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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 275 Documents
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U-TAPIS Sal-Tik : Typing Error Detection Using Random Forest Algorithm Overbeek, Marlinda Vasty; Glennardy, Bryan; Mediyawati, Niknik; Nusantara, Samiaji Bintang; Sutomo, Rudi
ULTIMATICS Vol 16 No 1 (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.v16i1.3563

Abstract

The development of technology in the field of journalism has grown very rapidly. However, in the field of journalism there are still frequent deviations from the language on online news portals. This can be seen from the aspect of spelling and word usage. Spelling mistakes that occur in the news can cause the information contained in the news to be unclear and ambiguous. Based on these problems, a study was conducted to create a model to detect type error in Indonesian. This model is created using the random forest algorithm. random forest is an algorithm that works by building several decision trees and then combining the decisions from each tree that has been built and taking the most votes from the predictions of each tree so that it will produce stable and accurate predictions. The results of the accuracy of the model in the research that has been done is 100%. However, it should be noted that this 100% result is that the model is able to detect words that are already contained in the dataset. Based on the evaluation results that have been carried out, because the detected word is contained in the dataset, the accuracy issued is 100%. The built model successfully detects type error in Tribunnews news articles.
Fuzzy TOPSIS Implementation for the Determination of Priority Scale in Improving Service Quality Hasanah, Novrindah Alvi; Faisal, Muhammad; Angreani, Linda Salma
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.v16i2.3564

Abstract

Service quality plays a crucial role in economic development, particularly in the service industry, such as hotel services. Despite this, many hotels lack a systematic approach to help management identify areas that require improvement based on customer feedback. This research aims to develop a system that supports efforts to enhance service quality, utilizing the Fuzzy TOPSIS method. The study incorporates 150 data points obtained from questionnaires distributed to hotel service customers. The research involves two trials: service improvement priority and service eligibility. The results indicate an 84.45% accuracy level for service improvement priority testing, based on 120 out of 150 data points. Additionally, the accuracy level for service eligibility testing is 85.34%, derived from 131 data points out of the total 150. The research findings highlight the cafeteria as a significant area requiring improvement in service quality, aligning with the insights of hospitality experts. These results can serve as a foundation for management to enhance service quality based on selected criteria and alternatives.
Recommendation System Coffee Shop using AHP and TOPSIS Methods Siagian, Christian Andreas; Surbakti, Eunike Endariahna; Khaeruzzaman, Yaman
ULTIMATICS Vol 16 No 1 (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.v16i1.3579

Abstract

Indonesian people generally like to spend time with friends, family and business colleagues while drinking coffee. This habit of consuming coffee can not only be done at home, but can also be done in other places such as traditional and modern coffee shops. This has also significantly influenced the growth of coffee shops, especially in Tangerang. So people are faced with so many choices and alternative coffee shops to visit. This research was conducted to create a system that can recommend coffee shops in Tangerang based on priority criteria input by the user. Therefore, this recommendation system uses the Multi Criteria Decision Making (MCDM) method, where the process of making decisions is based on several criteria. This research uses the method Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This research was tested using the Usefulness, Satisfaction, and Ease of Use (USE) Questionnaire and received a very good rating with an overall score of 87.6\%, so the conclusion was that the average respondent felt helped by this recommendation system.
Data Mining Klasifikasi Penjualan Motor Menggunakan Kombinasi Algoritma K-Means Dan Naí¯ve Bayes Sofiati, Eka
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.v16i2.3603

Abstract

The showroom, which has been established since 2006, which is located in Lapai, Padang city, has problems, namely the difficulty of analyzing consumer demand and a lot of accumulated sales data. In addition, there are many stocks of goods that are not available when consumer demand is high. From these problems a data mining application system is needed to improve sales patterns and process sales data to determine what is often purchased and not by using the data mining method, namely K-Means and Naí¯ve Bayes. The data is obtained directly from CV. Unique Motor in the form of motorcycle sales data and motorcycle inventory data. At the system analysis stage, system design will be carried out using data mining using the K-Means and Naive Bayes algorithms. Where the program will be executed in the PHP and MySQL programming languages. The existence of a classification data mining system using a combination of K-Means and Naive Bayes can speed up the showroom in making decisions from the data taken so that the showroom can increase the number of stocks that have a hot-selling classification, so that the showroom not out of stock. A data mining system designed using a combination of KMeans and Naí¯ve Bayes can assist showrooms in classifying motorcycle sales, as well as being able to align the availability and inventory of existing motorcycles by classifying sales volumes.
Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
ULTIMATICS Vol 16 No 1 (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.v16i1.3610

Abstract

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.
Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (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.v16i1.3652

Abstract

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.
Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm Samudera, Elfajar Bintang; Waworuntu, Alexander; Lumba, Ester
ULTIMATICS Vol 16 No 1 (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.v16i1.3653

Abstract

Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%. Index Terms”Analog Television; Digital Television; Sentiment; Twitter; Random Forest
APPLICATION OF DEEP LEARNING TECHNIQUES FOR ENHANCING ARABIC VOCABULARY ACQUISITION IN STUDENTS AT MTS DARUN-NAJAH Isnaini, Misbachur Rohmatul; kurniasari, arvita agus; Arifianto, Aji Seto; Dewi Puspitasari, Pramuditha Shinta
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.v16i2.3701

Abstract

Arabic vocabulary recognition is an important aspect of learning at MTs Darun - Najah, a school that emphasizes on Islamic religious education. This research proposes the application of Convolutional Neural Network (CNN) and EfficientNet B7 to create learning media for Arabic vocabulary recognition for students. This method is implemented in the form of a web-based application. The built application offers an innovative approach in learning by utilizing deep learning. The results of several trials conducted showed that the application of Convolutional Neural Network (CNN) and EfficientNet B7 achieved 90% accuracy with an average precision of 94.6%, recall 94.6%, and f1-score 94.6%. Tests using User Acceptence Testing (UAT) have a success accuracy rate of 87.2% which proves that users can accept quite well.
Sentiment Analysis in E-Commerce: Beauty Product Reviews Tumanggor, Gavrila Louise; Samosir, Feliks Victor Parningotan
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.v16i2.3708

Abstract

The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
Prostate Cancer Screening for Specific Races Using Bioinformatics and Artificial Intelligence on Genomic Data Agustriawan, David
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.v16i2.3735

Abstract

Prostate cancer is one of a deathly cancer worldwide. The higher incidence and mortality rate shows that it is an urgent call for all of us to fight against it in our own way. This study develops an artificial intelligence system to screening prostate cancer from normal patients in a specific race. Gene expression and its phenotype dataset was downloaded from xenabrowser.net Data preprocessing and filtering based on a particular race, bioinformatics computational analysis to determine the features and machine learning algorithm such as decision tree and random forest are used to develop AI model. All the procedure and analysis was performed using python programming The result show that only White and Black African American that has a proper number of dataset while Asian and American Indian has a very lack dataset. Differentially expression gene (DEG) analysis was performed to both White and Black African American cancer and normal dataset as a reference. 143 and 1 DEG are found in White and Black African American race respectively. ENSG00000225937.1 (PCA3) is identified as the highest up-regulated gene expression in cancer in both White and Black African American race. The results of DEG analysis then become features to develop Artificial Intelligence (AI) classification system. AI model was developed using decision tree and random forest with GriDSearch parameters optimization and stratified 10-fold cross validation. Both Decision tree and random forest model yield 96% accuracy in training dataset and 93% and 91% accuracy in testing dataset for decision tree and random forest, respectively.

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