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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
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UI/UX Design for Student Discussion Applications Based Felder Silverman Learning Style with the Design Thinking Method HNW Syahuda Nahatmasuni; Anisa Herdiani; Ati Suci Dian Martha
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.v9i2.754

Abstract

Telkom University is one of the educational institutions that implement asynchronous learning through its Learning Management System (LMS). Based on preliminary research conducted with 37 respondents, just 13.5% of the participants LMS using a smartphone, because responsiveness issue. The Discussion Forum is a frequently used feature with high student interaction. However, this feature has several shortcomings, such as the lack of responsiveness in the mobile web interface and the limited interaction between users and professors. This research will employ the Design Thinking methodology and adopt the Felder Silverman Learning Style (FSLS). The evaluation of the prototype design resulted in a System Usability Scale (SUS) testing score of 72.22 for the LMS Celoe website and 85.65 for the proposed UI/UX application. The SUS testing score for the LMS Celoe website falls within Quadrant C, indicating an acceptable level of acceptance with a grade C scale and a rating of "Good." On the other hand, the SUS testing score for the proposed UI/UX application falls within Quadrant B, with an acceptable level of acceptance, a grade B scale, and an "Excellent" rating.
The Development of High Availability Database Infrastructure for OSS Projects with Monitoring Systems in Cloud Computing Environments Ikhsan, Akmal; Kusumo, Dana
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.v9i2.761

Abstract

This study addressed a critical problem in the Kominfo's Operation Support System (OSS) project, which significantly impacted business operations. The outage of the MongoDB database, a vital component of OSS, resulted in server crashes and data damage. To overcome this issue, the OSS team initiated a project to migrate to PostgreSQL for high availability and improved database performance. The implementation involved the use of HA PostgreSQL technology, with multiple connected servers sharing data in real-time. Through functional and performance testing, the study has demonstrated that the HA PostgreSQL system increased database availability, managed server failures, and facilitated effective cluster administration. The findings of this research can guide the development of the OSS project's IT infrastructure and serve as a reference for similar projects utilizing HA PostgreSQL technology.
Sentiment Analysis on Acute Kidney Syrup Videos Using CNN and LSTM Algorithms: Analisis Sentimen Tentang Isu Obat Sirup Penyebab Ginjal Akut pada Video di Youtube Menggunakan Algoritma CNN dan LSTM Tamara, Guido; Kemas Muslim L
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.v9i2.818

Abstract

The issue of acute kidney failure, particularly caused by the consumption of cough syrup, was circulating around October 2022 and has become a serious public health concern. This issue has drawn extensive attention and sparked various reactions on social media. In this digital era, public opinion expressed in comments on social media platforms like YouTube significantly impacts societal perceptions. Therefore, in the context of the aforementioned issue, sentiment analysis on YouTube video comments can provide valuable insights into societal perceptions and people’s reactions. Therefore, this study focuses on the sentiment analysis of public opinions expressed in YouTube comments related to this matter. The methods employed for this analysis include Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with Word2Vec feature extraction. The findings of this study indicate that both these methods produce good performance results with an oversampling dataset with a 90:10 data proportion. In the performance comparison, CNN yielded the highest accuracy, at 0.92, while LSTM was at 0.90.
Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection Adly, Muhammad Ihsan; Mandala, Satria
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.v9i2.825

Abstract

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%
Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition Denny Hardiyanto; Dyah Anggun Sartika; Imam Junaedi; Sukamto
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.v9i2.839

Abstract

In today's digital era, the process of facial recognition has a very big role. Face recognition has benefits for authentication and identification processes. The development of facial recognition research continues to be carried out with the aim of being able to get the right algorithm, more accurate, faster processing, to be able to recognize faces from various angles. In this study, a performance test was performed on the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture, which is one of the deep learning algorithm developments for facial recognition. AlexNet has 8 convolution layers so that it will not leave even the slightest feature of the object. The process of training and testing the system uses the MATLAB programming language. The number of datasets used is 400 image data which is divided into 360 training image data and 40 test image data. The 400 data come from 4 classes of facial images that have been labeled with names and each classes have 100 images. The training process produces an accuracy of 100% and the testing process produces an accuracy of 95%.
The The Recognition of American Sign Language Using CNN with Hand Keypoint Ridwan, Muhamad Asep; Aradea; Mubarok, Husni
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

Abstract

Sign Language is a method used by the deaf community for their communication. In line with the advances of deep learning, researchers have widely interpreted neural networks for language recognition in recent years. Many models and hardware have been developed to help get high accuracy in language recognition, but generally, the problem of accuracy is still a concern of researchers, even the accuracy problem related to American language or American sign language (ASL) still requires further research to solve. This paper discusses a method to improve ASL recognition accuracy using Convolutional Neural Network (CNN) with hand keypoint. Pre-trained Keypoint detector is used to generate hand keypoints on the massey dataset as an input for classification in the CNN model. The results show that the accuracy of the proposed method is better than the previous studies, obtaining an accuracy of 99.1% in recognizing the 26 statistical signs of the ASL alphabet.
Machine Learning Sentiment Analysis in Cyber Threat Intelligence Recommendation System aji bawono, Marastika wicaksono aji bawono; Kasman , Sachlany; Dwi Utomo , Stevani
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.v9i2.849

Abstract

The use of the digital world is increasing every day. Attacks and data theft occur on various websites, both government-owned and commercial and banking sites. Therefore, this research aims to identify the threats of frequently occurring viruses in a country. There is a considerable amount of news explaining cybercrime incidents. The problem of this research is that unstructured data such as articles and technical reports are difficult to analyze and identify the types of cybercrime attacks. Previous research attempted to semantically extract unstructured cyber threats, but there were shortcomings in previous research. The novelty of this research is the development of a Cyber Threat Intelligence (CTI) machine learning model to identify the types of virus attacks or cybercrimes that frequently occur in e-commerce transactions, so that they can take rescue actions for incident handling in the digital world using tactics, techniques, and procedures (TTP). The method involves using machine learning, taking Cyber Threat Intelligence (CTI) documents as input regarding cybersecurity threat handling steps, and then processing the data using AI TF-IDF and Bags of Words for the identification of steps, tactics, techniques, and procedures required for each frequently occurring security incident.
Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals Rini, Dian Palupi; Kurnia Sari, Winda
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.v10i1.857

Abstract

EEG emotion is a research topic that has gained significant attention in the development of emotion classification systems. This study focuses on optimizing the hyperparameters of CNN (Convolutional Neural Network) and DNN (Deep Neural Network) for classifying EEG emotion signals. The data is divided into three train-test data ratio scenarios: 80:20, 70:30, and 60:40. After modeling and the classification process, hyperparameter tuning was conducted on both models to achieve the best results. Experimental results showed the highest accuracy of 98.36% for CNN, while DNN reached 98.18% in the 80:20 data ratio scenario. Despite the high accuracy, the differences in the loss curves between CNN and DNN reflect the complexity of the performance of both models. The train-test data ratio was also found to significantly impact the performance of both models, with the 80:20 data split yielding the best results, while the 70:30 and 60:40 splits resulted in slightly lower accuracies.
Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation Kusuma Adi Achmad; Lukito Edi Nugroho; Achmad Djunaedi; Widyawan
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.v9i2.858

Abstract

The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label to suggest context was uHijos, uCuisine, uAmbience, and uTransport. The accuracy of the socio-user context aware-based recommender system in suggesting the context of uHijos, uAmbience, and uTransport was 100% with an error rate of 0%. It was found that only the level of recognition of the model in suggesting uCuisine was less accurate (below 30%) with a classification error for more than 70%. Performance evaluation of the socio-user model context-based recommender system was considered efficient, particularly for the evaluation of the level of accuracy, completeness (recall/sensitivity), precision, and a harmonic average of precision and recall (F-score), mainly for label/context of uHijos, uAmbience, and uTransport.
Reducing Lending Risk: SVM Model Development with SMOTE for Unbalanced Credit Data Purba, Josya Ryan Alexandro; Muftikhali, Qilbaaini Effendi; Josaphat, Bony Parulian
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.v9i2.860

Abstract

Lending is an important activity for banks in managing available funds. However, lending is also an activity that has a high risk, because not all customers who borrow funds can fulfill the responsibilities of the existing agreement. Because of this, it is necessary to have a method that can predict creditworthiness to customers in order to minimize the risks that arise. This research uses machine learning method, namely Support Vector Machine (SVM) in predicting creditworthiness. This method is applied and compared before and after the Synthetic Minority Oversampling Technique (SMOTE) on historical bank credit data BPR NBP 16 Rantau Prapat, North Sumatra and find the best parameters with grid search. According to the results of the analysis based on Area Under the Receiver Operating Characteristic Curve (AUC-ROC), SVM with SMOTE shows better results, namely 96%, than SVM without SMOTE, namely 56%.

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