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Poverty in Central Java using Multivariate Adaptive Regression Splines and Bootstrap Aggregating Multivariate Adaptive Regression Splines Karisma, Ria Dhea Layla Nur; Juhari, Juhari; A Rosa, Ramadani
CAUCHY Vol 6, No 4 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v6i4.10871

Population poverty is one of the serious problems in Indonesia. The percentage of population poverty used as a means for a statistical instrument to be guidelines to create standard policies and evaluations to reduce poverty. The aims of the research are to determine model population poverty using MARS and Bagging MARS then to understand the most influence variable population poverty of Central Java Province in 2018. The result of this research is the Bagging MARS model showed better accuracy than the MARS model. Since, GCV value in the Bagging MARS model is 0,009798721 and GCV value in the MARS model is 6,985571. The most influential variable poverty population of Central Java Province in 2018 in the MARS model is the percentage of the old school expectation rate (X9). Then, the most influential variable in the Bagging MARS model is the number of diarrhea (X1).

Model Machine Learning CART Diabetes Melitus Ria Dhea Layla Nur Karisma; Bambang Widjanarko Otok
Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Vol 1 No 1 (2017): Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai Islami )
Publisher : Mathematics Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (352.645 KB)

Penyakit Diabetes secara perlahan dapat menimbulkan masalah yang dikenal dengan the silent killer. Penyakit Diabetes disebabkan oleh kerusakan pada hormon insulin Tipe penyakit Diabetes ada tiga jenis, yaitu Diabetes tipe I yang disebabkan oleh kurangnya produksi insulin, tipe II yang disebabkan oleh produksi hormon insulin yang berlebihan, dan Gestasional yaitu hiperglekemia yang terjadi selama kehamilan. Metode CART (Classification and Regression Tree) merupakan salah satu metode yang digunakan untuk pengklasifikasian. Metode CART dapat digunakan untuk data yang memiliki skala kontinu maupun rasio. Data yang digunakan pada penelitian ini merupakan data skunder dari penderita Diabetes Melitus tipe II dan bukan Tipe II. Variabel respon yaitu penderita Diabetes tipe II dan bukan tipe II, dengan variabel prediktor riwayat keluarga, usia, jenis kelamin, obesitas, pola makan, dan aktivitas fisik (olahraga). Faktor-faktor yang mempengaruhi penderita Diabetes Melitus menurut metode CART riwayat keluarga, obesitas, usia, dan jenis kelamin.

Analisis Ketahanan Hidup Pada Penderita Kanker Serviks Menggunakan Regresi Cox Proportional Hazard Ummi Hafildah; Ria Dhea Layla Nur Karisma
Jurnal Riset Mahasiswa Matematika Vol 2, No 2 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Survival analysis is a statistical method used to analyze data with time until the occurrence of a certain event which is commonly referred to as "failure". One of the objectives of survival analysis is to determine the effect of predictor variables on survival time. The purpose of this study was to determine the regression model and determine the hazard ratio of each factor that is thought to affect the survival of cervical cancer patients. The results of this study showed that the factors that influence patients with cervical cancer in their survival are stage II and stage III variables (the patient’s stage), complications, and a history of pregnancy (who have children 0-2).

Analisis Ketahanan Hidup Pada Penderita Kanker Serviks Menggunakan Regresi Cox Proportional Hazard Hafildah, Ummi; Karisma, Ria Dhea Layla Nur
Jurnal Riset Mahasiswa Matematika Vol 2, No 2 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v2i2.15042

Survival analysis is a statistical method used to analyze data with time until the occurrence of a certain event which is commonly referred to as "failure". One of the objectives of survival analysis is to determine the effect of predictor variables on survival time. The purpose of this study was to determine the regression model and determine the hazard ratio of each factor that is thought to affect the survival of cervical cancer patients. The results of this study showed that the factors that influence patients with cervical cancer in their survival are stage II and stage III variables (the patient’s stage), complications, and a history of pregnancy (who have children 0-2).

Implementasi Support Vector Machine (SVM) dalam Penentuan Klasifikasi Indeks Khusus Penanganan Stunting di Indonesia Syafika, Vicky Alfina Nur; Karisma, Ria Dhea Layla Nur
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1595

Stunting is a health problem that poses a challenge in various countries, including Indonesia. Several government stunting intervention programs can be evaluated based on the stunting-specific intervention index. Accurate evaluation results of stunting intervention programs will facilitate the government in determining the next policy. This study aims to obtain the classification and accuracy level of the stunting-specific intervention index in Indonesia using the Support Vector Machine (SVM) method. The results of the study showed that the best model for stunting-specific intervention index classification using the SVM method was the polynomial kernel with parameters h= 1 and C=100. The resulting classification showed that there were 4 Provinces with low stunting-specific intervention index categories, 21 Provinces with moderate stunting-specific intervention index categories, and 9 Provinces with high stunting-specific intervention index categories. The 100% accuracy level of the stunting-specific intervention index classification in Indonesia using the SVM method indicates that the SVM methods is highly effective in classifying the stunting-specific intervention index in Indonesia.

Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.

Optimalisasi Penentuan Klaster pada Indeks Khusus Penanganan Stunting Menggunakan Metode Agglomerative Hierarchical Pamungkas, Syahrul Aziz; Karisma, Ria Dhea Layla Nur; Alisah, Evawati
Jurnal Riset Mahasiswa Matematika Vol 4, No 2 (2024): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i2.31182

Stunting is a nutrition problem that is still the main focus in developing countries, one of which is Indonesia. One of the instruments designed to measure the performance of the implementation of the stunting reduction acceleration program at the national level is the Stunting-Specific Intervention Index (Indeks Khusus Penanganan Stunting/IKPS). This study aims to group provinces in Indonesia based on a special index for handling stunting consisting of six indicators, namely health, nutrition, housing, food, education, and social protection indicators using the agglomerative hierarchical clustering method. The agglomerative hierarchical clustering method is divided into several methods, including single linkage, complete linkage, average linkage, and ward methods. This study compares the four methods with the aim of obtaining the best cluster solution in the grouping of provinces in Indonesia based on the stunting-specific intervention index. The determination of the best method in agglomerative hierarchical clustering is determined by the value of the cophenetic correlation coefficient. The results show that the average linkage method provides a better cluster solution than other methods. The cluster solution in the average linkage method produces eight clusters, including, cluster 1 consists of one province, cluster 2 consists of nine provinces, cluster 3 consists of twelve provinces, cluster 4 consists of six provinces, cluster 5 consists of one province, cluster 6 consists of one province, cluster 7 consists of three provinces, and cluster 8 consists of one province in Indonesia.

Pendampingan Anggota Igra dalam Melaksanakan Proses Belajar Mengajar Kreatif dan Menyenangkan Berbasis Konten Kreator Karisma, Ria Dhea Layla Nur; Sa'adati, Hawzah; Khudzaifah, Muhammad; Ismiarti, Dewi; Alisah, Evawati; Aghniacakti, Ainindita
JRCE (Journal of Research on Community Engagement) Vol 6, No 1 (2024): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v6i1.29118

Education is an effective way to combat poverty, with teachers playing key roles as facilitators, motivators, and guides in the learning process. In early childhood education, particularly in Raudhatul Athfal (RA), teachers play a crucial role in instilling values of faith, independence, and good moral character in children during their golden age (0-5 years old). This research aims to empower RA teachers in Kecamatan Turen to create social media-based learning content as an innovative method to enhance student engagement.The research method employed is Participatory Action Research (PAR), involving 13 RA institutions in Kecamatan Turen, with a focus on training teachers to produce digital learning content. The findings reveal that most teachers already use digital tools in teaching, but there are still challenges in developing digital content that aligns with the cognitive development of young children. Additionally, the study emphasizes the importance of creating official social media accounts and websites for each RA, integrated into a unified platform at the district level.The outcomes of this program are expected to enhance teachers' abilities in utilizing social media as an interactive and engaging learning tool. Thus, empowering teachers to create educational digital content is key to improving the quality of education, especially for early childhood. Continuous evaluation through feedback allows for the refinement of teaching methods, fostering a more effective and creative learning environment.

Classification Cyber Harassment on Twitter using Multinomial Naïve Bayes Karisma, Ria Dhea Layla Nur
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.29866

Multinomial Naïve Bayes is a classification method in Naïve Bayes Classifier based on Bayes Theorem and multinomial distribution. This method works optimally in the multiclass classification of text data. Furthermore, it calculates the probability of occurrence of each word by multiplying the class prior probability by the likelihood value of the occurrence of each word in each class. The phenomenon of Cyber Harassment is defined as the behavior of utilizing technology to harm or humiliate people, which has four types of behavior, namely Physical Threats, Purposeful Embarrassment, Racist, and Sexual Harassment. The number of Cyber Harassment cases always increases every year even though the government has made policies to deal with Cyber Harassment cases. The study aims to classify results accurately regarding the types of Cyber Harassment on Twitter using the Multinomial Naïve Bayes method. The classification results obtained are 20 tweets classified as Physical Threats, 10 tweets classified as Purposeful Embarrassment, 25 tweets classified as Racist, and 22 tweets classified as Sexual Harassment. The accuracy of classification of types of Cyber Harassment on Twitter social media using Multinomial Naïve Bayes is 77%, and the results of the model performance test with K-fold cross-validation is 76.21%, showing that the Multinomial Naïve Bayes method can classify the types of Cyber Harassment on Twitter social media is well effective.

Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipseâ€™sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.

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