bluuebunny/crossref_metadata_2025_split huggingface dataset:
Need analysis for developing of a light waves module: a three-dimensional learning framework
Authors: Wandana I A P , Efendi R , Ramalis T R 
Published: November 2021

AbstractSome meaningful changes in science learning standards are listed in A Framework for K-12 Science Education and Next Generation Science Standards: For States, By States under a three-dimensional learning framework. Learning modules must be developed with the framework as the most advanced science learning standards. The research focus was to determine student needs for developing light waves module based on a three-dimensional learning framework. This study involved 147 respondents consisting of 22 teachers with different backgrounds and 125 high school students in the Purwakarta Regency. The instrument used was a 3DLQP questionnaire which included initial knowledge of the three-dimensional learning framework, learning modules in the school, criticism, and suggestions. Data obtained from questionnaires, interviews, national curriculum documents, NGSS complementary documents and then analyzed using triangulation of data sources. Based on the results, the teacher and student responses explain the need for the development of learning modules. This learning module is expected to facilitate the development of coherent and interconnected student knowledge, so that it can be used in various situations of everyday life.
Student needs for developing light waves test based on three-dimensional learning frameworks
Authors: Aziizah U , Efendi R , Muslim M 
Published: November 2021

AbstrakThe Framework for K-12 Science Education and NGSS: For States, By States represent a substantial change in science learning standards in the form of a three-dimensional learning framework as the latest science learning standard. The purpose of this study was to analyze the need for a test development based on a three-dimensional learning framework on the topic of light waves in high school. The research subjects were 140 respondents consisting of 20 teachers and 120 students in the high school of Purwakarta area. The research used a 3DLQP questionnaire as an instrument, which accumulates respondent characteristics, prior knowledge of three-dimensional learning, tests used in school, and criticism and suggestions. Data collection was carried out using interviews, questionnaires, national curriculum documents, and NGSS documents. Data analysis utilized the triangulation of data sources. The study results were to elaborate student needs for developing tests and assessment rubrics from the teacher and student perceptions. This research also produces characteristics of test development that are adapted to the national curriculum.
Utilization of wood sawdust as heavy metal adsorbent in paint industry waste
Authors: Sukmawardani Y , Pitriani  , Pitriana P , Subarkah C Z 
Published: November 2021

Abstract
Abstract This study aims to utilize wood sawdust waste in the paint industry waste treatment. The first thing to do is a qualitative analysis of heavy metals before and after the processing. Furthermore, the parameters measured are temperature, pH, TDS, and dissolved oxygen (Dislove Oxgen). The use of wood sawdust as a heavy metal adsorbent in paint industry wastes is able to provide efficient results with no sediment formation when conducting qualitative analysis of Cu2+ and Cd2+ metal ions and raising the waste parameters to be environmentally friendly. This shows that the adsorption process works optimally.
Literature review as a didactical content analysis: Sustainable magnetic lubricants based on ionic liquids
Authors: Jalil S A A , Mudzakir A , Hernani  
Published: November 2021

Abstract
Magnetic lubricants are usually petroleum-based, but not renewable and cannot be environmentally degraded. It can cause the environmental problems. Magnetic lubricants based on ionic liquids can be environmental friendly. The ionic liquid lubricants are synthesized from vegetable oil fatty acids, which is a locally sustainable and renewable sources. This molecular engineering can be used to integrate the concept of sustainability into teaching and learning. This study aimed to obtain the concept map and teaching learning sequence (TLS) from the scientist’s conception. The method used is a qualitative content analysis (literature analysis type), using an instrument in the form of a content analysis format. The first phase begins with collecting literatures in the form of textbooks, monographs, review results and research articles. The next phase is descriptive analysis, selecting categories, and evaluating the material didactically. This research produces the concept map, TLS and clarified chemical concepts. The scientist’s conception obtained is the application, function, characteristics of media magnetic lubricants, ionic liquids and examples of magnetic lubricants based on ionic liquids. Concept map and TLS can illustrate the relationship between one concept and another. They also show the relationship between science, technology and engineering. The results can be used as the basis for the preparation of teaching materials and didactical designs for teaching and learning.
Preparation of silver nanoparticle by green synthesis and application to leather in enhancements of antimicrobial activity of leather
Authors: Rohaeti E , Putri A , Budiasih K S , Ariyanti N A , Annisa A S N , Kasmudjiastuti E 
Published: November 2021

Abstract
The objectives of this study were to prepare nanoparticle by using extract of Tectona grandis L. leaf and to deposit nanoparticle on goat leather via ex situ method, and also to characterize the antimicrobial activity of modified goat leather. The stages in this study were preparing of silver nanoparticles via green synthesis method by using extract of teak leaf, characterizing of nanoparticle by using a UV-Vis spectrophotometer and Particle Size Analyzer (PSA), impregnating the nanoparticle on the pickle leather by ex situ method, and analysing the antibacterial and antifungal activities of modified leather by the diffusion method. The silver nanoparticles were greenish brown in colour, having absorption at the wavelengths of 449 nm which indicator the formation of silver nanoparticles. The silver nanoparticles have an average particle size of 109.9 nm. The results of antimicrobial tests against the bacteria Staphylococcus epidermidis DNCC 6018, Escherichia coli FNCC 0047, and the fungus Candida albicans ANCC 0048 showed that all variations of the leather have different abilities in inhibiting the growth of the bacteria and the fungi. The pickle leathers after modification with nanoparticle Ag showed the highest antibacterial activities against Staphylococcus epidermidis DNCC 6018 and Escherichia coli FNCC 0047.
STEAM-based project learning: the effect to middle school’s student’s collaboration competences
Authors: Nurramadhani A , Kumala F , Permana I 
Published: November 2021

Abstract
The Purposes of this research is to investigate the student’s collaboration competence in integrated STEAM learning approach. The method that is used in this research is pre-experimental design with type one shot case study. The subject that is included in this research is students of VIII grade at one of Junior High School in Depok. The Instrument in this research to take the data is using Co-Measure rubric based on Hero’s research. The rubric has four categories, there are: 1) Students Interaction- 2) Positive Communication- 3) Multi Inquiry- and 4) Interdisciplinary Approach. The data technique sampling is used observation during the activity. Data analysis in this research is used add the score average in each category, then describe it with the description Need Work, Acceptable, and Proficient. This research results are the collaboration competence category which has higher results is transdisciplinary approach and positive communications with proficient descriptions. Arts in STEAM has influence the collaboration, communication, cross disciplinary, and curiosity in learning. This study could be concluded that student’s collaboration competence has outperformed in STEAM project activity rather than in non-STEAM project activity.
The integration of STEAM-project-based learning to train students critical thinking skills in science learning through electrical bell project
Authors: Rahmawati Y , Adriyawati  , Utomo E , Mardiah A 
Published: November 2021

Abstract
This study uses qualitative methods describing the integration of STEAM-project-based learning with the aim of training the critical thinking skills of 18 male and 18 female students in science learning. The application of STEAM is done by integrating it with a project-based learning model on the topic of energy transformation through the electric bell project. Instruments are used to explore the development of critical thinking skills through interviews, observations, reflective journals, and critical thinking skills tests. The study found that students trained STEAM-PjBL to ask questions and have a good understanding of the energy change from electrical energy to sound energy. In addition, through problem solving and project creation, students are encouraged to connect ideas, make assumptions, and infer concepts. The challenges faced in this study are time management, project ideas related to teaching concepts, and student engagement. In addition, STEAM-PjBL integration provides an opportunity for teachers to develop their competencies in applying various methods to encourage students in the test results of the test is students achieve mastering (40%) and competent (30%) at a critical level. Only temporary small (3.80%) students no concept of transformation correctly, even after knowing the distance.
Hybrid SARIMA-FFNN model in forecasting cash outflow and inflow
Authors: Monica M , Suharsono A , Otok B W , Wibisono A 
Published: November 2021

Abstract
The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.
Spatial autocorrelation analysis of Covid-19 cases in South Kalimantan, Indonesia
Authors: Amaliah O N , Sukmawaty Y , Susanti D S 
Published: November 2021

Abstract
Coronavirus Disease 2019 (COVID-19) is a new coronavirus that was discovered in Wuhan, China, at the end of 2019. In March 2020, the outbreak extended throughout the world, including Indonesia and one of its provinces, South Kalimantan. This rapid expansion should belinked to people’s mobility between regions, hence the linkage across regions must be examined. In South Kalimantan Province, the purpose of this research is to evaluate the distribution and relationship across regions in terms of the number of positive COVID-19 cases, the number of additional positive COVID-19 cases, and the number of COVID-19 patients under treatment. The spatial autocorrelation analysis with the Moran Index and Local Indicator of Spatial Autocorrelation (LISA) tests were used to determine the spatial autocorrelation between what and what using what data/where the data obtained? from March 22 to September 30, 2020. Based on the results of the Moran Index test, it is known that there is a spatial autocorrelation in the number of cases, the number of additional cases and the number of positive confirmed COVID-19 patients in treatment between one region and another neighboring area. While the results of the LISA Index test show that Balangan Regency, Hulu Sungai Tengah Regency, Hulu Sungai Utara Regency, Banjarmasin City, Tabalong Regency and Banjar Regency affect the level of COVID-19 cases in their respective neighboring areas. Therefore, there is a need for policies to control community mobility in those spatially correlated areas and increase testing and tracing to control the spread of COVID-19 cases in South Kalimantan Province.
A note on the refinement of Hermite-Hadamard inequality
Authors: Idris Mochammad 
Published: November 2021

Abstract
In this paper, we give the sharper bounds for the mean value of a convex function using dyadic decomposition. Our result is related with classical Hermite-Hadamard inequality. Moreover, using the result, we can determine the maximum error before calculating the numerical (trapezoidal) integral of the convex function.
Forecasting of fuel oil supply using the transfer function approach (Case study: PT. Agrabudi Karyamarga Gas Station Division 64.706.07)
Authors: Labibah A U , Sukmawaty Y , Susanti D S 
Published: November 2021

AbstractIn everyday life, fuel oil is quite important. The need for fuel is increasing every day, which means that the supply of fuel oil must keep up with the demand. As a reason, we require a way for predicting future fuel needs. The forecasting method is one that is frequently utilized. Forecasting is a method for predicting future conditions based on historical data. The transfer function approach is one way to forecast data with several variables in time series analysis. The objective of this research is to estimate the parameters of the transfer function model and use a transfer function approach to predict the movement of fuel, particularly pertalite. The parameter estimation results in this research areω^0=0.033;ω^1=−0.0358;ω^2=0.0627;δ^1=−0.9713;δ^2=1;θ^1=−0.9141, and the forecast value for the 214th period is 8762.61, based on the data used, namely for 213 periods starting from the 1stperiod until the 213thperiod.
Modeling of poverty in Kalimantan with nonparametric spline regression approach
Authors: Halida A , Salam N , Lestia A S 
Published: November 2021

Abstract
Poverty is a problem that is always present in any country, including Indonesia. Kalimantan is one of the islands in Indonesia that has not been free from poverty. Therefore, more effective efforts to further reduce poverty are needed. The purpose of the is study was to develop a model that can explain significant factors to poverty in Kalimantan. To achive the objective, first, factors that may have significant influence to the changes in the percentage of poor people should be identified, using regression analysis. In this study, nonparametric regression analysis was used with a spline approach since the relationship between poverty and the explanatory factors did not have a particular pattern. The results of this study showed that, the best was using three knot points, where Open Unemployment Rate (OUR), Human Development Index (HDI), and Economic Growth have a significant effect on poverty in Kalimantan.
Analysis of rainfall classification over Tanah Laut disrict based on global climate indicators using support vector machine method
Authors: Hayah N , Soesanto O , Rahman M A 
Published: November 2021

Abstract
The Support Vector Machine (SVM) classification method can be applied in various fields, one of which is meteorology and climatology in rainfall forecasting. Thus, a study was conducted by classifying rainfall to recognize the relationship between global phenomena and rainfall and the results of applying the classification using the SVM method to rainfall in the Tanah Laut Regency. The analysis is carried out using the SVM Multiclass concept with 4 categories of rainfall classification: low, medium, high, and Extreme. The kernel used in SVM is the RBF kernel with optimization parameters used, namely Cost (C) 1,5,10,15 and Gamma (γ) 1,5,10,15. The dataset formed is based on the annual period, climatic conditions, and seasonality. The Spearman Rank correlation test describes the relationship between global phenomena and rainfall with a correlation range of (−0.1456 ) − (0.43144) for the entire dataset. The implementation of the SVM classification method shows that the Cost (C) 10 and Gamma (γ) ≥ 5 parameters obtained the highest accuracy of 100% on the training data. In contrast, in testing the data testing, the accuracy was good, namely the accuracy of 78.00% in La Nina and 81.38% in seasonal periods.
Constructing an optimum 4×4 S-Box with quasigroup
Authors: Pambekti G B , Rosdiana S 
Published: November 2021

Abstract
The efficiency of cryptographic algorithms is a problem that is often encountered. One solution of this problem is the use of lightweight cryptography. S-Box is one of the basic non-linear components in a cryptographic algorithm. Among all, 4 × 4 S-Box quasigroup is one kind of S-Box which can be used in lightweight cryptography, that formed by applying quasigroup transformation. The research described in this paper is the construction of the 4 × 4 S-Box using e-transformation of quasigroup as well as to know which leader pattern produces the highest number of optimum S-Box and mostly has higher Robustness value. The construction resulted in 6912 4 × 4 S-Boxes quasigroup by applying for each six leader patterns in four e-transformation rounds of 432 nonlinear quasigroups. The results of 4 × 4 S-Box quasigroup is calculated based on criteria of optimum 4 × 4 S-Box that has higher Robustness value. From all results of the 4 × 4 S-Box quasigroup, it is known that the leader pattern producing S-Box which meet the criteria and all S-Boxes have highest Robustness value are l
1
l
2
l
1
l
2 and l
1
l
2
l
1
l
2. The number of S-Box which meet the criteria is 18,75% of the total 5376 different 4 × 4 S-Boxes quasigroup and the highest Robustness value is 0,75.
Relation between the left and right cosets of an α-normal subgroup
Authors: Mahatma Y , Hadi I , Sudarwanto  
Published: November 2021

Abstract
Let G be a group and α be an automorphism of G. In 2016, Ganjali and Erfanian introduced the notion of a normal subgroup related to α, called the α-normal subgroup. It is basically known that if N is an ordinary normal subgroup of G then every right coset Ng is actually the left coset gN. This fact allows us to define the product of two right cosets naturally, thus inducing the quotient group. This research investigates the relation between the left and right cosets of the relative normal subgroup. As we have done in the classic version, we then define the product of two right cosets in a natural way and continue with the construction of a, say, relative quotient group.
Subordination of functions in subclass of Bazilevič Functions B
1(α, β)
Authors: Marjono  
Published: November 2021

Abstract
Let f be analytic in the unit disc D = {z : |z| < 1} with

f
(
z
)
=
z
+

∑

n
=
2

∞

a
n

z
n

, and for α ≥ 0 and 0 < β ≤ 1, let B
1(α, ß), denote for the class of Bazilevič functions satisfying the expression

|
arg

z

1
−
α

f
′

(
z
)

f

(
z
)

1
−
α

|
<

β
π

2

. We give sharp estimates for various coefficient problems for functions in B
1(α, β), which unify and extend well-known results for starlike functions, strongly starlike functions and functions whose derivative has positive real part in domain D.
Prime labeling of line and splitting graph of brush graph
Authors: Fran F , Putra D R , Pasaribu M 
Published: November 2021

Abstract
A bijective function f from V(G) to {1,2,…, n} be a prime labeling of a graph G with n order if for every u, v ∈ V(G) such that e = uv ∈ E(G), f(u) and f(v) relatively prime. A prime graph is a graph which admits prime labeling. In this study, we investigate and conclude that the line and splitting graph of the brush graph is a prime graph.
Application of GSTAR(1,1) model for layer peat soil predicted based on resistivity log data
Authors: Jonathan R , Yundari  , Nurhasanah  , Nada O Y E 
Published: November 2021

Abstract
In this study, GSTAR modeling was carried out with the inverse of distance weight matrix obtained from Geoelectrical Resistivity data at several peatland locations around the Universitas Tanjungpura, Pontianak. This data can identify the subsurface layer of the soil through the electric current that binds into the soil. However, due to the limitation of the tool to measure the resistivity value, it can only measure 1/5 of the depth of the observation length. To overcome this problem, predictions are made at the next depth using the GSTAR model. The study began by measuring the resistivity value of the land using the geoelectric method and mapping it. Through this GSTAR modeling, predictions are made for the unobserved subsurface to determine the type of soil layer. Knowing the type of deeper soil layer can help contractors build plant concrete stakes to keep buildings safe on peatland. The results of the GSTAR(1.1) model are not accurate enough to estimate the resistivity value data. This is possible because the correlation between rock ages is not the same, so further analysis is required.
Temporal analysis of the heavy metal concentration in road sediment and dust using statistical models
Authors: Zafra-Mejía C A , Rondón-Quintana H A , Echeverry-Prieto L C 
Published: November 2021

AbstractThe objective of this paper is to show a temporal analysis using autoregressive integrated moving average models of the heavy metal concentration in road sediment and dust of Soacha, a Colombian locality. The representative size fractions in the road sediment and dust were <250 μm and ⩽10 μm, respectively. The results suggest that lead is the best metallic element to study the relationship between the heavy metal concentration in the road sediment and dust (r-Pearson = 0.90). Univariate models (R2⩾ 0.58) suggest that the time series of lead concentrations in road sediment and dust have the same temporal structure. Namely, because they are first-order autoregressive processes, concentrations at a given moment of time are influenced by the immediately preceding concentrations. The transfer function model (R2= 0.91) suggests that there is no delay in impulse transfer from road dust concentration to lead concentration in the road sediment. The effect is immediate for a sampling interval of 3 days. The results show that modeling has a better fit during the rainy season compared to the dry season. In the context of the simulation of physical phenomena in engineering, this study is relevant to deepen knowledge in relation to the use of autoregressive integrated moving average models.