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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Proceeding Simposium Nasional Kelautan dan Perikanan Wave: Jurnal Ilmiah Teknologi Maritim Jurnal Abdi: Media Pengabdian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika MATHunesa: Jurnal Ilmiah Matematika Pattimura Proceeding : Conference of Science and Technology Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Journal of World Future Medicine, Health and Nursing
Danang Ariyanto
Universitas Negeri Surabaya
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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ANALISIS MATEMATIS PENGARUH LOKASI RUMAH TERHADAP HARGA JUAL, LUAS RUMAH DAN JUMLAH KAMAR DENGAN MANOVA Annisa Nur Afifah Kusuma Sayekti; A’yunin Sofro; Danang Ariyanto
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.494

Abstract

A house is a building that functions as a residence or residence and a means of family development (Law No. 4 of 1992). Every community needs a house as a necessity. With so many people needing a place to live, house construction often occurs. As time goes by, many houses have been built. From the number of houses, they can be classified into several groups, one of the classifications is based on the location where the house was built. The classification of houses based on the location where the house was built is classified into 3, namely houses in rural areas, urban areas and suburban areas. Based on this classification, data on houses being sold on the kaggle.com website was taken. From this data, the characteristics of the houses in the 3 groups can be described. The characteristics chosen are selling price, house area, and number of bedrooms. Using this data, carry out analysis using the MANOVA method. The aim of this research is to determine the influence between house location and selling price, house area and number of rooms
INTRODUKSI TEKNOLOGI PASCAPANEN SIMPLISIA KUNYIT HITAM DI DESA GETASANYAR, MAGETAN Purnomo, Aris Rudi; Purnama, Erlix Rakhmad; Ariyanto, Danang; Adiprahara, Mirwa Anggarani
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2024): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v9i2.26860

Abstract

Introduksi Teknologi Pascapanen Simplisia Kunyit Hitam Di Desa Getasanyar, Magetan
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA Pramoedyo, Henny; Ariyanto, Danang; Aini, Novi Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.915 KB) | DOI: 10.30598/barekengvol16iss4pp1411-1422

Abstract

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data.
LOGISTIC AND PROBIT REGRESSION MODELING TO PREDICT THE OPPORTUNITIES OF DIABETES IN PROSPECTIVE ATHLETES Ariyanto, Danang; Sofro, A'yunin; Hanifah, A’idah Nur; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu
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/barekengvol18iss3pp1391-1402

Abstract

Diabetes is among the most prevalent chronic diseases globally, posing significant health risks to individuals. The identification of individuals at risk of developing these conditions is of paramount importance, particularly in high-stress and physically demanding activities such as athletic training. To find out the chances of a prospective athlete suffering from diabetes or not, models for binary data can be used, including logistic regression and probit models. The data used is primary data from prospective athletes in East Java, including prospective athletes from the State University of Surabaya and East Java Koni Athletes. This study aimed to develop an early prediction model for diabetes in prospective athletic candidates using a bivariate logistic and probit regression approach while considering the influence of socio-demographic and anthropometric factors. To selecting the best model between logistic regression and probit regression using Akaike’s Information Criterion (AIC) value, the smaller the AIC value gets means that the model is closer to the actual value or being the best model. Logistic regression has a smaller AIC value (129,85) than probit regression, this means that the logistic model is the best model. In this paper, an attempt is made to explore the use of logistic and probit regression to determine the factors which significantly influence the diabetes disease and we got that the logistic model as the best model because it has a smaller AIC value than the probit model. Based on the result of analysis and discussion, it can be concluded that there are two factors called mother’s job and finance which are influenced to the response variable, diabetes disease at significance level of 5%.
RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD Iriany, Atiek; Ngabu, Wigbertus; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0627-0636

Abstract

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.
ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH Sofro, A'yunin; Riani, Rosalina Agista; Khikmah, Khusnia Nurul; Romadhonia, Riska Wahyu; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0837-0848

Abstract

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.
STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS Maulana, Dimas Avian; Sofro, A'yunin; Ariyanto, Danang; Romadhonia, Riska Wahyu; Oktaviarina, Affiati; Purnama, Mohammad Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp97-106

Abstract

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.
PEMODELAN SPASIAL PENYAKIT JANTUNG DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN FIXED DAN ADAPTIVE BANDWIDTH Prameswari, Yunita; Oktaviarina, Affiati; Ariyanto, Danang
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Penyakit jantung koroner (PJK) merupakan penyebab utama kematian di Indonesia. Prevalensi PJK bervariasi di setiap provinsi yang dipengaruhi oleh perbedaan faktor kesehatan dan gaya hidup. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi prevalensi PJK menggunakan metode Geographically Weighted Regression dengan pendekatan bandwidth fixed dan adaptive Gaussian. Hasil penelitian menunjukkan bahwa model GWR dengan fixed bandwidth lebih baik dibandingkan dengan adaptive bandwidth, dengan nilai AIC yaitu -31,32 dan sebesar 82,20%, sehingga lebih optimal dalam menjelaskan variasi spasial prevalensi PJK. Analisis menunjukkan bahwa prevalensi ginjal dan prevalensi obesitas merupakan variabel yang signifikan terhadap prevalensi PJK. Hal ini menunjukkan bahwa hubungan antara faktor risiko dan prevalensi PJK berbeda antarprovinsi, sehingga pendekatan berbasis spasial relevan.
AI-Driven Diagnostic Imaging: Enhancing Early Cancer Detection through Deep Learning Models Ariyanto, Danang; Chai, Napat; Krit, Pong
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i3.2369

Abstract

Early detection is critical for improving cancer survival rates, yet the interpretation of diagnostic images is subject to human error and variability. Artificial intelligence (AI), specifically deep learning, presents a transformative opportunity to enhance diagnostic accuracy and speed. This study aimed to develop and validate a deep learning model to improve the accuracy and efficiency of early-stage cancer detection in radiological images compared to human expert interpretation. A convolutional neural network (CNN) was trained and validated on a curated dataset of over 20,000 mammography images. The model's diagnostic performance was rigorously evaluated using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), against a biopsy-verified ground truth. The AI model achieved an overall accuracy of 97.2%, with a sensitivity of 98.1% and a specificity of 96.5%. The model's performance, with an AUC of 0.98, was comparable to that of senior radiologists and significantly reduced false-negative rates. AI-driven deep learning models are highly effective and reliable tools for augmenting diagnostic imaging. They can significantly enhance early cancer detection, reduce diagnostic errors, and serve as a powerful assistive tool for radiologists in clinical practice.
PENAMBAHAN METODE NEURAL NETWORK DALAM PEMODELAN GSTAR-SUR UNTUK MENGATASI KASUS NON LINIER PADA PERAMALAN DATA CURAH HUJAN Iriany, Atiek; Fernandes, Adji Achmad Rinaldo; Efendi, Achmad; Putri, Henida Ratna Ayu; Ariyanto, Danang; Ngabu, Wigbertus
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p226-236

Abstract

Salah satu model peramalan yang dapat yang menggabungkan unsur spasial (spatial) dan temporal (time) adalah Generalized Space Time Autoregressive (GSTAR). Pendugaan parameter yang digunakan adalah Seemingly Unrelated Regression (SUR). Peramalan iklim pada tanaman hortikultura pada masa kini sulit untuk diprediksi karena memiliki pola dan karakteristik yang sulit diidentifikasi dan dapat disebut aktivitas non linier. Unsur non linier ini dapat ditangkap oleh metode neural network. Penelitian ini ingin mengetahui hasil peramalan curah hujan pada 6 wilayah di Tengger menggunakan model GSTAR dengan pendugaan parameter menggunakan metode SUR dan digabungkan dengan neural network agar hasil peramalan yang lebih akurat. Data yang digunakan dalam penelitian ini adalah data curah hujan enam lokasi di wilayah Tengger, yakni Ngadirejo, Puspo, Wonokitri, Argosari, Ngadas, dan Wonokerto. Model yang tepat dalam melakukan peramalan pada data curah hujan pada 6 lokasi Tengger adalah model GSTAR (1,2,3,4,5,6,7,36(1)) Backpropagation Neural Network (96-120-6).
Co-Authors Achmad Efendi Adiprahara, Mirwa Anggarani AFFIATI OKTAVIARINA Annisa Nur Afifah Kusuma Sayekti Arditama Putra Rochmanullah Atiek Iriany A’yunin Sofro Bambang Dwi Argo Chai, Napat Dhanny Septimawan Sutopo Dian Cahya Cahya Rini Dian Cahya Rini Fernandes, Adji Achmad Rinaldo Fida Rachmadiarti Hanifah, A’idah Nur Henny Pramoedyo Henny Pramoedyo Khikmah, Khusnia Nurul Krit, Pong Marhen Andan Prasetyo Maulana, Dimas Avian Mitakda, Maria Bernadetha Moh. Fadli Muhamad Firdaus NI WAYAN SURYA WARDHANI Ni Wayan Surya Wardhani Ni Wayan Surya Wardhani Novi Nur Aini Novi Nur Aini, Novi Nur Prihanto, Junaidi Budi Purnama, Erlix Rakhmad Purnama, Mohammad Dian Purnomo, Aris Rudi Putri, Henida Ratna Ayu Redi Bintarto Riani, Rosalina Agista Romadhonia, Riska Wahyu Romi Setiawan Satoto, Sapto Wiratno sihabudin sihabudin Sofro, A'yunin Solimun Solimun Wigbertus Ngabu Yana Shanti Yunita Prameswari, Yunita