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Journal of Natural Sciences and Mathematics Research Published by Faculty of Science and Technology Universitas Islam Negeri Walisongo Semarang Jl Prof. Dr. Hamka Kampus III Ngaliyan Semarang 50185 Website: https://journal.walisongo.ac.id/index.php/JNSMR Email:jnsmr@walisongo.ac.id
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Journal of Natural Sciences and Mathematics Research
Published by Universitas Islam Negeri Walisongo Semarang
ISSN : 26146487     EISSN : 24604453     DOI : https://doi.org/10.21580/jnsmr
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Materials Science & Nanotechnology Physics
Journal of Natural Sciences and Mathematics Research, an international journal, provides a forum for publishing the original research articles, review articles from contributors, and the novel technology news related to natural sciences and mathematics research. Scientific articles dealing with fundamental sciences, material sciences, mathematics, materials characterization, etc. are particularly welcome. The journal encompasses research articles, original research report, reviews, short communications and scientific commentaries in fundamental sciences and mathematics including: fundamental sciences, fundamental physics, fundamental chemistry, fundamental biology, mathematics, applied sciences, applied physics, applied chemistry,applied biology,applied mathematics and materials characterization.
Arjuna Subject : Umum - Umum
Articles 8 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2023): June" : 8 Documents clear
Prediction of seawater salinity based on comparison of truncated spline estimators, Fourier Series and Kernel Faisol, Faisol; Mardianto, M. Fariz Fadillah; Yudistira, Ira; Yulianto, Tony; Hasanah, Sarmiatul
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Salinity is one of the factors that affect salt production. Salinity is defined as the level of saltiness or too much salt in water. The salt in question is a variety of ions dissolved in water, including table salt (NaCl). The higher the level of NaCl contained, the better the quality of the salt formed. This low quality causes Indonesia to import salt, both consumption salt and industrial salt. Because most of the quality of salt still does not meet the criteria of SNI. For this reason, it is necessary to predict the salinity of seawater to help determine the next steps or policies in improving the quality of salt in Indonesia, especially in the Madura area. This research is examined in the form of a nonparametric regression curve estimator with a truncated spline estimator approach, Fourier series and kernel. From the comparison results, the best model for predicting seawater salinity is the estimator of the Fourier series base sine cosine with an oscillation parameter (k) of 2 with a GCV value of 5.017987 and MSE and a coefficient of determination of 0.06299933 and 94.64373%. So the prediction results obtained in this study are close to accurate with MAPE values of 0.07225208%, MSE of 0.0001441417 and coefficient of determination of 99.99%.
Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data Rifqah Fahira, Nurul; Lawi, Armin; Aqsha, Masjidil
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection.
Precipitated calcium oxide nanosize from limestone and blood clam shells Pujiastuti, Caecilia -; Muljani, Srie; Sumada, Ketut
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Calcium oxide (CaO) is a product that is needed by various types of industries such as the pharmaceutical, chemical, agricultural and health industries. CaO is generally produced by the calcination method of CaCO3 materials such as calcium carbonate rocks or from various types of shells. The calcination method requires a large amount of energy because the operating temperature is above 1000 C and the resulting calcium oxide product is still micrometer size. This study developed nanosize precipitated CaO from two calcium sources, namely blood clam shells and limestone. For clam shells using hydrochloric acid as a solvent and sodium hydroxide as a precipitating agent, while for limestone using phosphoric acid as a solvent and potassium hydroxide as a precipitating agent. The effect of acidity (pH) and calcination temperature on the characteristics of the precipitated CaO was observed. The blood calm shell produces precipitated CaO 85-92 % with a particle size of 200-250 Nm and the limestone produces precipitated CaO 42-66% with a particle size of 250-300 Nm 
Formulation and analysis of physical properties of Turi leaf extract suspension (Sesbania grandiflora L.) Amananti, Wilda; Pratiwi‬, ‪Rosaria Ika
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Sesbania Grandiflora .L is a type of plant that has so many benefits in the treatment of various kinds Diseases include laxatives, pain relievers (analgesics), fever reducers (antipyretic), laxative urine (diuretic), and others. Almost all parts of This plant belonging to the genus Sesbania Grandiflora is efficacious as a medicine covering the skin stems, flowers, leaves and roots. Sesbania Grandiflora .L  contains alkaloid compounds, glycosides, flavonoids, saponins, tannins and phenols which give a positive reaction in the phytochemical screening assay. Turi leaf extract need to be made in the form of pharmaceutical dosage forms for ease of use. One preparation that can be applied is a suspension.In this study, turi leaf extract was used as the active substance. The suspension was made in 3 formulas with different concentrations of turi leaf extract. The finished suspension was tested for physical characteristics including organoleptic test, homogeneity test, pH test, density test and viscosity test.Based on the results of secondary metabolite tests, turi leaves contain alkaloids, flavonoids, tannins and saponins. Based on the results of testing the physical properties of the suspension, it showed that all formulas met SNI standards based on organoleptic, homogeneity, pH and density tests. while based on the viscosity test the suspension made does not meet SNI standards.
Geothermal potential analysis using 3d modeling of subsurface structures based on the Gravity Anomaly in the Mount Lawu area, Central Java Puspita Sari, Feby; Restiana, Andini; Firya Wardhianty, Nabila
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Over time, energy needs will continue to increase. Dependence on the use of fossil energy results in the availability of such energy will run out at any time, so it is necessary to develop research on geothermal energy that is environmentally friendly and renewable. One of the Geothermal Work areas is on Mount Lawu, located at coordinates between 111°15' east longitude and 7°30' south latitude in several districts Central Java Province and East Java Province. The purpose of this study is to provide information related to geothermal potential using the gravity method by knowing the temperature of the area and the 3D modeling of subsurface structures. Based on the results of the Second Vertical Derivative (SVD) analysis, three normal faults were found in geothermal sources. The modeling results show that the residual anomaly map depicts a distribution of high anomalies ranging between 2 mGal up to 6.5 mGal scattered from North to South and East to West, suspected to be caused by lava rock intrusion. The results of the 3D inversion model show there are three layers, namely clay rock, which is suspected to be cap rock at a depth of 500 - 2500 m, pyroclastic lava rock, which is suspected to be as a reservoir at a depth of 3000 - 4500 m, and lava, which is suspected to be as a reservoir heat source at a depth of 5000 - 8000 m. Based on the map in Land Surface Temperature (LST) obtained the temperature value of Mount Lawu ranging from 3.14 °C - to 23.25 °C.
Numerical computational approach for 6th order boundary value problems Adebisi, Folasade Ajimot; Ishola, Christie Yemisi; Uwaheren, Ohigweren Airenoni; Okunola, Kamilu Adedokun; Raji, Musiliu Tayo; Oseni, Wasiu
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

This study introduces numerical computational methods that employ fourth-kind Chebyshev polynomials as basis functions to solve sixth-order boundary value problems. The approach transforms the BVPs into a system of linear algebraic equations, expressed as unknown Chebyshev coefficients, which are subsequently solved through matrix inversion. Numerical experiments were conducted to validate the accuracy and efficiency of the technique, demonstrating its simplicity and superiority over existing solutions. The graphical representation of the method's solution is also presented.
Bioactivity mapping of secondary metabolite compounds of Pandanus amaryllifolius leaves as anti-inflammatory using in silico Arrohmah, Robiatus Sholichah; Ibtisam, Afina Anjani; Sa’adah, Siti Malihatus; Putri, Fensy Rania; Fitriyah, Fitriyah
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Inflammation is one of the primary responses of the immune system to infection and irritation. Anti-inflammatory drugs generally cause side effects. Anti-inflammatory developed by reducing side effects use many natural materials such as plants. The parts of the plant used include fruits, leaves, stem bark, rhizomes, and flowers. One of the plants that can be used as an anti-inflammatory is Pandanus amaryllifolius. P. amaryllifolius leaves contain several materials, such as flavonoids, alkaloids, saponins, tannins, polyphenols, and dyes. This study aimed to determine the anti-inflammatory potential of the secondary metabolites of Pandanus amaryllifolius leaves using the in silico method. The research used a descriptive exploratory method and was conducted from December 2022 – January 2023. In silico mapping of the bioactivity of active compounds was carried out using several software or websites: knapsack database (www.knapsackfamily.com), NCBI PubChem database (https://pubchem.ncbi.nlm.nih.gov/), PASS Online Way 2 Drug (http://www.way2drug.com/passonline/) and ADME Swiss Analysis (http://www.swissadme.ch/). The result shows P. amarylifolius has 31 active compounds. The compounds were then analyzed using Pass Online with 18 anti-inflammatory parameters. It explained that 3 compounds met the rules for Pa values > 0.7, namely compounds 6E-Pandanamine (0.758), Pandamenyamine (0.735), and Pandamarilactone 1 (0.709). The results of pharmacokinetic tests using Lipinski
Scalar fields as dark matter candidates in the modified left-right symmetry model Istikomah, Istikomah; Isnawati, Nurul Embun
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Dark matter is about 25% of the universe, but its existence is still a mystery. The Modified Left-Right Symmetry Model with the extension of the scalar field, is expected to explain dark matter candidate. The dark matter candidates were analyzed using the Higgs Potential and Lagrangian Yukawa to obtain information on decay and scattering interactions. The generation of dark matter can be determined by analyzing the temperature evolution of the universe, which is divided into three stages post-inflation reheating, symmetry breaking first step, and symmetry breaking second step. The analysis results show that the right-sector scalar field  can be Cold Dark Matter (CDM) candidate because it has non-relativistic characteristics, is stable, does not interact with fermions, and has an abundance of 0.004. The right-sector atom can also be a CDM candidate because it has non-relativistic characteristics, is neutral, and consists of the right nucleons and right electrons. The singlet scalar field  can be the Warm Dark Matter (WDM) candidate because it can decay into fermion, interact in the left and right sectors, is neutrally charged and does not interact with other particles electromagnetically and has an abundance of 0.003. Thus, based on the modified left-right symmetry model, the particle that can be a candidate for dark matter is the scalar field.

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