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All Journal Jurnal Gaussian Indonesian Journal of Mathematics and Natural Sciences Teknosains: Media Informasi Sains dan Teknologi GRADIEN SEMIRATA 2015 BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Inferensi Journal Of Human And Education (JAHE) Jurnal Matematika Integratif Limits: Journal of Mathematics and Its Applications
Idhia Sriliana
Universitas Bengkulu
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PENERAPAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (APARCH) TERHADAP HARGA MINYAK MENTAH DUNIA Famuji, Ahmad; Sriliana, Idhia; Agwil, Winalia
Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.13.1.99-109

Abstract

Heteroscedasticity poses a challenge in ARIMA modeling by causing residual variance to be non-constant, leading to less efficient estimates. This issue often arises in time series data due to volatility, which measures data fluctuation over time. To address heteroscedasticity, models like ARCH and GARCH incorporate variance changes into forecasting. However, they lack the ability to capture asymmetry, the difference in impact between good and bad news on volatility. The APARCH model, on the other hand, addresses this by modeling volatility with asymmetry elements. Daily world crude oil prices, known for high volatility, serve as a case study for this research. By employing the APARCH model, the study aims to forecast these prices accurately. Results indicate that the APARCH(1,1) model outperforms the best GARCH model, ARCH(2), as it yields a smaller Mean Absolute Percentage Error (MAPE) of 6.033487. This highlights the superior accuracy of APARCH in forecasting data with heteroscedasticity issues, particularly in the context of daily crude oil prices.
EVALUATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES ON IMBALANCED DATASET FOR POVERTY CLASSIFICATION IN BENGKULU PROVINCE Sriliana, Idhia; Nugroho, Sigit; Agwil, Winalia; Sihombing, Esther Damayanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1143-1156

Abstract

Classification is a statistical method that aims to predict the class of an object whose class label is unknown. The Multivariate Adaptive Regression Splines (MARS) classification method is a classification model that involves several basis functions with influential predictor variables. The MARS classification model is generally effective in classifying imbalanced data, including poverty data classification. The response variable used is the poverty status of households classified into poor and non-poor households, and the predictor variables consist of several poverty indicators. The problem that often arises in classification methods is a class imbalance in the response variable. Due to the poverty status included in the class imbalance data, the Bootstrap Aggregating (Bagging) and Synthetic Minority Over-sampling Technique (SMOTE) approaches will be used to improve classification accuracy on the MARS model. Bagging works by replicating data to strengthen the stability of classification accuracy, while SMOTE works by synthesizing data from minority data classes. The evaluation results showed that the classification model of poverty in Bengkulu Province using the SMOTE-MARS method provides the best classification accuracy compared to the MARS (25.81%) and Bagging-MARS (32.26%) methods based on the sensitivity value obtained, which is 85.36%.
Pengabdian Kepada Masyarakat FMIPA 2024: Desa Cantik, Desa Cinta Statistik: Visualisasi Data dengan Statistik Deskriptif di Desa Panca Mukti Kabupaten Bengkulu Tengah Susi Wijuniamurti; Nugroho, Sigit; Novianti, Pepi; Sriliana, Idhia; Dyah Pangesti, Riwi
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 8 No. 1 (2025): APRIL: Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jpmbr.v8i1.8195

Abstract

Pembangunan desa dikatakan berhasil dan dapat terwujud jika masing-masing desa dapat mengenali potensi yang dimiliki. Menggali potensi desa memiliki hubungan yang erat dengan memberikan data yang akurat sehingga tugas pemerintah dalam perancangan pembangunan dapat tepat sasaran. Peran data sangat penting untuk menentukan bagaimana strategi dalam pembangunan desa. Bagi perangkat desa, meningkatkan kemampuan manajemen pengolahan data dan penggunaan data serta literasi statistik menjadi hal yang sangat penting. Penerapan teknologi akan mempermudah aparat desa dalam memahami pengolahan dan penyajian data statistik sehingga desa dapat secara mandiri mengidentifikasi potensi daerahnya. Prodi S1 Statistika, Prodi S2 Statistika dan pojok statistik Universitas Bengkulu bekerjasama dengan BPS dan mitra BPS Kabupaten Bengkulu tengah melalui program pengabdian kepada masyarakat melaksanakan kegiatan pelatihan dan pendampingan terhadap perangkat desa untuk meningkatkan kemampuan pengolahan, penganalisaan dan penyajian data statistik di bidang sektoral serta memaksimalkan penggunaan data dalam bentuk visualisasi data dengan statistik deskriptif  di Desa Panca Mukti. Kegiatan pelatihan visualisasi data dengan statistik deskriptif di Desa Panca Mukti memberikan hasil yang positif bagi masyarakat Desa Panca Mukti. Masyarakat Desa Panca Mukti, khususnya agen statistik dan perangkat desa dapat menampilkan data-data hasil survei ataupun sensus dalam bentuk diagram atau grafik yang mudah dipahami oleh semua orang. Data yang ada di Desa Panca Mukti ditampilkan di website resmi Desa Panca Mukti.
ANALISIS HARGA SAHAM BANK MANDIRI MENGGUNAKAN REGRESI NONPARAMETRIK: PERBANDINGAN SPLINE TRUNCATED DAN DERET FOURIER Fadila, Risfa; Sriliana, Idhia; Hayadi, Ilham; Fhaeza, Veronnica Noer; Novianti, Pepi
Teknosains Vol 19 No 1 (2025): Januari-April
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v19i1.57387

Abstract

Saham Bank Mandiri merupakan salah satu bank terbesar di Indonesia yang masuk ke dalam Big four Bank. Harga saham Bank Mandiri tidak terhindar dari fluktuasi yang disebabkan oleh berbagai faktor ekonomi dan kebijakan pasar. Penelitian ini bertujuan untuk memahami pola pergerakan saham Bank Mandiri, menggunakan metode regresi nonparametrik dengan membandingkan metode Spline truncated dan Deret Fourier dalam memodelkan dan memprediksi harga saham Bank Mandiri. Metode Spline truncated menangkap perubahan lokal pada data dengan membaginya menjadi beberapa segmen, sedangkan Deret Fourier menggunakan fungsi sinus dan cosinus untuk mendeteksi pola periodik. Data yang digunakan pada penelitian ini meliputi harga penutupan saham BMRI bulanan, inflasi Indonesia dan BI Rate dari Januari 2021 hingga Desember 2024. Hasil penelitian menunjukkan bahwa kedua metode memiliki performa yang hampir sama. Namun, Deret Fourier sedikit lebih unggul dengan nilai R^2 sebesar 92,27% memiliki 5 titik osilasi. Penelitian ini menegaskan pentingnya model nonparametrik untuk menangkap sifat non-linier harga saham, mendorong pengembangan model yang lebih adaptif.
CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS Ermawati, Ermawati; Sriliana, Idhia; Sriningsih, Riry
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1617-1630

Abstract

Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.
BIRESPONSE SPLINE TRUNCATED NONPARAMETRIC REGRESSION MODELING FOR LONGITUDINAL DATA ON MONTHLY STOCK PRICES OF THREE PRIVATE BANKS IN INDONESIA Pahlepi, Reza; Sriliana, Idhia; Agwil, Winalia; Oktarina, Cinta Rizki
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2467-2480

Abstract

This study investigates the application of a truncated spline nonparametric regression model for biresponse analysis of longitudinal data, focusing on modeling monthly stock prices specifically opening and closing prices of three private banks in Indonesia: Bank Mayapada, Bank Mega, and Bank Sinar Mas. The data used in this research are secondary data sourced from the website Id.Investing.com and monthly financial statement publications of three private banks in Indonesia. Longitudinal data, combining cross-sectional and time-series dimensions, are utilized to capture trends and patterns not detectable in traditional cross-sectional analysis. The truncated spline method is selected for its adaptability to nonlinear relationships and abrupt data behavior changes. The model incorporates three predictor variables traded stock volume, total assets, and total liabilities and evaluates their influence on stock prices. Assumptions of longitudinal data are validated using the Ljung-Box autocorrelation test, Bartlett’s sphericity test, and Pearson correlation. Results confirm significant within-subject correlations, independence between subjects, and strong interdependence between response variables. The optimal configuration is determined using Generalized Cross Validation (GCV), with up to three knots considered for segmentation. Weighted Least Squares (WLS) is employed for parameter estimation, accounting for within-subject correlations. Model evaluation based on Mean Absolute Percentage Error (MAPE) indicates high accuracy, with all MAPE values below 5%. The highest MAPE value is 4.41% for the closing price of Bank Mayapada, while the lowest is 2.65% for the opening price of the same bank. The segmentation analysis reveals that traded stock volume and total assets positively influence stock prices, while total liabilities exhibit a predominantly negative impact. The model is limited to internal financial indicators and does not include external macroeconomic factors such as interest rates or inflation. This study is the first to apply a biresponse truncated spline nonparametric regression approach to analyze stock prices of private banks in Indonesia by simultaneously modeling both opening and closing prices, providing a flexible and effective method for capturing complex patterns in longitudinal financial data.
PELATIHAN ANALISIS DATA MENGGUNAKAN SPSS PADA MAHASISWA PROGRAM KEGURUAN UNIVERSITAS SWASTA DI KOTA BENGKULU Sriliana, Idhia; Dyah Pangesti, Riwi; Setyo Rini, Dyah; Novianti, Pepi; Swita, Baki; Dwi Lorenza, Kenny; Abdul Aziz, Ali
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 5 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i5.2135-2141

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperluas wawasan serta meningkatkan kemampuan mahasiswa Program Studi S1 Pendidikan Biologi Universitas Muhammadiyah Bengkulu (UM Bengkulu) terhadap analisis data statistik menggunakan software SPSS. Pelatihan ini dilatarbelakangi oleh kebutuhan mahasiswa untuk memahami teknik analisis statistik yang sering kali dianggap sulit bagi mahasiswa yang tidak memiliki latar belakang ilmu statistika. Metode pelaksanaan kegiatan ini mencakup persiapan, impelemntasi, dan evaluasi, diantaranya pembuatan materi edukatif berupa modul dan poster, presentasi, demonstrasi penggunaan SPSS, serta diskusi. Hasil yang diperoleh dari pelatihan ini memperlihatkan peningkatan pemahaman dan keterampilan mahasiswa terhadap penggunaan SPSS dan interpretasi hasil analisis statistik. Evaluasi dilakukan melalui pre-test dan post-test yang memperlihatkan terdapatnya peningkatan pengetahuan serta keterampilan mahasiswa setelah mengikuti pelatihan. Kegiatan pengabdian ini diharapkan mampu berkontribusi dalam mendukung penyelesaian skripsi mahasiswa serta meningkatkan mutu penelitian di lingkungan akademik.
Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka Andini, Agita; Sunandi, Etis; Novianti, Pepi; Sriliana, Idhia; Agwil, Winalia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3374

Abstract

Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt the stability of parameter estimates. Therefore, Ridge Regression and Jackknife Ridge Regression methods were used to address this issue. Both methods modify the least squares method by adding a bias constant value. This research uses the Open Unemployment Rate (OUR) data in Sumatra in 2022, and 3 predictor variables exhibit multicollinearity. Based on the analysis comparing the Mean Squared Error (MSE) values, the Jackknife Ridge Regression method yields the smallest MSE value, 0.004. Both methods are effective in addressing multicollinearity and identifying significant predictor variables for OUR in Sumatra Island, namely the Human Development Index (HDI), average years of schooling, number of poor people, Life Expectancy (LE), population density and inactive population
Desa Cantik, Desa Cakap Statistik Agwil, Winalia; Sriliana, Idhia; Rini, Dyah Setyo; Supianti, Filo; Oktarina, Cinta Rizki; Famuji, Ahmad
Journal Of Human And Education (JAHE) Vol. 4 No. 1 (2024): Journal Of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i1.708

Abstract

Dalam pembangunan desa tentunya diperlukan pengetahuan terkait dengan potensi desa yang dimiliki. Pengembangan potensi desa dapat dilakukan dengan penggalian data awal serta pengumpulan data untuk pemetaan potensi desa. Sayangnya, sering kali terdapat desa yang memiliki sumber daya dengan kompetensi yang masih belum memadai sehingga di perlukannya peningkatan kompetensi perangkat desa tentang pengumpulan dan pemanfaatan data. Program studi S1 Statistika dan Pojok Statistik Universitas Bengkulu bermitra bersama BPS melakukan pengembangan peningkatan kompetensi terhadap perangkat desa dengan melakukan pengabdian Desa Cantik yang diharapkan dapat meningkatkan kemampuan perangkat desa dalam memanfaatkan data. Pengabdian ini dilakukan pada Kelurahan Tanah Panah Kota Bengkulu. Melalui pengabdian ini, diharapkan pihak terkait dapat memanfaatkan data yang ada. Hasil dari pengabdian ini mampu memberikan pemahaman terkait dengan pemanfaatan data desa serta pengolahan data Microsoft excel dan Canva. Dimana perangkat desa mengetahui tipe-tipe data dan pengaplikasiannya dalam memvisualisasi data pada Microsoft Excel, mengetahui fitur-fitur pada canva yang dapat digunakan pada elemen-elemen infografis. Sebagai rekomendasi bentuk kegiatan pengabdian Desa Cantik patut dilakukan untuk desa-desa yang lain untuk meningkatkan kemampuan dalam memanfaatkan data-data desa.
Estimation of Stunting and Wasting in Sumatra 2022 with Nadaraya-Watson Kernel and Penalized Spline Oktarina, Cinta Rizki; Nugroho, Sigit; Sriliana, Idhia; Novianti, Pepi; Sunandi, Etis; Pahlepi, Reza
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23330

Abstract

This study aims to estimate the prevalence of Stunting and Wasting in Sumatra in 2022 using nonparametric regression methods, specifically the Nadaraya-Watson Kernel and Penalized Spline regression models. Both models were applied to assess the relationship between these two correlated response variables and various predictor variables, such as low birth weight, sanitary facilities, poor population, and exclusive breastfeeding. The results showed that the Nadaraya-Watson Kernel regression, particularly using the Gaussian kernel, provided the best fit with minimal prediction error, as indicated by its low Generalized Cross-Validation (GCV) value of 0.024 and high R-squared values (0.9992 for Stunting and 0.9995 for Wasting). In contrast, the Epanechnikov kernel and Biweight kernel produced higher GCV values (0.110 and 0.356, respectively), indicating less optimal performance. For the Penalized Spline model, optimal parameters were determined with a smoothing parameter λ of 5 and 3 knots, which balanced model flexibility and smoothness. This research underscores the potential of nonparametric regression techniques in capturing complex relationships in health data and provides insights for improving interventions aimed at addressing child malnutrition in Indonesia.
Co-Authors Abdul Aziz, Ali Agwil, Winalia Andini, Agita Baki Swita Cinta Rizki Oktarina Dian Agustina Dian Agustina Dian Agustina Dwi Lorenza, Kenny Dyah Pangesti, Riwi Dyah Setyo Rini Ermawati Ermawati Etis Sunandi Fadila, Risfa Famuji, Ahmad Fhaeza, Veronnica Noer Hayadi, Ilham Jose Rizal Pepi Novianti Rafflesia, U Reza Pahlepi, Reza Rini, Dyah Setyo Riry Sriningsih Setyo Rini, Dyah Sigit Nugroho Sihombing, Esther Damayanti Sunandi, Etis Sunandi, Etis Supianti, Filo Susi Wijuniamurti Swita, Baki Ulfasari Rafflesia Ulfasari Rafflesia