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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Sistemasi: Jurnal Sistem Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Contemporary Mathematics and Applications (ConMathA) ESTIMASI: Journal of Statistics and Its Application Basis : Jurnal Ilmiah Matematika
Anna Islamiyati
Department Of Mathematics, Hasanuddin University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square Islamiyati, Anna; Anisa, Anisa; Raupong, Raupong; Massalesse, Jusmawati; Sirajang, Nasrah; Sahriman, Sitti; Wahyuni, Alfiana
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 10 No. 2 (2022): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam had Accr
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v10i2.3197

Abstract

Abstract:Nonparametric regression is used for data whose data pattern is non-parametric. One of the estimators that can be developed is a segmented cubic spline which is able to show several segmentation changes in the data. This article examines the estimation of segmented cubic spline nonparametric regression models using the Penalized Least Square estimation criteria. The method involves knot points and smoothing parameters simultaneously. In addition, the model is used to analyze data on BPJS claims based on patient age. The results show that the optimal model is at two-knot points, namely 26 and 52 with a smoothing parameter of 0.89. There are three segmentation changes from the cubic data, which consist of young people up to 26 years old, 26-52 years old, and 52 years and over. Abstrak:Regresi nonparametrik digunakan untuk data yang pola datanya bentuk non parametrik. Salah satu estimator yang dapat dikembangkan adalah spline kubik tersegmen yang mampu menunjukkan beberapa segmentasi perubahan pada data. Artikel ini mengkaji estimasi model regresi nonparametrik spline kubik tersegmen melalui kriteria estimasi menggunakan Penalized Least Square. Metode tersebut melibatkan titik knot dan parameter penghalus secara bersamaan. Selain itu, model digunakan untuk menganalisis data klaim BPJS berdasarkan usia pasien. Hasil menunjukkan bahwa model optimal pada dua titik knot yaitu 26 dan 52 dengan parameter penghalus sebesar 0,89. Terdapat tiga segmentasi perubahan data secara kubik, yaitu usia muda hingga 26 tahun, usia 26-52 tahun, dan usia 52 tahun ke atas. 
Estimasi Model Perubahan Indeks Harga Saham Gabungan melalui Regresi Kuantil Spline Smoothing Ashwad K, Hajratul; Islamiyati, Anna; Siswanto, Siswanto
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.25198

Abstract

Regression of nonparametric quantile is conducted on purpose to help estimating the function of regression when the assumptions about the regression curve shape are not known involving quantile values. Spline is claimed as one of the estimators commonly applied in nonparametric regression. Patterns of platelet change in Jacarta Composite Indeks (JCI) based on Dow Jones Index (IDJ) were analyszed by quantile spline smoothing using τ 0.25, 0.50, and 0.75. The analysis results show two patterns of change in the relationship of JCI and the IDJ. It can be seen from the optimal knot point for each quantile, namely 28500, 35000 and 29600, which shows that before and after the IDJ value reaches the point from the knot point, there is a tendency to decrease and then increase in the JCI data. The optimal model with the one-knot point. According to the minimum GCV value, the optimal model with the smallest GCV vaue, which is 5243.45 on quantile 0.75.
Pengelompokan Kemiskinan di Provinsi Sulawesi Selatan Tahun 2023 dengan Metode K-Means Clustering Wulandari, A. Elisha; Baso, Andi M. Alfin; Fajri, Belia Nurul; Kalondeng, Anisa; Islamiyati, Anna; Pannu, Abdullah; Fadil, Muhammad; Vallarino, Alfian Akbar; Rahman, Anugrah Nur Isnaeni
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45824

Abstract

Poverty remains a significant social and economic issue in South Sulawesi Province. This study aims to classify districts/cities in South Sulawesi based on poverty levels using the K-Means Clustering method. The data used were obtained from the Central Bureau of Statistics (BPS) for 2023, including indicators such as the percentage of poor population, education level, and employment sector. The Silhouette Index method was applied to determine the optimal number of clusters. The results indicate that South Sulawesi is divided into two clusters, representing high and low poverty levels. The scatter plot further reveals that cluster 1 is more varied, while cluster 2 is more concentrated. These findings can serve as a foundation for formulating more targeted policies to reduce poverty in South Sulawesi.
THE USE OF PENALIZED WEIGHTED LEAST SQUARE TO OVERCOME CORRELATIONS BETWEEN TWO RESPONSES Islamiyati, Anna; Anisa, Anisa; Zakir, Muhammad; Sirajang, Nasrah; Sari, Ummi; Affan, Fajar; Usrah, Muhammad Jayzul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.447 KB) | DOI: 10.30598/barekengvol16iss4pp1497-1504

Abstract

The non-parametric regression model can consider two correlated responses. However, for these conditions, we cannot use the usual estimation process because there are violations of assumptions. To solve this problem, we use a penalized weighted least square involving knots, smoothing parameters, and weighting in the estimation criteria simultaneously. The estimation process involves a weighted criteria matrix in the estimation criteria. Estimation results show that the estimated two-response non-parametric regression function with penalized spline is a linear estimation class in y response observation and depends on the knot point and smoothing parameter. Furthermore, the use of the model on toddler growth data shows some changes in the pattern of weight and height gain. The pattern segmentation that experienced a gradual increase was age 7-43 months for weight and age 6-54 months for height
Comparison of Multinomial Naïve Bayes and Bernoulli Naïve Bayes on Sentiment Analysis of Kurikulum Merdeka with Query Expansion Ranking Yusran, Muhammad; Siswanto, Siswanto; Islamiyati, Anna
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3187

Abstract

Social media is one of the public services for conveying or obtaining news, opinions and comments on an issue. One of the social media that is in great demand by the people of Indonesia is Twitter. Kurikulum merdeka is one of the most discussed issues currently on Twitter. Kurikulum merdeka is a curriculum that incorporates varied intra-curricular learning with more optimal content to provide students adequate time to investigate ideas and build expertise. Until now, kurikulum merdeka still reaps the pros and cons. To process and analyze further regarding opinions on the kurikulum merdeka, it can be done using sentiment analysis. The high dimension of features in the classification process becoming a problem in sentiment analysis because it causes classification to be inefficient, so feature selection is needed to solve this problem. The purpose of this study was to obtain the results of the classification of kurikulum merdeka sentiments using the multinomial naïve bayes and bernoulli naïve Bayes, as well as query expansion rankings for feature selection and to compare the performance of the two classifications. Multinomial naïve bayes classification produces 106 tweets with positive sentiment and 164 tweets with negative sentiment with accuracy, recall, precision and f-measure respectively 98.889%, 98.131%, 99.057% and 98.591%, while bernoulli naïve bayes produces 95 tweets with positive sentiment and 175 tweets with negative sentiment with accuracy, recall, precision, and f-measure respectively 94.815%, 87.850%, 98.947% and 93.069% respectively. Therefore, multinomial naïve bayes classifies the kurikulum merdeka sentiment better than bernoulli naïve bayes.
Pemodelan Regresi Binomial Negatif menggunakan Estimator Jackknife Negative Binomial Ridge Regression pada Data Angka Kematian Bayi Provinsi Sulawesi Selatan Palinoan, Kezia Agra; Jaya, Andi Kresna; Islamiyati, Anna
Basis : Jurnal Ilmiah Matematika Vol. 3 No. 2 (2024): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v3i2.1140

Abstract

Analisis regresi Binomial Negatif adalah metode yang digunakan untuk menganalisis hubungan antara variabel prediktor terhadap variabel respon yang berdistribusi Poisson. Namun, regresi Poisson tidak dapat digunakan untuk memodelkan data dengan overdispersi maupun terdapat multikolinearitas. Untuk menyelesaikan masalah tersebut digunakan regresi Binomial Negatif dengan estimator Jackknife Negative Binomial Ridge Regression. Dalam penelitian ini, estimasi parameter regresi Binomial Negatif dengan estimator Jackknife Negative Binomial Ridge Regression diterapkan pada data tingkat kematian bayi di Sulawesi Selatan tahun 2017. Metode Jackknife berperan untuk mereduksi bias sehingga dapat diperoleh penaksiran parameter dengan bias yang kecil sedangkan metode ridge untuk menangani multikolinearitas. Metode pemilihan parameter ridge menggunakan nilai MSE terkecil. Model terbaik terbentuk pada model dengan parameter ridge k = 0.0081. Berdasarkan estimasi parameter yang terbentuk menunjukkan bahwa variabel jumlah bayi dengan berat badan lahir rendah (X1), jumah bayi yang diberi ASI eksklusif (X2), jumlah bayi yang mendapatkan vitamin A (X3), jumlah cakupan pelayanan K4 pada ibu hamil (X4), jumlah ibu hamil yang menerima imunisasi TT2+ (X5), dan jumlah kelahiran (X6) signifikan mempengaruhi jumlah kematian bayi.
Co-Authors Affan, Fajar Ainun Utari Budistiharah Alfiana Wahyuni Andi Kresna Jaya Anisa Anisa Anisa Anisa Anisa Anisa Anisa Anisa Anisa Ashwad K, Hajratul Baso, Andi M. Alfin Bunga Aprilia Bunga Aprilia Fajri, Belia Nurul Fitri Fitri Jusmawati Massalesse Jusmawati Massalesse Kalondeng, Anisa Marsya Anggun Prisila Muhammad Fadil Muhammad Jayzul Usrah Muhammad Yusran Muhammad Zakir Nasrah Sirajang Nirwan Ilyas Novilia Jao Nurmiati Nurmiati Nurdin NURTITI SUNUSI Palinoan, Kezia Agra Pannu, Abdullah Rahman, Anugrah Nur Isnaeni Raupong Raupong Raupong, Raupong Raupong, Raupong S Siswanto Sahriman, Sitti Sakinah Oktoni Samsul Arifin Sari, Ummi Sirajang, Nasrah Sitti Sahriman Sri Astuti Thamrin Try Widyaiswara Hairil Usrah, Muhammad Jayzul Utami, Mamik Vallarino, Alfian Akbar Wahyuni, Alfiana Widya Nauli Amalia Puteri Wulandari, A. Elisha Zhazha Alifkhamulki Ramdhani