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This study compares the K-Medoids and CLARA (Clustering Large Application) methods for livestock population data in Indonesian districts and cities. Calculating the distance between points and objects in the data, K-Medoids is a method for clustering based on data points (medoids). A larger dataset is divided into several samples for comparison in CLARA, an extension of the K-Medoids approach. The CLARA method analysis results show that three clusters are the ideal number. The ideal number of clusters in a K-Medoids cluster analysis is two. The Silhouette Score (SS), Davis-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) are the metrics that are measured. The evaluation of the comparison results shows that the CLARA method has an SS value of 0.66, while K-Medoids has an SS value of 0.62. The comparison of the CLARA and K-Medoids approaches yielded DBI values of 1.38 and 1.92, respectively, and 197.54 and 132.73 for CHI. The findings indicate that, in comparison to the K-Medoids approach, the SS value for the CLARA method is closer to 1, and that the CHI value derived from the CLARA method is likewise greater. The K-Medoids approach has a higher DBI value than the CLARA method, where a lower DBI value denotes superior performance. The CLARA approach is the most effective way to do cluster analysis on livestock population data in Indonesian districts and cities, according to the findings.

Clustering is a statistical method used to group data based on certain similar characteristics, particularly in the context of complex and diverse data. This study aims to cluster districts/cities in West Java Province based on infrastructure indicators, namely access to clean water, sanitation, electricity, and energy, using the K-Modes clustering method. The data used is categorical data sourced from SUSENAS West Java 2023. The cluster analysis resulted in four distinct clusters, each representing significant differences in infrastructure characteristics across regions. The first cluster consists of 8 regions, the second cluster includes 7 regions, the third cluster consists of 1 region, and the fourth cluster contains 11 regions. These characteristic differences among clusters indicate infrastructure disparities that need to be addressed in planning more equitable development to improve the quality of life of people in West Java

K-Means dan K-Medoids digunakan untuk menilai indikator sosial ekonomi dan kesehatan di Provinsi Jawa Timur tahun 2023 melalui metode klasterisasi. Dengan menggunakan Principal Component Analysis (PCA) untuk mereduksi dimensi variabel, penelitian ini mengelompokkan wilayah berdasarkan karakteristik sosial ekonomi dan kesehatan. Data yang dianalisis termasuk angka harapan hidup, tingkat kemiskinan, pengangguran, dan akses ke layanan kesehatan. Kebaruan penelitian ini terletak pada kombinasi unik antara PCA dan K-Medoids untuk menghasilkan klaster yang lebih akurat dan robust terhadap outlier, dibandingkan metode yang biasanya hanya menggunakan satu teknik klasterisasi atau tidak melibatkan reduksi dimensi. Hasil penelitian menunjukkan bahwa K-Medoids dengan PCA menghasilkan klaster yang lebih koheren dan terpisah daripada K-Means, terutama dalam menangani outlier. Menurut metode Elbow dan Silhouette, empat hingga lima klaster adalah pilihan terbaik. PCA meningkatkan akurasi dan efisiensi klasterisasi dengan mengurangi kompleksitas data, yang menghasilkan klaster yang lebih baik Diharapkan temuan ini akan membantu pemerintah membuat kebijakan yang lebih baik untuk mengatasi ketimpangan kesehatan dan sosial ekonomi di Jawa Timur. Kata kunci: Klasterisasi, Outlier, Principal Component Analysis (PCA)

Handling missing values in univariate time series analysis poses a challenge, potentially leading to inaccurate conclusions, especially with frequently occurring consecutive missing values. Machine Learning-based Univariate Time Series Imputation (MLBUI) methods, utilizing Random Forest Regression (RFR) and Support Vector Regression (SVR), aim to address this challenge. Considering factors such as time series patterns, missing data patterns, and volume, this study explores the performance of MLBUI in simulated Autoregressive Integrated Moving Average (ARIMA) datasets. Various missing data scenarios (6%, 10%, and 14%) and model scenarios (Autoregressive (AR) models: AR(1) and AR(2); Moving Average (MA) models: MA(1) and MA(2); Autoregressive Moving Average (ARMA) models: ARMA(1,1) and ARMA(2,2); and Autoregressive Integrated Moving Average (ARIMA) models: ARIMA(1,1,1) and ARIMA(1,2,1)) with different standard deviations (0.5, 1, and 2) were examined. Five comparative methods were also used in this research, including Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The research findings indicate that MLBUI performs exceptionally well in imputing successive missing values. The results of this study indicate that the performance of MLBUI in imputing consecutive missing values, based on MAPE, yielded values of less than 10% across all scenarios used.

The primary tool for assessing the extent of human development progress in a country is the Human Development Index (HDI). There are three components of Indonesia's Human Development Index (HDI). The method used to characterize the quality of human existence is based on these foundational aspects of HDI. The three elements include the role of economic advancement in human progress, as well as health, knowledge, and a decent standard of living. The objective of this research is to conduct binary logistic regression modeling to identify the key aspects that influence the Human Development Index of Regencies and Cities in Indonesia. If the response variable is binary and the predictor factors consist of one or more continuous or categorical variables, binary logistic regression is the statistical technique used to model the categorical response variable. The research results indicate that the percentage of Life Expectancy (X1), Average Length of Schooling (X2), Expected Years of Schooling (X3), and Per Capita Expenditure (X4), both partially and simultaneously, are independent variables that have the most significant impact on HDI at a real level of α = 5%. A balanced accuracy rating of 91.83% was achieved from the model evaluation, indicating that the model is useful

A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors

Unemployment is one of the indicators for measuring the economic conditions of a region. It is also a social and economic problem in many countries, including Indonesia, especially in areas with a density of economic activity, such as Java Island. The purpose of this study was to classify and analyze the factors that affect the open unemployment rate in cities and regions on Java Island, which are categorized as low, medium, and high. The research method used in this study was ordinal logistic regression analysis. The data source comes from the BPS website in 2023 with four predictor variables: population size, labor force participation rate, average years of schooling, and gross regional domestic product at constant prices. The research results show that the variables population size and labor force participation rate had a significant effect on the open unemployment rate, while the variables average years of schooling and gross regional domestic product at constant prices did not have a significant effect on the open unemployment rate with the accuracy of the ordinal logistic model is 77.27%.

Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study

In research, we often encounter problems of multicollinearity and outliers, which can cause coefficients to become unstable and reduce model performance. Robust Continuum Regression (RCR) overcomes the problem of multicollinearity by reducing the number of independent variables, namely compressing the data into new variables (latent variables) that are independent of each other and whose dimensions are much smaller and applying robust regression techniques so that the complexity of the regression model can be reduced without losing essential information from data and provide more stable parameter estimates. However, it is hampered in the computational aspect if the data has very high dimensions (p>>n). In the initial stage, it is necessary to reduce dimensions by selecting variables. The Least Absolute Shrinkage and Selection Operator (LASSO) can overcome this but is sensitive to the presence of outliers, which can result in errors in selecting significant variables. Therefore, we need a method that is robust to outliers in selecting explanatory variables such as Weighted Least Absolute Deviations with LASSO penalty (WLAD LASSO) in selecting variables by considering the absolute deviation of the residuals. This method aims to overcome the problem of multicollinearity and model instability in high-dimensional data by paying attention to resistance to outliers. Leverages the outlier resistant RCR and variable selection capabilities of LASSO and WLAD LASSO to provide a more reliable and efficient solution for complex data analysis. Measure the performance of RKR-LASSO and RKR-WLAD LASSO; simulations were carried out using low-dimensional data and high-dimensional data with two scenarios, namely without outliers (δ= 0%) and with outliers (δ= 10%, 20%, 30%) with a level of correlation (ρ = 0.1,0.5,0.9). The analysis stage uses RStudio version 4.1.3 software using the "MASS" package to generate data that has a multivariate normal distribution, the "glmnet" package for LASSO variable selection, the "MTE" package for WLAD LASSO variable selection. The simulation results show the performance of RKR-LASSO tends to be superior in terms of model goodness of fit compared to RKR-WLAD LASSO. However, the performance of RKR-LASSO tends to decrease as outliers and correlations increase. RKR-LASSO tends to be looser in selecting relevant variables, resulting in a simpler model, but the variables chosen by LASSO are only marginally significant. RKR-WLAD LASSO is stricter in variable selection and only selects significant variables but ignores several variables that have a small but significant impact on the model.

To produce superior human resources, the SPs-IPB Master Program must consider the factors influencing the GPA in the student selection process. The method that can be used to identify these factors is a machine learning algorithm. This paper applies the random forest and XGBoost algorithms to identify significant variables that affect GPA. In the evaluation process, the default model will be compared with the model resulting from Bayesian and random search optimization. Bayesian optimization is a method for optimizing hyperparameters that combines information from previous iterations to improve estimates. It is highly efficient in terms of computing time. Based on a balanced accuracy and sensitivity metrics average, Bayesian optimization produces a model superior to the default model and more time-efficient than random search optimization. XGBoost sensitivity metric is 25% better than random forest. However, random forest is 19% better in accuracy and 30% in specificity. Important variables are obtained from the information gain value when splitting the tree nodes formed. According to the best random forest and XGBoost model, variables that have the most influence on students' GPA are Undergraduate University Status (X8) and Undergraduate University (X6). Meanwhile, the variables with the smallest influence are Gender (X4) and Enrollment (X9).

Education is an important element for the Indonesian nation and must be felt by all citizens. The availability of educational facilities is important for the realization of overall educational equality for the Indonesian people. The aim of this research is to group sub-districts in West Java Province according to their level of access to Senior High School (SHS-equivalent). The data included in this study comprises both numerical and categorical variables, which were obtained from the 2021 Village Potential Data Collection (PODES) conducted by the Central Statistics Agency. A cluster analysis method that can be used to group objects based on numerical and categorical data is K-Prototypes. The results of the grouping divide the data into 2 groups, where the first group has the characteristics of an urban subdistrict, the topographic area is plain, access to the nearest high school is very easy, and has an average number of high school and equivalent schools of 22 schools per subdistrict, and has an average distance to the nearest high school of 1,86 km. Meanwhile, the second group has the characteristics of subdistricts with rural areas, topography in the form of slopes, easy access to the nearest high school, and has an average number of high schools of 7 per subdistrict, and the average distance to the nearest high school is 4,06 km. The second group is sub-districts that need to be given special attention because they have relatively fewer high schools and the distance to the nearest high school is further

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modelling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have major variance and overfitting in prediction results. The modeling solution uses LASSO and GLASSO. Binary categorical response data uses binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. Based on the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables, and the correlation between them is relatively high. The recommendation given by the Indonesian government, if it is to increase the status of VDI in Indonesia, especially in North Maluku province, is to increase the number of electricity users, food and beverage stalls, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centres, because they still need to be developed compared to other villages, and more than 50% of villages are underdeveloped. If the Village SDGs are formulated through increasing VDI status, it will support the achievement of SDGs goals nationally.

Cluster analysis is a statistical learning technique that aims to uncover hidden patterns in data by grouping it based on known explanatory variables. In multi-clustering algorithms, similar data in different sub-dimensions of categories are initially grouped separately and then re-categorized based on the obtained clusters, resulting in a more exploratory grouping. This research aims to conduct exploratory analysis of regencies/cities in West Java based on the Village Development Index (Indeks Desa Membangun/IDM), which consists of three sub-dimensions: Social Resilience Index, Economic Index, and Environmental Index. It also aims to observe how the regencies/cities in West Java are grouped based on these indices using a multi-clustering algorithm with KMeans for each sub-dimension. From the exploration and analysis results, regencies/cities are clustered based on the three sub-dimensions. Additionally, recommendations are obtained suggesting that the equal distribution of educational facilities, addressing crime rates, improving economic infrastructure, and enhancing environmental quality should be priorities for the government of West Java province

Tujuan dari penelitian ini adalah untuk mengkaji bagaimana kualitas air dan sanitasi mempengaruhi Kejadian Luar Biasa (KLB) Diare di Provinsi Jawa Barat, Indonesia, menggunakan data Pendataan Potensi Desa (PODES) tahun 2021. Diare merupakan permasalahan serius dalam kesehatan masyarakat Indonesia, terutama pada kelompok anak balita, dan salah satu faktor penyebab utamanya adalah rendahnya kualitas air dan sanitasi. Dalam konteks penelitian ini, kami menerapkan metode algoritma K-Nearest Neighbors (K-NN) untuk mengklasifikasikan wilayah-wilayah yang mengalami KLB Diare. Hasil eksplorasi data menunjukkan variasi yang signifikan dalam jumlah kasus diare di sejumlah kabupaten dan kota yang tersebar di wilayah Jawa Barat. Untuk menangani masalah ketidakseimbangan data, kami menerapkan teknik Pengurangan Acak (Random Under Sampling), Penambahan Acak (Random Over Sampling), dan Synthetic Minority Oversampling Technique (SMOTE).Hasil analisis menunjukkan bahwa model K-NN dengan penggunaan metode SMOTE menghasilkan tingkat akurasi tertinggi, yaitu sebesar 71.28%. Meskipun demikian, nilai F1 score untuk semua model cenderung rendah, yang mengindikasikan adanya tantangan dalam mengklasifikasikan wilayah-wilayah dengan KLB Diare. Penelitian ini memberikan wawasan yang penting mengenai korelasi antara kualitas air, sanitasi, dan KLB Diare di Jawa Barat, serta mengidentifikasi wilayah-wilayah yang memerlukan perhatian lebih dalam upaya pencegahan dan pengendalian penyakit diare. Hasil ini dapat digunakan sebagai dasar untuk merancang program-program kesehatan yang lebih efektif di daerah-daerah dengan tingkat insiden diare yang tinggi. Kata kunci: Algoritma K-Nearest Neighbors (K-NN), SMOTE, Ketidakseimbangan data dan teknik pengambilan sampel ulang, Kualitas air dan sanitasi, Program pencegahan dan pengendalian diare.

Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which is higher than the ordinal random forest. The four most important variables causing diabetes are body mass index, hypertension, age, and cholesterol.

Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.

The number of positive cases of Covid-19 in DKI Jakarta has contributed to the national issues, reaching 25% of the total cases in Indonesia. The research examined and modeled the distribution pattern of Covid-19 positive cases in DKI Jakarta based on 44 districts spreading over six administrative areas. The data were regarding positive Covid-19 cases in DKI Jakarta for the past year, from April 2020 to April 2021. The research related to the pattern of positive Covid-19 distribution in 44 districts was carried out by time series clustering through Dynamic Time Warping (DTW) distances and agglomerative hierarchical methods. Then, the effectiveness of the clustering process is evaluated by comparing the predicted value of Covid-19 cases between clustering and non-clustering forecast results at the city level for the next 14 days through the Autoregressive Integrated Moving Average (ARIMA) model. The results group 44 districts into 6 optimal clusters based on the pattern of positive cases of Covid-19 in each district. The highest distribution rate is in cluster A, and the lowest is in cluster F. Geographical characteristics are also indicated by clusters A, B, E, and F. Then, the results show that the Mean Average Percentage Error (MAPE) value of the clustering model ranges from 16% to 20%. The difference between MAPE values to the non-clustering model implies that the forecasting accuracy is not far apart, which is in the round of 5%−6%.

The high population growth rate can impact various fields due to several factors. Some of the impacts of this high rate are high poverty rates, unemployment, consumption levels, inequality in education figures, gender empowerment index, and increasingly narrow land or area. Therefore, research on the rate of population growth using data on poverty, unemployment, consumption levels, education rates, gender empowerment index, and area makes sense. This data was taken from the official website of the Central Statistics Agency for six provinces on the island of Java, Indonesia. The data used contains missing data so that the missing data is presumed by using the k-nearest neighbour method. The estimated missing data values were modelled using binary logistic regression. Variables that significantly affect the rate of population growth, namely the level of consumption, gender empowerment index, and area, are obtained using the backward stepwise method and are selected based on the smallest Aikakes criterion information value or the one with the most excellent accuracy rate.

Abstract Ceratobasidium ramicola is a fitopathogenic fungus that harmful and causes various levels of damage on several types of forestry and horticultural crops. The purpose of this study was to examine the effect of Acacia villosa, Myristica fragrans, Acacia mangium and Calliandra calothyrsus leaf extracts as tannin sources related to the in vitro inhibition of Ceratobasidium ramicola growth. The in vitro inhibition was performed by employing solid potato dextrose agar (PDA) medium to obtain the radial inhibition, while liquid potato dextrose broth (PDB) medium was used to obtain the biomass inhibition. Experimental design was based on in time nested-completely randomized design and statistical analysis was carried out with SAS software version 9.1. The result of radial growth inhibition of Ceratobasidium ramicola showed that tannin extracts of A. mangium and M. Fragrans were not significantly different to each other. Treatment of tannin extracts from A. villosa, M. fragrans, A. mangium and C. Calothyrsus with a concentration of 1% were significantly different with other concentrations and resulted the greatest inhibition values. Tannin extract of A. Mangium at 1% concentration produced the greatest radial inhibition by 33.2%. In most cases, the effective inhibition from tannin extract occurred at the 24h of incubation. The greatest biomass inhibition was produced on 1% tannin extract of A. mangium by 64.3%, while the lowest was produced from 1% tannin extract of M. fragrans by 27.0%.

Background: The high rate of maternal and infant mortality is due to the low level of knowledge of mothers and the frequency of irregular pregnancy checks. Purpose: The purpose of this study was to determine the knowledge of primigravida mothers about the regularity of prenatal care. Methods: This type of observational research, according to the analysis is descriptive research. The population is all primigravida mothers who examined in Gayaman Village and the sample size of 30 respondents was taken based on total sampling technique. The instrument used was a questionnaire. Data collection is done through several stages, namely editing, coding, tabulating and scoring data, data collection was carried out on August 17-21 2018 in Gayaman Village, Mojokerto Regency. Result: The results of the general data from primigravida mothers' knowledge of the regularity of prenatal care showed that of 30 respondents less than 50% had sufficient knowledge of 9 respondents (30%). The data analysis here is enough because the education level of the respondents is lacking, so that in selecting and filtering the information received because the higher the level of education someone will influence the intellectual ability to choose information. Conclusion: The conclusions from this study indicate that the knowledge of primigravida mothers about the regularity of prenatal care is sufficient. Therefore, it is necessary to improve communication, information and education from health workers in providing counseling on antenatal care, so that mothers understand more about the regularity of prenatal care.

This research aimed to identify types and ways of oral feedback used by teachers in EFL classroom interaction based on the theory of Tunstall & Gipss (1996), Lyster & Ranta (1997), Cullen (2002), and Mackiewicz & Thompson (2013). This research also investigated teachers’ reasons for using oral feedback and students’ perceptions of oral feedback used by teachers in EFL classroom interaction. This research was conducted at Senior high school in Indonesia. The researcher applied descriptive qualitative research method. The subjects of this research were 2 English teachers and 12 students. All of them were chosen by using purposive sampling technique. The instruments of this research were audio recording, field notes, and interview. The result of this research showed that the teachers used 5 types of oral feedback, namely evaluative feedback, corrective feedback, descriptive feedback, interactional feedback, and motivational feedback. The teachers used oral feedback through providing evaluative statements to students’ work or performance, indicating and correcting students’ errors explicitly or implicitly, informing students’ achievement and the improvement strategies of learning, clarifying and embellishing some ideas on students’ responses, and providing motivational statements to students. The teachers employed oral feedback because of teachers’ responsibility and obligation to provide it, the effective and efficient feedback mode, the utility of oral feedback, positive effect to students, and consequences of none of oral feedback. Mostly, the students perceived positively to oral feedback. Some students found felt sensitive to oral feedback. Nevertheless, they still realized to its positive effect on the result of their work or performance.

Pendugaan kepekatan kernel dan spline termasuk pendugaan kepekatan nonparametrik. Perilaku pemulus spline terletak dipertengahan antara pemulus kernel yang konstan dan pemulus kernel yang tidak konstan. Pada kasus n besar dan λ tertentu fungsi pembobot spline dapat didekati oleh fungsi kernel. Perbandingan pemulus spline dan kernel secara empirik dilakukan dengan menggunakan data simulasi yang dicobakan pada berbagai lebar jendela kernel serta fungsi spline pada berbagai jumlah knot.

Spektrum keluaran FTIR merupakan data dalam dua dimensi yang merupakan data berpasangan antara % transmitan (Y) dan bilangan gelombang (X). Data spektrum yang dihasilkan oleh FTIR tersebut adalah data kuantitatif yang umumnya memiliki ukuran dimensi yang besar. Sehingga bila ingin dilakukan analisis data akan dijumpai permasalahan dalam penggunaan paket program pengolah data dan kemampuan hardware yang dimiliki. Sehingga diperlukan suatu metode pendekatan untuk mereduksi data tanpa menghilangkan pola spektrum awal. Pada penelitian ini dilakukan kajian untuk mereduksi dimensi data yang sangat besar, dengan tidak mengabaikan informasi tentang kandungan senyawa kimia pada bilangan-bilangan gelombang tertentu. Salah satu metoda yang digunakan adalah membuat partisi atau sekatan dari pola spektrum yang dihasilkan. Pada setiap sekatan memiliki satu fungsi persamaan antara bilangan gelombang dan %transmitan. Sehingga pada setiap sekatan tersebut cukup diambil sebagian data dari kumpulan data yang ada. Data yang digunakan adalah adalah data spektrum senyawa aktif gingerol yang diamati menggunakan FTIR. Senyawa aktif gingerol tersebut berasal dari tanaman jahe dua daerah sentra produksi tanaman obat yaitu Kulonprogo, Jawa Tengah dan Karanganyar, D.I. Yogyakarta. Pengamatan dilakukan pada periode waktu 27 Juli 2003 sampai dengan 1 Agustus 2003. Penerapan pendekatan regresi linier pada setiap sekatan data spektrum yang dihasilkan ternyata memberikan hasil yang cukup baik. Tanpa menghilangkan informasi yang dibutuhkan, teknik ini dapat mereduksi data hingga menjadi lima persen. Jumlah data awal yang semula sekitar 2000 buah data berpasangan (X,Y) dapat direduksi hingga menjadi sekitar 100 buah.

Dalam bidang kimia, model kalibrasi merupakan suatu fungsi hubungan antara absorban (X) pada panjang gelombang yang dihasilkan oleh spektrometer dengan konsentrasi (y) larutan unsur atau senyawa yang akan dianalisis (Nur dan Adijuwana, 1989). Permasalahan yang sering dihadapi dalam pembentukan model kalibrasi adalah adanya multikolinear antara peubah bebas X serta jumlah pengamatan (n) yang jauh lebih kecil dibandingkan jumlah peubah bebas p (n<

Data keluaran FTIR umumnya adalah data dengan dimensi yang besar. Seringkali ditemukan permasalahan dalam keterbatasan perangkat lunak dan perangkat keras dalam pengolahan data. Pereduksian data keluaran FTIR perlu dilakukan tanpa menghilangkan informasi tentang pola spektrum awal. Pendekatan regresi sekatan (Segmented Regression) merupakan salah sat u alternatif yang baik untuk pereduksian data keluaran FTIR. Pendekatan ini memiliki algoritma yang sederhana, menggunakan koefisien Determinasi sebagai acuan untuk pereduksian data dan dapat mereduksi data hingga 99%.

Model Kalibrasi adalah suatu fungsi matematik dengan data empirik dan pengetahuan untuk menduga informasi pada y yang tidak diketahui berdasarkan informasi pada X yang tersedia (Martens dan Naes 1989). Dalam bidang kimia, model kalibrasi merupakan suatu fungsi hubungan antara absorban (X) pada panjang gelombang yang dihasilkan oleh spektrometer dengan konsentrasi (y) larutan unsur atau senyawa yang akan dianalisis (Nur dan Adijuwana, 1989). Dengan kalibrasi, konsentrasi larutan contoh dapat diketahui berdasarkan absorbannya. Beberapa penelitian telah dilakukakan untuk menyusun model kalibrasi Gingerol. Hasil Penelitian menunjukkan dari beberapa pendekatan yang dicobakan diperoleh nila Root Mean Squares Error of Prediction (RMSEP) masing-masing sebagai berikut: Regresi Komponen Utama (0.1096), metode Transformasi Wavelet Diskret (0.1072), Pendekatan Bayes (0.0622), Regresi Sinyal P-spline (0.0686) dan Regresi Kontinum (0.0453). Regresi Kontinum dengan melakukan pre-processing Transformasi Wavelet Diskret ternyata memberikan hasil nilai RMSEP terkecil dan persentase R2y vs yˆ terbesar dibandingkan pendekatan lainnya.

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This research is a morphological study that aims to find regular and irregular forms of verb inflection in Dutch. Several theories are used to achieve this goal, namely Verhaar (1999), Bauer (1988), and Boiij (2005). From the data of the interview with an informant, it was concluded that 1) verb inflection in Dutch has a visible productivity and has structured patterns; 2) morphological processes related to inflection do not change the word class; 3) there are five tenses in Indonesian, and 4) verbs in Dutch consist of regular and irregular verbs. [ABSTRACT FROM AUTHOR]