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<![CDATA[Python Model Predicts Covid-19 Cases since Omicron in Indonesia ]]>
<![CDATA[Muhammad Furqan Rasyid]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.18908
<![CDATA[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.]]>
<![CDATA[Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams ]]>
<![CDATA[Novan Wijaya]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.18681
<![CDATA[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]]>
<![CDATA[Pathfinding Solving in Maze Game Using Backtracking Algorithm ]]>
<![CDATA[Tegar Arifin Prasetyo]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.17109
<![CDATA[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. ]]>
<![CDATA[Optimization Of Histogram Equation With The Cukcoo Algorithm to Improve Fundus Image Quatlity ]]>
<![CDATA[Dafwen Toresa]]>;
<![CDATA[Keumala Anggraini]]>;
<![CDATA[Pandu Pratama Putra]]>;
<![CDATA[Edriyansyah Edriyansyah]]>;
<![CDATA[Taslim Taslim]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.23348
<![CDATA[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]]>
<![CDATA[Violation Types Determination of The Whistleblowing System Using the C4.5 Algorithm ]]>
<![CDATA[Dwi Vernanda]]>;
<![CDATA[Rian Piarna]]>;
<![CDATA[Helfira Lustiana]]>;
<![CDATA[Tri Herdiawan Apandi]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.22897
<![CDATA[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.]]>
<![CDATA[Computer Assessment Test at the Association of Indonesian Independent Housing Experts with Waterfall Model ]]>
<![CDATA[Elin Panca Saputra]]>;
<![CDATA[Risqi Nur Alfiyah]]>;
<![CDATA[Indriyanti Indriyanti]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.11483
<![CDATA[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.]]>
<![CDATA[Monitoring the pH and temperature of IoT-based fish farming using Arduino ]]>
<![CDATA[Kusdarnowo Hantoro]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.21893
<![CDATA[The Internet of Things (IoT) based intelligent Fish farming refers to the integration of various IoT devices and sensors into traditional fish farming practices to monitor and optimize various aspects of the fish farming environment such as water temperature, pH levels, oxygen levels, and feed schedules. This can help to improve the overall health and wellbeing of the fish, reduce waste, and increase productivity and efficiency. IoT devices can also be used to track the growth and health of individual fish, allowing farmers to make data-driven decisions about when to harvest or move fish to different tanks. The use of IoT technology can also help to detect potential health issues or environmental problems early on, allowing farmers to take preventative measures to minimize the impact on their fish. Overall, smart fish farming using IoT has the potential to revolutionize the way fish are farmed, making the process more sustainable, efficient, and profitable. The project focuses on an IoT-enabled smart fish farming system. In order to deal with them, the system is coupled with an irrigation system. Indonesia’s weather is erratic. This system's microcontroller is an Arduino ESP32. The temperature sensor DSb18B20 and the soil moisture sensor DF Robot are used to regulate the environment. Both a computer and a smartphone are used to display the results.]]>
<![CDATA[Efficiency of the Combination of Machine Learning Models in the Evaluation of Weather Parameters ]]>
<![CDATA[Yannick Mubakilayi]]>;
<![CDATA[Simon Ntumba]]>;
<![CDATA[Pierre Kafunda]]>;
<![CDATA[Salem Cimanga]]>;
<![CDATA[Gracias Kabulu]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.21713
<![CDATA[In this article we exploit the potential presented by the combination of machine learning models (Ensemble Learning) as one of the essential points of the Soft aspect, i.e. observation tools, monitoring, sampling and study of meteorological parameters in order to provide effective support and monitoring of measures taken at different levels in the fight against climate change and sustainable management of the environment by creating a learning model automatic composed of the measurements of the various meteorological parameters (Temperature, Rainfall, Humidity rate, Wind speed, etc.) by training this model using the Ensemble Learning technique called "BOOSTING" on the various measurements taken from each indicator so as to continuously train on past data and be able to predict the next weather forecast with high precision or even make annual or multi-year projections of the evolution of our climatic situation and present this to the various players in our environment and thus enable them to better anticipate possible extreme situations that could negatively affect our environmental situation.]]>
<![CDATA[Geographic Segmentation using Application Programming Interface (API) Geolocation on E-Marketplace Development ]]>
<![CDATA[Arif Amrulloh]]>;
<![CDATA[Yudha Saintika]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.19434
<![CDATA[Purbalingga is one of the regencies in the province of Central Java with many muffler artisans and is the largest exhaust producer in Indonesia. In this study, the development of an e-marketplace application will be carried out by implementing a geolocation Application Programming Interface (API). The geolocation API is used to detect the location of visitors so that price differences can be made based on the visitor's country. The system development method used is Rapid Application Development (RAD). The RAD method is used because application development can be done in a relatively fast time. At the system design stage, the Unified Modeling Language (UML) is used as a visual model to facilitate the application development. The final result of this research is an e-marketplace application that specifically sells exhaust products and accessories. The test results were carried out in 6 different locations with details of 4 countries of Indonesia, one country of Malaysia, and one country of Saudi Arabia. Other prices were obtained according to the location of the visitor's country.]]>
<![CDATA[Segmentation of Mentoring Customer Characteristics Using the K-Means Method and Hierarchical Clustering for Customer Relationship Management (CRM) ]]>
<![CDATA[Hanif Aristyo Rahadiyan]]>
<![CDATA[ Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023 ]]>
Publisher : <![CDATA[Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/coreit.v9i1.21567
<![CDATA[In the next 10-20 years, it is expected that Indonesia will enter a demographic bonus era, where the population of productive age exceeds that of non-productive age. This presents an opportunity for startups in the field of education to prepare better human resources in Indonesia. With the recent Covid-19 pandemic, the government has implemented regulations that require online teaching and learning. Startups, such as Outstanding Youth Indonesia (OYI), play a role in bridging distance learning, leading to increased competition in the education sector. To stay competitive, OYI is implementing a customer relationship management (CRM) strategy, using consumer characteristic segmentation through the K-means method and hierarchical clustering. The study aims to test the consumer characteristic cluster results and provide CRM recommendations based on the segmentation results. The results of the study revealed that the K-Means method was more optimal, with a score of 0.657, compared to hierarchical clustering of 0.644. The clusters tested included categories, intended education, and types of scholarships. Three clusters were produced: cluster 1, dominated by high school/vocational high school students; cluster 2, mostly university students; and cluster 3, dominated by employees of government agencies. Cluster one had the largest silhouette coefficient. Based on the clustering, a strategy was generated for each cluster to improve CRM in OYI.]]>
