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All Journal CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan
Rinda Nariswari
Institut Teknologi Sepuluh Nopember (ITS)
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Arifin, Samsul; Abdillah, Abdul Azis; Prasetyo, Puguh Wahyu; Susyanto, Nanang
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.8495

Abstract

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.
Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data Novitasari, Dian C Rini; Fatmawati, Fatmawati; Hendradi, Rimuljo; Nariswari, Rinda; Saputra, Rizal Amegia
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1501

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR.
OUTPUT VISUALIZATION FROM RESULT OF DISCRETE EVENT SYSTEM SIMULATION WITH ‘simmer’ R PACKAGE Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Arifin, Samsul
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 (565.269 KB) | DOI: 10.30598/barekengvol17iss1pp0581-0592

Abstract

This study aims to describe the various capabilities of the simmer package on R, especially in running a discrete event simulation model of a circular system, then develop a DES simulation model building technique, which is effective and can represent real systems well, and explore the simulation output on this simmer, both in statistical summary form and parameter estimation. The method used in this research is the literature study with descriptive and exploratory approaches. Model development is more effective when it is carried out starting from simple models, to more complex forms step by step, and describing the system using a flow chart. Replication for simulations is easy to perform so as to get standard error values ​​for model parameter estimators. The stages in developing a discrete event simulation model with a simmer, start with compiling a simple flowchart to a more complex form, and replication is carried out. The simmer output in the form of a data frame makes it very easy to process the output further. The simple R API on Simmer will also make it easier to simulate.
Co-Authors Abdillah, Abdul Azis Fatmawati Fatmawati I Gusti Agung Anom Yudistira Novitasari, Dian C Rini Puguh Wahyu Prasetyo, Puguh Rafikasari, Elok Fitriani Rimuljo Hendradi Rizal Amegia Saputra Samsul Arifin Susyanto, Nanang