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INDONESIA
Jurnal ULTIMATICS
Published by Universitas Multimedia Nusantara
ISSN : 20854552     EISSN : 2581186X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Jurnal ULTIMATICS merupakan Jurnal Program Studi Teknik Informatika Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang analisis dan desain sistem, programming, algoritma, rekayasa perangkat lunak, serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, kecerdasan buatan, pemrograman sistem mobile, serta topik lainnya di bidang Teknik Informatika. Jurnal ULTIMATICS terbit secara berkala dua kali dalam setahun (Juni dan Desember) dan dikelola oleh Program Studi Teknik Informatika Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 292 Documents
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Improved SVM for Website Phishing Detection Through Recursive Feature Elimination Putri, Farica Perdana; Marcello, Feliciano Surya
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3744

Abstract

Technology is developing faster every day, particularly in the information technology field. A website is one of the many information access points people use to do business activities, get information, and other purposes. Sophisticated websites are being developed and used, encouraging many naive individuals to commit crimes for financial gain. Phishing websites are a common method of using information technology to conduct fraud. One way to conduct phishing is by using the features on the website. One technique for identifying phishing websites is to use the Support Vector Machine (SVM) algorithm, which classifies websites based on features. However, the SVM algorithm is not able to detect many features so that the resulting accuracy and optimization level is also not good. Based on datasets, the SVM algorithm only gets around 60% to 70% accuracy. The use of Recursive Feature Elimination (RFE) feature selection is one way that can be done to cover the shortcomings of SVM. By eliminating features that irrelevant and redundance, RFE makes the SVM algorithm get a higher accuracy rate on the available dataset with an accuracy of 96.09%.
Implementation of Deep Learning Model for Identification of Skin Diseases by Utilizing Convolutional Neural Network Apriani, Lysa; Ulum, Muhamad Bahrul
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3753

Abstract

Skin diseases are health problems that affect many individuals worldwide. Rapid and accurate diagnosis of skin diseases is essential for effective treatment. In an effort to improve diagnosis, information technology and artificial intelligence have taken on increasingly significant roles. This study focuses on the implementation of deep learning models for skin disease identification using CNN architectures EfficientNetB0, Xception and VGG16. The models were trained and tested on a dataset of 1800 images with 5 dermatitis classes and 1 normal class. Confusion matrices were used to assess the performance of the three deep learning models on the components of accuracy, recall, precision, and F1-score. The results of the deep learning model that can classify dermatitis skin diseases with a performance of more than 90% for each evaluation matrix are deep learning models utilizing EfficientNetB0 transfer learning with an accuracy of 93%. In contrast, the Xception model indicates overfitting with a training accuracy of 99.96% and a validation accuracy of 86.38%. The VGG16 model indicates underfitting with a training accuracy of 69.71% and a validation accuracy of 46.79%.
Comparison of Multilinear Regression and AdaBoost Regression Algorithms in Predicting Corrosion Inhibition Efficiency Using Pyridazine Compounds Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa; Setiawan, Nabila Putri
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3809

Abstract

Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (µ) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.
Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3874

Abstract

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.
Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development Waworuntu, Alexander
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3936

Abstract

The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.
Hospital Virtual Tour Website Design Using Multimedia Development Life Cycle Kurniadi, Dede; Rahmi, Murni Lestari; Nurhaliza, Nabila Putri
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.3832

Abstract

In 2023, Indonesia recorded 3,155 hospitals spread across the country, comprising 2,636 general and 519 specialized hospitals. Although the number is significant, not all community members have easy, direct access to hospitals. To overcome this challenge, virtual tour technology has emerged as a relevant solution to facilitate access to information and increase the transparency of hospital services. This project aims to develop a virtual tour website for Medina Hospital in Garut Regency. The project uses a systematic development method known as the Multimedia Development Life Cycle (MDLC), which includes stages from concept to distribution. The resulting website allows users to explore various hospital areas, such as the Main Building, Emergency Room, Tulip Building, and Chemotherapy Poly, through a virtual 360-degree panoramic view. Additionally, building and floor selection features are designed to make it easy for users to navigate. This website is also equipped with a chatbot feature that helps users find the location of a specific room and video tutorial guides that provide instructions for using the website. The results of the black box test show that the website functions well without any significant technical problems, so it is ready for public use. This website is expected to increase accessibility and convenience for users in obtaining information about the facilities and rooms available at Medina Hospital.
Identifying Academic Performance Patterns Among PTIK Students Using K-Means Clustering Anwar, Rizak Al Hasbi; Liantoni, Febri
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.3998

Abstract

This study explores the identification of academic performance patterns among students in the Informatics and Computer Engineering Education Study Program (PTIK) at Sebelas Maret University, focusing on the 2022 cohort. Using the K-Means clustering method within the scope of Data Mining, this research analyzes student performance data across multiple course categories from the first to fourth semesters. Through the Elbow method, four optimal clusters were established, each representing distinctive patterns of academic achievement. The analysis was conducted using RapidMiner software to reveal nuanced insights into student learning outcomes. Cluster 1 consists of students with moderate achievements in most categories, with a particular strength in Multimedia. Cluster 2 includes students with generally lower academic performance but shows a relative strength in General Courses. Cluster 3 is composed of high-achieving students who excel across categories, particularly in Software Engineering (RPL), Multimedia, and Educational subjects, indicating well-rounded academic proficiency. Cluster 4 comprises students with notable strengths in Software Engineering and Computer Networking, yet demonstrates lower performance in certain specialized subjects. These findings highlight the potential to tailor educational programs to address the specific learning needs and strengths of each student group, facilitating more personalized and effective academic support.
Sentiment Analysis of University X Students: Comparing Naive Bayes and BERT Approaches David, Jonathan; Saputra, Kie Van Ivanky; Panjaitan, Andry Manodotua; Samosir, Feliks Victor Parningotan
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4034

Abstract

Student satisfaction with university facilities and services requires in-depth analysis to ensure improvements in unsatisfactory facilities or services while maintaining those that meet expectations. This study aims to analyze sentiment in student satisfaction surveys using Natural Language Processing (NLP) methods. Survey data collected from 2022 to 2024 were analyzed using two main approaches: Naive Bayes (NB) with n-grams (n=1,2,3) employing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), and Bidirectional Encoder Representations from Transformers (BERT). The analysis results indicate that BERT outperforms NB in terms of sentiment prediction accuracy, although the difference is not highly significant. This study also identified keywords for both positive and negative sentiments. These keywords were then analyzed across 11 categories of facilities and services to provide focused insights into aspects that need to be maintained or improved. This study concludes that sentiment analysis provides significant contributions to universities in evaluating and enhancing the quality of facilities and services according to student preferences.
Trends and Keyword Networks in Machine Learning-Based Click Fraud Detection Research Kevin, Kevin; Hermawan, Aditiya
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4131

Abstract

The rapid advancement of the digital economy has significantly increased the use of online advertising while concurrently giving rise to critical challenges, particularly in the form of click fraud”a manipulative act that harms advertisers by generating fraudulent clicks on digital advertisements. As click fraud attack patterns grow increasingly complex, machine learning (ML)-based research has emerged as a principal approach for detecting and mitigating these threats. This study aims to map the research landscape of ML-based click fraud detection through a bibliometric analysis to identify publication trends, patterns of international and institutional collaboration, and key thematic domains within this field. Employing a bibliometric methodology, the study analyzed 61 publications retrieved from Dimensions.ai spanning the years 2015–2024. The data were collected, refined using OpenRefine, and visualized with VOSviewer to examine keyword co-occurrences and research trends. The findings reveal a marked increase in publication volume since 2019, with dominant contributions from India, China, Saudi Arabia, and the United States. Furthermore, four principal research clusters were identified: cybersecurity, the relationship between click fraud and the digital advertising industry, dataset processing and evaluation techniques, and the development of ML-based detection systems. Each cluster offers practical contributions in areas such as system protection strategies, ad budget optimization, improved detection accuracy, and the development of scalable, real-time detection solutions. Recent trends highlight growing scholarly interest in model performance evaluation and the challenges posed by class imbalance (class skewness). This study concludes that more effective data management and the development of adaptive ML models capable of addressing evolving attack patterns are pivotal for future research. By providing a clearer mapping of current trends, this study aims to support the scientific community in developing more accurate and efficient click fraud detection strategies, thereby strengthening the integrity of the global digital advertising ecosystem.
Adagrad Optimizer with Compact Parameter Design for Endoscopy Image Classification Pariyasto, Sofyan; Suryani; Arfeni Warongan, Vicky; Vika Sari, Arini; Wijaya Widiyanto, Wahyu
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4225

Abstract

Research on CNN Model and Adagrad Optimizer is expected to help identify diseases in the medical world. Especially in the field of image classification in Gastrointestinal endoscopic procedures . The research is specifically for the process of classifying medical images of Diverticulosis, Neoplasm, Peritonitis and Ureters . Previously, there have been quite a lot of studies on CNN and its various optimizers. However, those who have studied the Adagrad optimizer are not too many, especially those discussing the use of minimum parameters. The use of minimum parameters is expected to be one of the contributions of researchers in the fields of computing and medicine. The research was conducted to determine the use of the best parameters and obtain the highest level of accuracy. The research was conducted using minimum epochs starting from epoch 1, epoch 5, and epoch 10. Then the combination process between epoch and the number of convolution layers between 1 and 5 was carried out, resulting in 15 combinations. The test was carried out using 4000 images with 1000 images in each class. From the results of the test, the highest accuracy value was obtained, namely 82.875%. Then the highest average accuracy value was 81.625%.

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