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
 10   11   12   13   14 
The Re-development of Proxsis Workspace with Responsive Design and Multiplatform approaches using Flutter Framework Puspitasari, Shinta Yulia; Ra'uf, Iqbal Abdul; Nurtantyana, Rio; Satrio, Cahyo Tri
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 Putri, Agista; Selviandro, Nungki; Wulandari, Gia Septiana
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 Anugerah, Christopher Arden; Jadied, Erwid Musthofa; Cahyani, Niken
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) Puspita, Juni Wijayanti; Yanto, Kevin Jieventius; Pettalolo, Andi Moh. Ridho; Dg. Matona, Moh. Ali Akbar; Lilies, Handayani
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 Prasetiyowati, Sri Suryani; Harini, Sri; Nur Fadila, Juniardi; Fahlena, Hilda
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.
Content Based Filtering on Culinary Tourism Recommendation System Based on Social Media X Using Bi-LSTM Khamil, Muhammad Khamil; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.967

Abstract

Advancing technology, especially on social media platforms like X, created a vibrant space for users to share culinary experiences and recommendations through opinions and reviews. X became critical in presenting reviews and recommending places to eat with an excessively high number of active users. Facing the challenge of information overload on X, this research proposed a culinary tourism recommendation system using the Content-Based Filtering (CBF) method with Word to Vector (Word2Vec) and Bidirectional Long Short-Term Memory (Bi-LSTM) for classification. Utilizing culinary tourism data from Tripadvisor and user threads on Twitter, the dataset used included 2,645 tweets and five web crawling results, resulting in a matrix with a total of 200 culinary places and 44 users. Data pre-processing, such as the calculation of sentiment polarity scores using TextBlob and the application of SMOTE technique to balance the data, contributed to the improved accuracy of this research. In addition, optimization of the Bi-GRU model with various optimization methods, such as Adam, and hyperparameter tuning using Learning Rate Finder, resulted in a maximum accuracy of 94.99%, an increase of 29.4% from the baseline. The results of this research contributed significantly to the development of a more accurate and personalized culinary tourism recommendation system.
Music Genre Classification Using Adam Algorithm of Convolutional Neural Network Abou Haidar, Gaby
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.978

Abstract

Even though technology has been evolving rapidly lately, music classification is still definitely a major task in the Music Information Retrieval (MIR) domain. Music genre classification is a key challenge in Music Information Retrieval (MIR), aiming to identify the genre, style, and mood of audio tracks. This study explores the use of Convolutional Neural Networks (CNNs) with the Adam optimizer for music genre classification. We conducted experiments to evaluate the performance of our proposed model, which incorporates advanced machine learning techniques to improve classification accuracy. Our approach involves extracting features from audio files, converting them into Mel spectrograms, and training the CNN model using Python. The results demonstrate a high classification accuracy of 98.5%, significantly improving upon previous methods. Additionally, GPU acceleration enhanced the training speed by five times. Future work includes developing a mobile application for real-time classification and exploring integration with speech recognition technologies
Sentiment Analysis of Game Review in Steam Platform using Random Forest Dwifebri Purbolaksono, Mahendra
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1007

Abstract

Steam provides a platform for buyers to write reviews of the software or games they have purchased. Developers will benefit from knowing the criticisms and suggestions given by their community. The number of reviews users give is so large that developers find it difficult to determine whether users like or dislike the games they create. In the Steam application, there is a rating system, but the ratings given by users do not always represent the content of the comments. Therefore, sentiment analysis is used to facilitate developers in understanding the sentiment of the reviews given by users. Sentiment analysis is used to solve this problem. In this research, the sentiment analysis method used is Random Forest with TF-IDF feature extraction in Bigram and Trigram. Based on the research results, scenario testing using Bigram TF-IDF instead of Trigram then in the preprocessing stage without Lemmatization achieved the highest performance. The average F1 score obtained was 62%.
Regional Mapping Based on Tourism Destinations in West Java: K-Medoid Clustering Analysis Almajid, Nafis; Dina Atika, Prima; Fadhilla Ramdhania, Khairunnisa
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1011

Abstract

The growth of the tourism sector in West Java demands an optimal development strategy. This study aims to cluster regions in West Java based on the characteristics of their tourist destinations using the K-Medoid algorithm. This algorithm was chosen because of its superiority in producing optimal clusters and robustness to outliers. Data on tourist destination characteristics were analyzed using the K-Medoid algorithm and the Elbow method to determine the optimal number of clusters. As a result, three clusters with different characteristics were formed. The first cluster, "Medium potential and achievement", consists of 1 region with unoptimized potential for campsite tourism. The second cluster, "High potential and moderate achievement", consists of 25 regions with a diversity of attractions and a high number of visits. The third cluster, "Medium potential and high achievement", consists of 1 region with popular historical and cultural attractions and high visitation. The model evaluation showed a DBI score of 0.08, indicating good clustering quality. This research is expected to provide insights for the government and related stakeholders to formulate targeted tourism development policies in West Java. The K-Medoid algorithm helps identify certain patterns, providing deeper insights into regional differences in terms of tourism.

Page 13 of 14 | Total Record : 140

 10   11   12   13   14 

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