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Jurnal CoreIT
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 162 Documents
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Phishing Detection in Deep Learning: Systematic Literature Review Abdillah (Scopus ID: 57210600304), Rahmad; Syafitri, Wenni
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.31009

Abstract

Abstract. Phishing is an attack that is harmful to organizations and individuals in cybersecurity. Many researchers use deep learning techniques to detect phishing. However, the proposed techniques still have shortcomings in terms of performance, especially in detecting unknown attacks, even though they have been developed in such a way. Therefore, to gain a more comprehensive understanding of the current state of research on the use of deep learning to detect phishing, a systematic literature review (SLR) is needed. This SLR aims to identify deep learning techniques, performance measures, overfitting techniques, datasets, parameters, phishing types, and recommendations for future phishing detection research. The method used by SLR consists of a research question and research objective, Search strategy, Inclusion and exclusion criteria, and Data extraction and Analysis. Over the past five years, SLR successfully identified 25 quality articles on phishing detection using deep learning. The contribution of this SLR is to provide insight into the current state of research and identify future research areas of phishing detection using deep learning techniques.
Web-based Information System for Processing Student Report Grade Using Waterfall Method (Study Case: SMPN 3 Talaga) Lisan, Fauzan Fashihul; Riadi, Daffa Rayhan; Nugraha, Aditya Rizkiawan; Shalma, Hastin Ajeng; Adhinata, Faisal Dharma
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.21954

Abstract

Assessment is an activity or method used by educators to measure students' abilities in the processes and learning outcomes at school. Junior Highschool 3 Talaga in its assessment process still uses the conventional method which causes delays in the assessment report process, not minimizing errors in writing on the assessment report is quite difficult. Based on the problems experienced, it is presented as an assessment information system that helps the student assessment process. This information system was created using the waterfall method which produces a ready-to-use system with sufficient features. The information system presented uses the PHP and MySQL programming languages to facilitate and lighten student assessment work. This assessment is used as a reference standard for achieving student competency and a basis for helping students. Not only that, but the assessment is also carried out continuously and aims to monitor the learning process and progress of students. With the existence of an information system, the assessment will be more efficient and can facilitate its implementation.
Comparison Of The Performance Of C4.5 And Naive Bayes Algorithms For Student Graduation Prediction baskoro, baskoro; Triraharjo, Bambang; Wibowo, Adi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.24931

Abstract

Along with the development of technology, especially the development of increasingly large data storage. One organization that has large data storage is an educational organization. Educational organizations use data to obtain information, especially information about students. Student data has many attributes so that we can make predictions such as predictions of student performance, predictions of scholarship recipients and predictions of student graduation. Data mining methods in education are classified into five dimensions, one of which is prediction, such as predicting output values based on input data. From the results of the research conducted from the initial stage to the testing stage of the application of the C4.5 Algorithm, the accuracy results are higher than Naïve Bayes because in its classification stage, C4.5 processes attribute data one by one. The difference is with naïve Bayes which is influenced by the amount of data used, the comparison of the amount of training and testing data. The feasibility of the model obtained is supported by the high accuracy, precision, recall and AUC obtained from the two algorithms that have been tested. The C4.5 algorithm has an accuracy rate of 79.91%, 89.06% precision and 81.38% recall and an AUC value of 0.823. Meanwhile, Naïve Bayes has an accuracy rate of 76.95%, precision of 75.95% and recall of 98.38% and an AUC value of 0.838.Keywords: Graduation, Prediction, Data Mining, C4.5, Naïve Bayes
Enhancing Hepatitis Patient Survival Detection: A Comparative Study of CNN and Traditional Machine Learning Algorithms DIQI, MOHAMMAD; HISWATI, MARSELINA ENDAH; DAMAYANTI, EKA
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28241

Abstract

Hepatitis patient survival prediction is a critical medical task impacting timely interventions and healthcare resource allocation. This study addresses this issue by exploring the application of a Convolutional Neural Network (CNN) and comparing it with traditional machine learning algorithms, including Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The research objectives include evaluating the algorithms' performance regarding confusion matrix metrics and classification reports, aiming to achieve accurate predictions for both "Live" and "Die" categories. The dataset of 155 instances with 20 features underwent preprocessing, including data cleansing, feature conversion, and normalization. The CNN model achieved perfect accuracy in hepatitis patient survival prediction, outperforming the baseline algorithms, which exhibited varying accuracy and sensitivity. These findings underscore the potential of advanced machine learning techniques, particularly CNNs, in improving diagnostic accuracy in hepatology.
Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing Iskandar (Scopus ID: 55316114000), Iwan
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26655

Abstract

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9
DDoS Attack Using GoldenEye, DAVOSET, and PyLoris Tools Mahadiv Wikrama, Kadek Sudewo; Firdaus, Rangga; Medes Mendrofa, Linda Zal; Jude Saskara, Gede Arna; Edy Listartha, I Made
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.20020

Abstract

IMPROVING PERFORMANCE OF RANDOM FOREST ALGORITHM USING ABC FEATURE SELECTION FOR SOFTWARE DEFECT PREDICTION Laila Hidayati, Zaina Fadia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.29283

Abstract

Defects that may arise in software during the development process can affect the quality of the software. The classification method is used to predict software defects to minimize defects. However, the dataset used in the classification process may contain less relevant or have too many features. This can be overcome by selecting features in the dataset. In this research, the Random Forest algorithm is applied for the classification process, and the Artificial Bee Colony (ABC) algorithm is used as a feature selection method. The research aims to determine the accuracy of Random Forest with ABC feature selection. From the results of research conducted on 3 Relink datasets, without feature selection, an average accuracy of 73% was obtained. After implementing ABC feature selection, the average accuracy increased to 82%.
Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification Ramadhan, Aweldri; Budianita, Elvia; Syafria, Fadhilah; Ramadhani, Siti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26834

Abstract

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN
PREDICTION OF ANEMIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) AND NAÏVE BAYES ALGORITHM tri utami, septiana; Sriyanto, Sriyanto; Lestari, sri; Widi Nugroho, Handoyo; zarnelly, zarnelly
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28428

Abstract

Purpose: Anemia is a nutritional disorder that is still often found in Indonesia. The main risk factors for iron deficiency anemia are low iron intake, poor iron absorption, and periods of life when the need for iron is high such as during growth, pregnancy, and breastfeeding. Anemia can generally occur in pregnant women, teenagers, the elderly and even babies who have anemia.Methods/Study design/approach: This research uses the Naive Bayes and PSO algorithms, and the dataset used comes from the kaggel.com Anemia dataset. The number of data records is 1421 data consisting of 5 attributes and 1 label. This data set is used to predict whether a patient is likely to suffer from anemia.Result/Findings: Based on the results of testing the Naïve Bayes and PSO algorithm models which were carried out through confusion matrix evaluation, it was proven that the tests carried out by the Naïve Bayes algorithm were 93.88% and the tests carried out with Naïve Bayes and PSO had a high accuracy value, namely 94.02%.Novelty/Originality/Value: The purpose of selecting information acquisition features is to select features or attributes that significantly influence anemia. Keywords: Prediction, Anemia, Naive Bayes, Particle Swarm Optimization (PSO)
Audit Of Information Technology Governance In The Boss PT. Bahtera Pesat Lintasbuana Using Cobit 2019 Dewi, Yumi Novita; Nurlaila, Nurlaila
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.21933

Abstract

This research, starting from the frame of mind that Audit of Technology Information Governance on Enterprise  information system using COBIT 2019 is an important thing to do now and the future to align information technology investment with Enterprise Goals. COBIT 2019 framework provides realization of the benefits of implementation information systems, optimizing risks and optimizing resources according to Enterprise needs. Design factor of COBIT 2019 can determine the domains that must be assessed for their maturity level, so that companies can find out what needs to be improved in implementing information systems. This research is focus to the EDM02 and APO04 with expected maturity level 5. The EDM02 domain focuses on the process of optimizing the business value contribution of business processes, IT Service and IT assets resulting from investments made by IT in accordance with acceptable cost for the Enterprise. APO04 domain, it aims to organize and manage innovation by increasing operational efficiency in companies with the latest technology.

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