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All Journal CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization MEDIA BINA ILMIAH
Rinda Nariswari
Institut Teknologi Sepuluh Nopember (ITS)
Arts Humanities Computer Science & IT Education Social Sciences

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PERBANDINGAN METODE ANALISIS DISKRIMINAN, NEURAL NETWORK, DISKRIMINAN KERNEL, REGRESI LOGISTIC, MARS UNTUK DATA BANGKITAN (KOMBINASI VARIANS, OVERLAP DAN KORELASI) Nariswari, Rinda; Rafikasari, Elok Fitriani
MEDIA BINA ILMIAH Vol 13, No 11: Juni 2019
Publisher : BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (606.397 KB) | DOI: 10.33758/mbi.v13i11.273

Abstract

Metode untuk pengklasifikasian data diantaranya menggunakan analisis diskriminan, analisis diskriminan kernel, analisis regresi logistik, neural network, dan MARS. Secara keseluruhan masing-masing metode jika diterapkan pada data mempunyai kelebihan maupun kekurangan. Pada pengelompokan data iris virginica dan vercicolor, metode MARS dan NN FeedForward paling baik digunakan. Sedangkan pada pengelompokan data iris setosa dan vercicolor, metode Analisis Diskriminan, NN RBF dan NN FeedForward adalah metode yang paling baik digunakan dalam pengelompokan. Namun berbeda dengan hasil analisis data simulasi yang dibangkitkan melalui Minitab, metode MARS adalah satu-satunya metode yang paling baik digunakan untuk data simulasi karena mempunyai rata-rata ketepatan klasifikasi yang paling besar diantara metode lainnya.
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.
Co-Authors Abdillah, Abdul Azis Fatmawati Fatmawati Novitasari, Dian C Rini Puguh Wahyu Prasetyo, Puguh Rafikasari, Elok Fitriani Rimuljo Hendradi Rizal Amegia Saputra Samsul Arifin Susyanto, Nanang Yudistira, I Gusti Agung Anom