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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 154 Documents
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Deep learning technique for plant disease detection Samson Adekunle, Temitope; Oladayo Lawrence, Morolake; Omotayo Alabi, Oluwaseyi; A. Afolorunso, Adenrele; Nse Ebong, Godwin; Abiola Oladipupo, Matthew
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p55-62

Abstract

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.
Smart irrigation system using node microcontroller unit ESP8266 and Ubidots cloud platform Sutikno, Tole; Nur Wahyudi, Ahmad; Wahono, Tri; Arsadiando, Watra; Purnama, Hendril Satrian
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p168-175

Abstract

The agricultural irrigation system is extremely important. For optimal harvest yields, farmers must manage rice plant quality by monitoring water, soil, and temperature on agricultural fields. If market demand rises, traditional rice field irrigation in Indonesia will make things harder for farmers. This modern era requires a system that lets farmers monitor and regulate agricultural fields anywhere, anytime. We need a solution that can control the irrigation system remotely using an internet of things (IoT) device and a smartphone. This study employed the Ubidots IoT cloud platform. In addition, the study uses soil moisture and temperature sensors to monitor conditions in agricultural regions, while pumps function as irrigation systems. The test results indicate the proper design of the system. Each trial collected data. The pump will turn on and off automatically based on soil moisture criteria, with the pump active while the soil moisture is less than 20% and deactivated when the soil moisture exceeds 20%. In simulation mode, the pump operates for an average of 0–5 seconds of watering. The monitoring system shows the current soil temperature and moisture levels. Temperature sensors respond in 1-3 seconds, whereas soil moisture sensors respond in 0–4 seconds.
Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method Purnama Harahap, Eka; Dwi Purnomo, Hindriyanto; Iriani, Ade; Sembiring, Irwan; Nurtino, Tio
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p19-28

Abstract

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.
Transfer learning: classifying balanced and imbalanced fungus images using inceptionV3 Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Karisma, Aulia Haritsuddin; Maulana, Bayu Rizky; Pinem, Josua Geovani
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p112-121

Abstract

Identifying the genus of fungi is known to facilitate the discovery of new medicinal compounds. Currently, the isolation and identification process is predominantly conducted in the laboratory using molecular samples. However, mastering this process requires specific skills, making it a challenging task. Apart from that, the rapid and highly accurate identification of fungus microbes remains a persistent challenge. Here, we employ a deep learning technique to classify fungus images for both balanced and imbalanced datasets. This research used transfer learning to classify fungus from the genera Aspergillus, Cladosporium, and Fusarium using InceptionV3 model. Two experiments were run using the balanced dataset and the imbalanced dataset, respectively. Thorough experiments were conducted and model effectiveness was evaluated with standard metrics such as accuracy, precision, recall, and F1 score. Using the trendline of deviation knew the optimum result of the epoch in each experimental model. The evaluation results show that both experiments have good accuracy, precision, recall, and F1 score. A range of epochs in the accuracy and loss trendline curve can be found through the experiment with the balanced, even though the imbalanced dataset experiment could not. However, the validation results are still quite accurate even close to the balanced dataset accuracy.
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia Hizqil, Ahmad; Ruldeviyani, Yova
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p63-71

Abstract

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Nuzulastri, Sari; Stiawan, Deris; Satria, Hadipurnawan; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
The impact of usability in information technology projects Putra Hulu, Freddy Richard; Raharjo, Teguh; Simanungkalit, Tiarma
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p7-18

Abstract

Achieving success in information system and technology (IS/IT) projects is a complex and multifaceted endeavour that has proven difficult. The literature is replete with project failures, but identifying the critical success factors contributing to favourable outcomes remains challenging. The triad of Time-Cost-Quality is widely accepted as key to achieving project success. While time and cost can be quantified and measured, quality is a more complex construct that requires different metrics and measurement approaches. Utilizing the PRISMA Methodology, this study initiated a comprehensive search across literature databases and identified 142 relevant articles pertaining to the specified keywords. A subset of ten articles was deemed suitable for further examination through rigorous screening and eligibility assessments. Notably, a primary finding indicates that despite recognizing usability as a critical element, there is a tendency to neglect usability enhancements due to time and resource constraints. Regarding the influence of usability on project success, the active involvement of end-users emerges as a pivotal factor. Moreover, fostering the enhancement of Human-Computer Interaction (HCI) knowledge within the development team is essential. Failure to provide good usability can lead to project failure, undermining user satisfaction and adoption of the technology.
Improving support vector machine and backpropagation performance for diabetes mellitus classification Prastyo, Angga; Sutikno, Sutikno; Khadijah, Khadijah
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p140-149

Abstract

Diabetes mellitus (DM) is a glucose disorder disease in the human body that contributes significantly to the high mortality rate. Various studies on early detection and classification have been conducted as a DM prevention effort by applying a machine learning model. The problems that may occur are weak model performance and misclassification caused by imbalanced data. The existence of dominating (majority) data causes poor model performance in identifying minority data. This paper proposed handling the problem of imbalanced data by performing the synthetic minority oversampling technique (SMOTE) and observing its effect on the classification performance of the support vector machine (SVM) and Backpropagation artificial neural network (ANN) methods. The experiment showed that the SVM method and imbalanced data achieved 94.31% accuracy, and the Backpropagation ANN achieved 91.56% accuracy. At the same time, the SVM method and balanced data produced an accuracy of 98.85%, while the Backpropagation ANN method and balanced data produced an accuracy of 94.90%. The results show that oversampling techniques can improve the performance of the classification model for each data class.
The best machine learning model for fraud detection on e-platforms: a systematic literature review Yussiff, Alimatu-Saadia; Frank Prikutse, Lemdi; Asuah, Georgina; Yussiff, Abdul-Lateef; Dortey Tetteh, Emmanuel; Ibrahim, Norshahila; Wan Ahmad, Wan Fatimah
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p195-204

Abstract

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on eplatforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.
Safeguarding data privacy: strategies to counteract internal and external hacking threats Jamal, Hassan; Ahmed Algeelani, Nasir; Al-Sammarraie, Najeeb
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p46-54

Abstract

In the digital age, the protection of data privacy has become increasingly important. Hackers, whether internal or external to an organization, could cause significant damage by stealing sensitive data, causing financial loss, compromising the privacy of individuals, or damaging the organization's reputation. This scientific research aimed to make substantial contributions by emphasizing the importance of addressing both internal and external hacking threats to protect sensitive information. The main theme of their work revolved around building a multi-layered defense system that included technological solutions like firewalls, encryption, and intrusion detection systems. The specific goals of their design and development approach were to establish clear policies and procedures for data handling, access control, and incident response, as well as to enhance data privacy strategies to stay ahead of evolving hacking techniques. The authors also highlighted the significance of employee awareness and training programs, collaboration with cybersecurity experts, and staying up-to-date with regulatory requirements to create a robust data privacy framework.

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