bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling of Human Development Index in Papua Province Using Spline Smoothing Estimator in Nonparametric Regression
Authors: Rahmawati D P , Budiantara I N , Prastyo D D , Octavanny M A D
Published: February 2021

The development goal of a country must be focused on the quality of human life to achieve prosperity. One important indicator for measuring the success of a country’s development is the Human Development Index (HDI). In 2018, Papua was the province with the lowest HDI in Indonesia. Special attention is needed to improve HDI in Papua Province, one of them is by paying attention to the variables that affect HDI such as population growth rate, percentage of poor population, and economic growth. The relationships between HDI and the predictor variables do not have a clear pattern and tend to change at certain subintervals. This case can be approached using Spline Smoothing in multivariable nonparametric regression. Spline Smoothing is a type of estimator in nonparametric regression that has an excellent ability to handle data that tend to change at certain subintervals. Therefore, the purposes of this study are to obtain the form of Spline Smoothing estimator function in multivariable nonparametric regression, estimate the function and apply it to the HDI in Papua Province. The empirical results of modeling HDI in Papua Province show that it can be adequately applied which gives GCV = 58.108, R2 = 99.77% and RMSE = 0.0505.
Monitoring ZA Fertilizer Production using Multivariate Maximum Chart Based on Bootstrap Control Limit
Authors: Kruba R , Mashuri M , Prastyo D D
Published: February 2021

Control charts are extensively used to monitor the production process. When there is more than one variable process are considered, the multivariate control charts are typically employed to monitor the mean vector and the variability process separately. In recent years, control charts have been developed for monitoring mean process and variability process simultaneously in a chart. A Maximum multivariate control chart (Max-Mchart) is one of the simultaneous multivariate control charts relying on exact distribution control limit. The objective of this paper is to evolve Max-Mchart based on Bootstrap control limits. This paper also compares Max-Mchart over the Hotelling T2 and Generalized Variance (GV) control chart. The interpretive examples are implemented to demonstrate the applications of the ZA fertilizer production dataset in carbonation step.
Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset
Authors: Talita A S , Nataza O S , Rustam Z
Published: February 2021

The security of a network might be threatened by an intrusion aim to steal classified data or to find weaknesses on the network. In general, network main security systems use a firewall to control and monitor both incoming and outgoing network traffic. Intrusion Detection System can be used to strengthen network security. Several data mining methods have been used to solve Intrusion Detection System (IDS) problem on a network. On this paper we will use Naïve Bayes Classifier along with Particle Swarm Optimization (PSO) as the feature selection method specifically on one of the benchmark dataset on IDS problem, KDD CUP’99. The dataset consists of more than 40 features with more than 400 thousands records. To solve IDS problem on the dataset, it needs a quite expensive cost either on time computation or memory usage hence the use of PSO as the feature selection method. The best classification result was reached when we use 38 features where the accuracy is 99.12%. Particle Swarm Optimization method has several parameters that may affect the classification performance. For future improvement, it is possible to use a parameter optimization method to ensure the best classifier performance.
Bayesian Mixture Generalized Extreme Value Regression with Double-Exponential CAR Frailty for Dengue Haemorrhagic Fever in Pamekasan, East Java, Indonesia
Authors: Rantini D , Iriawan N , Irhamah
Published: February 2021

There is a lot of research on infectious diseases. One of the dangers is caused by dengue hemorrhagic fever (DHF) viruses. Almost all the world there are dengue viruses that spread. In health science, this research continues to increase over time. It has been widely studied by medical circles, but from a statistical point of view, patient survival can be investigated probabilistically. The survival time of DHF patients could refer to the time of arrival of DHF patients to the hospital with all the health conditions of the patient to be discharged from the hospital. It is because the patient has been declared medically cured or further improved from the initial hospital admission. In this case, survival analysis will be appropriate to be implemented, where the event of the study is DHF patients who are discharged from the hospital due to recovery. DHF patient survival data is found following the multimodal Generalized Extreme Value distribution so that a mixture model would be employed. There is a lot of transmission media for diseases transmitted by the DHF virus, but the fastest transmission medium occurs in a short time, called the Aedes Aegypti Mosquito. This transmission of the DHF virus by mosquitoes can occur between adjacent neighboring regions. This article brings together two fields of research, namely survival models with spatial random effects. The Bayesian analysis will be used as a model parameter estimation approach, whereby unmeasured autocorrelations between intersecting regions will be captured through the conditional autoregressive (CAR). Two different distributions approach for these unmeasured autocorrelations will be given, i.e. the Normal CAR and the Double Exponential CAR. Four predictor variables, namely sex, age, hematocrit level, and platelet count in each DHF patient are used to explain the variability of the survival. The Cox Proportional Hazard regression is employed to find out the significant influence of those variables on the survival spatial DHF model. The result shows that the two-component mixture Generalized Extreme Value regression model coupled with a random Double Exponential CAR effect is the best model. Based on the best model obtained, the significant predictor variables for the DHF spatial survival model are sex, hematocrit level, and platelet count.
Highest Posterior Distribution (HPD) Control Chart for Individual Observation
Authors: Khusna H , Mashuri M , Ahsan M
Published: February 2021

Highest Posterior Distribution (HPD) is one of the methods to determine the interval estimator of data having both symmetric and asymmetric distributions. On the symmetric distribution, the Lower Control Limit (LCL) and the Upper Control Limit (UCL) of Individual control chart will automatically have the equilibrium density value. However, the LCL and the UCL of Individual control chart for asymmetric distribution will never have the equilibrium density value. This research is aimed to propose HPD control chart based on the concept of equilibrium density. The simulation studies provide the control limits of Individual and HPD control charts for both symmetric and asymmetric distributions of data. The Average Run Length (ARL) of both Individual and HPD control charts are calculated by generating the data which follow normal distribution and some asymmetric distributions such as weibull, gamma, beta, and lognormal. The in-control ARL (ARL0) of Individual control chart for various asymmetric distributions violate the ARL0target. Even for asymmetric distribution, the ARL0of HPD control chart is convergent to certain value of ARL0target. Hence, HPD control chart is appropriate to monitor both symmetric and asymmetric distribution of data.
Logistic Regression Model of Relationship between Breast Cancer Pathology Diagnosis with Metastasis
Authors: Bustan M N , Poerwanto B
Published: February 2021

Management of breast cancer requires a diagnosis of breast cancer, including an accurate pathology diagnosis to direct the provision of appropriate treatment. Part of inseparable diagnosis of breast cancer itself is determining the presence of metastasis. This study aims to determine the relationship model of pathological diagnosis with the possibility of metastasis to other organs of the breast. Pathology diagnosis results include tumor position, topography, behavior, grade, and tumor status. This study used secondary data from the Hospital Information System (SIRS) of the hospital in Makassar, Indonesia. Data obtained from the results of pathology and clinical examinations of breast tumor as many as 204 patients were hospitalized during the year of 2018. Binary regression analysis modeling was performed to see the relationship between types of pathological diagnosis (laterality, topography, behavior, grade, tumor status) of breast cancer with the possibility of metastases to other organs. The results of the analysis found a significant relationship between the position and status of the tumor with the presence of metastasis. The results of the metastatic binary logistic regression model wasL(Y) = 1.330 – 0.670lat1 – 0.779lat2 – 0.724statusChi-square analysis between metastasis with surgery and chemotherapy found the significant relationships between metastasis with surgery (X2= 15.951; p value = 0.001) and metastasis with chemotherapy (X2= 20,796; p value = 0.001). The results of this analysis indicate that the position and status of the tumor have a relationship with the likelihood of metastasis. In addition, this metastasis further determines the management of treatment whether surgery or chemotherapy or both.
Compound-Target Prediction and Network-Target Analysis on Jamu Formula
Authors: Syahrir N H A , Sumarheni  , Hasbi M
Published: February 2021

Jamu is the Indonesian traditional herbal medicine composed of several plants that have been practised for many centuries to maintain good health and to treat diseases. Unlike chemical drugs that have only one single target, the components of jamu have multiple channels and targets. Therefore, the identification of potential target and their actions is a major challenge in jamu research. We used a computational method called drugCIPHER-CS to predict the target profile for each herbal ingredient from jamu formula by combining the drug chemical interaction information in a heterogeneous network that correlates chemical, pharmacological, and genomic spaces. We also analysed network target to evaluate the importance protein of compound and pharmacological actions to help explain the action mechanisms of the jamu formula. In this work, we presented a case study of antidiabetic Jamu formula we previously developed. The jamu formula composed of four medicinal plants, namely Tinospora crispa, Zingiber officinale, Momordica charantia, and Blumea balsamifera. After determining 55 bioactive compounds in the jamu formula, we used DrugCIPHER-CS to predict their potential targets. The top 100 target profiles predicted for each compound were selected, and the target profiles of all collected compounds within a medicinal plant were combined to form integrative target profiles of this formula. We obtained 1250 potential targets of the jamu formula among 9500 listed targets on protein-protein interactions data. The potential targets then are involved in the protein interaction network and measured their topological networks by combining their degree, centrality, and betweenness. The network topology showed 20 unique potential targets hit by four medicines in jamu formula. DrugCHIPER-CS as a computational approach can speed the genome-wide identification of drug target on jamu formula efficiently. The potential target in each compound and the network demonstrate new insight to understand the mechanism on Jamu formula. However, experimental validation is still needed.
Stability Analysis of Genotype the Local Red Union Palu to determine the Cultivation Location using Additive Main Effect and Multiplicative Interaction (AMMI)
Authors: Fadjryani  , Junaidi  , Riski
Published: February 2021

Additive Main Effect and Multiplicative Interaction (AMMI) is a method used to learn the interaction between genotype and location. The objective of this research is to apply the AMMI model in verifying stability of several genotypes of the local red union Palu towards the growth of genotype location based on biplot and stability value AMMI (SVA). This research used seven genotypes red union. Four genotypes namely, Sumenep, Sanren F1, Tuk-Tuk and Kawak are not from Palu while the three genotypes are the native palu plant (Batu, Kertas and Palasa). These are planted in different five experiment locations, Wombo, Guntarano, Bolupounto, Oloboju and Soulove with three replications. The Interaction Principle Component AMMI (IPCA) which is significant is included into the model based on analysis of variance of AMMI. These are IPCA1 and IPCA2 with the total of variance interaction equals 88,02%. Biplot picture shows that the Batu genotype can only be able to adapt in Oloboju while Kawak, Sumenep and Kertas genotypes location are far away from the main line. This indicates that the three genotypes tend to unstable or in another word those genotypes could not adapt well in the five plantation locations. Furthermore, Biplot picture shows the genotype which is stable in all location is Palasa, Sanren F1 and TukTuk due to nearby the main line. The SVA based on the index stability denotes that the first ranking is Palasa followed by Sanren F1 and TukTuk. This indicates that from the three genotypes of the red union Palu, it can be concluded that Palasa is the best genotype.
Kernel Based Comparison between Fuzzy C-Means and Support Vector Machine for Sinusitis Classification
Authors: Putri R A , Rustam Z , Pandelaki J , Salmi N
Published: February 2021

Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both side of the nose, and behind the eyes. Sinusitis is caused by infection, growth of nasal polyps, allergies, and others. This condition can effect adults, teenagers, and even children. To classify sinusitis, we used Kernel Based Fuzzy C-Means, which is the development of Fuzzy C-Means (FCM). FCM algorithm groups data using Euclidean distance. However, when non-linear data is separated, the convergence is inaccurate and need a long-running time. To overcome this problem, a Kernel Based Fuzzy C-Means that use kernel functions as a substitute for Euclidean distance. It maps objects from data space to a higher dimension feature space, so they can overcome FCM deficiencies. Beside we used Kernel Based Support Vector Machine to do the same thing, that separate the data set by hyperplane. From the result of both methods, we will compare both of them to get the best method for the data set. Data that is used is sinusitis data set obtained from the laboratory of radiology at Cipto Mangunkusumo National General Hospital, Jakarta. From the experiment we got 100% accuracy of Kernel Based Fuzzy C-Means and 100% accuracy of Kernel Based Support Vector Machine using the same parameter sigma for the kernel.
Fuzzy C-Means Clustering with Minkowski and Euclidean Distance for Cerebral Infarction Classification
Authors: Khairi R , Fitri S G , Rustam Z , Pandelaki J
Published: February 2021

Cerebral infarction is a condition in which the death of neuronal cells, glial cells and blood vessel system is caused by a lack of oxygen and nutrients. The cause of nerve damage is hypoxia, which is a decrease in oxygen pressure in the alveoli which can cause hypoxemia in brain tissue. Cerebral infarction can also be caused by obstruction of blood flow to the brain so that the brain does not get enough oxygen. This situation is called ischemia. The initial stage of ischemic neurons is characterized by the formation of micro vacuolization, which is characterized by cell size that is still normal or slightly reduced, vacuoles occur in the perikaryon area, which can be found in neurons in the hippocampus and cortical 5-15 minutes after hypoxia. The final sign of cell damage due to ischemia is the nucleus which becomes pyknotic and fragmented. To diagnose the presence or absence of cerebral infarction in the brain it is not enough just to use a CT scan, therefore machine learning will also be used to diagnose the presence or absence of cerebral infarction in the brain. For this reason, the authors propose Fuzzy C-Means Clustering with Minkowski and Euclidean Distance as a classification method that has good accuracy, good precision, good memory, and a good F1-score in calcifying patients whose brains experience infarction or not. In this proposed method, Fuzzy C-Means Clustering with Minkowski and Euclidean Distance is a modification of the Fuzzy C-Means Clustering Algorithm. This modification is proposed to increase the detection capacity of Fuzzy C-Means Clustering. The parameterized Minkowski distance metric is adjusted for implementation with FCM with various settings. The experimental results show that this method can improve the results of the FCM grouping with an accuracy of around 88%.
Preprocessing Unbalanced Data using Support Vector Machine with Method K-Nearest Neighbors for Cerebral Infarction Classification
Authors: Sari A G M , Putri A M , Rustam Z , Pandelaki J
Published: February 2021

Cerebral infarction is focal brain necrosis due to complete and prolonged ischemia that affects all tissue elements, neurons, glia, and vessels. Stroke infarction or known as cerebral infarction is a condition of damage in the brain due to insufficient oxygen supply, due to obstruction of blood flow to the area. Research shows stroke infarction does not only occur in the elderly, but occurs at a young age of around 15-55 years, especially with certain risk factors, such as diabetes, hypertension, heart disease, smoking, and long-term alcohol consumption. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. Therefore, it requires timely detection and more accurate methods of classification. This study aims to use Support Vector Machine (SVM) as preprocessing and K-Nearest Neighbors (KNN) algorithm to classify Infarction Cerebral. In this study, discusses the application of SVM to deal with class imbalances. The first strategy is to balance data using SVM as a preprocessor and the actual target value of the training data is then replaced by trained SVM predictions. Then, the modified training data is used to classify with K-NN method. We use data CT scan result from a Department of Radiology at Dr. Cipto Mangunkusumo Hospital (RSCM). This accuracy in this paper shows around 69,85 %.
Classification of sinusitis using kernel three-way c-means
Authors: Hartini S , Rustam Z , Pandelaki J , Prasetyo M , Yunus R E
Published: February 2021

Sinusitis can be defined as acute and chronic sinusitis, according to the duration of symptoms. In this study, kernel three-way c-means, as the modification of the three-way c-means method that used kernel distance instead of Euclidean distance, was used. Three-way c-means itself is the upgrade version of the rough k-means algorithm that integrates three-way weight and three-way assignments to assign data points into clusters with the appropriate weight. The performance was later compared using the sinusitis dataset taken from Cipto Mangunkusumo Hospital, Indonesia, which was consists of 102 acute and 98 chronic sinusitis samples. From the experiments, three-way c-means was obtained 62.09% accuracy, 55.21% sensitivity, 62.76% precision, 68.77% specificity, and 58.59% F1-Score in 1.82 seconds. Meanwhile, kernel three-way c-means with the 8th polynomial kernel was provided 67.48% accuracy, 74.82% sensitivity, 64.52% precision, 60.77% specificity, and 69.12% F1-Score in 2.24 seconds. Therefore, it was concluded that kernel three-ways c-means performs better with the slower running time than the three-way c-means.
Kernel Entropy Based Fuzzy C-Means (KEFCM) for Acute Sinusitis
Authors: Rustam Z , Shandri N , Siswantining T , Pandelaki J
Published: February 2021

Sinusitis is a condition when sinuses membranes are plugged or inflamed or swollen due to infection. There are several types of sinusitis, one of them, which will be explained in this study, is acute and chronic sinusitis. There are many ways to diagnose sinusitis such as allergy tests, nasal endoscopy, CT Scans and MRI. In this study, a diagnosis will be made whether someone has acute sinusitis or chronic sinusitis by using clustering techniques with machine learning. In medical field machine learning can be used to help to analyse medical data more quickly and accurately therefore the patient can get the treatment sooner. in this study, the machine learning method used is kernel entropy fuzzy c-means (KEFCM). The kernel will be used in the Entropy Fuzzy C-means (EFCM) method which can represent multiplication in a high-dimensional space and the kernel that will be used is RBF and Polynomial. This sinusitis data used in this study were obtained from the Laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia with this method it will get 97% Accuracy.
Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier
Authors: Rukmawan S H , Aszhari F R , Rustam Z , Pandelaki J
Published: February 2021

Cerebral infarction is one of the causes of stroke in the brain and is included in ischemic stroke. To detect infarction in the brain, classification in machine learning can be used. They are k-Nearest Neighbor (kNN) and Naive Bayes (NB). kNN is a simple and well-known machine learning method with high accuracy values. however, kNN can produce sub-optimal results if very little training data is used. Because it will produce accuracy from a biased model and has less than optimal performance. Meanwhile, Naive Bayes Classifier has a better level of accuracy compared to other classifier models. And only requires a small training test to get high accuracy. Therefore, this study will compare 2 different classifications to get the highest accuracy in the brain infarction dataset obtained from the Department of Radiology, dr. Cipto Mangunkusumo Hospital (RSCM). The accuracy of this method reaches 91% for kNN and 97% for Naive Bayes.
Spatial Econometric Model for Mapping Poverty Area in West Sulawesi
Authors: Asrirawan Asrirawan , Rahmawati  , Hikmah  , Abdy M
Published: February 2021

This paper depicts the frameworks of spatial regression methods which cover the spatial interaction effects and apply it to analyze the spatial distribution of poverty area for five districts in West Sulawesi. These methods consider queen contiguity-based spatial weights to create spatially lagged variables and apply to Spatial Lag Model, Spatial Error Model (SEM), Spatial Durbin Model and Ordinary Least Square (OLS) Model. The percentage of poverty area represents the observed dependent variable in this article while several independent variables are recorded by the previous research and correlated to poverty. The analysis results showed that the spatial effects should be involved to the econometric model because an estimated spatial parameter is statistically significant. To select model criteria, AIC and Likelihood ratio test are used and this criteria highlight Spatial Lag Model is better than the other model.
Handling Incomplete Data with Regression Imputation
Authors: Annas S , Kartikasari P , Arisandi R
Published: February 2021

Regression analysis is widely used in various fields due to easy-to-understand. One of purposes of regression analysis is to predict the response variable using the predictor variables. Unfortunately, in real cases, some values may be missing. This circumstance will produce large error, indeed, poor prediction. Missing value lead us to trade-off remove the paired data points or replace. The purpose of this study was to estimate the missing value with regression imputation. This study conducted two scenarios of amount of missing values, 10% and 15%. The study results showed that the higher of amount of missing values, the higher the value of MSE was. The first section in your paper.
Effect of Brotowali Extract Fortification of Papaya Leaves And Sugar Cane Caries on Diabetes Mellitus Type II
Authors: Purnamasari A.Bida , Rusdianto
Published: February 2021

Diabetes Mellitus Type II is a metabolic disorder characterized by an increase in blood sugar due to decreased insulin secretion by pancreatic beta cells or impaired insulin function (insulin resistance). Risk factors for diabetes mellitus are age, sex, obesity, hypertension, genetic, food, smoking, alcohol, and lack of activity. Increasing cases of diabetes in Indonesia is a concern that must be followed up in real terms, especially for educators starting from the stage of detection to treatment to reduce prevalence. Diabetes Mellitus Type II is the focus of attention because it is a non-communicative disease that can be prevented through by changes of healthier lifestyle. In the prevention and treatment of Diabetes Mellitus Type II can utilize several types of local medicinal plants, namely brotowali plants, papaya and sugar cane which have antidiabetic substances. This study aims to determine the effect of brotowali fortification extracts of papaya leaves and sugar cane juice induced in mice (Mus musculus). This type of research is an experimental study that was be held in Laboratory of Department Biology Makassar State of University. Brotowali extract of fortified papaya leaves and sugarcane extract at a dose of 250 mg/kg BW and 500 mg/kg BW orally by mixing in drinking water of mice. Brotowali extract of fortified papaya leaves and sugar cane juice is expected to reduce blood glucose levels in mice. The results of this study will be used as a reference or reference in making Diabetes Mellitus Type II drugs from various types of herbal plants including extracts of fortification brotowali, papaya leaves and sugar cane juice.
Isolation And Identification of Secondary Methabolites Compound of Moringa Oleifera Lamk Leaf Acetic Ethyl Extract
Authors: Sudding  , Herawati N
Published: February 2021

This study aims to isolate and identify secondary metabolite compounds in the ethyl acetate extract of Moringa leaves (Moringa oleifera Lamk). This isolation includes several stages, namely extraction, fractionation, purification and identification. Identification includes solubility test, UV-VIS spectroscopy test, FTIR and GCMS. Based on the solubility test results, it is known that the isolate is soluble in ethyl acetate solvent and dissolves well in the chloroform solvent, this shows that the isolate obtained is semi-polar and the compound glows under UV light. UV-VIS spectroscopy test results showed a maximum wavelength of 210.52 nm showing the characteristics of the ester group. FTIR spectroscopy data showed wave numbers (cm-1) namely: 3450.65 (OH alcohol), 2926.01 and 2560.43 (C-H); 1456.26 and 1382.96 (CH2 and CH3), 1722.43 (C=O); 1616.26 (C = C aromatic); 1039,63 (C-O). The result of GC-MS showed molecular weight of compound is 166. From the results of the analysis it was suggested that the isolate was a phenolic group that are 4-hydroxy benzoyl ethyl esthers.
Analysis The Effect of the Use Seaweed (Eucheuma cottonii) as The Substitution Material to Tapioca Flour on The Quality of Snakehead Fish (Channa striata) Nugget
Authors: Kaseng E S , Suhaeb F W
Published: February 2021

The study aimed to determine the effect of the use of seaweed (Eucheuma cottonii) as a substitution material to tapioca flour on the quality of snakehead fish (Channa striata) nugget. The method used was experimental method and composed as non-factorial, Completely Randomized Design (RAL). The factor of the treatment was substitution of tapioca flour with seaweed in the processing of snakehead fish nugget, consisting of 4 treatment levels, namely: 75 gs tapioca flour and 0g wet seaweed (N0), 0 g tapioca starch and 75 gs wet seaweed (N1), 50g tapioca flour and wet seaweed 25 gs (N2), 25gs tapioca starch and 50 gs wet seaweed (N3), in each 500 gs of snakehead fish formulation. The results indicated that the snakehead fish nugget substituted by tapioca flour and seaweed showing the significat effect on the value of organoleptic (appearance, odor, taste, texture, folding test), and proximate (water, protein, fiber content). The quality of snakehead fish nugget using the tapioca starch and seaweed (N2) was showing the highest value of appearance 7.92 aroma 7.14, flavor 7.5, and texture 8,29; as well as the highest value of folding test 5.0 (AA). The content of moisture was 36.63%, protein 13.10%, and crude fiber 1.02%. So that, the best product of snakehead fish nugget proximat with subtitution of 50g tapioca flour and wet seaweed 25 gs (N2) was appropriate to the Indonesia National Standard (SNI 7758-2013).
Gas Chromatography-Mass Spectrometry (GC-MS) Analysis and Antioxidant Activity of Sea-Cucumber (Holothurian atra and Holothurian edulis) From Selayar Island
Authors: Muflihunna A , Mu’nisa A , Hala Yusminah , Hasri
Published: February 2021

The present investigation was carried out to characterize the bioactive compounds present in extract of Holothurian atra and Holothurian edulis using Gas Chromatography-Mass Spectrum (GC-MS) and antioxidan activity. The results of the GC-MS analysis provide different peaks determining the presence of 4 phytochemical compounds with different therapeutic activities. The measurement of antioxidant activity carried out using 1,1difenil-2-pikrihidazil (DPPH) method. The major, 9-Octadecenoic acid (Z)-, methyl ester (47.29), hexadecanoic acid, methyl ester (28.41), octadecanoic acid, methyl ester (4.47), and [1,1′-bibicyclo[2.2.2]Octane]-4-carboxylic acid (92.11) and minor compounds were also present. Antioxidant activity of the ethanolic extract of Holothurian edulis have low antioxidant activity with the value IC50 41.6828 mg/mL and ethanolic extracts of Holothurian atra have strong antioxidant activity with value IC50 33.514 mg/mL. From the results, it could be concluded that Holothurian atra and Holothurian edulis contains various bioactive compounds have various biological activities and antioxidant activity.
The Effect Of Microwave Irradiation on Synthesis of Gold Nanoparticles Using Ethanol Extract of White Bol Guava Leaves
Authors: Putri Suriati Eka , Pratiwi Diana Eka , Side Sumiati
Published: February 2021

The aim of this research was to determine the effect of microwave radiation on the synthesize of gold nanoparticles using ethanol extract of white bol guava leaves. 50 mL of 25 ppm HAuCl4 solution was synthesized using ethanol extract of white bol guava leaves to convert Au3+ to Au0 with 0.75 mL and synthesized using microwave and without microwave. Characterization of gold nanoparticles (Au-NPs) using Scanning Electron Microscope (SEM) and X-ray Diffraction (XRD) instruments. Based on the results, the microwave irradiation has a significant effect on the formation of Au-NPs using ethanol extracts of white bol guava leaves, based on the analysis using XRD, the size of Au-NPs was 17.13 nm with microwave irradiation and 17.57 nm without without microwave irrdaiation. Based on the results of SEM-EDS obtained Au-NPs by the microwave irradiation of 54.92%, while without a microwave irradiaton of 42.17 %.
Type Curiosity of Students Learned by Discovery-Based Contextual Models and Direct Learning Models on Acid Base Topic
Authors: Sugiarti  , Husain Halimah
Published: February 2021

This research is a quasi-experimental that aimed to compare the curiosity of students with discovery-based contextual models and direct learning models. The independent variables in this study are discovery-based contextual models and direct learning models, while the dependent variable is the students’ curiosity in acid-base topic. The population in this study is all students of class XI MIA SMAN 1 Gowa, while the sample was class XI MIA 1 as experimental group I and class XI MIA 7 as experimental group II with the number of students each 30 people. The research data was obtained through an observation sheet and student activity sheet. The results of the descriptive analysis showed that the average value obtained by the experimental group I was 79 while in the experimental group II it was 68,63. The results of hypothesis testing using t-test values obtained at tcount = 5,99 and significance level α = 0,05 with df = 3 obtained ttable = 1,64. It shows that the curiosity of student that are taught by discovery-based contextual models was higher that their taugh by direct learning models in class XI MIA SMAN 1 Gowa on acid-base topic.
Using Cooperative Learning Models of The Bamboo Dancing Type to Improve the Skills of 4C Students
Authors: Arvyaty  , Salim  , Kadir  , Kodirun  , Ruslan  , Hariani
Published: February 2021

Mathematical learning is essentially related to problem solving, reasoning and abstraction, which can be used to level 4C skills. One interesting mathematical learning model for practicing creative and critical thinking skills is a cooperative learning model of the bamboo dancing type. This Model emphasizes the occurrence of cooperation between students in pairs in large groups, shifting each other to move position according to the clockwise direction to solve a problem, students actively communicate and Collaborate to resolve specific issues and within specific time constraints. The results of this study show that: (1) Students can explain and exchange information with their friends, (2) students can learn how to convey information correctly, so that their friends can understand it, (3) students can grow and increased cooperation in a group to resolve a particular problem, (4) students can increase its tolerance to differing cognitive abilities of students, (5) students can strive to think critically and creatively.
Analyzing Conceptual and Procedural Knowledge of Geometry Among Prospective Teachers: Indonesian Perspective
Authors: Yurniwati  , Soleh D A
Published: February 2021

It is well established that to scaffold students’ learning of mathematics, teachers themselves need a substantial body of content knowledge of mathematics (Ma, 1989) to drive their lessons and design of learning experiences. In this paper, we address the above issue among prospective elementary mathematics teachers in Indonesia. Evidence suggests that elementary school Indonesian teachers tend to acquire surface-level knowledge of geometry, which does not support deep learning. This paper aims to bring a higher degree of insight into the above problem by analyzing prospective teachers’ geometry knowledge along two knowledge strands: conceptual and procedural. Examples of these knowledge components are provided with suggestions for future research.
Gap Analysis of University Online Learning Website from Students’ Perspectives: A Case from Telkom University, Indonesia
Authors: Pradana M , Rahmawan M D , Wahyuddin S , Imam R
Published: February 2021

The purpose of this research is to observe the perceived quality of Telkom University online learning website from students’ perspectives. Students here act as the main users of the learning platform and we would like to see whether the perceived quality and the ideal quality already fulfil users’ expectation. The data used are the primary and secondary data with a set of sample taken from 100 students. We use the question items from website quality method and analyse the data with importance performance analysis (gap analysis). The research shows that the usability and service interaction aspects of the website have quite significant gaps between perceived and ideal qualities.
Pre-service elementary school teachers’ reasoning profile in solving geometry problems based on mathematics ability
Authors: Rosdiana  , Budayasa I K , Lukito A
Published: February 2021

A person’s reasoning can be seen in the solution of the problem he solved. Geometry is effective for developing the ability to think logically or reasoning students. Solve geometry problems that have an abstract object of study required a process of reasoning or reason. The reasoning of a student with a high mathematical ability may be different from the reasoning of a moderate or low-ability student in solving geometrical problems. The purpose of this study is to obtain information about pre-service elemantary school teachers’ reasoning profiles in solving geometry problems based on mathematical ability. The type of research that will be used is descriptive explorative research with qualitative approach. The subject of this research is 3 pre-service elementary school teachers’. Subject selection is based on a written math ability test. The instruments of this study include the main instruments and supporting instruments. The reasoning profile data was obtained through mathematical skills tests and problem-solving test-based interviews. The credibility of the data collected is tested through triangulation. Student reasoning profile of student of pre-service elementary school teacher is able to explain again the thinking process (reasoning or logical argument) clearly.
Development of the Basic Concept Assessment of SD/MI Mathematics Based KKNI Curriculum
Authors: Munir N. P , Anas A.
Published: February 2021

The assessment of mathematics learning in college needs to be a concern for lecturers to avoid subjectivity in assessing students. So far, the assessment of mathematics learning in the PGMI Study Program is only dominant in the knowledge aspect. A good assessment should not only focus on the domain of knowledge but the three domains, namely, attitudes/values, knowledge, and general skills/special skills in accordance with learning outcomes in the KKNI curriculum. The purpose of this research was to develop basic concepts of SD/MI mathematics based on a valid and practical KKNI. This research was R & D (research and development) research using ADDIE method. The target of this research was PGMI Study Program of IAIN Palopo semester 1 of 2019/2020 Academic Year. The assessment instruments developed were in the form of performance assignments, product assignments, and project assignments. The results of this research indicated that: (1) The validity of the developed assessment instrument was on the very valid category with an average rating validators 93%; (2) The practicality of the developed assessment instruments is in a very practical category. From the lecturer’s point of view, it shows that the instrument has a clear and integrated category in measuring three domains in Learning Outcomes. Whereas from the student’s point of view it showed that the instruments developed was practically used and was able to reveal the understanding of the basic concepts of mathematics of SD/MI students.
Managing Hybrid Learning Model: Web Enhanced Course Media in Physics Learning
Authors: Yani A , Nurhayati  , Dahlan A
Published: February 2021

This research was study about managing hybrid learning model: web enhanced course media in physics learning. The research conducted in SMA Negeri 2 Pare-pare and XI grade MIPA Class were chosen as study object. The study method is modified research and developing model which use to manage Hybrid learning media. The study result show that (1) web enhanced course media is suitable to develop by using LMS based website, (2) web enhanced course media have to have four service components i.e (1) administration, (2) assessment, (3) content delivery, and (4) community service, and (4) the implementation of web enhanced course media makes the student physics learning motivation gain to high grade and increase the number of student which interest in physics.
The Use of Metacognitive Strategies in Solving Mathematical Problems
Authors: Anggo M , Masi L , Haryani M
Published: February 2021

The ability to solve mathematical problems is useful not only for the absorption of knowledge, but also for the formation of thinking and attitude in solving the problems. This research was conducted to describe the metacognitive strategies that students undertake when solving mathematical problems. Metacognitive strategy is one of the methods used in problem solving that emphasizes to build the well-structured of thinking, by deciding what to do and where to go in realizing and overcoming obstacles. Data obtained through problem solving by the subject and then analyzed qualitatively. Through the application of metacognitive strategies, a systematic and well-structured problem solving is implemented based on the logical thinking steps, and allows to the right solution. For students, metacognitive strategies are very important to build a strong foundation of thinking and to be applied in various situations and levels of complexity problems.
Study of Clarification Android Based Worksheet of Topic Cartesian Coordinate at Level Junior Secondary
Authors: Nasrullah  , Suradi  , Hamda
Published: February 2021

This study aims to design an Android-based student worksheet on the topic of cartesian coordinate learning and obtain evidence for clarification of research on prototype design. The design research method was carried out based on stages, namely research clarification, descriptive study 1, prescriptive study, and descriptive study II. This research revealed the results of the research clarification stage by involving 35 Mathematics and Natural Sciences students of FMIPA UNM. They were asked to test the prototype and provide responses through the questionnaire provided. There are three main things to be responded by respondents, namely the suitability of the format, the material submitted, and worksheet form. The data obtained were analysed descriptively quantitatively. The results show that the prototype format meets the suitability. Likewise, the criteria for the material presented and the prototype designed. In other words, this Android-based worksheet prototype meets the stated design objectives.
Learning Trajectory in Mathematical Proof
Authors: Minggi I , Mulabar U , Assagaf S F
Published: February 2021

This study aims to obtain a learning trajectory framework for mathematical proofing and to develop teaching materials for the real number system topic. The method is Design research. The results showed the five developed trajectories: (a) experience and fundamentals, (b) simple proof (c) proof of existence, (d) complex proof. and (e) proofing through construction. In proofing, the students still need scaffolding from the lecturer. The difficulties was still existed are writing symbols and writing mathematical reasoning especially in linking a series of statements in the proof..
Students’ Understanding on Formal Definition of Limit
Authors: Nurdin  , Assagaf S F , Arwadi F
Published: February 2021

This paper reports our preliminary findings of students’ understanding on the formal definition of limit - epsilon delta definition. The aim of this study is to develop students understanding of definition of limit by construction learning trajectories in teaching limit. The subjects were five students majoring in mathematics. The methods were test and interview. The test is used to identify how the subjects proof the limit using formal definition. Interview aims to confirm and explore information about the students’ difficulties in proving the limit using the definition. The interviews were then transcribed and analyzed along with the test results to get conclusions about the difficulties of students proving the limit by using formal definition. The result shows that the subjects were still facing difficulties in interpreting the definition of limits.
Viterbi Algorithm and Its Application to Indonesian Speech Recognition
Authors: Hatala Z , Puturuhu F
Published: February 2021

An algorithm used to extract HMM parameters is revisited. Most parts of the extraction process are taken from implemented Hidden Markov Toolkit (HTK) program under the name HInit. The HMM model is introduced briefly based on the theory of Markov Chain. We schematically outline the Viterbi method implemented by HInit. The iterative formal definition of the method which directs computer implementation is reviewed. We also illustrate the method calculation precisely using manual calculation and extensive graphical illustration. The distribution of observation probability used is simply independent Gaussians. The performance of the algorithm for phone recognition rate on small Indonesian vocabulary is 80%, while the result is near perfect 95% for word recognition.
Estimation of Vehicle Speed at Night Based on Rear Lights
Authors: Setiyono B , Ratna D , Ilmi M F , Usadha I G N R
Published: February 2021

All Speed violations are common things. One way to prevent it is detecting the vehicle and estimating the speed of vehicle that is running. To detect the vehicle, we can use the vehicle lights. The rear lights of the vehicle could be detected easier than the front lights of the vehicle because the color of the rear lights was different than the common lights on the road at night. This paper focuses on estimating the speed of a vehicle that runs at night based on the rear lights of a vehicle. The first step is calibrating the camera used and extracting the video into frames. Then we tracked the vehicle position in each frame in the Region of Interest (ROI). The next step is bounding and labelling each detected vehicle. After that, the conversion from the camera angle and the camera height become the actual distance in each frame. The final step is to find the movement of the vehicle from one frame to another using Euclidean distance to determine the speed of the vehicle. This research results in high estimation accuracy when in the ROI II area for the actual vehicle speed between 20-40 km/h, and for vehicle with actual speed between 40-60 km/h, the accuracy was high if it is in the ROI III area.
Mapping groundwater recharge in Africa from ground observations and implications for water security
Authors: MacDonald Alan M , Lark R Murray , Taylor Richard G , Abiye Tamiru , Fallas Helen C , Favreau Guillaume , Goni Ibrahim B , Kebede Seifu , Scanlon Bridget , Sorensen James P R , Tijani Moshood , Upton Kirsty A , West Charles
Published: February 2021

Groundwater forms the basis of water supplies across much of Africa and its development is rising as demand for secure water increases. Recharge rates are a key component for assessing groundwater development potential, but have not been mapped across Africa, other than from global models. Here we quantify long-term average (LTA) distributed groundwater recharge rates across Africa for the period 1970–2019 from 134 ground-based estimates and upscaled statistically. Natural diffuse and local focussed recharge, where this mechanism is widespread, are included but discrete leakage from large rivers, lakes or from irrigation are excluded. We find that measurable LTA recharge is found in most environments with average decadal recharge depths in arid and semi-arid areas of 60 mm (30–140 mm) and 200 mm (90–430 mm) respectively. A linear mixed model shows that at the scale of the African continent only LTA rainfall is related to LTA recharge—the inclusion of other climate and terrestrial factors do not improve the model. Kriging methods indicate spatial dependency to 900 km suggesting that factors other than LTA rainfall are important at local scales. We estimate that average decadal recharge in Africa is 15 000 km3 (4900–45 000 km3), approximately 2% of estimated groundwater storage across the continent, but is characterised by stark variability between high-storage/low-recharge sedimentary aquifers in North Africa, and low-storage/high-recharge weathered crystalline-rock aquifers across much of tropical Africa. African water security is greatly enhanced by this distribution, as many countries with low recharge possess substantial groundwater storage, whereas countries with low storage experience high, regular recharge. The dataset provides a first, ground-based approximation of the renewability of groundwater storage in Africa and can be used to refine and validate global and continental hydrological models while also providing a baseline against future change.