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INDONESIA
EKSAKTA: Journal of Sciences and Data Analysis
Published by Universitas Islam Indonesia
ISSN : 27160459     EISSN : 27209326     DOI : 10.20885
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology
Ekstakta is an interdisciplinary journal with the scope of mathematics and natural sciences that is published by Fakultas MIPA Universitas Islam Indonesia. All submitted papers should describe original, innovatory research, and modelling research indicating their basic idea for potential applications. The Journal particularly welcomes submissions that focus on the progress in the field of mathematics, statistics, chemistry, physics, biology and pharmaceutical sciences.
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
Articles 7 Documents
 1 
Search results for , issue "VOLUME 3, ISSUE 2, August 2022" : 7 Documents clear
Identification of Elemental Content and Rock Types in West Lampung Regency Nadya Fitra Kurnia; Hamdi Rifai; Syafriani Syafriani; Letmi Dwiridal; Fatni Mufit
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art1

Abstract

Rock is a constituent of the earth's crust from a collection of minerals that harden due to natural processes. Rocks formed from volcanic eruption materials in each place have different characteristics in distribution patterns, types of minerals and the content of elements contained therein. These rocks contain elements that form magnetic minerals. However, in rocks in West Lampung Regency the elemental content in the rocks has not been identified. This study aims to determine the composition of rock-forming elements and rock types found in West Lampung Regency. The elemental content in rocks can be identified using X- Ray Fluorescence (XRF) and determine rock types using SiO2and K2O diagrams. The results of the analysis using XRF show that Si (Silica) is the most dominant element found in 4 rocks in Lampung Regency. West. In addition, the elements found in rocks in West Lampung are Al, Si, K, Ca, Fe, P, Cl, Ti, Mn, Ni, Cu, Zn, Ga, As, Rb, Sr, Y, Zr, Ag, In, Eu, and Pb , from the content of these elements there are elements that form magnetic minerals, namely Fe and Ti , with rock types of Foidite and Decite.
Penerapan K-Means untuk Clustering Berdasarkan Tingkat Keparahan COVID-19 di Rumah Sakit Swasta Indonesia: Penerapan K-Means untuk Clustering Berdasarkan Tingkat Keparahan COVID-19 Kathina Deswiaqsa; Endang Darmawan; Sugiyarto Sugiyarto
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art5

Abstract

In December 2019, coronavirus (COVID-19) caused by SARS-CoV-2 was first discovered in Wuhan, China. This virus has a high transmission rate and can be transmitted through droplets, airborne, and aerosols. The clinical manifestations are very diverse ranging from mild, moderate, and severe. Therefore, this study aims to conduct a clustering of the spread of the Covid-19 pandemic to facilitate the identification and handling. The method of the K-Means algorithm can be used as a method to obtain the desired clustering. The implementation and evaluation were conducted using RapidMiner tools and Davies Bouldin Index (DBI) respectively. Furthermore, the data sources by Kangdra (2020) were used with a total sample of 110 for the period March-June 2020. The results showed that the optimal cluster is located at k: 2 with a DBI value: 0,094 as the lowest value. Therefore, the cluster is strong since a smaller DBI value gives a better cluster. The clustering obtained is Cluster 1 and 2 with mild and moderate severity. The results are expected to facilitate a better zone identification of the COVID-19 severity level and rising people awareness.
Clustering of PDQ Participant Student in Faculty of Mathematics and Natural Sciences UII using the ROCK Method Rahmadi Yotenka; sekti kartika dini
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art7

Abstract

The Qur’anic Self-Development (PDQ)-Ta'lim Program is one of the student activities that must be followed by diploma and bachelor program students in Universitas Islam Indonesia (UII). The implementation of PDQ is coordinated by each faculty which is carried out for 4 semesters with 12 meetings for each semester. After carrying out PDQ activities, it is necessary to know the student profiles that can be used as the basis for policy making in the implementation of PDQ activities in the next period. In order to find out the profile of students after participating in PDQ activities, it is necessary to group these students based on related variables. This study uses the ROCK method to group students participating in the PDQ Faculty of Mathematics and Natural Sciences (FMIPA) UII batch 2020. The ROCK method is a robust agglomerative hierarchical-clustering algorithm based on the notion of links. The ROCK method is a suitable clustering method for grouping data with categorical variables. Based on the results of the analysis of the ROCK method of student data for the batch 2020 FMIPA UII, obtained three optimum clusters (k=3) at a threshold value of θ of 0.20. Threshold 0.20 has the smallest SW/SB ratio value of 0.0514 or 5.14% and the largest R-squared value is 61.76% compared to other thresholds.
Pemodelan Proporsi Kasus Tuberkulosis di Sulawesi Selatan Menggunakan Sparse Least Trimmed Squares Trigarcia Maleachi Randa; Georgina Maria Tinungki; Nurtiti Sunusi
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art6

Abstract

The deadliest infectious disease in Indonesia is tuberculosis (TB), and South Sulawesi is one of the provinces that contributed the most tuberculosis cases in Indonesia in 2018 with 84 cases per 100,000 population. This study aims to identify variables that could explain the proportion of TB cases in South Sulawesi. The data used has many explanatory variables, and there are outliers. Sparse Least Trimmed Squares (LTS) analysis can be used to handle data that has many explanatory variables and outliers. The resulting sparse LTS model successfully selects and shrinks the variables to 14 variables only. In addition, based on the value of R2 and RMSE for the model evaluation, the sparse LTS shows satisfying results rather than classical LASSO. The government can focus on these factors if they want to reduce the proportion of TB cases in South Sulawesi.
Comparison of the Naïve Bayes Classifier and Decision Tree J48 for Credit Classification of Bank Customers Alifia Tanza; Dina Tri Utari
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art2

Abstract

The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.
The The Potential Implementation of Biomass Co-firing with Coal in Power Plant on Emission and Economic Aspects: A Review Meiri Triani; Fefria Tanbar; Nur Cahyo; Ruly Sitanggang; Dadan Sumiarsa; Gemilang Lara Utama
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art4

Abstract

Applying coal-biomass co-firing power generation is the strategy to accelerate the renewable energy share in the energy mix to reach 23% by 2025. Although biomass co-firing trials have been carried out at several Coal-Fired Power Plants (CFPP), the potential for implementing biomass co-firing on a larger scale and for the long-term propose still needs to be identified. This article evaluates emission characteristics and economic aspects of implementing biomass and coal in power plants. The traditional review method is used by identifying journal articles as data sources and further elaborating according to the context of the study. The primary emissions from co-firing biomass with coal contain CO, SO2, NOx, and particulate matter. The coal-biomass co-firing power generation has been widely adopted due to its various positive effects. However, it is still necessary to consider the cost of retrofitting, OM, biomass prices, and incentives in its application.
In-silico Analysis Potential Of Curcuma zedoaria As A Candidate For Degenerative Disease Therapy Fitria Dwi Damayanti; Risma Ayu Setiawan; Tri Luthfiana Maretha; Trikxy Viori Andhani; Faiznanda Awwaluddin; Resi Puguh Prihantono; Ahmad Shobrun Jamil
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art3

Abstract

Indonesia has a high and diverse biodiversity, particularly in plant species. There are numerous advantages to using various plants that grow in Indonesia. Indonesia is also known for its abundance of spices and other natural resources. Rapid research is required in the use of this plant in order for bio-based products to be widely accepted. Using in-silico predictions by utilizing meta data provided by several credible sites is one of the important rapid methods in analyzing the benefits of the chemical content of Curcuma zedoaria (Temu putih). The goal of this in-silico analysis-based study is to gain an understanding of the pharmacology of a plant known as potency simplicia Curcuma zedoaria. Analyzing metadata from various sources is the research method. Prediction of absorption, distribution, metabolism, and excretion (ADME) was obtained from http://www.swissadme.ch/. The prediction of target proteins for phytochemical compounds of Curcuma zedoaria is available at http://www.swisstargetprediction.ch/, while the construction of active protein networks and interactions after induction of compounds contained in Curcuma zedoaria rhizome is available at https://string-db.org. According to the in-silico analysis performed with some of the software mentioned above, the rhizome of Curcuma zedoaria (Temu Putih) contains 71 active compounds, 64 of which are highly bioavailable. According to in-silico research, Curcuma zedoaria (Temu Putih) contains curcumin compounds (diarylheptanoid) and its derivatives have antioxidant activity, which functions to prevent stress from physiological stimulation that can increase the number of leukocytes.

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