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Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution, . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.

One of the efforts that can be done to optimize health services and the distribution of facilities and infrastructure efficiently in a wide scope is by profiling and clustering areas in the province of West Java to the scope of sub-districts that have similar characteristics of disease category. The methods that will be compared to get the best clustering are hierarchical clustering and ensemble clustering. The data used as the object of research is the BPJS Kesehatan capitation primary service sample data for the 2017-2018 period. Some of the important variables used include: primary disease diagnosis data (ICD-10) of patients at the puskesmas, service time, type of visit, and location of service sub-district. This study uses several evaluation metrics Silhouette coefficient, Dunn index, Davies-Bouldin index, and C-index to determine the optimal number of clusters formed. In addition, descriptive analysis and visualization of the clustering results are also used as considerations in selecting the optimal cluster. Based on the evaluation results, the optimal method is hierarchical clustering with complete linkage. This method produces three clusters: cluster 1 consists of 5 sub-districts that have a high/dominant mean value in almost all disease categories, cluster 2 consists of 26 sub-districts that have a medium mean value, and cluster 3 consists of 589 sub-districts that have a low mean value. Most of the members of clusters 1 and 2 are sub-districts located in the districts/cities around the national capital (DKI Jakarta) and the provincial capital (Bandung) while the members of cluster 3 are mostly sub-districts located in suburban districts/cities or far from the central government.

Inequality in the Human Development Index (IPM) in Papua Province amid the disbursement of development funds needs to be studied adequately so that the policies and programs that have been planned can be more directed and on target. For this reason, research is needed that can map the priority needs of each district in Papua Province by identifying regional characteristics, namely villages. By using Cluster Analysis and Factor Analysis, the results of this research show that 4 district clusters in Papua Province were formed with different priority focuses on increasing the HDI. The main focus of the district HDI improvement priorities in Papua Province is divided into three through factor analysis: the infrastructure-telecommunication factor, the sanitation-economic factor, and the health-education factor. Each cluster is generally still dominated by districts with a low HDI category. The main obstacle to increasing HDI in Papua Province is the transportation and telecommunications infrastructure factor. Local governments are expected to be able to formulate human development programs and policies concerning the priority needs of each district as a result of this research.

Covid-19 merupakan penyakit yang disebabkan oleh infeksi virus SARS-CoV-2, pertama kali masuk ke Indonesia pada awal tahun 2020. Adanya wabah covid-19 menyebabkan setiap daerah yang ada di Indonesia khususnya kabupaten Garut harus terbagi ke dalam beberapa risiko zona covid-19 sesuai dengan kondisi dari suatu daerah tersebut. Beberapa faktor yang dapat memengaruhi suatu daerah masuk pada risiko zona tertentu dapat ditentukan berdasarkan jumlah kasus positif covid-19, kasus suspek, kasus kontak erat, jumlah desa, dan kepadatan penduduk daerah tersebut. Regresi logistik ordinal merupakan salah satu analisis regresi yang digunakan untuk menganalisa hubungan antara variabel prediktor dan variabel respon, dimana variabel respon tersebut bersifat kategorik dengan skala ordinal. Oleh karena itu, maka digunakan regresi logistik ordinal untuk mengetahui faktor apa saja yang memberikan pengaruh terhadap pembagian risiko zona covid-19 di kabupaten Garut pada bulan Juli tahun 2021. Sebelum melakukan pemodelan regresi logistik ordinal dilakukan terlebih dahulu proses teknik resampling dengan metode oversampling untuk menangani data yang tidak seimbang pada peubah respon. Berdasarkan pemodelan hasil dari pemodelan serta pengujian secara parsial, didapatkan bahwa peubah bebas yang memiliki pengaruh terhadap risiko zona covid-19 di kabupaten Garut yaitu jumlah desa, kepadatan penduduk, kasus suspek, dan kasus konfirmasi positif dengan nilai akurasi sebesar 85.71%.

Salah satu bentuk akibat dari kurangnya asupan gizi kronis pada balita adalah stunting. Stunting merupakan permasalahan kesehatan yang saat ini sedang digencarkan untuk diturunkan angka prevalensinya. Permasalahan kesehatan ini berhubungan erat pada pertumbuhan tinggi badan yang lebih rendah dengan anak seusianya. Berdasarkan data Kemenkes per Agustus 2021, Provinsi Jawa Barat menduduki posisi pertama dengan angka balita stunting paling tinggi di Indonesia. Penelitian ini bertujuan untuk mengetahui faktor apa saja yang memengaruhi terjadinya stunting pada balita dan memodelkan dengan metode regresi logistik biner untuk wilayah Jawa Barat. Metode ini dapat menunjukkan faktor yang memengaruhi berdasarkan peubah yang signifikan. Regresi logistik biner akan memodelkan hubungan antara satu atau beberapa peubah prediktor dengan peubah respon yang kategorik. Peubah respon didefinisikan sebagai persentase angka balita stunting dan dibagi kedalam dua kategori yakni tinggi dan rendah. Pengategorian kelas didasarkan pada nilai median pada persentase angka balita stunting. Unit penelitian menuju pada 27 wilayah Kabupaten/Kota di Jawa Barat. Hasil analisis menunjukkan dari 11 peubah prediktor, setelah dilakukan pemodelan terdapat 3 peubah yang berpengaruh signifikan pada taraf nyata 0,10 yakni imunisasi dasar lengkap, tempat pengelolaan makanan yang memenuhi syarat kesehatan, dan penduduk miskin. Model yang terpilih berdasarkan nilai akurasi seimbang terbesar dibandingkan model lainnya yakni dihasilkan nilai akurasi seimbang sebesar 81,59%.

Java is one of the islands in Indonesia which has good establishment acceleration. Even though economic growth was good, poverty is still a serious problem. Three of six provinces, including DI Yogyakarta, Central Java, and East Java still have poverty rates above national rates in March 2020. This problem indicates that an imbalance in poverty happens between those regions. Several regions have extreme conditions or known as outliers. Besides that, poverty gap data have a complex pattern so modeling using a non-parametric approach is suitable. This study aims to build an appropriate model to support the success of poverty alleviation in Java and the identification of outliers was carried out using an adjusted boxplot. The best-penalized regression spline model for Poverty Gap Index in Java Island was obtained by Generalized minimum Cross-Validation (GCV) using optimum smoothing parameter (λ) 0,12 and knot combination (1, 2, 4, 1, 5, 3, and 1) for seven predictor variables. The result shows that penalized spline regression model has a higher R2 than OLS regression. The R2 is obtained 69,10%, so the model is feasible to explain the variability of the poverty gap in Java. Moreover, based on the outliers’ identification shows a dependency between outlier in data and residual because some districts/cities are identified as outliers in both.

The diversity of each region causes different potentials in each region. The potential of the village can map how rich the area is, the advantages of the area, and the population and welfare. Tourism is one of them; this sector is potential for the area because it can lift its economy if it is adequately managed. Good management is born from the policies/regulations of the local government. Nusa Tenggara Barat is a province with many tourist attractions. However, from an economic and socio-cultural perspective, Nusa Tenggara Barat has yet to be able to compete with other major provinces in Indonesia, such as the Special Region of Yogyakarta (DIY). The 2018 Village Potential Data by BPS can assist the government in compiling efforts for the village's progress. In the process of data processing, especially big data, in-depth exploration is needed to produce meaningful insight. Clustering is one of the exploration techniques that can map areas in Nusa Tenggara Barat based on the tourism potential in each village. K-Prototypes are used in cases with mixed variables (numeric and categorical). Determination of the best number of clusters is using the silhouette index. It produced 5 clusters with their respective diversity. There are five clusters in Nusa Tenggara Barat by the villages based on tourism aspects and factors that support tourism. Cluster 3 is an ideal cluster, meaning tourism development in that cluster is complete. Cluster 5 has considerable potential in tourism because the supporting factors are analytically good. There are villages dispersed across Sumbawa Barat, Sumbawa, Lombok Tengah, Lombok Barat, Dompu, and Bima that are part of cluster 1. In Sumbawa Barat and Lombok Tengah, cluster 1 predominates numbers. The settlements in cluster 2 are then more prevalent in Sumbawa and Bima. Furthermore, Sumbawa, Dompu, and Bima have the highest concentrations of cluster 4. Unlike clusters 3 and 5, special attention should be paid to clusters 1, 2, and 4 in tourism development. Implications of this research are the government could take toward each cluster to increase the GDP-oriented service product, namely tourism; whether it is an improvement or reconstruction, clustering analysis works its role in learning the data to make the policy more focused.

The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems. The study aims to compare resampling techniques in linear regression. The original data used in the study is clean, without any influential observations, outliers and leverage points. The ordinary least square method was used as the primary method to estimate the parameters and then compared with resampling techniques. The variance, p-value, bias, and standard error are used as a scale to estimate the best method among random bootstrap, residual bootstrap and delete-one Jackknife. After all the analysis took place, it was found that random bootstrap did not perform well while residual and delete-one Jackknife works quite well. Random bootstrap, residual bootstrap, and Jackknife estimate better than ordinary least square. Is was found that residual bootstrap works well in estimating the parameter in the small sample. At the same time, it is suggested to use Jackknife when the sample size is big because Jackknife is more accessible to apply than residual bootstrap and Jackknife works well when the sample size is big.

High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier. Outliers are part of observations that do not follow the data distribution pattern and are located far from the data center. The existence of outliers needs to be detected because it can lead to deviations from the analysis results. One of the methods used to detect outliers is the Mahalanobis distance. To obtain a robust Mahalanobis distance, the Minimum Vector Variance (MVV) method is used. This study will compare the MVV method with the classical Mahalanobis distance method in detecting outliers in non-invasive blood glucose level data, both at p>n and n>p. The test results show that the MVV method is better for n>p. MVV shows more effective results in identifying the minimum data group and outlier data points than the classical method.

Drug-drug interactions is defined as the modification of the effect of a drug as a result of another drug given simultaneously or with an interval or when two or more drugs interact so that the effectiveness or toxicity of one or more drugs changes. Pharmacodynamic interactions are one type of interaction that needs special attention because these interactions work directly on the body's physiological systems and compete on the same receptors so that they can be antagonistic, additive, or synergistic. The use of medicinal plants is becoming an alternative because in addition to their relatively safer side effects, medicinal plants consisting of active compounds are appropriate in treating degenerative metabolic diseases triggered by mutations in many genes. As in the case of polypharmacies, interactions of active compounds in medicinal plants can also lead to phapharmodynamic interactions. Therefore, it is also necessary to identify the active compounds so that it can then be known whether the interaction of the compounds will be beneficial or detrimental. In this study, pharmacodynamic identification was applied to Diabetes Mellitus Type 2 medicinal plant compounds by using the independent variables Target Protein Connectedness (TPC), Side Effect Similarity (SES), and Chemical Similarities (CS) using Random Forest classification method. From a search of various databases, 21 active compounds were obtained and then only 100 compound interactions could be calculated as independent variables. With an accuracy value and AUC of 0,96, there were 93 pairs of compounds that interacted pharmacodynamically and the remaining 7 did not interact.

The human development index (HDI) is one of the important things to note in Indonesia today. The growth of HDI in Indonesia in 2021 is not evenly distributed in all regencies/cities and has high disparities. This study aims to find out the description of HDI data, get the best model to determine the factors that significantly affect the HDI of regencies/cities in Indonesia in 2021 and identify the classification accuracy results of the best model. The independent variables used in this study are average years of schooling, open unemployment rate, population growth rate, population density, percentage of poor people and sex ratio. The independent variables in this study contained missing values, so they were handled using k-nearest neighbour (KNN) imputation and continued modelling using ordinal logistic regression using the backward elimination technique to obtain significant factors. The results showed that the proportion of the low HDI category was 4.28%, the medium HDI category was 48.64%, and the high HDI category was 47.08%. Based on logistic regression modeling using backward elimination which has the smallest AIC value of 293.387, a model with independent variables of average years of schooling (X1), population density (X4), percentage of poor people (X5) and sex ratio (X6) is a variable that significantly affects the HDI of regencies/cities in Indonesia in 2021. The accuracy value of the classification accuracy of training data and test data from the ordinal logistic regression model of HDI of regencies/cities in Indonesia in 2021 is 83.46% and 86.61%, respectively, which means that the model is good for prediction.

This article highlights a new approach to finding a copula model that most closely matches extreme rainfall characteristics in South Sulawesi, Indonesia. In climatology research, data that does not meet normal assumptions are often found due to extreme observations; therefore, a method is needed to overcome the dependency of variables that do not meet the normality assumption. The copula approach is a method that can determine the relationship between such variables. Copula can describe the dependency structure between variables with different margins and model its tail dependencies. This study discusses the application of Archimedean copula in modeling the dependency structure of both variables, namely the intensity of the 75th percentile (I75) and volume of the 75th percentile (P75). For explaining the dependencies of both variables, the best copula model was selected using the empirical copula method. The results showed that 16 stations followed the Clayton copula model, 23 stations followed the Gumbel copula model, and 14 stations followed the Frank copula model. After knowing the distribution model characteristics and the intensity and volume of extreme rainfall, the return period value on the intensity and volume of extreme rainfall was calculated. The return period value can be useful as information and evaluation valuable material for preparing natural hazard mitigation programs.

The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However, some fundamental assumptions must be fulfilled to provide good parameter estimates for the OLS estimation. The error term in the regression model must be identically and independently comes from a Normal distribution. The failure to fulfill the assumptions will result in a poor estimation of parameters. The violation of assumptions may occur due to the presence of unusual observations (which is known as outliers or high leverage points. Even in the case of only one single extreme value appearing in the set of data, the result of the OLS estimation will be affected. The parameter estimates may become bias and unreliable if the data contains outlier or high leverage point. In order to solve the consequences due to unusual observations, robust regression is suggested to help in reducing the effect of unusual observation to the result of estimation. There are four types of robust regression estimations practiced in this paper: M estimation, LTS estimation, S estimation, and MM estimation, respectively. Comparisons of the result among different types of robust estimator and the classical least square estimator have been carried out. M estimation works well when the data is only contaminated in response variable. But in the case of presence of high leverage point, M estimation cannot perform well.

This study aims to compare and determine the best model to describe the relationship between National Education Standard (NES) and CBNE scores using generalized structured component analysis. Model 1 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of National Education and the Ministry of Religion (2010), Model 2 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2012), and Model 3 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2017). The results of the structural model evaluation have found that in Model 1, the SI path coefficient to Academic Achievement (PA) is not significant, in Model 2, the SI path coefficient to PA and SPT to SPN is not significant and in Model 3, the SI path coefficient to PA is also not significant. The coefficient of determination of each endogenous latent variable for each model ranges from 0.20 - 0.75. While the resulting Q-square value for all models is more than 0.9 to represent very good predictive relevance. Based on the overall goodness of fit, it is found that Model 3 produces the largest FIT and AFIT values. So it can be said that model 3 is better than other models. This model produces 11 invalid indicator variables, namely points 17, 39, 51, 55, 57, 59, 73, 75, 76, 80, and 108. The study found that National Education Standards that significantly affect academic achievement are graduate competency standards, process standards, and educational assessment standards

This article highlights an alternative approach to identify a slight shift of the process mean for resistor production. Commonly, the industries use exponentially weighted moving average (EWMA) or classic I-MR charts for this kind of product. The parametric control chart consists of few underlying assumptions, especially observations that come from a normal distribution. A misleading conclusion was mainly made when non-normal distributed data were analyzed using a parametric control chart. A chip resistor manufacturing company provided the data for the study for future quality monitoring purposes. This study aims to determine a more appropriate analysis method according to the characteristics of the chip resistor data distribution. This article discusses the results of implementing one of the nonparametric methods that are still rarely known. The company’s current I-MR, corrective I-MR, parametric EWMA, and NPEWMA-SR control charts are used and compared in the analysis part. In the comparison, the I-MR control chart cannot detect a slight shift in the process mean. In contrast, the parametric EWMA control chart is not robust for data from a non-normal population. Since the data was not naturally from a normally distributed population, the nonparametric control chart is more appropriate, and the NPEWMA-SR control chart is suggested.

Sektor pertanian memiliki sumbangsih besar dalam perekonomian Indonesia, tetapi menjadi sektor yang seringkali diidentikkan dengan kemiskinan. Hingga saat ini, pembangunan sektor pertanian belum mampu sepenuhnya meningkatkan kesejahteraan rumah tangga petani. Provinsi Sulawesi Tenggara selama 2013-2021 tercatat memiliki Indeks Nilai Tukar Petani (NTP) untuk subsektor tanaman pangan, stabil di bawah 100. Indeks NTP di bawah 100 menunjukkan bahwa kesejahteraan petani belum begitu baik. Penelitian ini bertujuan menentukan faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara. Status kesejahteraan merupakan peubah respon berskala ordinal dengan tiga kategori yaitu miskin, rentan miskin, dan tidak miskin. Metode regresi yang sesuai untuk peubah respon berskala ordinal adalah regresi ordinal, dengan beberapa kemungkinan fungsi hubung. Dalam kajian ini menggunakan fungsi hubung logit dan probit. Hasil analisis regresi menunjukkan bahwa umur kepala rumah tangga (X3), kepemilikan telepon seluler (X6), sumber penghasilan utama rumah tangga (X9), akses terhadap kredit usahatani (X11), dan keberadaan jaminan sosial rumah tangga (X13) berpengaruh positif dan signifikan terhadap status kesejahteraan rumah tangga petani tanaman pangan, sedangkan jumlah anggota rumah tangga (X4) dan usia kawin pertama (X5) berpengaruh negatif dan signifikan. Dengan membandingkan nilai R2 dan balanced accuracy model logit dan probit, disimpulkan bahwa model logit lebih baik dalam mengidentifikasi faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara daripada model probit.

Kemiskinan seringkali berhubungan dengan masalah kesejahteraan dan menjadi salah satu masalah utama di Indonesia. Kondisi ekonomi akibat Covid-19 berdampak pada tingkat kemiskinan di masyarakat. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi tingkat kemiskinan di Indonesia tahun 2021 menggunakan regresi logistik biner. Regresi logistik biner digunakan untuk memodelkan hubungan antara variabel respon yang terdiri dari dua kategori dengan satu atau lebih variabel prediktor. Hasil penelitian ini menunjukkan proporsi tingkat kemiskinan rendah lebih tinggi dibandingkan tingkat kemiskinan tinggi. Terdapat 18 provinsi dengan tingkat kemiskinan rendah serta 16 provinsi dengan tingkat kemiskinan tinggi. Faktor-faktor yang berpengaruh terhadap kategori pada tingkat kemiskinan yaitu Indeks Pembangunan Manusia (X1) dan Gini Ratio (X2). Ketepatan klasifikasi dari model sebesar 83.33%, yang artinya model baik digunakan.

Kesehatan adalah keadaan sehat, baik secara fisik, mental, spiritual maupun sosial yang memungkinkan setiap orang untuk hidup produktif secara sosial dan ekonomis. Indeks Membangun Desa 2018 Provinsi Papua merupakan peringkat terendah dalam kategori “tertinggal” dan Papua adalah provinsi dengan IPM paling rendah dengan angka 59,09 pada tahun 2017. Dalam kaitannya dengan kesehatan, Papua yang terdiri dari 29 kota/kabupaten masih memiliki pelayanan kesehatan dengan kinerja yang tergolong rendah. Hal ini diantaranya berkaitan dengan keberadaan fasilitas pelayanan dan tenaga kesehatan di Papua. Dalam penelitian ini ingin dilakukan pengelompokkan provinsi-provinsi di Papua berdasarkan fasilitas dan tenaga kesehatan menggunakan algoritma K-Medoids. Hasilnya menunjukkan terdapat hubungan linear positif antara total populasi dengan jumlah tenaga kesehatan artinya bertambahnya populasi diikuti dengan peningkatan jumlah tenaga kesehatan selain itu bertambahnya jumlah fasilitas kesehatan (meliputi rumah sakit, puskesmas, fasilitas kesehatan terkait persalinan, dan fasilitas kesehatan lainnya) diikuti dengan peningkatan jumlah tenaga kesehatan. Hasil clustering sudah sesuai dimana Cluster 1 berpenduduk banyak dengan memiliki jumlah puskesmas, fasilitas kesehatan terkait persalinan, dan tenaga kesehatan lainnya (selain dokter, dokter gigi, dan tenaga kesehatan terkait persalinan) sedangkan Cluster 2 berpenduduk sedikit dan memiliki jumlah fasilitas maupun tenaga kesehatan yang paling sedikit. Walaupun hasil menggambarkan jumlah penduduk berbanding lurus dengan jumlah fasilitas kesehatan dan jumlah tenaga kesehatan, namun masih dibawah standar yang ditetapkan oleh perundang-undangan. Papua masih perlu lebih banyak membangun fasilitas pelayanan kesehatan dan merekrut lebih banyak tenaga kesehatan dengan strategi khusus yang disesuaikan dengan Kata Kunci : Sehat, Fasyankes, Nakes, Papua.

Beef is a food ingredient that has a high selling value. Such high prices make some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. In addition to socialization related to understanding the differences between the two types of meat, another solution is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Convolutional Neural Network (CNN) is one of the Deep Learning methods and the development of Artificial Intelligence science that can be applied to classify images. Several regularization techniques include Dropout, L2, and Max-Norm were applied to the model and compared to obtain the best classification results and may predict new data accurately. It has known that the highest accuracy of 97.56% obtained from the CNN model by applying the Dropout technique using 0.7 supported by hyperparameters such as Adam's optimizer, 128 neurons in the fully connected layer, ReLu activation function, and 3 fully connected layers. The reason that also underlies the selection of the model is the low error rate of the model, which is only 0.111.Keywords: Beef and Pork, Model, Classification, CNN

This study aimed to formulate a suitable statistical model to determine acetylcholinesterase enzyme's half-maximal inhibitory (IC50) by a series of synthetic compounds. It was done with the same core structure for acetylcholinesterase inhibition for anti-Alzheimer’s disease (AD). The IC50 of eighteen synthesized compounds on anticholinesterase activities was obtained and statistical methods were applied. Regression models were fitted to the dose-response curve to look for their IC50. Simple linear regression is the simplest model for the dose-response curve. However, polynomial regression models or non-linear regression models fit the data more accurately. The adjusted coefficient of determination (𝑅𝑎𝑑𝑗 2 ) was used to determine the best model among the linear models, while the root mean square error (RMSE) is more suitable in determining the goodness of fit between linear and non-linear model. Four-parameter logistic (4-PLR) regression often fits the dose-response data closely. Based on the RMSE value, a polynomial regression fitted better than 4-PLR with the IC50 of 245.52.

Indeks Pembangunan Manusia dibangun dari tiga dimensi dasar, dimensi ini merupakan pendekatan yang dipilih dalam penggambaran kualitas hidup manusia dan tidak mengalami perubahan hingga saat ini. Ketiga dimensi ini mencakup umur panjang dan hidup sehat, pengetahuan, dan standar hidup layak Penelitian ini bertujuan untuk menentukan model regresi logistik biner dan faktor-faktor yang mempengaruhi Indeks Pembangunan Manusia di Pulau Jawa menggunakan regresi logistik biner. Regresi logistik biner adalah analisis statistika yang digunakan untuk mengetahui pengaruh suatu satu atau lebih variabel independen terhadap satu variabel dependen, dimana variabel dependen bersifat biner atau dichotomous. Hasil penelitian ini menunjukkan model regresi logistik biner yang terbentuk adalah Y = -111.32025 + 0.58148X1 + 3.07145X2 + 2.40689X3 + 0.79014X4 – 0.87295X5 + 0.02139X6 + 0.11445X7. Faktor-faktor yang berpengaruh terhadap kategori pada nilai IPM terhadap variabel bebas dengan taraf nyata α = 5% adalah Persentase umur harapan hidup (X1), Persentase harapan lama sekolah (X2), dan Persentase rata-rata lama sekolah (X3). Evaluasi model diperoleh nilai balanced accuracy sebesar 96.43%, yang berarti model baik digunakan.

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

Pengukuran kadar glukosa darah menggunakan metode invasif, yaitu melukai bagian tubuh, seperti jari, merupakan metode yang kurang disukai oleh sebagian besar masyarakat. Untuk itu, diperlukan pengembangan teknologi berupa alat pengukur kadar glukosa darah non-invasif. Alat ini menggunakan prinsip kerja spektroskopi inframerah. Oleh karena itu, lama waktu pengukuran menjadi hal yang harus dipertimbangkan. Keoptimalan lama waktu pengukuran diperlukan agar proses pemeriksaan kadar glukosa darah efisien dan bisa merekam seluruh informasi. Tujuan penelitian ini adalah menentukan lama waktu optimal pada alat pengukur kadar glukosa darah non-invasif. Data yang digunakan merupakan data primer hasil pengukuran kadar glukosa darah dari tiga responden. Data tersebut dianalisis menggunakan metode eksplorasi dan regresi linier. Hasil pemodelan dengan persamaan , lama waktu optimal tersebut berada pada waktu perlakuan sebesar 1700 ms dengan menggunakan metode gradien pada kurva.