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FUNCTIONAL PREDICTION REGRESSION VIA BOOSTING UNTUK PEMODELAN CURAH HUJAN DI PROVINSI JAWA TIMUR Khusnia Nurul Khikmah
MATHunesa: Jurnal Ilmiah Matematika Vol 9 No 2 (2021)
Publisher : Universitas Negeri Surabaya

Natural phenomena that can cause natural changes on earth caused by increasing greenhouse gases and decreasing landthat absorbs carbon dioxide are called climate change. The elements that cause changes include rainfall and temperature.The constantly rising temperature of the earth results in changing rainfall patterns and can have various effects on theenvironment. Therefore, research on rainfall modeling with annual average temperature and rainfall data from theprovince of East Java from 2006 to 2017 which was taken from the official website of the Central Statistics Agency ofEast Java Province makes sense. This data is a data multivariate time series that is approached with Functional DataAnalysis and modeled using Functional Prediction Regression. Functional Prediction Regression is a form of modelingwith functional data that can test the overall model for high-dimensional data and one of the improved methods ofregression methods for functional data. One way to model Functional Prediction Regression is through boosting. Thisresearch conducted rainfall modeling with Functional Prediction Regression through boosting from East Java Provinceand obtained modeling results using an additional predictor model with 5-fold bootstrap and adjustment of the splineregression (knots) used, namely 16 indicated by the iteration value-boosting bootstrap where the model used to have alinear functional effect of ???????????????????? for the mu parameter and 100 for the sigma parameter.

Penerapan Principal Component Analysis dalam Penentuan Faktor Dominan Cuaca Terhadap Penyebaran Covid-19 di Surabaya Khusnia Nurul Khikmah
ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 1, Januari, 2021 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v2i1.11943

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the transmission can mediate human-to human by enviroment. According to Indonesian Meterological, Climatological, and Geophysical Agency found that weather and climate were supporting factors of COVID-19 outbreak so, research and analysis is carried out regarding the most factor were supporting the spread of COVID-19. In this study, using secondary data obtained from data reported by Indonesian Meterological, Climatological, and Geophysical Agency. According the aims of this study by using Principal Component Analysis (PCA) there are three principal components which represents the most factor were supporting the spread of COVID-19 they are temperature, humidity, and length of sunshine.

Clustering Regency and City in East Java Based on Population Density and Cumulative Confirmed COVID-19 Cases Khusnia Nurul Khikmah; A'yunin Sofro
ComTech: Computer, Mathematics and Engineering Applications Vol. 12 No. 2 (2021): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v12i2.6891

Coronavirus is a big family of viruses that causes acute respiratory syndrome and mediates human-to-human by the environment. A factor that affects the spread of infectious diseases is population density. Therefore, it is necessary to study the effect of population density on infectious diseases like COVID-19. The research analyzed the effect of the population density of each regency in East Java on cumulative confirmed COVID-19 cases until December 9, 2020. The research applied quantitative method using the agglomerative hierarchical clustering method. The clustering method included single, average, and complete linkages. The results of clustering using single linkage and average linkages have the same results for the population density of Jember Regency. This regency has the lowest effect for the cumulative confirmed COVID-19 cases. Then, complete linkage obtains that Banyuwangi Regency and Gresik Regency has the population density with the lowest effect for the cumulative number of confirmed COVID-19 cases. The results of clustering with single, average, and complete linkages have the same results for population density with a big effect on the cumulative number of confirmed COVID-19 cases in Surabaya City. The results of best clustering regencies or cities that population density affects the number of

Synthetic Minority Oversampling Technique Pada Model Logit dan Probit Status Pengangguran Terdidik Fatimah Fatimah; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.17050

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.

Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process A'yunin Sofro; Ika Nurwanitantya Wardani; Khusnia Nurul Khikmah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.19653

COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.

The Comparison between Ordinal Logistic Regression and Random Forest Ordinal in Identifying the Factors Causing Diabetes Mellitus Assyifa Lala Pratiwi Hamid; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20289

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.

Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java Khusnia Nurul Khikmah; Bagus Sartono; Budi Susetyo; Gerry Alfa Dito
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1012

This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. The dataset used in this research is based on the Socio-Economic Survey by Statistics Indonesia in 2020. The novelty of this research is comparing the performance of the four methods used, which all are the tree ensemble approaches. In addition, due to the imbalance class problem, the authors also applied three imbalance handling techniques in this study. The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. This approach has a balanced accuracy value of 65.795%. The best classification method results show that the food insecurity experience scale in West Java can be identified by considering the factors of floor area (house size), the number of depositors, type of floor, health insurance ownership status, and internet access capabilities.

Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process Sofro, A'yunin; Wardani, Ika Nurwanitantya; Khikmah, Khusnia Nurul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.19653

COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.

Analisis Pelaksanaan Pelatihan Penulisan Karya Tulis Ilmiah di MGMP Matematika SMP Kabupaten Lumajang Sofro, A'yunin; Khikmah, Khusnia Nurul; Fuad, Yusuf; Maulana, Dimas Avian; Lukito, Agung; Auliya, Elok Rizqi
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 8 No 2 (2024): Mei
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v8i2.13610

Wabah Covid-19 merupakan ancaman nyata bagi kesehatan global dan menjadi beban dan tantangan serius bagi semua negara. Covid-19 berdampak pada melemahnya perekonomian, tetapi dampaknya juga dirasakan dalam dunia pendidikan. Keprofresionalan seorang guru sangatlah dibutuhkan untuk menghadapi berbagai tantangan. Guru memegang peranan yang sangat penting dalam mendukung program pemerintah khususnya peningkatan kualitas pendidikan, terutama di masa pandemi saat ini. Seorang guru yang profesional juga diharapkan selalu melakukan penelitian yang dituangkan dalam suatu karya tulis ilmiah. Untuk mendukung kualitas dari karya tulis ilmiah, analisis data dalam penelitian juga sangat diperlukan. Di sisi lain, MGMP Matematika SMP Kabupaten Lumajang membutuhkan pelatihan untuk meningkatkan kinerja guru. Sehingga, menggiatkan guru untuk melakukan penulisan karya ilmiah dengan analisis statistika adalah salah satu solusi yang tepat dilakukan. Dari hasil yang didapatkan bahwa kriteria keberhasilan dari sisi output telah terpenuhi. Lebih dari 90 persen kelompok telah mencapai target kinerja yang ditetapkan. Sedangkan dari sisi proses, sekitar 80 persen lebih peserta memberikan kesan positif terhadap workshop yang telah dilakukan. Dengan adanya pelatihan tersebut juga ada peningkatan kinerja guru dalam penulisan karya ilmiah sebesar 82 persen.

Data Analysis of Diabetes Mellitus with Joint Modeling Method Sofro, A'yunin; Mukaromah, Muizzatul; Khikmah, Khusnia Nurul
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.20519

Diabetes mellitus is a dangerous disease that requires long-term medical treatment. The cause of this disease is high blood sugar levels. If not treated immediately, complications will occur and even cause death. The data is taken from the Indonesia Family Life Survey (IFLS). IFLS is a longitudinal measurement that is performed repeatedly every five years. More data is needed for repeated measures. Therefore, this research needs to be done to accommodate the missing data, and it is assumed that it is missing at random (MAR). This study aims to analyze the causative factors that are thought to affect the recovery time of patients with diabetes mellitus using the joint modeling method. This model is a relationship between event time data and repeated measurement data. The joint modeling method uses a linear mixed model for longitudinal measurements and a Cox proportional hazard model for survival. The variables were taken from IFLS4 and IFLS5 data with 293 observations: measurement time, treatment history, gender, comorbidities, and complications. The results in this study obtained a significant influence, namely the variables of measurement time, gender, and complications, on the recovery time of patients with diabetes mellitus. With the reduced measurement time, the patient has a lower chance of recovering 8.7184 times. The variables of gender also have a lower possibility of recovery of 9.1032 times, respectively.

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