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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 Jurnal SUTASOMA Indonesian Journal of Statistics and Its Applications
Handajani, Sri Sulistijowati
Universitas Sebelas Maret
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Application of Generalized Space Time Autoregressive Model on Farmer Exchange Rate Data in Three Provinces of The Sumatera Island Aryani, Fadhilatul Nida; Handajani, Sri Sulistijowati; Zukhronah, Etik
Jurnal ILMU DASAR Vol 21 No 2 (2020)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v21i2.17226

Abstract

The agricultural sector has a big role in the development of the Gross Regional Domestic Product (GDP). Therefore the agricultural sector is very important. Besides the agricultural sector, the farmer's welfare also needs to be considered because the agricultural sector will be good if the welfare of farmers is good also. In measuring the level of farmers' welfare, the method used is the farmer's exchange rate. The farmer's exchange rate has a location relationship and a previous time relationship. The Generalized Space-Time Autoregressive (GSTAR) model is a good method of forecasting data that contains time series and location relationships by assuming that the data has heterogeneous characteristics. The purpose of this study is to model the farmer exchange rate data with GSTAR using normalization of cross-correlations weighting and inverse distance in three provinces namely West Sumatra, Bengkulu and Jambi Provinces. Based on data analysis, the best GSTAR model obtained by using the best weighting with the model is GSTAR (11) − I(1) using normalization of cross-correlations because the assumption of normal white noise and multivariate are fulfilled with an RMSE value of 1.097775. The best GSTAR model explains that the exchange rate of West Sumatra farmers is only the previous time, Bengkulu farmers' exchange rate is the previous time and is the exchange rates of farmers of West Sumatra and Jambi, whereas for the exchange rate of farmers of Jambi is the exchange rates of farmers of Bengkulu and West Sumatra and influenced by previous times.Keywords: GSTAR, RMSE, farmers exchange rate, normalization of cross-correlations, inverse distance.
Pemodelan Faktor-Faktor Yang Mempengaruhi Tingkat Pengangguran Terbuka (Tpt) Di Provinsi Jawa Tengah Menggunakan Regresi Spline Truncated Multivariabel Azhar, Zenitha Amalia; Handajani, Sri Sulistijowati; Slamet, Isnandar
Jurnal SUTASOMA (Science Teknologi Sosial Humaniora) Vol 2 No 2 (2024): Juni 2024
Publisher : Universitas Tabanan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58878/sutasoma.v2i2.264

Abstract

Human life depends on work as it brings self-actualization to families, societies, and nations. Increasing the Open Unemployment Rate (OPR) is an employment problem. Statistically speaking, regression analysis is a tool for discovering how one or more variables (the predictors) affect another (the response variables). For this TPT case study in Central Java, researchers looked into the nonpatometric regression model of spline reduced using the UBR and GCV approaches for knot selection. The results demonstrated that the GCV model produced MSE values of 1.381e-01 and R2 of 95.69%, while the UBR model generated MSE value of 1.380e-01, and R2.
Pemodelan faktor-faktor yang memengaruhi angka kesembuhan tuberkulosis di Jawa Barat menggunakan regresi spline truncated Evitasari, Niken; Handajani, Sri Sulistijowati; Pratiwi, Hasih
Majalah Ilmiah Matematika dan Statistika Vol 22 No 2 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i2.30356

Abstract

Tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis. Transmission of tuberculosis (TBC) can occur due to environmental factors and community behavior. West Java is Indonesia's province with the highest number of tuberculosis cases. Curing tuberculosis is critical to reducing cases and breaking the transmission chain. The Human Development Index (IPM), good sanitation, comprehensive tuberculosis treatment, public spaces (PS) meeting health criteria, and residents having health insurance are all assumed to influence the tuberculosis cure rate. This research aimed to model the elements that have a substantial impact on tuberculosis cure rates.The tuberculosis cure rate in West Java in 2020 was modeled using nonparametric spline truncated linear regression with a combination of knot points (3,3,3,3,2). The lowest Generalized Cross Validation (GCV) value of 26.7579 was used to find the best knot point. The adjusted coefficient of determination for this study was 96.35 percent, indicating that the linear truncated spline regression model with a combination of knot points is feasible to use in modeling. The five predictor variables simultaneously affect the tuberculosis cure rate of 96.35 percent, while 3.65 percent is influenced by other variables not used in the study. Keywords: Spline truncated, tuberculosis cure, knots, GCVMSC2020: 62G08
Penerapan Model Epidemic Type Aftershock Sequence (ETAS) pada Data Gempa Bumi Sulawesi dan Jawa Mutiah, Siti; Pratiwi, Hasih; Handajani, Sri Sulistijowati
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (672.23 KB)

Abstract

Gempa bumi merupakan suatu kejadian yang bersifat acak baik dalam waktu maupun lokasi. Suatu kejadian gempa bumi yang berkekuatan besar, biasanya diikuti oleh kejadian gempa susulan. Oleh karana itu, diperlukan upaya untuk meminimalkan dampak yang diakibatkkan peristiwa gempa bumi, salah satunya menggunakan pendekatan probabilistik yaitu proses titik. Model yang dibahas pada penelitian ini adalah model epidemic type aftershock sequence (ETAS), dimana model ini merupakan fungsi intensitas bersyarat yang memberikan informasi tentang laju kejadian gempa bumi dengan mempertimbangkan variabel magnitudo dan waktu. Penelitian ini bertujuan untuk membahas model ETAS dengan magnitudo mengikuti distribusi gamma dan penerapannya pada gempa bumi yang terjadi di Pulau Sulawesi dan Pulau Jawa dari tahun 2000 sampai 2018. Langkah yang dilakukan adalah mengestimasi parameter model ETAS dengan metode estimasi likelihood maksimum. Hasil estimasi parameter gempa bumi di Pulau Sulawesi menunjukkan bahwa laju kegempaan dasar, produktivitas gempa susulan, dan efisiensi gempa bumi dengan magnitudo tertentu yang menghasilkan gempa susulan lebih tinggi dari hasil estimasi di Pulau Jawa. Akan tetapi, pada laju peluruhan gempa susulan menurut waktu dan secara keseluruhan di Pulau Sulawesi lebih rendah dari Pulau Jawa.
Modeling Human Development Index of East Java Using Spatial Autoregressive and Spatial Error Ensemble Jelita, Nadia Aulia; Handajani, Sri Sulistijowati; Susanto, Irwan
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 19 No. 2: December 2024
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v19i2.78621

Abstract

The human development index (HDI) is an indicator used to monitor the government's success in developing the quality of human life. East Java Province's HDI is the lowest compared to other provinces on Java Island. Therefore, it is necessary to improve human development in this province. Attention must be paid to all aspects of human development, including the relationship between neighboring regions. The spatial regression method is an analysis method that considers the spatial dependency of the data. Ensemble spatial regression combines several spatial models by adding noise to the response variable, which is expected to reduce the diversity in the data. This research aims to use ensemble spatial regression to examine the East Java HDI. East Java HDI has spatial lag and spatial error dependence, modeled with SAR and SEM. Queen contiguity is used as a spatial weight. The SEM model does not fulfill the homogeneity assumption, so it is continued with the ensemble method. The ensemble method is proven to reduce diversity, so  SEM Ensemble fulfills the assumption of homoscedasticity. After analysis using SAR and SEM Ensemble, the SAR model was chosen as the best model with the largest  and lowest AIC value. Significant variables on East Java HDI are life expectancy, expected years of schooling, average years of schooling, and expenditure per capita.
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM Lingga Aji Andika; Hasih Pratiwi; Sri Sulistijowati Handajani
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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.
COMPARISON OF B-SPLINE AND TRUNCATED SPLINE REGRESSION MODELS FOR TEMPERATURE FORECAST Handajani, Sri Sulistijowati; Pratiwi, Hasih; Respatiwulan, Respatiwulan; Qona’ah, Niswatul; Ramadhania, Monica; Evitasi, Niken; Apsari, Nindya Eka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp1969-1984

Abstract

The spline regression model is a nonparametric model and it is applied to data that do not have a certain curve shape and do not have information about it. In this study, the results of the analysis of the B-Spline regression model and the Spline Truncated model were compared on temperature data at several stations on Java Island to obtain the best model that can be used to forecast the temperature for the next few days. Daily temperature data were obtained from BMKG at Semarang, Juanda, Serang, Sleman, Bandung, and Kemayoran stations. The temperature data were modeled with the B-Spline and Spline Truncated regression using the optimal knot point of the GCV, and the best model was obtained. The analysis shows that the B-Spline regression models are better than the truncated Spline models with a fairly small MSE value and a greater coefficient of determination than the truncated Spline model.
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%.
Co-Authors 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 Mahmudah, Arik Martini, Titin Sri Muhammad Bayu Nirwana mutiah, siti Nareswari, Lintang Pramesti Nindya Eka Apsari 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