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<![CDATA[Movie Recommendation System Based on Synopsis Using Content-Based Filtering with TF-IDF and Cosine Similarity ]]>
<![CDATA[Armadhani Hiro Juni Permana Juni Permana]]>;
<![CDATA[Agung Toto Wibowo]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.747
<![CDATA[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.]]>
<![CDATA[Analysis The Impact of E-Service Quality on E-Customer Satisfaction in Cinema Ticket Booking Application ]]>
<![CDATA[Ditya Ilmi Rizqi]]>;
<![CDATA[Rio Guntur Utomo]]>;
<![CDATA[Muhammad Al Makky]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.748
<![CDATA[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.]]>
<![CDATA[UI/UX Design for Student Discussion Applications Based Felder Silverman Learning Style with the Design Thinking Method ]]>
<![CDATA[HNW Syahuda Nahatmasuni]]>;
<![CDATA[Anisa Herdiani]]>;
<![CDATA[Ati Suci Dian Martha]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.754
<![CDATA[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.]]>
<![CDATA[The Development of High Availability Database Infrastructure for OSS Projects with Monitoring Systems in Cloud Computing Environments ]]>
<![CDATA[Akmal Ikhsan]]>;
<![CDATA[Dana Kusumo]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.761
<![CDATA[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.]]>
<![CDATA[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 ]]>
<![CDATA[Guido Tamara]]>;
<![CDATA[Kemas Muslim L]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.818
<![CDATA[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.]]>
<![CDATA[Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection ]]>
<![CDATA[Muhammad Ihsan Adly]]>;
<![CDATA[Satria Mandala]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.825
<![CDATA[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%]]>
<![CDATA[Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition ]]>
<![CDATA[Denny Hardiyanto]]>;
<![CDATA[Dyah Anggun Sartika]]>;
<![CDATA[Imam Junaedi]]>;
<![CDATA[Sukamto]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.839
<![CDATA[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%.]]>
<![CDATA[The The Recognition of American Sign Language Using CNN with Hand Keypoint ]]>
<![CDATA[Muhamad Asep Ridwan]]>;
<![CDATA[Aradea]]>;
<![CDATA[Husni Mubarok]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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<![CDATA[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.]]>
<![CDATA[Machine Learning Sentiment Analysis in Cyber Threat Intelligence Recommendation System ]]>
<![CDATA[Marastika wicaksono aji bawono aji bawono]]>;
<![CDATA[Sachlany Kasman]]>;
<![CDATA[Stevani Dwi Utomo]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v9i2.849
<![CDATA[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.]]>
<![CDATA[Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals ]]>
<![CDATA[Dian Palupi Rini]]>;
<![CDATA[Winda Kurnia Sari]]>
<![CDATA[ International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/ijoict.v10i1.857
<![CDATA[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.]]>
