Article Per Year (5 Year)
p-Index From 2020 - 2025
1.456
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Engineering, Mathematics and Computer Science Journal (EMACS) Parameter: Journal of Statistics MATHunesa: Jurnal Ilmiah Matematika
Purnama, Mohammad Dian
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search

 1 
An Implementation of Ordinal Probit Regression Model on Factor Affecting East Java Human Development Index Purnama, Mohammad Dian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12094

Abstract

An instrument for measuring human development, the Human Development Index (HDI) looks at how well human development has been achieved in relation to a few fundamental aspects of quality of life. In 2023, East Java's HDI showed an increase in the last three years with the latest value of 73.38. Despite the increase, East Java still has the lowest HDI in Java and Bali. This situation suggests the need for an in-depth analysis of the factors that influence HDI. This study aims to identify factors that contribute to HDI to formulate more appropriate policies in the future. The data used is the HDI of East Java in 2023 with ordinal categories. To analyze the ordinal data, the ordinal probit regression method was applied. The results show that the percentage of poor people has a significant influence on HDI. In addition, the classification accuracy of the model is obtained with a value of 50.5%, which indicates that the accuracy of the model in predicting HDI into the right category reaches 50.5%.
PENGELOMPOKAN KABUPATEN /KOTA DI JAWA TIMUR BERDASARKAN INDIKATOR KASUS DBD MENGGUNAKAN COMPLETE LINKAGE DAN AVERAGE LINKAGE Mustafidah, Mutia Eva; Purnama, Mohammad Dian
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

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

Abstract

Demam Berdarah Dengue (DBD) merupakan permasalahan serius dalam sektor kesehatan masyarakat Indonesia, dengan tingkat kematian yang cenderung meningkat setiap tahunnya. Menurut data, kasus DBD tersebar di 449 kabupaten/kota atau sekitar 87,3% dari total wilayah administratif di 34 provinsi Indonesia. Pada tahun 2022, Dinas Kesehatan mencatat tingginya jumlah kasus DBD, mencapai 131.265 kasus. Provinsi Jawa Timur menduduki peringkat kedua dalam jumlah kasus DBD tertinggi, dengan 8.894 kasus tercatat di wilayah tersebut. Oleh karena itu, penelitian ini dilakukan untuk mengidentifikasi faktor-faktor yang berpengaruh terhadap kasus DBD menggunakan sembilan indikator, termasuk persentase penduduk miskin, kepadatan penduduk, persentase Indeks Pembangunan Manusia (IPM), jumlah sarana kesehatan, persentase keluhan kesehatan, persentase rumah tangga dengan sanitasi layak, persentase tempat umum yang memenuhi syarat kesehatan, persentase air minum layak, dan persentase jaminan kesehatan. Algoritma yang digunakan dalam penelitian ini adalah algoritma hierarki clustering dengan metode complete linkage dan average linkage. Tujuan utama dari penelitian ini adalah untuk menghasilkan kelompok-kelompok Kabupaten/Kota di Provinsi Jawa Timur berdasarkan indikator yang berkaitan dengan kasus DBD. Hasil penelitian menunjukkan adanya empat kelompok, yakni kelompok 1 dengan 28 Kabupaten/Kota, kelompok 2 dengan 5 Kabupaten/Kota, kelompok 3 dengan 4 Kabupaten/Kota, dan kelompok 4 dengan 1 Kabupaten/Kota.
OPTIMASI WAKTU PENJEMURAN DAN SUHU PENGGORENGAN TERHADAP KEMEKARAN KERUPUK UDANG Purnama, Mohammad Dian
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n1.p168-173

Abstract

PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA JAWA TIMUR DENGAN REGRESI LOGISTIK ORDINAL Purnama, Mohammad Dian; Sofro, A'yunin
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n3.p654-661

Abstract

Indeks Pembangunan Manusia (IPM) merupakan tolok ukur utama kemajuan dan kesejahteraan wilayah, serta indikator penting dalam menilai peningkatan mutu hidup manusia. Pada tahun 2023, IPM Jawa Timur mengalami kenaikan 3 tahun terakhir dengan nilai terakhir 73,38. Walaupun mengalami kenaikan, Jawa Timur merupakan provinsi dengan IPM terendah di Pulau Jawa dan Bali. Sehingga dari permasalahan tersebut tentunya diperlukan untuk memahami secara menyeluruh faktor-faktor yang berpengaruh terhadap IPM. Tujuan penelitian ini tentunya untuk memberikan gambaran tentang faktor-faktor yang mempengaruhi IPM dalam merancang kebijakan yang tepat di masa mendatang. Data yang digunakan merupakan tingkat IPM Jawa Timur 2023 dalam skala kategori ordinal. Untuk menginterpretasikan data ordinal, diperlukan metode yang sesuai yakni regresi logistik ordinal. Hasil penelitian menunjukkan bahwa variabel persentase penduduk miskin berpengaruh signifikan terhadap IPM.
Relationship Between Temperature and Humidity on Rainfall: A Multiple Linear Regression Analysis Purnama, Mohammad Dian; Mustafidah, Mutia Eva
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11466

Abstract

Indonesia is one of the tropical countries in the world that has two seasons, the dry season and the rainy season. One of the biggest challenges in tropical countries is flooding caused by heavy rainfall. Not only does it cause flooding, rainfall also affects several sectors especially agriculture. Areas that have a lot of rain-fed agricultural land, especially rice fields, depend on rainfall because it determines crop yields. This study uses data from 12 sub-districts in Mojokerto district where agricultural activities are one of the pillars of the economy in the region. There are various factors associated with rainfall such as temperature and humidity. The data used is the year 2022 using multiple linear regression. Based on the results of the study, both predictor variables have a strong and positive relationship with rainfall with a correlation coefficient of 0.760007. With a significance level of 5% or 0.05, in the partial test, only the humidity variable has a significant effect on the amount of rainfall. While in the simultaneous test, both variables have a significant effect. These factors together have a coefficient of determination of 0.57761 or the contribution of the influence of the two predictor variables of 57.761% while the remaining 42.239% by other variables.
CLUSTER ANALYSIS OF HIGHEST EDUCATION COMPLETED IN EAST JAVA PROVINCE WITH SPHERICAL K-MEANS METHOD Purnama, Mohammad Dian
Parameter: Journal of Statistics Vol. 5 No. 1 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i1.17440

Abstract

One of the key pillars of development that greatly aids in the social and economic advancement of civilization is education. The purpose of this study is to use the Spherical K-Means Clustering method to evaluate the distribution and degree of educational attainment in districts/cities in East Java Province. This approach was selected because it can group vector-based data according to directional similarity, making it appropriate for multidimensional data. Based on education-related variables, including never attending school, not graduating from primary school, graduating from primary school, graduating from junior high school, graduating from senior high school, and graduating from university, this analysis groups regions. Based on the clustering results, several significant clusters were found. Areas with strong secondary and tertiary education levels make up Cluster 1. There is a more equitable distribution of schooling between primary and secondary education in Cluster 2. Regions with a higher percentage of basic education and lower secondary education levels are included in Cluster 3. The results can help stakeholders create more focused and efficient education policies by offering significant insights into the differences in educational attainment in East Java.
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.
Aplikasi Topologi Jaringan Pada Akun Twitter Paling Berpengaruh Terkait Redenominasi Rupiah dengan Metode SNA Purnama, Mohammad Dian; Aisyah, Ivon Tressyta Nanda; Rasyidah, Salma Azmi; Juniati, Dwi; Yulistina, Fika
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.p141-148

Abstract

Masyarakat sering memanfaatkan media sosial sebagai platform untuk mengungkapkan minat dan pandangan mereka terhadap berbagai topik. Di Indonesia, masyarakat juga sering menggunakan media sosial sebagai wadah untuk mengekspresikan minat dan pandangan mereka terhadap berbagai isu. Kemajuan teknologi informasi telah memperluas cakupan dan meningkatkan kecepatan dalam penyebaran informasi melalui media sosial. Salah satu isu yang ramai dibahas di Twitter adalah terkait Redenominasi Rupiah, yang tercermin dari tingginya jumlah retweet pada tweet yang terkait. Penelitian ini menerapkan Metode Analisis Jaringan Sosial (SNA) sebagai teknik untuk memetakan dan mengukur hubungan serta komunikasi di antara akun-akun. Hasil penelitian menunjukkan bahwa akun Twitter @BigAlphaID memunculkan nilai Degree Centrality tertinggi sebesar 1387, nilai Betweeness Centrality sebesar 1386, dan nilai Closeness Centrality mencapai 1.0. Closeness Centrality yang mencapai 1.0 menggambarkan bahwa akun tersebut menjadi simpul terdekat dengan akun lain dalam jaringan. Dengan kata lain, akun Twitter @BigAlphaID memiliki dampak signifikan dalam menyuarakan isu Redenominasi Rupiah
Enhancing Tourism Demand Forecasting Accuracy Through Clustering Time Series: A Comparison MAPE Analysis of Indonesian Provincial Domestic Tourist Flows Purnama, Mohammad Dian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14112

Abstract

The post-pandemic recovery period of the Indonesian tourism sector poses new challenges for accurate tourism demand forecasting across Indonesia's diverse provincial richness. This research aims to enhance the predictive accuracy of domestic tourism demand by comparing conventional single-provincial forecasting methods with clustering-based time series techniques. The Geometric Brownian Motion (GBM) model analyzed data regarding the monthly influx of domestic tourists to 34 provinces from January 2021 to May 2025. This study utilized average linkage agglomerative nesting (AGNES) clustering to discern structural similarities among provinces. Subsequently, silhouette analysis was employed to determine the optimal number of clusters. The findings demonstrate that the cluster-based forecasting approach markedly improved accuracy relative to the non-clustered model. The Mean Absolute Percentage Error (MAPE) for the traditional provincial forecasts was 16.48%. The first cluster-based model had an MAPE of 13.38% and the second cluster-based model had an MAPE of 6.54%. These findings indicate that grouping provinces with analogous temporal patterns enhances the model's ability to identify the underlying dynamics in domestic tourism flows. The work underscores the efficacy of combining stochastic models with hierarchical clustering to enhance evidence-based tourist planning and policy development. This study improves sustainable tourism management by providing an empirical foundation for enhanced forecasting precision, particularly in post-crisis recovery periods.
Co-Authors AFFIATI OKTAVIARINA Aisyah, Ivon Tressyta Nanda Danang Ariyanto Dwi Juniati Maulana, Dimas Avian Mustafidah, Mutia Eva Rasyidah, Salma Azmi Romadhonia, Riska Wahyu Sofro, A'yunin Yulistina, Fika