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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Komatika: Jurnal Pengabdian Kepada Masyarakat Proceeding of International Conference on Humanity Education and Society
Respatiwulan, Respatiwulan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS Rifa, Isna Hidayatur; Pratiwi, Hasih; Respatiwulan, Respatiwulan
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.194-205

Abstract

Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.
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 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.
SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, and Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
SIMULATION OF THE DISCRETE TIME MARKOV CHAIN SUSCEPTIBLE INFECTED RECOVERED (DTMC SIR) EPIDEMIC MODEL FOR COVID-19 TRANSMISSION IN CENTRAL JAVA Valentino, Yohanes Felix; Respatiwulan, Respatiwulan; Slamet, Isnandar
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An epidemic is a situation when an area has a very high number of cases of individuals infected with an infectious disease in a short time frame. Susceptible Infected Recovered (SIR) epidemic models that explain changes in the number of infected individuals in discrete time intervals are called Discrete Time Markov Chain SIR (DTMC SIR) epidemic models. This research aims to discuss the DTMC SIR epidemic model and its simulation of the COVID-19 outbreak. The research methods used are literature reviews and simulation of the dynamics of COVID-19 transmission in Central Java. Central Java's COVID-19 dynamics are analyzed using the obtained DTMC SIR model with a contact rate and cure rate . This research has yielded a DTMC SIR epidemic model that uses transition probabilities to study the dynamics of COVID-19 transmission. The model applied with an initial value of and , and shows that COVID-19 stops when and occurs at . The model was also applied when the contact rate was reduced and increased. The conclusion is that the smaller the contact rate, the longer the epidemic ends and the fewer individuals are infected at the time the epidemic ends.
Pelatihan Manajemen dan Visualisasi Data Menggunakan Excel untuk Guru Matematika SMP di Kabupaten Karanganyar: Data Management and Visualization Training using Excel for Junior High School Mathematics Teacher in Karanganyar Regency Nirwana, Muhammad Bayu; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Handayani, Sri Sulistijowati; Wijaya, Andreas Rony; Pratama, Alfito Putra Fajar; Ferawati, Kiki
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2024): November 2024
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v4i2.1023

Abstract

Literasi statistik merupakan kemampuan untuk memahami beragam informasi statistik yang dimunculkan di berbagai media. Kemampuan ini meliputi keterampilan dalam menginterpretasikan grafik dan tabel, serta mampu membaca dan memahami statistik dalam berita, media, jajak pendapat, dan lain-lain. Kabupaten Karanganyar merupakan salah satu kabupaten di Provinsi Jawa Tengah yang berbatasan dengan Kota Surakarta dan termasuk sebagai wilayah Karesidenan Surakarta. Pengetahuan mengenai literasi statistik dan implementasinya di wilayah Kabupaten Karanganyar merupakan hal yang penting untuk disampaikan kepada masyarakat, karena berkaitan langsung dengan pemahaman mengenai informasi data statistika dan bagaimana merepresentasikannya. Sebagai ilmu yang mempelajari tentang cara pengumpulan, analisis, dan pengambilan keputusan dari data, pengetahuan tentang statistika merupakan ilmu penunjang yang penting untuk dimiliki oleh masyarakat. Sebagai sasaran peningkatan literasi statistika kali ini Grup Riset Statistika dan Sains Data Bidang Lingkungan dan Kesehatan Program Studi Statistika FMIPA UNS akan melaksanakan pengabdian masyarakat dengan bentuk pelatihan untuk guru dan siswa SMP di Kabupaten Karanganyar melalui forum Musyawarah Guru Mata Pelajaran (MGMP) Matematika. Literasi statistik memerlukan pengetahuan tentang analisis dan visualisasi data yang diberikan untuk meningkatkan pemahaman terkait penerapan metode statistika dengan menggunakan Excel yang sudah banyak dikenal oleh masyarakat.
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%.
IS THE BOX-COX TRANSFORMATION NEEDED IN MODELING TELKOM’S STOCK PRICE USING NNAR AND DESH METHODS? Noven, Michela Sheryl; Respatiwulan, Respatiwulan; Sulandari, Winita
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.185-196

Abstract

Accurate stock price forecasting requires appropriate preprocessing, particularly for time series data with high variability and nonlinear patterns. This study investigates whether applying the Box-Cox Transformation (BCT) improves forecasting performance when modeling Telkom Indonesia's stock price using Neural Network Autoregressive (NNAR) and Double Exponential Smoothing Holt (DESH) methods. The NNAR model architecture is selected based on nonlinearity testing of lag variables, while DESH parameters are optimized by minimizing mean square error. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), root Mean Square Error (RMSE), and Mean Percentage Error (MPE), comparing models built with and without BCT. Results show that BCT does not enhance forecasting accuracy for either NNAR or DESH. Moreover, the NNAR model without BCT outperforms DESH, producing approximately 50% lower MAPE, RMSE, and MPE values on the testing dataset. These findings suggest that BCT may not be necessary for time series modeling in this case, and NNAR without transformation is recommended for forecasting Telkom's stock price.
Co-Authors Andreas Rony Wijaya Apsari, Nindya Eka Arnandya, Evelyn Regita Evitasi, Niken Handajani, Sri Sulistijowati Handayani, Sri Sulistijowati Isnandar Slamet, Isnandar Kiki Ferawati Mahmudah, Arik Nareswari, Lintang Pramesti Nirwana, Muhammad Bayu Noven, Michela Sheryl Pratama, Alfito Putra Fajar pratiwi, hasih Qona’ah, Niswatul Ramadhania, Monica Rifa, Isna Hidayatur Susanti, and Yuliana Valentino, Yohanes Felix Winita Sulandari Yuliana Susanti, Yuliana