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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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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
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.
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.
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.
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