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All Journal VALENSI TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal on Mathematics Education (JME) Journal on Mathematics Education (JME) Jurnas Nasional Teknologi dan Sistem Informasi International Conference on Coastal and Delta Areas BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Telematika Jurnal Matematika UNAND Jurnal Restikom : Riset Teknik Informatika dan Komputer STATISTIKA
Atje Setiawan Abdullah
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, Padjadjaran University, Indonesia
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION Munandar, Devi; Ruchjana, Budi Nurani; Abdullah, Atje Setiawan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1056.381 KB) | DOI: 10.30598/barekengvol16iss1pp099-112

Abstract

Over a long time, atmospheric changes have been caused by natural phenomena. This study uses the Principal Component Analysis (PCA) model combined with Vector Autoregressive Integrated (VARI) called the PCA-VARI model through the data mining approach. PCA reduces ten variables of climate data into two principal components during ten years (2001-2020) of climate data from NASA Prediction Of Worldwide Energy Resources. VARI is a non-stationary multivariate time series to model two or more variables that influence each other using a differencing process. The Knowledge Discovery in Database (KDD) method was conducted for empirical analysis. Pre-processing is an analysis of raw climate data. The data mining process determines the proportion of each component of PCA and is selected as variables in the VARI process. The postprocessing is by visualizing and interpreting the PCA-VARI model. Variables of solar radiation and precipitation are strongly correlated with each measurement location data. A forecast of the interaction of variables between locations is shown in the results of Impulse Response Function (IRF) visualization, where the climate of the West Java region, especially the Lembang and Bogor areas, has strong response climate locations, which influence each other.
THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19 IN BANDUNG Monika, Putri; Soetikno, Christophorus; Abdullah, Atje Setiawan; Ruchjana, Budi Nurani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.323 KB) | DOI: 10.30598/barekengvol17iss1pp0085-0094

Abstract

Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease.
INTEGRATED OF WEB APPLICATION RSHINY FOR MARKOV CHAIN AND ITS APPLICATION TO THE DAILY CASES OF COVID-19 IN WEST SUMATERA Monika, Putri; Ruchjana, Budi Nurani; Parmikanti, Kankan; Abdullah, Atje Setiawan
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/barekengvol17iss4pp2397-2410

Abstract

Discrete-time of Markov chains, starting now referred to as Markov chains, have been widely used by previous researchers in predicting the phenomenon. The predictions were made by manual calculations and using separate software, including Maple, Matlab, and Microsoft Excel. The analysis takes a relatively long time, especially in calculating the number of transitions from each state. This research built an integrated R script for the Markov chain based on the web application RShiny to quickly, easily, and accurately predict a phenomenon. The Markov chain integrated R script is built via command-command to predict the day-n distribution with the n-step distribution and long-term probability using a stationary distribution. The RShiny web application built is limited to state two and three. The integrated web application RShiny for the Markov chain is used to predict the daily cases of COVID-19 in West Sumatra. Based on the analysis carried out in predicting the daily cases of COVID-19 in West Sumatra from March 26, 2020, to October 20, 2020, for the next three days and in the long term, the results show that there is a 51.2% probability of an increase in COVID-19 cases, a 43% probability that cases will decrease, and 5.8% chance of stagnant cases
Pengembangan Sistem Informasi Geografis Kebun Binatang Berbasis Progressive Web Application (PWA) dengan Metode Prototype (Studi Kasus Kebun Binatang Bandung) Arsa, Muhammad Fadillah; Abdullah, Atje Setiawan; Rejito, Juli
Jurnal Nasional Teknologi dan Sistem Informasi Vol 7 No 3 (2021): Desember 2021
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v7i3.2021.119-129

Abstract

Kebun binatang merupakan tempat yang memiliki peran strategis terhadap aspek konservasi satwa, sosial ekonomi masyarakat, maupun lingkungan. Kebun Binatang Bandung sebagai salah satu kebun binatang di Indonesia sudah seharusnya dapat dikelola dengan baik dengan memberikan fasilitas pelayanan yang mumpuni. Namun pada saat ini Kebun Binatang Bandung masih belum memiliki fasilitas layanan petunjuk arah, peta, dan informasi satwa yang memadai. Hal tersebut melatarbelakangi adanya penelitian pengembangan aplikasi Sistem Informasi Geografis Kebun Binatang Bandung ini. Tujuannya agar pengunjung Kebun Binatang Bandung dapat lebih mudah dan nyaman dalam menjelajahi kebun binatang, serta bisa mendapatkan informasi lebih dalam mengenai satwa yang dilihatnya. Pengembangan aplikasi menggunakan metode pengembangan perangkat lunak Prototype yang dinilai baik untuk pengembangan aplikasi berskala kecil. Metode Prototype terdiri dari lima tahap pengembangan yakni Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, dan Deployment Develivery and Feedback. Sementara itu, aplikasi dibuat berbasis Progressive Web Application (PWA) yang mudah diakses namun tetap memberikan fitur-fitur yang menarik layaknya aplikasi native. Hasil pengembangan aplikasi kemudian diujicobakan dengan menggunakan metode System Usability Scale (SUS) dan Retrospective Think Aloud (RTA). Dari hasil pengujian, didapat didapat nilai usabilitas sebesar 81,43 (Skor SUS) yang tergolong ke dalam kategori Acceptable. Dengan demikian dapat disimpulkan bahwa aplikasi ini diterima dan layak untuk digunakan.
Comparison of Spatial Weight Matrices in Spatial Autoregressive Model: Case Study of Intangible Cultural Heritage in Indonesia Sobari, Muhamad; Desiyanti, Armalia; Yanti, Devi; Monika, Putri; Abdullah, Atje Setiawan; Ruchjana, Budi Nurani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 1 (2023): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i1.10757

Abstract

Intangible Cultural Heritage (ICH) can effectively contribute to Sustainable Development Goals (SDGs) in all economic, social, and environmental dimensions, along with peace and security. Studying ICH in Indonesia cannot be separated from the spatial aspect of how an area's attributes are related to other areas located close to each other. Spatial regression modeling needs to be done by considering the selection of spatial weight matrix. Using the wrong spatial weight matrix will increase the standard error in parameter estimation. Therefore, this study aims to determine: the best spatial weight matrix to accommodate the spatial autocorrelation in analyzing the description of the spread of ICH in Indonesia; and the variables that are thought to influence the number of ICH determination in Indonesia. The spatial regression modeling used in this study is the Spatial Autoregressive (SAR) model and the spatial weight matrices compared in this study are queen contiguity and inverse distance. The best model is the SAR model used the queen contiguity spatial weight matrix because it has minimum values of AIC, BIC, RMSE and MAPE which are 310.397, 319.555, 18.857 and 57.169 respectively. Simultaneously, involved in performing arts, wearing traditional dress, knowing Indonesian folklore and the spatial lag contribute significantly to number of ICH determination in Indonesia. Partially, only knowing Indonesian folklore have a significant effect on number of ICH determination in Indonesia at significance level α=5%. Each additional 1% of population that knowing Indonesian folklore in an area increases number of ICH determination in that area by 0.6719 units . 
Co-Authors Aditya Pradana Aditya Pradana, Aditya Akik Hidayat Amini, Raissa Anggraeni A Arsa, Muhammad Fadillah Budi Nurani Ruchjana Desiyanti, Armalia Devi Munandar, Devi Devi Yanti, Devi Dhita Diana Dewi Dian Islamiaty Puteri Eddy Hermawan Hamim Tsalis Soblia Hardianto A Husein Hernadi Bahti I Gede Nyoman Mindra Intan Nurma Yulita, Intan Nurma Juli Rejito Kankan Parmikanti Maryanto Rompon Monika, Putri Muhammad Fadillah Arsa Muthalib A Mutia Karimah Nurdeni, Nurdeni Nurul Qisthi Putri Monika Putri Monika Rizka Pradita Prasetya Rolly Maulana Awangga, Rolly Maulana Shailla Rustiana Sobari, Muhamad Soetikno, Christophorus