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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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Search results for , issue "Vol 10, No 2 (2024): December 2024" : 10 Documents clear
Sentiment Analysis on Hate Speech Post 2024 Election for Elected President Using a Hybrid Model Machine Learning Handaya, Ken Ken; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.31927

Abstract

One of the important events in the democratic life of a country is the general election. In addition, the possibility of hate speech appearing on social media increases as political tensions increase. This hate speech can take the form of negative comments, insults, or even threats against the elected president. This research uses the content of tweets as a data source to analyze public opinion and sentiment towards the elected president. This research aims to analyze sentiment towards hate speech held by twitter users towards the elected president after the 2024 election by building a hybrid model using 3 algorithms, namely k-nearest neighbors, long short-term memory and naive bayes. The results of tests carried out with 12,000 tweet data that show the naive bayes method classification results have an accuracy of 72%, the long short-term memory classification results show an accuracy of 78%, the k-nearest neighbors method accuracy value is 83%, and the hybrid model accuracy value is 78%. Considering the accuracy values of the three algorithm method, by using a hybrid model we can increase the accuracy by combining the three algorithm models. from previously having the lowest accuracy of 72%, by using a hybrid model we can increase the accuracy to 78%.
Performance Comparasion of Adaboost and PSO Algorithms for Cervical Cancer Classification Using KNN Algorithm Ubaidilah, Romdan Muhamad; Sutedi, Sutedi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.31711

Abstract

Abstract. Cervical cancer impacts the female reproductive organs and stands as the second most common cancer among women worldwide. The World Health Organization (WHO) reports that annually, approximately 500,000 women are diagnosed with cervical cancer, and about 300,000 die from it. Many of these deaths result from insufficient early detection and preventive measures. There are four primary screening techniques for detecting cervical cancer cells: Hinselmann, Schiller, Cytology, and Biopsy. In this study, patient health history data is analyzed using the KNN algorithm, which is further optimized with Adaboost and PSO techniques. These optimization strategies are evaluated to identify the most precise model for detecting patterns in cervical cancer patients and predicting their screening outcomes. This study employs the RapidMiner tool. Findings reveal that the KNN algorithm effectively performs multilabel classification, and when optimized with PSO, there is a slight improvement in accuracy.Purpose: The aim of this research is to assess the performance of the K-Nearest Neighbor (KNN) algorithm in multilabel classification of cervical cancer and to optimize it using Adaboost and Particle Swarm Optimization (PSO) techniques. This research is significant as it offers a potentially more accurate diagnostic method for detecting cervical cancer using medical records.Methods/Study design/approach: The Cervical Cancer Risk Classification dataset from Kaggle was used in this study. Data preprocessing was conducted before applying the KNN algorithm. The KNN algorithm's performance was evaluated using a 10-fold cross-validation method, and results were measured using the Confusion Matrix. Additionally, the KNN algorithm was optimized using Adaboost and PSO to assess improvements in accuracy.Result/Findings: Experimental results indicated that the KNN algorithm achieved optimal accuracy with k=5, reaching 95.81%, 91.26%, 94.64%, and 93.01% for Hinselmann, Schiller, Cytology, and Biopsy targets, respectively. Adaboost did not significantly improve accuracy, while PSO slightly enhanced the Hinselmann target accuracy from 95.81% to 95.92%. The average training time for this experiment was around two minutes. These results demonstrate the effectiveness of the KNN algorithm in conducting multilabel classification for cervical cancer diagnosis.Novelty/Originality/Value: This research demonstrates that optimizing the KNN algorithm with PSO can enhance accuracy, though not significantly. This suggests potential for further development to improve cervical cancer diagnostic accuracy. Testing the model with the latest data and optimizing parameters may lead to better models and useful tools for early cervical cancer diagnosis.
Design Of The Information System Model For Kodim 1416/Muna Administration Process Using The RAD Method With Whatsapp Based Notification Ruslan, Fisabilia Adipati; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.31871

Abstract

Globalization, driven by advancements in information technology, demands fundamental changes across various life aspects, including the operations of companies and organizations. Kodim 1416/Muna faces issues in daily reporting and administration due to reliance on WhatsApp, varying report formats, and delays in administrative approvals. This research aims to design an information system model to address these problems using the Rapid Application Development (RAD) method, encompassing requirements planning, design, and implementation. The designed system is expected to standardize daily reports, reduce the risk of overlooked information, expedite the submission process, and facilitate monitoring by leaders through WhatsApp notifications. System Usability Scale (SUS) testing results show a score of 81, categorized as "Good Performance," indicating user satisfaction with the designed system. Thus, this research contributes to improving operational efficiency and administrative performance at Kodim 1416/Muna.
Review of Original Differential Evolution Algorithm: Research Trends, Original Setting Parameters Wang, ShirLi; Budiman, Haldi; Ramadhani, Siti; FooNg, Theam; Morsidi, Farid
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.29903

Abstract

Abstract: Differential Evolution (DE) has emerged as a widely embraced optimization algorithm, consistently showcasing robust performance in the IEEE Congress on Evolutionary Computation (CEC) competitions.Purpose: This study aims to pinpoint key regulatory parameters and manage the evolution of DE parameters. We conduct an exhaustive literature review spanning from 2010 to 2021 to identify and analyze evolving trends, parameter settings, and ensemble methods associated with original differential evolution.Method: Our meticulous investigation encompasses 1,210 publications, comprising 543 from ScienceDirect, 12 from IEEE Xplore, 424 from Springer, and 231 from WoS. Through an initial screening process involving title and abstract skimming to identify relevant subsets and eliminate duplicate entries, we excluded 762 articles from full-text scrutiny, resulting in 358 articles for in-depth analysis.Findings: Our findings reveal a consistent utilization of tuning parameters, self-adaptive mechanisms, and ensemble methods in the final collection. These results deepen our understanding of DE's success in CEC competitions.Value: offer valuable insights for future research and algorithm development in optimization fields.  
Public Opinion Sentiment Analysis on Train Transport in Jakarta Using a Hybrid Model Machine Learning Savero, Adriel; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.31865

Abstract

Transportation is a key element in smoothing the wheels of the economy and connecting various regions, especially in big cities like Jakarta which has a high population density. This leads to dense and complex traffic conditions. Improving the quality and facilities of public transportation is important to overcome these problems. However, people are still reluctant to use public transportation for various reasons. Therefore, it is important to understand public sentiment towards public transportation in Jakarta. This research focuses on sentiment analysis of train-based transportation, namely KRL, MRT, and LRT. Sentiment analysis is conducted using a hybrid learning model with a voting model method, which combines SVM, logistic regression, and CNN algorithms. The data used is labeled with InSet sentiment dictionary and extracted features using TF-IDF method. The modeling results show that this hybrid model produces 89% accuracy for the KRL dataset, 88% for the MRT dataset, and 81% for the LRT dataset. However, this model still has difficulty in predicting neutral and positive classes. The results of this study show that hybrid learning with the voting model method can provide quite good results in public transportation sentiment analysis, but there is still room for improvement in the classification of neutral and positive sentiments. The findings provide important insights for the development of strategies to improve the quality of public transportation and encourage people to use the service more.
The Implementation of Internet of Things (IOT) for Aquaponic Cultivation Zuriati, Zuriati; Widyawati, Dewi Kania; Dulbari, Dulbari; Zarnelly, Zarnelly
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.29541

Abstract

Aquaponic is a plant cultivation technique that is widely used by farmers and today’s communities due to its efficiency and ability to increase the agricultural productivity. The aquaponic cultivation in general still uses simple systems, such as manually feeding the fish by spreading the feed at predetermined times, monitoring water pH using a pH meter and monitoring water height or level through measurements, requiring farmers to spend time and special labor to care for and maintain plants and fish. Therefore, a solution is needed in the form of a system that can monitor and control plants and fish conditions automatically and continuously for 24 hours. The system should have the ability to control and monitor feeding activities, water pH, water and environmental temperature, water level and environmental humidity. The system in question is the internet of things (IoT) system that can be used as a tool for automatic control and monitoring through an application. The IoT system consists of several sensors that are connected to a microcontroller which can measure water pH, temperature, water level and environmental humidity. The data obtained by the sensor will be sent to a server via Wi-Fi protocol and stored in a database. The system is equipped with a web application that can be accessed through a computer device. The application provides a visual display of data: time, water pH, temperature, water level and environmental humidity, making it easier for farmers to monitor aquaponic conditions from a distance without having to come to the land. Through the implementation of IoT in aquaponic cultivation, farmers can increase efficiency and agricultural productivity by reducing the time, labor and costs required for control and monitoring.
Comparative Analysis Between Advanced Encryption Standard and Fully Homomorphic Encryption Algorithm to Secure Data in Financial Technology Applications Nurdin, Nurdin
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.30809

Abstract

This research discusses the comparison between two encryption algorithms, namely Advanced Encryption Standard (AES) and Fully Homomorphic Encryption (FHE), in the context of data security in Financial Technology (Fintech) applications. The main aim of this research is to analyze the speed and efficiency of the two algorithms to provide information and motivation to Fintech Application business actors to determine the right algorithm for securing data. The research results show that AES is faster and more efficient in terms of encryption and decryption compared to FHE. For encryption, the AES algorithm is 1,100 times faster than the FHE algorithm. For decryption, the AES algorithm is 581 times faster than the FHE algorithm. For arithmetic processing, AES is 132 times faster than FHE. CPU consumption for AES encryption is 35.93% lower CPU usage than FHE. In AES decryption 10.31% lower than FHE for CPU usage. In the arithmetic process AES is 9.33% lower in usage than FHE. For memory usage in the FHE encryption process, it has an advantage, namely 2.3 times lower than AES for memory usage. During decryption, AES memory usage is superior with memory consumption 54 times lower than FHE. For the arithmetic process, AES uses 4.3 times lower memory than FHE. Overall AES provides speed and low resource consumption, this makes AES very suitable for use in Fintech applications that require speed and efficiency. Even though FHE has advantages in memory usage during encryption alone, this is not enough because it takes a long time to carry out the encryption process. This research suggests that further research will attempt to make the FHE algorithm more efficient and faster in processing data, this is considering the potential of FHE which is able to process encrypted data
An Analysis And Forecasting The Foodstuffs Prices In Surabaya Traditional Market Using LSTM Ericko, Teddy; Lauro, Manatap Dolok; Winata, Andry; Handhayani, Teny
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.27855

Abstract

Food is one of the essential things in society. Foodstuffs prices are important factors in the stability economy. In Indonesian society, some foodstuffs, e.g., rice, beef, chicken egg, cooking oil, and sugar are the main ingredients in their cuisine. Analyzing and predicting the foodstuffs price is interesting job. This research is conducted to develop models for forecasting the price of rice, beef, chicken egg, cooking oil, and sugar. It implements the Long Short-Term Memory (LSTM) model and a daily time-series dataset from a traditional market in Surabaya. Surabaya is the capital city of East Java province, and it is one of the densest cities in Indonesia. The experiments run univariate time-series forecasting. The experimental results show that LSTM works well to forecast the price of rice, beef, chicken egg, cooking oil, and sugar. The evaluation results obtain MAPE scores as 0.12%, 0.03%, 0.72%, 0.36%, and 0.08% for models of rice, beef, chicken egg, cooking oil, and sugar, respectively. The annual average price of beef, chicken egg, and cooking oil show an increasing trend and those foodstuffs have positive correlations with each other.
The Implementation of Data Mining to Determine the Level of Students' Understanding in Utilizing E-Learning Using the K-Nearest Neighbor Method Iskandar (Scopus ID: 55316114000), Iwan; Candra, Reski Mai
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.33728

Abstract

The implementation of Information Technology is increasingly developing due to the growing demand. According to data obtained from the Indonesian Internet Service Providers Association (APJII) 2022 report, the number of internet users in Indonesia is 210.02 million, an increase of 27.9 million from the previous year. The application of E-Learning in various schools, campuses, and educational courses has been carried out. The utilization of e-learning media undoubtedly facilitates educators in transferring their knowledge to students. This research evaluates the level of understanding of each student who has used E-Learning during Covid-19 as a learning medium. In obtaining this level of understanding, the K-Nearest Neighbor (K-NN) method is applied. The data analyzed are based on assignment scores, quizzes, mid-term exams, and final exams from various related courses, namely Science and Mathematics Course Group, Programming Course Group, and Basic Informatics Course Group. A total of 1,627 data points were collected from the period between 2020 and 2021 when online learning was conducted using E-Learning. The data was processed using the KNN method with an 80:20 split between training and testing data. The analyzed K values were 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21. The calculation results showed an accuracy of 75.69% at K=17 for the Basic Informatics Course Group, 77.61% at K=15 for the Science and Mathematics Course Group, and 96.20% at K=3 for the Programming Course Group.
THE INFLUENCE OF MAJOR EXPERTISE COURSES ON ALUMNI EMPLOYMENT USING THE APRIORI METHOD Irsyad (Scopus ID: 57204261647), Muhammad; Iskandar, Iwan; Gusti, Siska Kurnia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 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.v10i2.34144

Abstract

The role of alumni in university progress and quality is vital. This study used data from the tracer study application to analyze the relationship between skill courses and alumni employment. The data mining technique of association was employed to find linkages between different parameters. The Apriori algorithm was used to identify patterns that described the relationship between skill courses and alumni employment. The findings revealed that the most sought-after professions by alumni of the Informatics Engineering Study Program were educators, such as teachers and lecturers, with a support value of 18.7692%. Programmers were also in high demand, with a support value of 15.3846%. The subjects that were found to have the greatest influence on employment were Database, Computer Network, Computer Human Interaction, and Software Engineering. These findings provide valuable insights for the Informatics Engineering Study Program to prioritize and enhance these influential courses in terms of curriculum, teaching methods, and teaching materials, with the aim of improving the relevancy and quality of the courses in supporting alumni employment.

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