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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Budi Juarto
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Safe-Deposit Box Using Fingerprint and Blynk Yulianto Yulianto; Budi Juarto; Ika Dyah Agustia Rachmawati; Risma Yulistiani
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 1 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i1.8080

Abstract

The criminal act of robbery really makes people nervous, especially in urban areas. There are many ways that can be done to avoid robbery at home and office, such as increasing the security system in the house to protect valuables. Safe-deposit boxes are items that are used to store valuables. Safe-deposit box is used to prevent against theft who want to take valuable things. To increase security, technology has begun to develop for security in various ways, such as fingerprints, passwords, and buzzers. This research will focus on a safe security system using a fingerprint that is connected to the internet with the Blynk application so that the user will get a safe notification when the servo condition is open or closed. The fingerprint sensor is an access to open doors, the Arduino Uno microcontroller is a storage for command logic on the system, the stepper motor acts as an activator for opening and closing servo and the Esp8266 module as a Wi-Fi module that connects equipment components using the internet network with the Blynk application which is used as distance control and notification of incoming access to homes with the concept of Internet of Things (IoT).
Breast Cancer Classification Using Outlier Detection and Variance Inflation Factor Budi Juarto
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 1 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i1.9223

Abstract

In terms of malignant tumors, breast cancer is one of the most prevalent. Breast cancer is a form of cancer that develops in the breast tissue when the surrounding, healthy breast tissue is overtaken by the uncontrollably growing cells in the breast tissue. Several features or patient conditions can be used in a machine learning approach to predict breast cancer. Machine learning will be utilized in these situations to determine if the cancer is malignant or benign. The Wisconsin Breast Cancer (Diagnostic) Data Set, which contains 32 characteristics and 569 collected data, was the dataset used in this research.. Feature selection in this study is done by eliminating outliers using the upper and lower quartile of each feature then feature selection is also carried out on features that have features that have a high variance inflation factor. The machine learning methods used in this research are Logistic Regression, Random Forest, KNN, SVC, XG Boost, Gradient Boosting, and Ridge Classifier. The selection of this method is based on the target that will be predicted by 2 labels, namely benign cancer, and malignant cancer. The result obtained is that the selection of features using the variance inflation factor increases the accuracy of the previous Logistic Regression and Random Forest methods from 98.25% to 99.12%. The method that has the highest level of accuracy is the Logistic Regression and Random Forest methods which have a value of 99.12%. The next research will be developed by trying other optimization techniques for hyperparameter tuning.
Co-Authors Ika Dyah Agustia Rachmawati Risma Yulistiani Yulianto Yulianto