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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Dasar Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan INTEK: Informatika dan Teknologi Informasi Majalah Ilmiah Bijak Majalah Ilmiah Matematika dan Statistika (MIMS) Prosiding Konferensi Nasional PKM-CSR Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Science and Education Journal Jurnal SUTASOMA Indonesian Journal of Statistics and Its Applications
Handajani, Sri Sulistijowati
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION TO HANDLE MIXED PATTERN DATA IN MODELING THE RICE PRODUCTION IN EAST JAVA PROVINCE Handajani, Sri Sulistijowati; Pratiwi, Hasih; Respatiwulan, Respatiwulan; Susanti, Yuliana; Nirwana, Muhammad Bayu; Nareswari, Lintang Pramesti
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/barekengvol19iss4pp2597-2608

Abstract

Climate change can affect rice production through changes in temperature, precipitation patterns, extreme weather events, and atmospheric carbon dioxide levels. A statistical model can be used to understand the correlation between rice production and factors that affect it. The existence of some patterns that are formed from independent variables and others that do not show data patterns due to volatility in weather element data makes semiparametric regression modeling more appropriate. In forming a parametric model, the data pattern needs to be regular to make the model more precise. Irregular data patterns are more appropriately modeled with nonparametric regression models. The existence of several patterns formed from independent variables to their dependent variables, and several others, does not show a particular pattern due to the volatility in climate data, making truncated spline semiparametric regression modeling more appropriate to use. This research aims to model rice production in several regions in East Java Province in 2022 using a semiparametric regression model. The data used were from the Meteorology, Climatology, and Geophysics Agency and the Central Statistics Agency for East Java Province in 2022. The response variable is the rice production (tons) in 2022 in Tuban, Gresik, Nganjuk, Malang, Banyuwangi, and Pasuruan Regency (Y). The predictor variables are paddy harvested area (hectares), average temperature (℃), humidity (percent), and rainfall (mm). The semi-parametric spline truncated regression model is obtained by combining the parametric and non-parametric models based on truncated splines. The analysis showed a spline truncated semiparametric regression model with a combination of knot points (3,3,1) with a minimum GCV value of 12,642,272. The variables significantly affecting rice production were rice harvest area, temperature, air humidity, and rainfall, with an adjusted value of 98.522%.
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM Andika, Lingga Aji; Pratiwi, Hasih; Handajani, Sri Sulistijowati
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.560

Abstract

Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.
Pemodelan Regresi Robust Menggunakan Estimasi Skala Umum (GS) Dan M Umum (GM) Pada Tingkat Kemiskinan Di Provinsi Jawa Timur Lutfiya Eka Rahmawati; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
Science and Education Journal (SICEDU) Vol 5 No 2 (2026): Science and Education Journal 2026
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/sicedu.v5i2.247

Abstract

Poverty is a global social issue that hinders human welfare and socioeconomic development. In Indonesia, poverty remains a serious and complex problem that continues to be a major challenge for national development. One of the main goals of Sustainable Development Goals (SDGs) is the elimination of all forms of poverty. Analysis of factors related to poverty levels in East Java Province was conducted in this study. The data used are sourced from the Central Statistics Agency (BPS), covering 38 districts/cities in East Java Province in 2023. The independent variables used in this study include Life Expectancy (UHH), Expected Years of Schooling (HLS), per capita expenditure, and economic growth. The data are indicated to be non-normally distributed and contain outliers, so modeling is carried out using robust regression. This study was conducted using two methods, Generalized Scaled (GS) and Generalized M (GM) by comparing the two results. The selection of the best model is based on the largest value, the lowest AIC value, and the number of outliers. The results of the study showed that the robust regression model of GM estimation was a better model with an value of 81.33%, an AIC value of 153. 2172, and one outlier.
Penerapan GWR dan MGWR dengan Pembobot Kernel Adaptive Tricube pada Pemodelan Prevalensi Stunting di Provinsi Jawa Tengah Ningrum, Imas Fitri; Handajani, Sri Sulistijowati; Respatiwulan, Respatiwulan
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.94657

Abstract

Jawa Tengah merupakan salah satu provinsi dengan prevalensi stunting yang tinggi di Indonesia pada tahun 2022 sebesar 20,8% dan hampir mendekati prevalensi stunting di Indonesia sebesar 21,6%. Wilayah di Jawa Tengah beragam dalam hal geografi, ekonomi, sosial budaya, kualitas sumber daya manusia, dan ketersediaan sumber daya alam. Regresi spasial digunakan untuk memodelkan faktor-faktor yang mempengaruhi prevalensi stunting di Jawa Tengah dengan mempertimbangkan pengaruh lokasi. Perbedaan karakteristik antar lokasi menyebabkan heterogenitas spasial, sehingga masalah tersebut diatasi dengan pemodelan menggunakan geographically weighted regression (GWR). Pemodelan dengan GWR memberikan hasil bahwa tidak ada heteroskedastisitas pada salah satu variabel, sehingga pemodelan juga dilakukan menggunakan mixed geographically weighted regression (MGWR) dengan kernel tricube tertimbang adaptif. Namun, dari analisis, model GWR memberikan hasil yang lebih baik daripada model MGWR. Unit sampel dalam penelitian ini adalah 35 kabupaten/kota di Provinsi Jawa Tengah. Model GWR untuk memodelkan prevalensi stunting di Jawa Tengah menghasilkan model yang lebih baik dengan nilai AIC yang lebih kecil dibandingkan dengan model MGWR. Hasil penelitian menunjukkan bahwa balita yang kekurangan gizi memiliki pengaruh positif terhadap stunting, sedangkan bayi baru lahir yang menerima IMD, balita yang menerima vitamin A, dan pengeluaran riil per kapita memiliki pengaruh negatif terhadap stunting.kata kunci: tricube adaptif; GWR; MGWR; stuntingCentral Java is one of the provinces with a high prevalence of stunting in Indonesia in 2022 at 20.8% and is almost close to the prevalence of stunting in Indonesia at 21.6%. The regions in Central Java are diverse in terms of geography, economy, socio-culture, quality of human resources, and availability of natural resources. Spatial regression was used to model the factors that influence the prevalence of stunting in Central Java by considering the influence of location. The characteristics between locations cause heterogeneity, so the modeling used is Geographically Weighted Regression (GWR). Because one variable is not locally significant, modeling is also carried out using Mixed Geographically Weighted Regression (MGWR) with adaptive tricube kernel weighted. However, from the analysis, the GWR model gave better results than the MGWR model. The GWR model for modeling stunting prevalence in Central Java produces a better model with an AIC value of 148.883 and R^2 of 88.01% compared to the MGWR model, which only provides an AIC value of 190.371 and R^2 value of 47.66%. Based on the analysis results with the GWR model using adaptive tricube weighted, the factors influencing the prevalence of stunting in Central Java Province are newborns getting early breastfeeding initiation (IMD), toddlers with malnutrition, toddlers getting vitamin A, and real expenditure per capita.Keywords: adaptive tricube; GWR; MGWR; stunting
Co-Authors Andika, Lingga Aji Apsari, Nindya Eka Aryani, Fadhilatul Nida Azhar, Zenitha Amalia Diari Indriati Elnatan Dimas Aditya Evitasari, Niken Evitasi, Niken Fransiska Grace S.W. Handajani Handajani Ilham Asyifa Maulana Rosyid Irwan Susanto Isnandar Slamet, Isnandar Jelita, Nadia Aulia Kiki Ferawati Lingga Aji Andika Lutfiya Eka Rahmawati Mahmudah, Arik Martini, Titin Sri Muhammad Bayu Nirwana mutiah, siti Nareswari, Lintang Pramesti Nindya Eka Apsari Ningrum, Imas Fitri Nirwana, Muhammad Bayu Niswatul Qona’ah Pratiwi, Hasih pratiwi, hasih Qona’ah, Niswatul Ramadhania, Monica Respatiwulan Respatiwulan Respatiwulan, Respatiwulan Rina Safitri Setiyowati, Ririn Setyowati Nurhaningsih Sugiyanto Sugiyanto Sugiyanto Sugiyanto Trionika Dian Wahyuningsih Yuliana Susanti Yuliana Susanti Yuliana Susanti, Yuliana Zukhronah, Etik