cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota bandung,
Jawa barat
INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 140 Documents
 8   9   10   11   12 
Sentiment Analysis on Social Media Using Fasttext Feature Expansion and Recurrent Neural Network (RNN) with Genetic Algorithm Optimization Inggit Restu Illahi; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.905

Abstract

Social media is a place to express opinions or feelings, both positive and negative. One of them is to express opinions or feelings about a topic that is currently being discussed. The number of opinions or sentiments related to a topic can be challenging to assess if it leans towards positivity or negativity. Therefore, Sentiment analysis is essential for examining the viewpoints or sentiments on the topic. In this study, 37,391 Twitter user comments on the 2024 Indonesian presidential election were tested. This research employs the RNN methodology, TF-IDF feature extraction, and FastText feature expansion utilizing an IndoNews corpus of as much as 142,545 data and using Genetic Algorithm optimization. The outcomes of this study yielded the highest accuracy when combining TF-IDF feature extraction with max 7000 features, FastText feature expansion on top 5 features, and implementing Genetic Algorithm optimization with a value of 82.72%, accuracy increased by 3.4% from the baseline.
AgroSense: An IoT-Based Manual Crops Selection Farming Safaet Hossain; Md. Payer Hamid Bijoy Chowdhury
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.918

Abstract

This capstone project introduces an Intelligent Irri-gation System leveraging IoT technology to enhance agriculture. Using the ESP32 microcontroller and various sensors for soil moisture, water levels, and environmental conditions, the system automates irrigation based on real-time data. It communicates through the Blynk platform, allowing remote monitoring via a mobile app. The project includes a smart algorithm for crop selection and irrigation control, displayed on an LCD and acces- sible through the Blynk app. By considering soil moisture and water availability, the system adapts to different crops like rice, wheat, potato, and corn. The project promotes sustainability by optimizing water usage and encourages efficient crop growth. The integration of a manual crop count for field feedback enhances decision-making. Overall, this system presents a user-friendly and innovative solution for precision agriculture, showcasing the transformative potential of IoT, data analytics, and machine learning in modernizing farming practices.
The Re-development of Proxsis Workspace with Responsive Design and Multiplatform approaches using Flutter Framework Shinta Yulia Puspitasari; Iqbal Abdul Ra'uf; Rio Nurtantyana; Cahyo Tri Satrio
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.919

Abstract

Most of previous studies implemented the responsive design approach for the web-based application platform only since it had several difficulties to apply in the mobile-based application platform. In addition, the mobile application required different codebases since there were several platforms like Android and iOS. However, this study tried to redevelopment the Proxsis Workspace website to mobile application with responsive design and multiplatform approaches using Flutter Framework, in order to explore the potentials and counter the difficulties these two approaches for mobile development. In addition, we provide the detailed improvement, and the software testing results of our redevelopment app. Eight participants were participated in this study to measure the improvement of the redevelopment application. The results showed that the redevelopment version of the Proxsis Workspace could implement the responsive design and multiplatform approaches well. Furthermore, the software testing found that the redevelopment version passed the responsive design and multiplatform testing. In addition, there was significant different and enhancement of the usability score from 52.50 with marginal category to 72.81 with acceptable category. Hence, the authors suggest implementing the responsive design and multiplatform with Flutter Framework to enhance and make efficient with single code base only.
The Implementation of Titian for Data Provenance on DISC Systems Automated Debugging Agista Putri; Nungki Selviandro; Gia Septiana Wulandari
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.929

Abstract

Data-Intensive Scalable Computing (DISC) systems are critical for managing large datasets while prioritizing fault tolerance, cost effectiveness, and user accessibility. However, the presence of input errors in processed data presents considerable hurdles to programmers. The Snowfall Analysis program, which is well-known for its anomalous data that causes forecasting failures, serves as a key case study in this research. To solve this problem, this study leverages Titian, an extended library designed to speed debugging by methodically tracing the provenance of incorrect data back to its original source. Through thorough analysis, we analyzed Titian's accuracy using confusion matrix and compared its efficiency to standard manual debugging approaches, showing solid evidence of its utility in improving data provenance in DISC systems.
An Impact Analysis of Damage Level caused by Malware with Dynamic Analysis Approach Christopher Arden Anugerah; Erwid Musthofa Jadied; Niken Cahyani
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.940

Abstract

Malware, short for malicious software, is software or code specifically designed to damage, disrupt computer systems, or gain unauthorized access to sensitive information. Based on type classification, one of the well-known types of malware is ransomware. Usually, ransomware will encrypt the files on a computer system and then demand a ransom from the owner of the computer system so that the owner can regain access to the encrypted files. Sometimes in some cases, ransomware is able to delete files without input from the computer system owner. This research includes the analysis process of three ransomware samples that are known for successfully causing losses to many computer systems throughout the world, namely WannaCry, Locky, and Jigsaw, using a dynamic approach and the use of tools to track the processes carried out by the ransomware. The purpose of this research is to determine which of the three samples has the highest to lowest level of damage based on metrics based on file access capabilities and file modification capabilities for various types of files such as system files, boot-related files, program files, etc. The findings of this research indicate that WannaCry has the highest impact followed by Locky and then Jigsaw.
Web-based Application for Diagnosis of Diabetes using Learning Vector Quantization (LVQ) Juni Wijayanti Puspita; Kevin Jieventius Yanto; Andi Moh. Ridho Pettalolo; Moh. Ali Akbar Dg. Matona; Handayani Lilies
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.941

Abstract

Diabetes is a chronic disease that causes the most deaths in the world. This disease can cause long-term complications that develop gradually, such as heart attacks, strokes, and problems with the kidneys, eyes, skin, and blood vessels. Therefore, early diagnosis of diabetes is crucial for patients to know their diabetes status. In this study, we designed a web-based application for diabetes diagnosis using Learning Vector Quantization (LVQ). The dataset was collected from Kaggle's Diabetes Dataset which contains eight attributes, namely pregnancy, glucose, blood pressure, insulin, skin thickness, BMI, diabetes lineage function, and age, with two classes, namely negative diabetes (healthy) and positive diabetes. The results show that the best accuracy is 73.1% with a learning rate of 0.001. These findings can help patients detect diabetes problems early.
Enhancing Cybersecurity Against DDOS Attacks Evaluating Supervised Machine Learning Techniques Janaki; Karthikeyan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.964

Abstract

An individual or group launches a cyber attack when they intentionally try to get into another person's or group's computer system. Typically, the goal of an attacker is to gain an advantage by interfering with the victim's network. Now that COVID-19 has wreaked havoc on businesses throughout the world, it's cybercriminals' ideal storm. When it comes to cyber threats, Distributed Denial-Of-Service attacks (DDoS) are the most common and dangerous for corporate networks, apps, and services. Distributed denial of service attacks aim to flood a server, service, or network with malicious traffic in an effort to interrupt regular traffic. Financial losses, decreased productivity, damaged brands, worse credit and insurance ratings, damaged relationships with suppliers and customers, and IT budget overruns are all possible outcomes. Developing Network Intrusion Detection Systems (NIDSs) that can reliably foretell DDoS attacks is an urgent issue. This study used the CICDDoS2019 dataset to assess supervised Machine Learning (ML) methods. The machine learning algorithms that were assessed include AdaBoost, Naïve Bayes, MLP-ANN, Random Forest, and SVM. We use the assessment metrics: Area Under the Curve (AUC), Accuracy, F-measure, Precision, and Recall. This study demonstrates that of the algorithms tested, AdaBoost shows the highest promise in detecting DDoS attacks
Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification Sri Suryani Prasetiyowati; Sri Harini; Juniardi Nur Fadila; Hilda Fahlena
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.965

Abstract

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.
Movie Recommendation System Based on Synopsis Using Content-Based Filtering with TF-IDF and Cosine Similarity Juni Permana, Armadhani Hiro Juni Permana; Agung Toto Wibowo
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.747

Abstract

Recommendation systems have become an interesting topic in the field of artificial intelligence and data analysis. In the current era of technological advancement, the entertainment industry is rapidly growing, particularly the film industry, which is highly popular among the public due to their enthusiasm for watching movies. The increasing number and variety of films with various genres and titles have made it challenging for users to choose a film. To assist them in selecting movies, the presence of a recommendation system is necessary to provide information or film recommendations based on user interests and preferences. In this research, the development of the recommendation system will utilize the content-based filtering method, employing the TF-IDF algorithm and cosine similarity. The dataset used in this study is derived from publicly available data (MovieLens). The results of this research demonstrate that the TF-IDF and cosine similarity algorithms provide recommendations that align with the viewers' interests, as measured by precision, recall, and f1-score calculations.
Analysis The Impact of E-Service Quality on E-Customer Satisfaction in Cinema Ticket Booking Application Ditya Ilmi Rizqi; Utomo, Rio Guntur; Al Makky, Muhammad
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.748

Abstract

The development of technology and information has made everything easier and more efficient for us to obtain. One of its applications is online ticket booking in cinema networking applications. Innovations in the implementation of this technology can attract a significant number of buyers as it is considered to facilitate users in ticket booking transactions. Cinema networking service providers offer various features and information on their applications to attract buyers and create a positive image and trust for users to reuse their online cinema service applications. In order to maintain and enhance user satisfaction, service providers must also improve the quality of the services provided. This research aims to examine the influence of service quality on user satisfaction. Data collection was conducted through questionnaires distributed to respondents who are users of cinema ticket booking applications. The data processing technique used is SmartPLS (Smart Partial Least Squares) to analyze the measurement and structural models. The method employed is E-Service Quality with seven dimensions as indicators, namely Efficiency, Fulfillment, Reliability, Privacy, Responsiveness, Compensation, and Contact. The results of this study indicate the influence of E-Service Quality variables on E-Customer Satisfaction variables.

Page 10 of 14 | Total Record : 140

 8   9   10   11   12 

Filter by Year

2015 2024


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024 Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024 Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 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): December 2021 Vol. 7 No. 1 (2021): June 2021 Vol. 6 No. 2 (2020): December 2020 Vol. 6 No. 1 (2020): June 2020 Vol. 5 No. 2 (2019): December 2019 Vol. 4 No. 2 (2018): December 2018 Vol. 2 No. 2 (2016): December 2016 Vol. 2 No. 1 (2016): June 2016 Vol. 1 No. 1 (2015): December 2015 More Issue