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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 399 Documents
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OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES Ulima Inas Shabrina; Muhammad Iskandar Java; Siti Rochimah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1204

Abstract

YouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa's adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB's accuracy of 79.59%. However, it is observed that RoBERTa's performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa.
MOBILE-BASED ONLINE QUEUE APPLICATION DEVELOPMENT AT GRIBIG PUBLIC HEALTH CENTER IN REALTIME Dwiky Satria Hutomo; Chaulina Alfianti Oktavia
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1197

Abstract

Puskesmas is one of the health service facilities that organizes community and individual health efforts at level one. Gribig Health Center is one of the health centers in the city of Malang, East Java, which has several health services. To get services from the Puskesmas, each patient is required to register and complete the required files for further processing by the administration. However, the imbalance between the number of patients and the availability of services is the cause of queues. This study aims to create a mobile-based queuing application at the Gribig Health Center in real-time. The ar-chitectural concept used in developing applications is client-server. The queuing method used in the system is a combina-tion of FCFS (First Come First Served) and PS (priority service) methods. In system development, the development method used in this research is the waterfall method. For system testing, the author uses the Black Box Testing method to ensure that all application functionality is appropriate. The purpose of developing this application is to make it easier for pa-tients to get queue numbers for Gribig Health Center services anywhere and anytime, make it easier for patients to make registration bookings for other days in advance, exchange queue numbers, notifications when their turn is approaching, find out the estimated time to get service, and queue information. up-to-date for each service at the Puskesmas. The results of this study are successful in developing an online queuing application at the Gribig Health Center in real-time by utiliz-ing the QR Code to verify the queue and there is also a notification feature as a patient reminder.
COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS Kristina Febriani; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1209

Abstract

Forecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5.11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1.07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 and R-Square 0.88, RNN with MAPE value of 5.19%, MAE 2.91 and R-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error values among the other models. With forecasting results that have a small error rate, it can help management in making decisions to minimize costs in coal ordering and warehouse management.
ANALYSIS OF USER EXPERIENCE OF DANA E-WALLET USING USER EXPERIENCE QUOSTIONNAIRE (UEQ) AND UX HONEYCOMB Nyimas Silvia; Allsela Meiriza; Nabila Rizky Oktadini; Pacu Putra
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1207

Abstract

In the era Society 5.0, economic growth and digital products have mushroomed, leading to digital transaction product innovation becoming a daily necessity for society. Easy, cashless payment are made through an E-wallet application with various options, including the DANA application. However, in several reviews, user have complained about the performance and issues with the DANA application such as, the display is unclear when logging into the application, and it loads slowly when entering the amount of money. This research was conducted to evaluate DANA application users and provide recommendation for improvements and enhancements to enhance the DANA application user experience. From the result of the value proposition on the implementation of the UX Honeycomb method in the DANA application between the people of Palembang City, users generally agree that DANA deserves a good above-average rating. Meanwhile, the User Experience Questionnaire (UEQ) method show that the DANA application received positive scores on the attractive, perspicuity, dependability, efficiency, and stimulation variables, but it obtained a negative score on the novelty variable. Thus, the DANA application need to design and create more creative, intentional, and innovative products.
SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK Eka Alifia Kusnanti; Lauretha Devi Fajar Vantie; Umi Laili Yuhana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1199

Abstract

Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).
EXPLORING CONSUMER RESPONSE TO TEXT-BASED CHATBOTS IN F-COMMERCE: A QUALITATIVE STUDY ON BANGLADESHI SME’S Md Mehedi Hasan Emon; Tahsina Khan; Md. Adnan Rahman
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1181

Abstract

This qualitative study examines the consumer response to text-based chat bots in F-commerce, specifically in the context of Bangladeshi SMEs. The study aims to explore the benefits and challenges of using chat bots in F-commerce and identify the factors that influence consumer response to chat bots. The study uses semi-structured interviews to collect data from 15 Bangladeshi consumers who have experience using chat bots in F-commerce. The findings suggest that chat bots can improve customer service, save time and effort, and provide convenience for consumers, but they also face challenges such as technical issues, language barriers, and privacy concerns. The study also identifies several factors that influence consumer response to chat bots, including perceived usefulness, perceived ease of use, trust, familiarity, and personalization. The study concludes by discussing the practical implications of the findings for SMEs in Bangladesh and suggesting directions for future research.
DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT Muhammad Faisal Fanani; Umi Laili Yuhana; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1238

Abstract

The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance. 
OVERSAMPLING HYBRID METHOD FOR HANDLING MULTI-LABEL IMBALANCED Dara Tursina; Sherly Rosa Anggraeni; Chastine Fatichah; Misbakhul Munir Irfan Subakti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1208

Abstract

Data and information continue to increase along with the development of digital technology. Data availability is becoming increasingly numerous and complex. The existence of unbalanced data causes classification errors due to the dominance of majority-class data over the minority class. Not only limited to the binary class, but data imbalance is also often encountered in multi-label data, which become increasingly important in recent years due to its vast application scope. However, the problem of class imbalance has been a characteristic of many complex multi-label datasets, making it the focus of this research. Handling unbalanced multi-label data still has a lot of potential for development. One approach, Synthetic Oversampling of Multi-Label Data Based on Local Label Distribution (MLSOL) and Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-Label Data (UCLSO), has been developed. UCLSO's attention only focuses on the majority class, which can lead to data imbalance and excessive overfitting. Although effective in preventing majority class domination, this approach cannot overcome the lack of variation within the minority class. By contrast, MLSOL focuses on minority classes, allowing for variations in multi-label data and significantly improving classification performance. This research aims to overcome the problem of data imbalance by combining the MLSOL and UCLSO oversampling methods. Combining these two approaches is expected to exploit the strengths and reduce the weaknesses of each, resulting in significant performance improvements. The trial results show that the hybrid oversampling method produces the highest value on biological data with an F-1 score of 88%. Meanwhile, the single oversampling methods UCLSO and MLSOL on biological data produce an F-1 score of 67% and 62%, respectively.
AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT Ary Shiddiqi; Muhammad Raihan Anindita; Wahyu Suadi; Rully Soelaiman; Suhadi Lili; Ilham Gurat Adillion
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1191

Abstract

The development of automatic plant watering systems has recently gained popularity due to the need to conserve water and ensure healthy plant growth. This study focuses on integrating fuzzy logic, sensors, and algorithms to provide an automatic watering system. Fuzzy logic is a powerful tool that allows the system to interpret sensor data and make informed decisions. The sensors measure soil moisture, humidity, temperature, and light intensity. The data collected from these sensors is analyzed using algorithms to determine the appropriate watering schedule. The system’s ability to analyze and interpret data ensures that the plants receive the necessary moisture without over-watering or under-watering. Integrating the Telegram Bot is a significant feature of the system, enabling users to monitor and control the system remotely. The Telegram Bot sends users notifications when the system is activated, or the plants require attention. The system can also be controlled remotely through the Bot, enabling users to adjust the watering schedule or turn the system on or off. This research shows that the designed features of the system function effectively and can be used on a daily household scale. The system’s automated features reduce the need for constant monitoring and manual watering, making it ideal for those who engage in gardening at home. This innovation is particularly relevant in increasing the productivity of plants. In addition, the system’s ability to be controlled remotely through the Telegram Bot is a significant advantage, making it accessible and convenient for users.
DC-SAM: DILATED CONVOLUTION AND SPECTRAL ATTENTION MODULE FOR WHEAT SALT STRESS CLASSIFICATION AND INTERPRETATION Wijayanti Nurul Khotimah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 2, July 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i2.a1219

Abstract

Salt stress can impact wheat production significantly and is difficult to be managed when the condition is critical. Hence, detecting such stress whet it is at an early stage is important. This paper proposed a deep learning method called Dilated Convolution and Spectral Attention Module (DC-SAM), which exploits the difference in spectral responses of healthy and stressed wheat. The proposed DC-SAM method consists of two key modules: (i) a dilated convolution module to capture spectral features with large receptive field; (ii) a spectral attention module to adaptively fuse the spectral features based on their interrelationship. As the dilated convolution module has long receptive fields, it can capture short- and long dependency patterns that exist in hyperspectral data. Our experimental results with four datasets show that DC-SAM outperforms existing state-of-the-art methods. Also, the output of the proposed attention module reveals the most discriminative spectral bands for a given wheat stress classification task.

Page 37 of 40 | Total Record : 399

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