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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
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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Model Deteksi Krisis Indonesia dengan Indikator Suku Bunga Simpanan Riil Rina Safitri; Sugiyanto Sugiyanto; Sri Sulistijowati Handajani
Majalah Ilmiah Bijak Vol 16, No 2: September 2019
Publisher : Institut Ilmu Sosial dan Manajemen STIAMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.245 KB) | DOI: 10.31334/bijak.v16i2.510

Abstract

The financial crisis is a condition where a country's finances experience a disruption which is characterized by a drastic increase in the inflation rate, a weakening currency exchange rate, and a decrease in other economic activities. Indonesia experienced financial crises in 1997 and 1998 which resulted in a collapse of financial conditions and national stability. Therefore, it is necessary to have a model to find out the crisis, so that efforts to recover the impact of the crisis can be done as early as possible from the model. This study aims to apply the Markov Switching Error Correction Model to detect a crisis. Based on the indicator of real deposit interest rates it can be concluded that the MS-ECM can explain the crisis that occurred in mid-1997 and late 2005
Implementation of Transfer Learning for Covid-19 and Pneumonia Disease Detection Through Chest X-Rays Based on Web Nindya Eka Apsari; Sugiyanto Sugiyanto; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 5, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i1.59442

Abstract

Coronavirus disease 2019, known as COVID-19, attacks the human respiratory system caused by severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2). COVID-19 disease and pneumonia show similar symptoms such as fever, cough, even headache. Diagnosis of pneumonia can be tested through diagnostic tests, including blood tests, chest X-rays, and pulse oximetry, while the diagnosis of COVID-19 recommended by WHO is with swab test (RT-PCR). But in fact, the swab test method takes a relatively long time, for about one to seven days, for the result, and is not cheap. For that, there needs to be a development that can be one of the options in diagnosing COVID-19 and pneumonia at once, especially since both diseases have similar symptoms. One option that can be done is the diagnosis using a chest X-ray. This research aims to detect COVID-19 disease and pneumonia through chest X-rays using transfer learning to increase the accuracy of disease diagnosis with a more efficient time. The architecture used is EfficientNet B0 with variations in optimization parameters, learning rates, and epochs. EfficientNet B0 Adam optimization with a learning rate of 0.001 in the 6th epochs is a great model that we obtained. Furthermore, the evaluation of the model got accuracy, precision, recall, and f1-score of 92%. Then the model visualization is done using Grad-CAM. To implement the best model, web application development is done to make it easier to detect COVID-19 disease and pneumonia.Keywords: COVID-19; pneumonia; EfficientNet; transfer learning; web
Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah Trionika Dian Wahyuningsih; Sri Sulistijowati Handajani; Diari Indriati
Indonesian Journal of Applied Statistics Vol 1, No 2 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i2.26250

Abstract

Sweet Potato is a useful plant as a source carbohydrates, proteins, and is used as an animal feed and ingredient industry. Based on data from the Badan Pusat Statistik (BPS), the production fluctuations of the sweet potato in Central Java from year to year are caused by many factor. The production of sweet potato and the factors that affected it if they are described into a pattern of relationships then they do not have a specific pattern and do not follow a particular distribution, such as harvest area, the allocation of subsidized urea fertilizer, and the allocation of subsidized organic fertilizer. Therefore, the production model of sweet potato could be applied into nonparametric regression model. The approach used for nonparametric regression in this study is smoothing spline regression. The method used in regression smoothing spline is generalized cross validation (GCV). The value of the smoothing parameter (λ) is chosen from the minimum GCV value. The results of the study show that the optimum λ value for the factors of harvest area, urea fertilizer and organic fertilizer are 5.57905e-14, 2.51426e-06, and 3.227217e-13 that they result a minimum GCV i.e 2.29272e-21, 1.38391e-16, and 3.46813e-24. Keywords: Sweet potato; nonparametric; smoothing spline; generalized cross validation.
Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta Fransiska Grace S.W.; Sri Sulistijowati Handajani; Titin Sri Martini
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24086

Abstract

Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble
Uji Asumsi Proportional Hazard pada Faktor yang Mempengaruhi Waktu Tahan Hidup Pasien Kanker Paru Elnatan Dimas Aditya; Sri Sulistijowati Handajani; Ririn Setiyowati
Indonesian Journal of Applied Statistics Vol 1, No 2 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i2.26496

Abstract

Lung cancer is the disease that its death risk always increase, because of that the survival time of its patient is interesting to be researched. One of the method that can be used to research survival time of lung cancer patient is Cox regression. It has an assumption that called proportional hazard assumption. Proportional hazard assumption can be tested by graph method that is log-log graph, but the result is only used as temporary suspicion. For a better result, the goodness of fit test can be used by calculate the correlation between rank of survival time and schoenfeld residual. The result is age variabel doesn’t satisfy proportional hazard assumption. Keywords : cox regression; proportional hazard assumption; log-log graph; goodness of fit test.
Model Penyebaran Penyakit SIR Tipe Rantai Binomial dengan Kontak Random dan Waktu Penyembuhan Bernilai Tak Hingga Ilham Asyifa Maulana Rosyid; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.44307

Abstract

Susceptible-Infected-Recovered (SIR) epidemic model is an epidemic model that illustrates the pattern of disease spread with the characteristics of individuals who have recovered cannot be re-infected and have a permanent immune system. The binomial chain type epidemic model assumes that infection spreads in discrete time units and the number of the infected individuals follows a binomial distribution. This research aims to discuss  binomial chain type SIR epidemic model by simulating the model. The transition probability depends on  the number of infected individuals in the period   the number of individuals encountered, and  the transmission probability. This model also assumes an infinite recovery time ( = ∞). This situation illustrates that infected individuals remain contagious during the period of spread of the disease. This situation can arise when the causative agent of the disease has a long life. Then simulations are performed by giving different transmission probability  The results show that the greater transmission probability will cause the probability of a new individual being infected in the next period to be greater.Keywords : SIR epidemic model, binomial chain, infinite recovery time
A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia Hasih Pratiwi; Yuliana Susanti; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24090

Abstract

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator
Implementasi Algoritma C5.0 Untuk Klasifikas Penyakit Gagal Ginjal Kronik Setyowati Nurhaningsih; Yuliana Susanti; Sri Sulistijowati Handajani
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 1 (2019)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v2i1.89

Abstract

Chronic kidney failure is one of the deadly diseases in many countries, including in In-donesia. This disease has a prevalence value increasing with the increasing population. Method that can be used to predict chronic kidney failure in the form of classification trees, namely C5.0. The purpose of this study is to apply the C5.0 to the classification of chronic kidney failure and to calculate the accuracy. Method C5.0 is a classification method in selecting its attributes to be processed using gain information. The independ-ent variables that are influential in this study are erythrocytes, urea, creatine, and plate-lets. The results of this study are in the form of a classification tree for chronic kidney failure. The C5.0 method produces 6 classification segments with an accuracy value of 99.3%.
Penggunaan Geoda untuk Pemetaan Bencana Alam di Kabupaten Karanganyar Hasih Pratiwi; Niswatul Qona’ah; Kiki Ferawati; Sri Sulistijowati Handajani; Handajani Handajani; Yuliana Susanti; Muhammad Bayu Nirwana
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 3 (2020): Peran Perguruan Tinggi dan Dunia Usaha Dalam Pemberdayaan Masyarakat Untuk Menyongsong
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.354 KB) | DOI: 10.37695/pkmcsr.v3i0.817

Abstract

Kemampuan mengolah data menjadi kebutuhan di masa kini, apalagi dengan banyaknya data yang tersedia yang dapat diakses secara bebas. Statistika dapat digunakan untuk membantu masyarakat dalam menjelaskan dan memahami gambaran tentang kejadian bencana alam. Karanganyar, yang terletak di Provinsi Jawa Tengah, merupakan salah satu kabupaten di Indonesia yang rawan bencana alam. Oleh karena itu, diperlukan visualisasi data sebagai upaya untuk memberikan pemahaman kepada masyarakat tentang bencana alam yang terjadi di wilayah Kabupaten Karanganyar. Pemetaan bencana alam dengan Geoda dapat memberikan informasi kondisi kecamatan-kecamatan di Karanganyar yang rawan bencana alam. Untuk menyusun peta, diperlukan data bencana alam serta file peta wilayah. Setelah program Geoda terinstal, peta dapat disusun melalui menu toolbar, mengurutkan kolom kode kabupaten, create project file, dan map. Peta spasial menunjukkan bahwa tanah longsor sering terjadi di wilayah Kabupaten Karanganyar bagian timur yang berbatasan dengan Kabupaten Magetan di Jawa Timur, kebakaran di bagian tengah, dan angin ribut di bagian utara.
Pemodelan Produksi Padi di Provinsi Jawa Timur dengan Regresi Non Parametrik B-Spline Handajani, Sri Sulistijowati; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Nirwana, Muhammad Bayu; Mahmudah, Arik
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 18 No. 2: December 2023
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.v18i2.67475

Abstract

Kebutuhan pangan merupakan kebutuhan primer masyarakat yang harus terpenuhi. Makanan pokok yang banyak dikonsumsi masyarakat Indonesia salah satunya beras. Beras yang berasal dari padi selalu diusahakan memenuhi untuk kebutuhan konsumsi masyarakat terutama di sekitarnya. Jawa Timur adalah salah satu provinsi penyumbang terbesar produksi padi di Indonesia.  Oleh sebab itu perlunya melihat pengaruh faktor-faktor iklim di beberapa wilayah produksi padi terbesar di provinsi Jawa Timur yaitu kabupaten Tuban, Nganjuk dan Gresik terhadap besarnya produksi padi di wilayah tersebut. Tujuan penelitian ini adalah menganalisis faktor-faktor meliputi suhu, kelembaban, curah hujan dan luas panen padi terhadap jumlah prodiksi padi. Data diambil dari website BMKG dan BPS tahun 2020-2022 di Kabupaten Tuban, Nganjuk dan Gresik. Metode analisis yang digunakan dengan memodelkan regresi non parametrik B-spline dengan beberapa kombinasi titik knot dari beberapa variable prediktor yang menghasilkan GCV terkecil dari kemungkinan banyaknya titik knot yang dicobakan. Hasil pemodelan mendapatkan knot optimum pada variabel X1 (suhu) berorde 2 dengan tiga titik knot bernilai 23,45584; 24,32467; 26,93116. Knot optimum pada variabel X2 (kelembaban) berorde 2 dengan satu titik knot bernilai 83,3828. Knot optimum pada variabel X3 (curah hujan) berorde 2 dengan dua titik knot bernilai 5,177247 dan 15,51238. Knot optimum pada variabel X4 (luas panen padi) berorde 2 dengan satu titik knot bernilai 16939,25. Nilai GCV minimum yang diperoleh adalah 18462458. Hasil analisis menunjukkan semua variable berpengaruh signifikan walaupun untuk variable iklim terdapat beberapa segmen yang kurang signifikan, dengan nilai adjusted R-Square sebesar 0,987. The need for food is a primary requirement of society that must be fulfilled. One of the staple foods widely consumed by the Indonesian society is rice. Rice, which comes from paddy fields, is always cultivated to fufill  the consumption needs of the community, especially in the surrounding areas. East Java is one of the largest contributors to rice production in Indonesia. Therefore, it is necessary to examine the influence of climate factors in several rice-producing regions in East Java, namely Tuban, Nganjuk, and Gresik regencies, on the level of rice production in those areas. The aim of this research is to analyze factors such as rainfall, humidity, temperature, and rice cultivation area on rice production quantity.  The data was collected from BMKG (Meteorology, Climatology, and Geophysics Agency) and BPS (Central Statistics Agency) websites for the years 2020-2022 in Tuban, Nganjuk, and Gresik regencies. The analysis method used involves modeling non-parametric B-splines with various combinations of knot points from multiple predictor variables, resulting in the smallest Generalized Cross-Validation (GCV) among the possible knot points tested. The modeling results obtained the optimal knots for variable X1 (temperature) of order 2 with three knot points at values 23.45584, 24.32467, and 26.93116. The optimal knot for variable X2 (humidity) of order 2 was at one knot point with a value of 83.3828. The optimal knots for variable X3 (rainfall) of order 2 were two knot points with values of 5.177247 and 15.51238. The optimal knot for variable X4 (rice cultivation area) of order 2 was at one knot point with a value of 16,939.25. The minimum GCV value obtained was 18,462,458. The analysis results indicate that all variables have a significant influence, although for climate variables, there were some segments that were less significant, with an value adjusted R-Square of 0.987.
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