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Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), is a multi-disciplinary and engineering journal published since 2021 byLLDikti Wilayah 1. Each issue consists of a minimum of 5 articles, the scope of this journal is Mathematics, Chemistry, Physics, Biology, Statistics, Agricultural and Biological Sciences, Biotechnology and Agro-industry, Chemical and Pharmaceutical Sciences, Computer Science, Materials Science, Physics and Astronomy, Biomedical Engineering.
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Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)
Published by LLDIKTI Wilayah 1
ISSN : 28073142     EISSN : 28073142     DOI : https://doi.org/10.54076/jumpa
Core Subject : Science, Education, Social, Engineering,
Aerospace Engineering Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Mathematics Mechanical Engineering Neuroscience Physics
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), 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, Physics, applied physics, applied chemistry, Biology, applied biology, Statistics, applied mathematics, Agricultural and Biological Sciences, Chemical and Pharmaceutical Sciences, Computer Science, Materials Science, Physics and Astronomy, Biomedical Engineering and materials characterization, and which are included in the related fields.
Arjuna Subject : Matematika - Ilmu Komputer Teoritis
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 1 (2023): March: Mathematics and natural science" : 5 Documents clear
Application of x-means alghorithm for district/city clustering based on povetry rate in Maluku Islands and Papua Matdoan, M. Y.; Igo, La
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.270

Abstract

Based on the Central Statistics Agency in 2022, the percentage and number of poor people according to islands in March 2022 explained that the largest number of poor people are on Maluku Island and Papua, 19.89 percent. This research used data mining techniques with the X-Means Clustering method. The data in this study was taken from the website of the Central Statistics Agency (BPS) of Maluku, North Maluku, Papua, and West Papua consisting of 63 regencies/cities with 8 variables. Then all the data was processed with Rapidminner and produced 3 clusters, namely cluster 0 consisting of Buru, Western Seram, Central Halmahera, Sula Islands, South Halmahera, North Halmahera, East Halmahera, Morotai Island, Jayapura, Nabire, Biak Numfor, Paniai, Pucak Jaya, Mimika, Tolikra, Nduga, Puncak and Manokwari. Cluster 1 consists of Tanimbar Islands, Southeast Maluku, Aru Islands, Eastern Seram, Southwest Maluku, South Buru, Tual, West Halmahera, Taliabu Island, Ternate, Tidore Islands, Yapen Islands, Boven Digoel, Mappi, Asmat, Star Mountains, Sarmi, Keerom, Waropen, Supiori, Mamberamo Jaya, Central Mamberamo, Yalimo, Dogiyai, Intan Jaya, Deiyai, Fakfak, Kaimana, Wondama Bay, Bentuni Bay, South Sorong, Sorong, Raja Ampat, Tambrauw, Maybrat, South Manokwari and Arfak Mountains and cluster 2 consisting of Central Maluku, Ambon, Merauke, Jayawijaya, Yahukimo, Lanny Jaya, Jayapura City, and Sorong City.
Tannin determination in young coconut coir (Cocos nucifera L.) by FTIR and UV-Vis spectroscopy Sari, Yelfira
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.271

Abstract

The coir of young coconut is the largest by product of coconut. Young coconut coir has no important industrial uses and until now has been incinerated or dumped without control. This is because the utilization of young coconut coir is not easy as the old one. Tannin has pharmacological activities including anti-inflammatory, antioxidant, anti-tumor This study aims to determine tannin in young coconut coir (Cocos nucifera L.) using a complementary analytical technique, Fourier Transform Infra-RedFTIR) and Ultraviolet-Visible (UV-Vis) spectroscopy. Sample of young coconut coir was obtained from the waste of bargainer at the local market. Sample was maserated using ethanol as the solvent and then was evaporated to get concentrated extract.  The FTIR and UV-Vis spectra showed that the sample was indicated to contain tannin. The presence of tannin was indicated by appearance of peak at 283 nm and 326 nm in UV-Vis spectra and in the region between 1610-1445 cm-1 by FTIR. This research is an early stage in the characterization of tannin compounds from young coconut coir extract. It is expected that waste have economic value in the future.
Antibacterial S. Aureus docking test from compounds contained from karamunting (rhodomyrtus tomentosa (aiton) hassk.) Hadi, Samsul; Nastiti, Kunti
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.273

Abstract

Infection is a disease that causes high mortality in Indonesia, therefore it is necessary to search for antibacterial S. aureus compounds, one of which is the in silico method. The method in this study was docking using the PLANT software, the materials used were 10 compounds derived from karamunting and protein with the 3mzd code from RCSB, the results of the docking were then seen for interacting residues and types of bonds (hydrogen, electrostatic and hydrophobic) using discovery studio, besides that to predict activity using PASSonline and the data obtained is the value of Pa. The results of this study are docking scores -92.3386 to -66.8339 and Pa values of 0.242 to 0.598. The conclusion of this antibacterial research is that three compounds have the potential to be developed as antibacterials S. aureus, namely 5'-Desgalloylstachyurin, Rhodomyrtosone A and Rhodomyrtosone C.
Comparison performance analysis of autoregressive integrated moving average and deep learning long-short term memory forecasting weather data Hasibuan, Alfiansyah; Tinambunan, Medi Hermanto; Putra, Purwa Hasan
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.302

Abstract

Information about the weather is crucial in assisting human activities and labor because the weather is a factor that cannot be separated and is closely related to all human activities. The purpose this study to compare performance the Autoregressive Integrated Moving Average (AIMA) and Long-Short Term Memory (LSTM) algorithm models with case studies of weather forecasting. This study uses comparison of two methods, forecasting using AIMA and LSTM methods. LSTM method provides the best forecasting performance for attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.45 and the Mean Absolute Error value below 1.14. For attributes of average humidity and solar radiation with a Root mean squared error value of 2.62 to 3.82 and a Mean Absolute Error value of 2.21 to 3.2. Precipitation forecasting has the highest error value with a root mean squared error value of 9.99 and a mean absolute error of 6.5. The AIMA method provides the best forecasting performance on the attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.47 and the Mean Absolute Error value below 1.16. For the sun exposure attribute with a Root mean squared error value of 2.91 to 3.05. Whereas the average humidity attribute has the highest error with the Root mean squared error value reaching 4.97 and the Mean Absolute Error reaching 3.99. LSTM method is better in terms of forecasting results and in terms of computation time. From every forecast made, the LSTM method produces a smaller error value.
Implementation of k-means clustering for the job provision in urban village Lubis, Andre Hasudungan; Utami, Widya Rizki; Lubis, Juanda Hakim
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.312

Abstract

Unemployment is one of critical issue in society. It may creates snowball effect towards economic development in a country and leads to the economic recessions. Hence, it is important to solve this issue by implementing the clustering to provide groups of people that have chance for job provision. K-Means Clustering is employed in this study by using 378 of data samples. Ages, marital status, amount of land owned, and income are selected as the attributes. The clustering result pointed out that there are 3 clusters that represent the people chances to get job, namely “High”, “Medium”, and “Low”. To evaluate the proposed cluster, Davis-Boulden index is utilized and presents a proper score. The practical implications are presented and discussed, then suggestions for future research are provided.

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