Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada
Monika, Putri
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
USULAN IMPLEMENTASI 5S DAN SISTEM MANAJEMEN PENYIMPANAN BERBASIS IT PADA PENYIMPANAN ALAT BANTU PRODUKSI DI PT. X Joanda, Alfian Destha; Monika, Putri
Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada Vol. 13 No. 2 (2023): Jurnal Sains & Teknologi
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70746/jstunsada.v13i2.455

Abstract

Penelitian ini bertujuan untuk memperbaiki tata kelola tempat penyimpanan alat bantu produksi yang ada di dalam ruangan PT. X dengan metode 5S serta menggunakan sistem informasi manajemen untuk pengelolaan administrasi peminjaman maupun pengembalian barang sehingga proses peminjaman dan pengembalian barang dapat lebih terta rapih, mudah, cepat dan tercatat dengan baik. Metode dalam penelitian yaitu: seiri, seiton, seiso, seiketsu, dan shitsuke dilakukan dengan tahap-tahapan yang tersusun dengan sistematis, kemudian merancang sistem informasi manajemen dengan navigasi tammpilan antarmuka (desain interface). Penelitian ini menghasilkan rancang tata letak barang yang terdokumentasi dengan baik lagi,yang dilengkapi rancangan sistem informasi manajemen sebagai media pendukung untuk meberikan informasi dalam proses peminjaman dan pengembalian alat bantu di ruangan.
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
Co-Authors Atje Setiawan Abdullah Budi Nurani Ruchjana Joanda, Alfian Destha Kankan Parmikanti Soetikno, Christophorus