bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring ZA Fertilizer Production using Multivariate Maximum Chart Based on Bootstrap Control Limit
Authors: Kruba R , Mashuri M , Prastyo D D 
Published: February 2021

Abstract
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

Abstract
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

Abstract
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

AbstractHighest 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

AbstractManagement 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractSinusitis 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

Abstract
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

Abstract
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

Abstract
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.