cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Suwanto Sanjaya
Contact Email
suwantosanjaya@uin-suska.ac.id
Phone
-
Journal Mail Official
coreit@uin-suska.ac.id
Editorial Address
-
Location
Kab. kampar,
Riau
INDONESIA
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.
Arjuna Subject : -
Articles 162 Documents
 11   12   13   14   15 
Monitoring the pH and temperature of IoT-based fish farming using Arduino Kusdarnowo Hantoro
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (709.435 KB) | DOI: 10.24014/coreit.v9i1.21893

Abstract

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.
Efficiency of the Combination of Machine Learning Models in the Evaluation of Weather Parameters Yannick Mubakilayi; Simon Ntumba; Pierre Kafunda; Salem Cimanga; Gracias Kabulu
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i1.21713

Abstract

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.
Geographic Segmentation using Application Programming Interface (API) Geolocation on E-Marketplace Development Arif Amrulloh; Yudha Saintika
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (703.085 KB) | DOI: 10.24014/coreit.v9i1.19434

Abstract

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.
Segmentation of Mentoring Customer Characteristics Using the K-Means Method and Hierarchical Clustering for Customer Relationship Management (CRM) Hanif Aristyo Rahadiyan
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i1.21567

Abstract

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.
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.

Page 13 of 17 | Total Record : 162

 11   12   13   14   15 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol 11, No 2 (2025): December 2025 Vol 11, No 1 (2025): June 2025 Vol 10, No 2 (2024): December 2024 Vol 10, No 1 (2024): June 2024 Vol 9, No 2 (2023): December 2023 Vol 9, No 1 (2023): June 2023 Vol 8, No 2 (2022): December 2022 Vol 8, No 1 (2022): June 2022 Vol 7, No 2 (2021): Desember 2021 Vol 7, No 1 (2021): Juni 2021 Vol 6, No 2 (2020): Desember 2020 Vol 6, No 1 (2020): Juni 2020 Vol 5, No 2 (2019): Desember 2019 Vol 5, No 1 (2019): Juni 2019 Vol 4, No 2 (2018): Desember 2018 Vol 4, No 1 (2018): Juni 2018 Vol 3, No 2 (2017): Desember 2017 Vol 3, No 1 (2017): Juni 2017 Vol 2, No 2 (2016): Desember 2016 Vol 2, No 1 (2016): Juni 2016 Vol 1, No 2 (2015): Desember 2015 Vol 1, No 1 (2015): Juni 2015 More Issue