the model has also contributed to the variation of household poverty status. Based on research, the fixed effects in mixed models do not need to be linear and GMET may be employed in grouped data structures, giving the GMET technique the ability to compete with other approaches/methods.
The effect of ripeness level, storage and heating conditions on vitamin C in Fig (Ficus carica L.) fruit juice using bivoltammetry sensor NERDY NERDY; NILSYA FEBRIKA ZEBUA; TJUT XENA; MAYA KUSUMA; RANI FARAH BUTSAINAH TANJUNG
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.32158

Abstract

Vitamin C, as a crucial nutrient, plays a vital role in human health and is known to be sensitive to various factors such as ripeness level, storage and heating conditions. This study aimed to determine the effect of ripeness level, storage and heating conditions on vitamin C in fig (Ficus carica L.) fruit juice using bivolmetric sensor. Fig (Ficus carica L.) fruit juice samples with different ripeness level were collected and analyzed the vitamin C. Full-ripe fig (Ficus carica L.) fruit juice samples subjected at different storage (cold, cool, and room temperature) and heating (30 °C, 60 °C, and 90 °C) conditions. The vitamin C level was monitored using a bivoltammetry sensor. The results showed that the higher ripeness level of fig (Ficus carica L.) fruit the lower level of the vitamin C. The results also showed that the higher storage and heating conditions generally accelerate with degradation of vitamin C. Degradation of vitamin C content in fig (Ficus carica L.) fruit juice with all different storage temperature had the best kinetic model fit zero order. Meanwhile, heating temperature at 30 °C and 60 °C showed that the best kinetic model fit first order. But, heating temperature at 90 °C showed that the best kinetic model fit second order. The kinetic modeling analysis showed that storage and heating conditions significantly influenced the degradation kinetics of vitamin C in the fig (Ficus carica L.) fruit juice.
Application of SHAP on CatBoost classification for identification of variabels characterizing food insecurity occurrences in Aceh Province households MUHAMMAD SUBIANTO; INA YATUL ULYA; EVI RAMADHANI; BAGUS SARTONO; ALFIAN FUTUHUL HADI
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.33548

Abstract

Classification is the process of building a model that can distinguish between different classes of data. The model aims to predict the class of testing data based on patterns or relationships learned from training data. One of the data processing algorithms used to build classification models is Categorical Boosting (CatBoost). However, in general, the resulting models are difficult to interpret. To facilitate the interpretation of complex classification models, methods such as SHAP (SHapley Additive exPlanations) are needed. SHAP is a method to explain individual predictions. SHAP is based on the game theoretically optimal shapley values. In this study, an analysis of important SHAP variables was conducted on the CatBoost classification model to identify variables characterizing occurrences of food insecurity in households. The data used in this study was obtained from the Survei Sosial Ekonomi Nasional (Susenas) in March 2021 in Aceh Province, sourced from the Badan Pusat Statistik (BPS). There are 13,126 observations in the research data. The results from four evaluated classification models on the testing data showed that the best model had accuracy, sensitivity, specificity, and AUC values of 0.703, 0.349, 0.798, and 0.637, respectively. Furthermore, the results of the analysis of important SHAP variables showed that the variables number of household members who smoke ( ), education of the household head ( ), wall types ( ), drinking water source ( ), and decent sanitation ( ) significantly contributed to the occurrences of food insecurity in households in Aceh Province in the year 2021.
Diversity of ABO blood groups, ethnic groups, and medical histories of university students in Indonesia PUJI RIANTI; ZAHRA RODLIYATAM MARDLIYAH; DEDY DURYADI SOLIHIN
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.30596

Abstract

. Blood group systems are heritable characteristics controlled by multiple alleles that determine the presence of antigens A, B, or the absence of antigens (O). These systems are crucial in categorizing humans' four main blood groups. ABO blood groups have been identified to correlate with evolution, migration, local adaptations, dietary patterns, and human diseases. Unfortunately, this knowledge is poorly known in Indonesia. Therefore, we initiate this study to record university students' blood groups' diversity through the frequency of ABO alleles, ethnicities, and medical histories through questionnaires. The data analysis involved the responses of 992 students aged 17 to 23 from IPB University. The ABO estimator version 17.3 and Program R ver. 3.6.3 were utilized for data analysis, with the sample size determined using the Slovin formula. Our analysis revealed allele frequencies of 0.19 for IA, 0.20 for IB, and 0.61 for IO. The O blood group exhibited the highest prevalence, while the AB blood group was the rarest. Individuals with the O blood group identified as Javanese and Sundanese were more likely to have histories related to typhoid/typhoid symptoms and dengue fever. Individuals with O and B blood groups from the Sundanese population experienced allergies. In contrast, those with blood group B from the Javanese population were more prone to gastric ulcers and asthma. There are no significant differences between ethnicities for each illness record except asthma. All ABO blood groups for each illness record show significant differences with a weak correlation between the blood groups and type of illness.
Anatomical and micromorphological characteristics of Pogostemon heyneanus Benth. (Lamiaceae) AMALIA AMALIA; NORAINI TALIB; JALIFAH LATIP; AMINAH ABDULLAH; ISMAIL BIN SAHID
Jurnal Natural Volume 23 Number 3, October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i3.33213

Abstract

Foliar 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.

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