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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 11 Documents
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Search results for , issue "Vol 5, No 3: November 2024" : 11 Documents clear
Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search Taufik, Ichsan; Agra, Agra; Gerhana, Yana Aditia
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p306-314

Abstract

For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly.
Capabilities of cellebrite universal forensics extraction device in mobile device forensics Sutikno, Tole; Busthomi, Iqbal
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p254-264

Abstract

The powerful digital forensics tool cellebrite universal forensics extraction device (UFED) extracts and analyzes mobile device data, helping investigators solve criminal and cybersecurity cases. Advanced methods and algorithms allow Cellebrite UFED to recover data from erased or obscured devices. Cellebrite UFED can pull data from call logs, texts, emails, and social media, providing valuable evidence for investigations. The use of smartphones and tablets in personal and professional settings has spurred the development of mobile device forensics. The intuitive user interface speeds up data extraction and analysis, revealing crucial information. It can decrypt encrypted data, recover deleted files, and extract data from multiple devices. The sector's best data extraction functionality, Cellebrite UFED, helps forensic analysts gather crucial evidence for investigations. Legal and ethical considerations are crucial in mobile device forensics. Legal considerations include allowing access to data, protecting privacy, and adhering to chain of custody protocols. Ethics include transparency, defamation, and information exploitation protection. Using Cellebrite UFED, researchers can navigate complex data on mobile devices more efficiently and precisely. Artificial intelligence (AI) and machine learning (ML) algorithms may automate data extraction in future tools. Examiners must train, maintain, and establish clear protocols for using Cellebrite UFED in forensic investigations.
Adversarial attacks in signature verification: a deep learning approach Hazra, Abhisek; Maity, Shuvajit; Pal, Barnali; Bandyopadhyay, Asok
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p215-226

Abstract

Handwritten signature recognition in forensic science is crucial for identity and document authentication. While serving as a legal representation of a person’s agreement or consent to the contents of a document, handwritten signatures de termine the authenticity of a document, identify forgeries, pinpoint the suspects and support other pieces of evidence like ink or document analysis. This work focuses on developing and evaluating a handwritten signature verification sys tem using a convolutional neural network (CNN) and emphasising the model’s efficacy using hand-crafted adversarial attacks. Initially, handwritten signatures have been collected from sixteen volunteers, each contributing ten samples, fol lowed by image normalization and augmentation to boost synthetic data samples and overcome the data scarcity. The proposed model achieved a testing accu racy of 91.35% using an 80:20 train-test split. Additionally, using the five-fold cross-validation, the model achieved a robust validation accuracy of nearly 98%. Finally, the introduction of manually constructed adversarial assaults on the sig nature images undermines the model’s accuracy, bringing the accuracy down to nearly 80%. This highlights the need to consider adversarial resilience while designing deep learning models for classification tasks. Exposing the model to real look-alike fake samples is critical while testing its robustness and refining the model using trial and error methods.
Unraveling Indonesian heritage through pattern recognition using YOLOv5 Rosalina, Rosalina; Sahuri, Genta
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p265-271

Abstract

This research focuses on three iconic Indonesian batik patterns-Kawung, Mega Mendung, and Parang-due to their cultural significance and recognition. Kawung symbolizes harmony, Mega Mendung represents power, and Parang signifies protection and spiritual power. Using the YOLOv5 deep learning model, the study aimed to accurately identify these patterns. Results showed mean average precision (mAP) scores of 77% for Kawung, 80% for Parang, and an impressive 99% for Mega Mendung. The highest precision results were 91% for Kawung, 88% for Parang, and 77% for Mega Mendung. These findings highlight the potential of pattern recognition in preserving cultural heritage. Understanding these designs contributes to the appreciation of Indonesia s culture. The research suggests applications in cultural studies, digital archiving, and the textile industry, ensuring the legacy of these patterns endures.
Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets Rachman Manga, Abdul; Putri Utami, Aulia; Azis, Huzain; Salim, Yulita; Faradibah, Amaliah
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p205-214

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.
Optimizing development and operations from the project success perspective using the analytic hierarchy process Nugraheni, Sani Novi; Raharjo, Teguh; Trisnawaty, Ni Wayan
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p272-282

Abstract

By merging development and operation disciplines, the approach known as development and operations (DevOps) can significantly improve the efficiency and effectiveness of software development. Despite its potential benefits, successfully implementing DevOps within traditional project management frameworks presents significant challenges. This study explores the critical factors influencing the implementation of DevOps practices from the project management perspective, specifically focusing on software development projects in the Ministry of Finance. This study utilizes the analytic hierarchy process (AHP) to prioritize the critical elements of project success criteria and DevOps factors necessary for effective implementation. The findings indicate that stakeholder satisfaction, quality, and value creation are the primary criteria for project success. Moreover, knowledge and skills, collaboration and communication, and robust infrastructure are pivotal factors for facilitating DevOps within project management. The study provides actionable insights for organizations aiming to improve their project outcomes by incorporating DevOps and offers a systematic approach to decision-making using AHP. This study recognizes limitations due to its focus on specific contexts and emphasizes the need for future research in diverse organizational environments to validate and expand these findings.
Securing DNS over HTTPS traffic: a real-time analysis tool Dhiya Eddine, Abid; Abdelkader, Ghazli
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p227-234

Abstract

DNS over HTTPS (DoH) is a developing protocol that uses encryption to secure domain name system (DNS) queries within hypertext transfer protocol secure (HTTPS) connections, thereby improving privacy and security while browsing the web. This study involved the development of a live tool that captures and analyzes DoH traffic in order to classify it as either benign or malicious. We employed machine learning (ML) algorithms such as K-Nearest Neighbors (K-NN), random forest (RF), decision tree (DT), deep neural network (DNN), and support vector machine (SVM) to categorize the data. All of the algorithms, namely KNN, RF, and DT, achieved exceptional performance, with F1 scores of 1.0 or above for both precision and recall. The SVM and DNN both achieved exceptionally high scores, with only slight differences in accuracy. This tool employs a voting mechanism to arrive at a definitive classification decision. By integrating with the Mallory tool, it becomes possible to locally resolve DNS, which in turn allows for more accurate simulation of DoH queries. The evaluation results clearly indicate outstanding performance, confirming the tool's effectiveness in analyzing DoH traffic for network security and threat detection purposes.
Electro-capacitive cancer therapy using wearable electric field detector: a review Sutikno, Tole; Fadlil, Fadlil; Ardiansyah, Ardiansyah; Taruno, Warsito P; Handayani, Lina
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p292-305

Abstract

Electro-capacitive cancer therapy (ECCT), a less invasive and more targeted approach using wearable electric field detectors, is revolutionizing cancer therapy, a complex process involving traditional methods like surgery, chemotherapy, and radiation. The review aims to investigate the safety and efficacy of electric field exposure in vital organs, particularly in cancer therapy, to improve medical advancements. It will investigate the impact on cytokines and insulation integrity, as well as contribute to improving diagnostic techniques and safety measures in medical and engineering fields. Wearable electric field detectors have revolutionized cancer therapy by offering a non-invasive and personalized approach to treatment. These devices, such as smart caps or patches, measure changes in electric fields by detecting capacitance alterations. Their lightweight, comfortable, and easy to-wear nature allows for real-time monitoring, providing valuable data for personalized treatment plans. The portability of wearable detectors allows for long-term surveillance outside clinical settings, increasing therapy efficacy. The ability to collect data over extended periods provides a comprehensive view of electric field dynamics, aiding researchers in understanding tumor growth and progression. Technology advancements in electro-capacitive therapy, including wearable devices, have revolutionized cancer treatment by adjusting electric field intensity in real-time, enhancing personalized medicine, and improving treatment outcomes and patient quality of life.
Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset Belluano, Poetri Lestari Lokapitasari; Rahma, Reyna Aprilia; Darwis, Herdianti; Rachman Manga, Abdul
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p235-242

Abstract

This study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adaboost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with Adaboost being an exception that shows a decrease in accuracy after applying imbalanced data methods.
Technology adoption model for smart urban farming-a proposed conceptual model Zhahir, Amirul Asyraf; M Shuhud, Mohd Ilias; Mohd, Siti Munirah; Kamarudin, Shafinah; Ahmad, Azuan; Salleh, Rossly; Md Norwawi, Norita
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p283-291

Abstract

Technological advancements have made their way into the heart of human civilization across numerous fields, namely healthcare, logistics, and agriculture. Amidst the sprouting issues and challenges in the agriculture sector, particularly, the growing trend of integrating agriculture and technologies is roaring. The public and private sectors work hand in hand with regard to addressing these complex issues and challenges that arise, aiming for efficient and sustainable possible solutions. This study is a continuation of a previous systematic literature review; hence, the main objective is to deliver a proposed conceptual model for technology adoption specifically for smart urban farming. Innovation diffusion theory (IDT) is used as the main foundation of the proposed conceptual model, supplemented with additional factors drawn from other exisiting technology adoption models both the originals and extended versions. The outcome of the study is expected to reveal valuable insights into the components affecting the technology adoption model in smart urban farming, which will be further laid out upon in the upcoming study, offering a robust framework for future studies and applications in smart urban farming.

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