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Jurnal CoreIT

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Published by Universitas Islam Negeri Sultan Syarif Kasim Riau

ISSN : 2460738X EISSN : 25993321 DOI : -

Core Subject : Science,

Computer Science & IT

Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi published by Informatics Engineering Department – Universitas Islam Negeri Sultan Syarif Kasim Riau with Registration Number: Print ISSN 2460-738X | Online ISSN 2599-3321. This journal is published 2 (two) times a year (June and December) containing the results of research on Computer Science and Information Technology.

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Articles 124 Documents

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Detection of Certain Objects Wearing Masks in Real Time To Prevent the Spread of the Virus (Yolov3) Kusdarnowo Hantoro; Rusdianto Rustam; Amir Dahlan
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

A significant increase in the spread of the corona virus (COVID-19) in the community is currently happening due to people not following the health protocol rules set by the ministry of health. One of the rules is to require people to wear masks while they are outside the home. Measures need to be implemented in anticipation of situations where people do not wear masks in public spaces. Therefore, the establishment of a mask detection system is chosen as a solution in order to solve the mentioned problem above. A real-time identification system for people wearing masks is proposed to be developed in this paper. The system utilizes Yolov3 with Darknet -53 as a deep learning mask detector and OpenCV as a real-time computer vision library, so that people doing activities in a public space captured by a video can be recognized and detected when they do not wear masks . In implementing deep learning, a data set of 4000 images is divided into two classes, i.e.,2000 images with masks f or data testing purposes and another 2000 images without masks for training custom objects. The Extreme Programming (XP) method as part of the Agile Process Model is adopted for system development. Computer language support and the latest system development tools have made it possible to utilize this method in an effort to develop this system rapidly. Requirement Analysis is conducted to obtain required processes before designing system. Writing code and testing the system will be the next step before the system is declared ready to be implemented in the public space. By adopting the XP development method, all of the above steps can be implemented repeatedly until the system delivers the expected results

Level Set Interactive Segmentation on Perforated Road Image using Region Based Active Countours Method with im’4 Anike, Marleni
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

An image will have a lot of interpretation meaning when compared to a text. However, there is nothing wrong with the original image being manipulated to achieve a certain goal. In digital image processing (PCD) there are image manipulation techniques without touching other objects, this is due to the difficulty of separating one object from another. One of the digital image segmentation techniques that is widely used for PCD and computer vision is level set. In this research, the strength of active contour will be tested which will segment area-based objects with potholes using Matlab R2016a. inner region taking into account im = 4 with imresize(mask, 8) iterations of 500.

Netflix Stock Price Trend Prediction Using Recurrent Neural Network Irani H
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able to determine stock market projections. Predictions are used to reduce risk when making transactions. In this study, predictions of stock price trends were made using the Recurrent Neural Network (RNN). The approach taken is to perform a time series analysis using the RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in the LSTM model testing simulation can estimate stock prices with maximum percentage accuracy. The results showed that the prediction model produced a loss function of 0.0012 and a training time of 73 m/step. The evaluation was carried out with the RMSE which resulted in a score of 17.13325. Predictions are obtained after doing machine learning using 1239 data. The RMSE and LSTM models are calculated by changing the number of epochs, the variation between the predicted stock price and the current stock price. Computations are carried out using a stock market dataset that includes open, high, low, close, adj prices, closes, and volumes. The main objective of this study is to determine the extent to which the LSTM algorithm anticipates stock market prices with better accuracy. Code can be seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Stock Prediction, Time Series, Recurrent Neural Network (RNN), Long Short Term Memory (LSTM).

Combination RC4 Algorithm and Base64 Encryption on The Least Significant Bit Method Soleman, Soleman; Budiman, Dendi; Mubaroq, Sefty
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Steganography is an art form of hiding data or information on a medium. Steganography was created as a way to secure data by hiding it in other media so that it is "invisible". In steganography, secret data is hidden in a carrier such as sound, image or video. Securing messages using steganography is still vulnerable to third parties because the message inserted is still the original message, so that when it is successfully deleted from the message carrier it can be immediately identified. Given these problems, the steganography method needs to be combined with other methods to strengthen the level of data security, in this case a combination of steganography and cryptography will be carried out so that the embedded message will be different from the original message. Even if the data is deleted from the user, the message cannot be known immediately. In this trial, the RC4 cryptographic method which has a symmetric key and also Base64 will be implemented in the PHP and Android programming languages using the Least Significant Bit (LSB) steganography method which will create encrypted secret messages embedded in JPG format, JPEG image media formats on each the last bit of a pixel so that the eye does not see the difference between the inserted and non-inserted images with the original image variable/result of 131.91KB after the Encryption process the amount of data is 31.92KB with very small differences so that data security is maintained without being visible to the naked eye

Python Model Predicts Covid-19 Cases since Omicron in Indonesia Muhammad Furqan Rasyid
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

The proposed work uses Support Vector Regression model to predict the new cases, recovered cases, and deaths cases of covid-19 every day during sub-variant omicron spread in Indonesia. We collected data from June 14, 2022, to August 12, 2022 (60 Days). This model was developed in Python 3.6.6 to get the predictive value of the issues mentioned above up to September 21, 2022. The proposed methodology uses a SVR model with the Radial Basis Function as the kernel and a 10% confidence interval for curve fitting. The data collected has been divided into 2 with a size of 40% test data and 60% training data. Mean Squared Error, Root Mean Squared Error, Regression score, and percentage accuracy calculated the model performance parameters. This model has an accuracy above 87% in predicting new cases and recovered patients and 68% in predicting daily death cases. The results show a Gaussian decrease in the number of cases, and it could take another 4 to 6 weeks for it to drop to the minimum level as the origin of the undiscovered omicron sub-variant. RBF (Radial Basis Function) very efficient and has higher accuracy than linear or polynomial regression as kernel of SVR.

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams Novan Wijaya
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

The purpose of this study is to compare Euclidean and Mahalanobis geometry as a means of identifying a person using their iris. Iris is the only biometric that is truly unique and is extremely difficult to perform, making it the single most important consideration in the improvement of system security. To obtain the desired results, namely preprocessing and feature extraction, various methods will be used in image processing. Methods like the Gaussian filter, the operator sobel, and thresholding will be used in the pengolahan. Utilize the United Moment Invariant method to extend the circle (UMI). For projects that use the method of comparing the strengths of FAR and GAR, a smaller FAR was obtained for the eulidean to mahalanobis ratio. Additionally, value distance mahalanobis is smaller compared to FAR for GAR penetration. Keywords: Gaussian Filter, Iris, Sobel Operator, United Moment Invariat

Pathfinding Solving in Maze Game Using Backtracking Algorithm Tegar Arifin Prasetyo
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Games are a means of entertainment that great demand by the community. Besides games as entertainment, games can also practice thinking skills to find solutions. A game that contains elements of artificial intelligence requires algorithms in its implementation. One type of the game is Maze Game, where players are required to find a way out of the maze. Backtracking algorithm was chosen to solve this game. This algorithm works recursively to solve problems by finding possible solutions. If the path being traced is not the right solution, it will be backtracked and traced to other paths. This solution will not be ignored or deleted. But if the path taken is right, it will continue to check the next path until the player reaches the final solution.

Optimization Of Histogram Equation With The Cukcoo Algorithm to Improve Fundus Image Quatlity Dafwen Toresa; Keumala Anggraini; Pandu Pratama Putra; Edriyansyah Edriyansyah; Taslim Taslim
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

This study discusses strategies for identifying Diabetic Retinopathy (DR) using fundus images and the efficiency of image pre-processing techniques to improve their quality. Fundus images in medical image processing often experience problems with non-uniform lighting, low contrast, and noise, thus requiring pre-processing of images to improve their quality. This study evaluates the effectiveness of standard histogram equation techniques and optimized histogram equations with cukkoo optimization in order to choose the best technique to improve fundus image quality to identify DR. The proposed technique to produce better image quality improvements will be tested in several performance metrics, such as NIQE, PSNR, and Entropy. the results of this study, the average PNSR before optimization was 50,8, whereas after optimization it became 49,8239. The average entropy before optimization is 4.5514, while after optimization it becomes 3.8577. The average NIQE before optimization was 3,4046, while after optimization it was 4,73. In general, the results of this study indicate that the quality of the fundus image is better using the histogram equation before optimization than after optimization. In other words, Cukcoo optimization is not suitable for increasing the performance of the histogram equation in improving fundus image quality

Violation Types Determination of The Whistleblowing System Using the C4.5 Algorithm Dwi Vernanda; Rian Piarna; Helfira Lustiana; Tri Herdiawan Apandi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Whistleblowing is a complaint system and follow-up management of each violation report. The problem that arises is when determining the follow-up, namely determining the severity or severity of the violation and the sanctions given are only based on the superior's assessment without adhering to standard guidelines or rules. This results in the sanctions given not in accordance with the violations committed. The purpose of this study is to classify the types of violations so as to facilitate the determination of sanctions on the whistleblowing system using the C4.5 Algorithm. The partition was performed three times with the highest additional value of 0.8516 and a decision tree was obtained. Based on the decision tree, the final node that has been generated is then extracted into 27 rules. The classification results from the C4.5 Algorithm can be used to classify the types of violations with an accuracy rate of more than 80%. The first validation with 15 tests obtained an accuracy rate of 86.66%. The second validation is the combination of data on 125 cases of combination data and obtained an accuracy rate of 84.8%. The decision tree generated from three partitions consists of 27 rules that can be used as a pattern to classify the types of violations.

Computer Assessment Test at the Association of Indonesian Independent Housing Experts with Waterfall Model Elin Panca Saputra; Risqi Nur Alfiyah; Indriyanti Indriyanti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

The Association of Indonesian Self-Help Housing Experts (PERAPSI) is a government organization that was just inaugurated in 2020 with an official certificate from the Ministry of Law and Human Rights of the Republic of Indonesia (KEMENKUMHAM) under the Directorate of Public Housing, Ministry of Public Works and Public Housing (PUPR).The Association of Indonesian Self-Help Housing Experts (PERAPSI) is tasked with directly gathering people in remote parts of the archipelago, an estimated 4,500 people who are not recorded by the Ministry of PUPR who are working on building self-help houses.For this reason, the Association of Indonesian Self-Help Housing Experts (PERAPSI) itself will record data and go through a process of data collection and assessment of people who are not recorded.The assessment process itself is through the Computer Assessment Test (CAT).The system development method used is the Waterfall method.Methods of Requirement Definition, System and Software Design, Construction, Deployment, which ends with support for the resulting software.With this designed system, it is a test with a computer in real time.This system can provide several advantages compared to the currently running system, namely efficiency and effectiveness in processing information and managing computer test data.

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Contact Name
Suwanto Sanjaya

Contact Email
suwantosanjaya@uin-suska.ac.id

Phone
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Journal Mail Official
coreit@uin-suska.ac.id

Editorial Address
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Location
Kab. kampar,

Riau

INDONESIA