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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Fourier 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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Infografis Dampak Pandemi Covid-19 sebagai Upaya Edukasi Pemberdayaan Masyarakat Desa Katonsari Kecamatan Demak Arum, Prizka Rismawati; Andy Purnomo, Eko; Imron, Ali; Haris, M. Al; Fauzi, Fatkhurrokhman; Alambara, Ach Ridoi
Jurnal Pengabdian Masyarakat: Tipis Wiring Vol 1 No 2 (2022): Tepis Wiring: Jurnal Pengabdian Masyarakat
Publisher : Fakultas Ekonomi dan Bisnis Unversitas Islam Raden Rahmat

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Abstract

Based on observations in the Katonsari Village, Demak District, Demak Regency, the delivery of information about Covid-19 and its impacts is still traditional, namely using text, tables or diagrams that are less attractive. Katonsari Village apparatus must have innovations in conveying information about Covid-19 and its impacts, such as new methods or media for conveying information. One of them is through infographics. Through the training, mentoring, mentoring and coaching that will be carried out, it is hoped that it will be able to make the officials of Katonsari Village, Demak District, Demak Regency more professional in utilizing IT to convey information on the impact of Covid-19 to the public through infographics. So that it can increase the awareness of the people of Katonsari Village in preventing Covid-19. This program consists of several stages which include delivering conceptual material on infographics, compiling data on the impact of Covid-19 into IT-based information, especially infographics, and socializing the results of infographics to the public.
OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION Amri, Saeful; Ningrum, Ariska Fitriyana; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.44-50

Abstract

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.
STOCK PRICE FORECASTING OF PT. BANK CENTRAL ASIA USING HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE-NEURAL NETWORK (ARIMA-NN) METHOD Azizah, Apipah Nur; Fauzi, Fatkhurokhman; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.1.2024.48-59

Abstract

PT. Bank Central Asia is a private company that has superior shares in the Lq45 category but has share prices that fluctuate every period. So forecasting is needed to predict stock prices in the next period. These fluctuations can cause linear and nonlinear relationships in historical stock price data. This research uses the Hybrid ARIMA-NN approach, where the ARIMA model is able to overcome data non-stationarity while the Neural Network is used to capture nonlinear patterns that cannot be explained by the ARIMA model by using the residuals as NN input, the hybrid model can increase forecasting accuracy. The data used is weekly data on closing stock prices for the period January 2019 to June 2024. Prediction measurements use Mean Absolute Percentage Error. The research results show that forecasting with Hybrid ARIMA(2,1,2)-NN(1-5-1) obtained a MAPE value of 3.99% smaller than the ARIMA(2,1,2) a MAPE value of 4.13%, that the accuracy of the forecasting model is good.
Prediksi Jumlah Penumpang Di Bandara Nasional Ahmad Yani Semarang Menggunakan Holt Winter’s Exponential Smoothing (HWES) Gautama, Rahmad Putra; Fadlurohman, Alwan; Arum, Prizka Rismawati; Dhani, Oktaviana Rahma
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Pesawat terbang memberikan kenyamanan dan kecepatan bagi penggunanya terutama bagi mereka yangmemiliki keterbatasan waktu. Peningkatan jumlah penumpang terus terjadi beberapa bulan ini, sehinggadibutuhkan suatu peramalan dalam mengambil keputusan untuk memprediksi jumlah penumpang gunamemaksimalkan kinerja yang ada. Karena metode Holt Winters Exponential Smoothing tidak sangat akuratdan sesuai dengan asumsi awal dari pola data penelitian, metode ini digunakan. Studi ini bertujuan untukmenggunakan metode Holt Winters Exponential Smoothing untuk meramalkan jumlah penumpang pesawatdi Bandara Nasional Ahmad Yani Semarang. Hasil analisis menunjukkan bahwa metode ini memiliki nilaiMAPE sebesar 13,98%, yang menunjukkan bahwa metode ini adalah pilihan yang baik dan tepat untukmeramalkan jumlah penumpang pesawat di Bandara Nasional Ahmad Yani Semarang. Kata Kunci : Holt Winters Exponential Smoothing, Mape, Penumpang, Peramalan
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

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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
Co-Authors Agi Khoerunnisa Alambara, Ach Ridoi Alfidayanti, Ina Alfisyahrina Hapsery Ali Imron Ali Imron Alwan Fadlurohman 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 Rahmadini Eko Andy Purnomo Elvia Nanda Sofiyanti Endang Tri Wahyuni Maharani Erlinda, Relly Evida Oktaviana Fatimahthus Zahra, Diandra Fauzi, Fatkhurokhman Fauzi, Fatkhurrokhman Fenny Amalia Adani fitriani, ine Fitriyah, Novina Indah Gautama, Rahmad Putra Haris, M Al Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indah Sulistiya Indriani, Anita Retno Isna Nur Azizah Izzadin Muhamad Nur Irfan Jihan Syafiqoh, Amalia Kurnia Nur Annisa Laila Khoirun Nisa Laras Indah Setyaningsih Linda Puspitasari Lisa Anggraini M. Al Haris Manfaati Nur, Indah Miftakhul Haris Mohammad Ridwan Muhammad Saifudin Nur Nerisa Rahma Nursamsiah Nursamsiah Pratiwi, Alda Putri Prissy Nusaiba Yulisa Puspitasari, Listi Putro, Dwi Purnomo Rahmi, Mulya Asy-syifa Rizky Utami, Hanief Rochdi Wasono Rochdi Wasono Siti Mutiah Siva Alfian Sugito Sugito Supriyadin, Supriyadin Tiani Wahyu Utami Tresiani Yunitasari Ulinuha, Samikoh Velia Arni Widyasari Wisudawati, Dinda Tri Yuciana Wilandari Yulia Fitri Yulia Fitri