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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 7 Documents
 1 
Search results for , issue "Vol 9, No 2 (2023): December 2023" : 7 Documents clear
Science Interest Detection Using Computerized Adaptive Testing Based on Fuzzy Item Response Theory Wulandari, Fitri
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.v9i2.27258

Abstract

Choosing a major or interest at the beginning of High School is a very important process for the future development of students.  A test  may be performed to determine the learner's ability in a particular field In this research, an interest test was developed to determine the students' ability in science. Students will be measured for their cognitive ability in Mathematics and Science subjects for junior high school level. The research was developed using an adaptive test system called Computerized Adaptive Testing (CAT). CAT is an adaptive media based model,  test participants will receive the test according to their ability. The test item selection procedure uses the fuzzy algorithm using item difficulty parameters, item strengths and participants' response data as input data. While the rule or procedure for terminating the test is done with the maximum likelihood estimation method, MLE. Based on the test results, each student received different test items according to their ability level and the difficulty indexs that received by the students according to the characteristics of the item information. Therefore, the CAT program with the fuzzy item response theory can be used as a support for measuring the students' ability and interest in a major.
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
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

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
Implementation of BiLSTM-SVM Algorithm to Detect Fake News on Text-Based Media Liman, Felix; Carsten, Carsten; Sufinata, Sufiandy; Irviantina, Syanti; Winardi, Sunaryo
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.v9i2.18982

Abstract

Online media is one of the places where news can spread quickly and everyone can access it easily and freely. Not only real or valid news is spread on online media, but fake news can also be easily spread on online media, and readers sometimes do not realize that the news they read is fake. As a result, wrong opinions arise that can lead to disputes, as well as divisions between individuals or groups. This study implements the BiLSTM-SVM algorithm to detect fake news that is spread on one of the online media, namely Twitter. The steps taken are tidying up the news text (text preprocessing), converting every word from the news text into numbers in vector form (word embedding), processing the numbers, and then classifying the results of the processing with the BiLSTM-SVM model formed with TensorFlow 2.0 help, and see the performance generated by the BiLSTM-SVM algorithm. The results obtained include an accuracy rate of 86% and an F1 Score value of 87.5% in detecting news from data validation with the same news topic.

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