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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Pendidikan Matematika (Kudus) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika EIGEN MATHEMATICS JOURNAL Community Development Journal: Jurnal Pengabdian Masyarakat LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Litbang Edusaintech Fraction: Jurnal Teori dan Terapan Matematika Tepis Wiring : Jurnal Pengabdian Masyarakat Journal of Data Insights Prosiding Seminar Nasional Unimus
Prizka Rismawati Arum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Pemodelan HIV dan AIDS di Provinsi Jawa Timur Menggunakan Metode Regresi Bivariat Poisson Invers Gaussian (BPIG) Fitriyah, Novina Indah; Arum, Prizka Rismawati; Wasono, Rochdi
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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

Abstract

Regresi Poissonnadalah metode regresi yang digunakannuntuk memodelkannhubungannantara variabeldependen diskrittdalam bentuk data hitungan (count). Namun, data hitungan pada variabel dependenseringgkali mengalami masalah overdispersi atau underdispersi, yang berarti bahwa variansinya lebih besaratau lebih kecil daripada rata-rata. Masalah ini tidak sesuai dengan asumsi regresi Poisson, di manadiasumsikan bahwa rata-rata sama dengan varians (equidispersi). Untuk mengatasi masalah ini, salah satumodel yang dapat digunakan adalah Bivariate Poisson Inverse Gaussian. Model ini dapat menjelaskanhubungan antara dua variabel dependen, seperti HIV dan AIDS, dengan beberapa variabel independen.Kesehatan dianggap sebagai unsur kunci dalam perkembangan ekonomi Negara dan permasalahankesehatan, terutama HIV dan AIDS menjadi isu utama dalam rangka mencapaiSSustainable DevelopmentGoals (SDGs) di Indonesia. Sehingga diperlukan penelitiannuntukkmengetahuiifaktor-faktor yangberpengaruh terhadap jumlah kasus HIV dan AIDS di Provinsii Jawa Timur tahun 2022. Penaksir parameterdilakukan dengannmetodeeMaximum Likelihood Estimation (MLE). Hasil penelitian menunjukkan modelregresi Bivariat Poisson Invers Gaussian adalah λ̂1 = exp(4,30692 + 0,00004X1 + 0,00048X2 + 0,00006X3+ 0,01657X4 + 0,00403X5 - 0,02719X6) dan λ̂2 = exp(2,52020 + 0,00034X1 + 0,00560X2 + 0,00006X3 –0,00257X4 + 0,00303X5 + 0,00497X6), di mana variabel kepadatan peduduk per kilometer, presentasedaerah yang berstatus desa, presentase pasangan usia subur pengguna kondom, presentase pendudukk yangmaksimal tamat SMA, presentase penduduk miskin, dan presentase penderita infeksi menular seksual,berpengaruh secara signifikan terhadap kasus HIV dan AIDS dengan nilai AIC sebesarr 5994.888.Kata Kunci : AIDS, HIV, Overdispersi, Regresi Poisson Bivariat, Poisson Invers Gaussian.
GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION AND GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION MODELING ON PROPERTY CRIME CASES IN CENTRAL JAVA Arum, Prizka Rismawati; Gautama, Rahmad Putra; Haris, M. Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1469-1484

Abstract

Property crime in Indonesia remains one of the most prevalent categories of crime across various regions of the country. This category encompasses a range of criminal acts, including theft, illegal appropriation of goods, robbery, motor vehicle theft, arson, and property damage. One of the commonly used regression analysis methods is Poisson regression. The assumption violation of overdispersion in Poisson regression is often found in property crime data in Central Java. This study also considers spatial aspects, depicting local regional characteristics and the integration of local and global variables. Therefore, this study employs Geographically Weighted Generalized Poisson Regression (GWGPR) and Geographically Weighted Negative Binomial Regression (GWNBR) methods with Adaptive Bisquare Kernel weighting. The aim of this research is to develop a model for each district/city in Central Java using Adaptive Bisquare Kernel weighting, thus providing a more accurate representation of the factors influencing property crime in each region. The AIC value criterion of 411.3652 indicates that the GWNBR method is the most suitable for modeling the number of property crime cases in each district/city in Central Java compared to Poisson regression, negative binomial regression, and GWGPR methods.
Implementasi Algoritma Random Forest untuk Mengklasifikasikan Data Gempa Bumi di Indonesia Pratiwi, Alda Putri; Arum, Prizka Rismawati
Eigen Mathematics Journal Vol 8 No 1 (2025): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i1.185

Abstract

Earthquakes are shocks that occur on the surface of the earth due to shifts in the earth's plates. Geographically, Indonesia is located in the Pacific Ring of Fire (King of Fire) region, this makes Indonesia prone to earthquakes. Earthquakes can cause environmental damage and tsunami disasters, but not all earthquakes can cause tsunamis. Classifying earthquakes that have the potential for a tsunami is very important to mitigate the damage caused. One classification method that has a high level of accuracy is random forest. The advantage of random forest is that this algorithm tends to be resistant to overfitting and can handle large data. This research uses real-time earthquake data from July to August 2023, sourced from the website of the Meteorology Climatology and Geophysics Agency (BMKG). The training data and test data used in this research are 70% and 30%. Confution Matrix is used as model evaluation, to measure the accuracy of the classification model. The results of this research obtained a high accuracy, equal 0.97 or 97%.
ANALISIS ALGORITMA DECISION TREE DALAM PENGKLASIFIKASIAN INDEKS PENCEMARAN UDARA KOTA JAKARTA DENGAN METODE CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING Anisa Putri Arla Vatwa Lubu; Siti Mutiah; Arya Praditya; Nerisa Rahma; Prizka Rismawati Arum
Fraction: Jurnal Teori dan Terapan Matematika Vol. 5 No. 1 (2025): Fraction: Jurnal Teori dan Terapan Matematika
Publisher : Jurusan Matematika, Fakultas Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/fraction.v5i1.87

Abstract

Kualitas udara yang bersih sangat penting untuk kelangsungan hidup manusia. Namun, DKI Jakarta saat ini menghadapi tantangan serius dengan kualitas udara terburuk di dunia, yang disebabkan oleh aktivitas manusia, termasuk industri dan penggunaan bahan bakar fosil. Dalam konteks ini, model klasifikasi, khususnya algoritma Decision Tree, dapat berperan dalam memahami faktor-faktor yang mempengaruhi kualitas udara serta mengklasifikasikan Indeks Standar Pencemar Udara (ISPU). Kajian ini bertujuan untuk menganalisis klasifikasi dengan menggunakan metode CRISP-DM guna mengidentifikasi pola dan parameter yang memengaruhi pencemaran udara. Penelitian ini mengevaluasi enam parameter, yaitu karbon monoksida (CO), sulfur dioksida (SO2), nitrogen dioksida (NO2), ozon (O3), serta partikel debu PM2.5 dan PM10. Kategori level ISPU yang dianalisis meliputi Baik, Sedang, dan Tidak Sehat. Hasil penelitian menunjukkan bahwa model yang digunakan memiliki performa yang sangat baik, dengan akurasi mencapai 97,01%. Dari analisis, PM2.5 ditemukan memiliki korelasi tertinggi terhadap Indeks Standar Pencemar Udara, sementara ozon terbukti efektif dalam membedakan antara kualitas udara yang sedang dan tidak sehat.
NEGATIVE BINOMIAL REGRESSION AND GENERALIZED POISSON REGRESSION MODELS ON THE NUMBER OF TRAFFIC ACCIDENTS IN CENTRAL JAVA Haris, M Al; Arum, Prizka Rismawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.677 KB) | DOI: 10.30598/barekengvol16iss2pp471-482

Abstract

Traffic accidents that always increase along with the increasing population growth and the number of vehicles impact the national economy. The number of traffic accidents is a count data that a Poisson distribution can approximate. The Poisson regression model often found violations of the overdispersion assumption by modeling the factors that affect the number of traffic accidents. Alternative models proposed to overcome the emergence of overdispersion in the Poisson regression model are the Generalized Poisson Regression and Negative Binomial Regression Models. Based on the analysis results, it was found that the overdispersion assumption violates the Poisson regression model, and the Generalized Poisson regression model is the best because it has the smallest AIC value of 485.50. Factors that significantly affect the number of traffic accidents in Central Java Province are the percentage of adolescents and the percentage of accidents occurring in the road area of the district/city.
FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME Arum, Prizka Rismawati; Indriani, Anita Retno; Haris, M Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1183-1192

Abstract

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city
ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION WITH LEAST ABSOLUTE DEVIATION (LAD) ESTIMATION AND M-ESTIMATION ON GRDP OF WEST JAVA PROVINCE Arum, Prizka Rismawati; Ridwan, Mohammad; Alfidayanti, Ina; Wasono, Rochdi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1573-1584

Abstract

Geographically Weighted Regression (GWR) is an analytical method for data that contains spatial heterogeneity effects. However, parameter estimation in the GWR model has a weakness, namely it is prone to outliers and can cause the parameter estimation to be biased. This can be overcome by the Robust Geographically Weighted Regression (RGWR) method which is more robust against the presence of outliers. This method is suitable for Gross Regional Domestic Product (GRDP) data in West Java Province, which contains outliers and also has spatial effects. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of West Java Province. The purpose of this study is to compare the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation (LAD) Estimation and M-estimation and also to find out the factors that affect the Gross Regional Domestic Product (GRDP) in West Java Province in 2021 based on the model resulting from. Selection of the best model is seen based on the value of the coefficient of determination (R2) and Mean Squared of Error (MSE). The research results show that the Robust Geographically Weighted Regression (RGWR) method with M-estimation is much more effective in estimating the distribution of GRDP in West Java Province in 2021, seen from the larger coefficient of determination and the smaller Mean Square Error (MSE). The variables that have a significant influence on GRDP in West Java Province in 2021 are the variables of foreign investment and local income.
Pengelompokan Wilayah Kecamatan di Kabupaten Kendal Berdasarkan Hasil Produksi Buah dan Sayur Dengan Metode K-means Clustering Arum, Prizka Rismawati; Nur, Indah Manfaati; Fitriyani, Indah; Amri, Saeful
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 7 No. 1 (2023): Mei (2023)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v19i1.8212

Abstract

Indonesia dikenal dengan sebutan negara agraris dimana sebagian besar penduduk bekerja di sektor produksi pertanian. Kabupaten Kendal merupakan salah satu kabupaten di Provinsi Jawa Tengah yang sebagian besar wilayahnya merupakan daerah produksi pertanian yang sangat subur. Data yang digunakan dalam kasus ini adalah hasil produksi produksi pertanian buah dan sayur pada 20 kecamatan di Kabupaten Kendal tahun 2022. Salah satu cara untuk mengetahui potensi produksi pertanian dari wilayah kecamatan di Kabupaten Kendal adalah dengan mengelompokkan wilayah yang memiliki karakteristik hampir sama menggunakan K-means clustering. Tujuannya adalah mendapatkan hasil pengelompokkan yang optimal dari masing-masing kelompok yang terbentuk. Berdasarkan hasil analisis, diperoleh pengelompokkan wilayah kecamatan di Kabupaten Kendal menggunakan K-means menjadi 3 cluster. Dimana Klaster 1 terdiri dari 2 kecamatan dengan identifikasi bawang merah, mangga, pisang, dan jambu air memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Klaster 2 terdiri dari 2 kecamatan dengan identifikasi pepaya, nangka, petai, dan melinjo memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Dan klaster 3 terdapat 16 kecamatan dengan identifikasi cabai rawit, cabai keriting, memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Dengan nilai evaluasi yang didapatkan dari Silhouette Index sebesar 0,5546 yang berarti termasuk kedalam kriteria medium structure.
Panel Data Spatial Regression Modeling with a Rook Contiguity Weighting Function on the Human Development Index in West Sumatera Province Arum, Prizka Rismawati; Anggraini, Lisa; Nur, Indah Manfaati; Purnomo, Eko Andy
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.16675

Abstract

The achievement of the level of welfare of a region or country can be seen from the level of human development as measured by the Human Development Index (HDI). West Sumatra is one of the provinces with HDI achievements above the national average. However, there are still regencies/cities in West Sumatra Province that have HDI achievements below the national average and HDI achievements in West Sumatra Province Regencies/Cities have changed in 2017-2021. Therefore, in this study, spatial analysis of panel data was used. The aim of this research is to find out the general description of the HDI of West Sumatra Province, obtain a panel data spatial regression model and obtain variables that significantly influence on HDI in West Sumatra Province 2017─2021because differences in HDI achievement were suspected to have influences from areas that were side by side and the area was observed more than once. The model formed from this analysis using the rook contigutiy weighting function is Random Effect Spatial Autoregressive because the spatial interactions formed in human development index data in West Sumatra Province are real at lag. This model is a suitable model based on panel spatial model selection and has an R2 value of 92.94%. Analysis of human development index data in regencies/cities in West Sumatra Province using spatial regression panel data obtained results that expectations of school length (X1), average length of schooling (X2), and population density (X3) significantly directly influenced the human development index in regencies/cities in West Sumatra Province.  
Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters Fauzi, Fatkhurokhman; Erlinda, Relly; Arum, Prizka Rismawati
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20385

Abstract

Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster. 
Co-Authors Agi Khoerunnisa Alambara, Ach Ridoi Alfidayanti, Ina Alfisyahrina Hapsery Ali Imron Ali Imron Alwan Fadlurohman Amalia Jihan Syafiqoh Amri, Saeful Andy Purnomo, Eko Anisa Putri Arla Vatwa Lubu Anjelina Rafika Wulandari Aqsal Maulana Ariska Fitriyana Ningrum Arya Praditya Azizah, Apipah Nur Cika Awani Ayuwida Cintadea Amanda Dwi Aryani Dannu Purwanto Dhani, Oktaviana Rahma Dheanyta Alif Shafira Dominikus Leki Sogen Dwi Purnomo Putro Dwi Rahmadini Eko Andy Purnomo Elvia Nanda Sofiyanti Erlinda, Relly Evida Oktaviana Fauzi, Fatkhurokhman Fauzi, Fatkhurrokhman Fenny Amalia Adani Fitriyah, Novina Indah Gautama, Rahmad Putra Hanief Rizky Utami Haris, M Al Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indah Sulistiya Indriani, Anita Retno ine fitriani Isna Nur Azizah Izzadin Muhamad Nur Irfan Kurnia Nur Annisa Laila Khoirun Nisa Laras Indah Setyaningsih Linda Puspitasari Lisa Anggraini Listi Puspitasari M. Al Haris Miftakhul Haris Mohammad Ridwan Muhammad Saifudin Nur Nerisa Rahma Nursamsiah Nursamsiah Pratiwi, Alda Putri Prissy Nusaiba Yulisa Rochdi Wasono Rochdi Wasono Samikoh Ulinuha Siti Mutiah Siva Alfian Sugito Sugito Supriyadin Supriyadin Tiani Wahyu Utami Tresiani Yunitasari Velia Arni Widyasari Wisudawati, Dinda Tri Yuciana Wilandari Yulia Fitri