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INDONESIA
IT JOURNAL RESEARCH AND DEVELOPMENT
Published by Universitas Islam Riau
ISSN : 25284061     EISSN : 25284053     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Information Technology Journal Research and Development (ITJRD) adalah Jurnal Ilmiah yang dibangun oleh Prodi Teknik Informatika, Universitas Islam Riau untuk memberikan sarana bagi para akademisi dan peneliti untuk mempublikasikan tulisan dan karya ilmiah di Bidang Teknologi Informatika. Adapun ruang lingkup dalam jurnal ini meliputi bidang penelitian di teknik informatika, ilmu komputer, jaringan komputer, sistem informasi, desain grafis, pengelolaan citra dan multimedia.
Arjuna Subject : -
Articles 4 Documents
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Search results for , issue "Vol. 9 No. 2 (2025)" : 4 Documents clear
Text Mining Approach to Emotion Analysis in Translation of Surah Yusuf With NRC Emotion Lexicon Syafiqah, Annisa; Zaman, Syahiduz; Imamudin, Mochamad
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.17765

Abstract

In the digital era, the accessibility of vast textual data, including the Quran, has facilitated broader comprehension of its teachings. This study analyzes the emotions in the English translation of Surah Yusuf using the NRC Emotion Lexicon. The findings show that trust is the most dominant emotion (22.89%), followed by joy (15.66%), anticipation (13.25%), sadness (12.05%), fear (10.84%), anger (9.64%), surprise (8.43%), and disgust (7.23%). These results confirm the text's diverse emotional expressions and the effectiveness of the lexicon-based method. The research aligns with the initial goals and highlights the potential of emotion analysis in understanding religious texts. Future research can expand the analysis to more verses and use machine learning for improved accuracy. This study aids scholars and students in exploring the Quran's emotional and spiritual dimensions and can be adapted to other texts for broader applications.
CNN-based Classification of Bladder Tissue Lesions from Endoscopy Images Lutviana, Lutviana; Rian Ardianto; Purwono
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.17867

Abstract

Bladder cancer is one type of tumor that frequently occurs in the urinary system, and early diagnosis is essential to improve the prognosis and survival of patients. The study aims to develop a Convolutional Neural Network (CNN) model for bladder tissue lesion classification from endoscopic images. This study uses a dataset consisting of 1754 images, which are divided into four classes: High-Grade Cancer (HGC), Low-Grade Cancer (LGC), Non-Specific Tissue (NST), and Non-Tumorous Lesion (NTL). The proposed CNN model showed a validation accuracy of 96.29%, with high recall, precision, and F1-score in most classes. The results show that CNN-based automated methods can improve efficiency and accuracy in the early diagnosis of bladder cancer, reduce manual visual interpretation errors, and improve the quality of patient care. This study suggests increasing the training data, especially for the NTL class, and applying more complex model architecture to better results.
Forecasting Used Car Prices Using Machine Learning Khotimah, Eni Khusnul; Swasono, Dwiretno Istiyadi; Fajarianto, Gama Wisnu
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.18031

Abstract

In an increasingly competitive era, it is crucial for car dealers and retailers to address the challenges of accurately determining the prices of used cars. To tackle these challenges, this study implements Machine Learning models to predict used car prices accurately. By applying the Artificial Neural Network (ANN) and Random Forest Regression algorithms, this research aims to evaluate the performance of these methods in predicting used car prices. The used car price data was obtained from the Kaggle repository, consisting of 14,657 data entries that provide comprehensive information about used cars. The analysis focuses on six main columns, including Brand, Model, Variant, Year, and Mileage, to estimate used car prices. Model evaluation was conducted using Mean Absolute Error (MAE) as the primary metric. The results show that the ANN model achieved a lower MAE (0.035) compared to the Random Forest Regression (0.047), indicating better performance in predicting used car prices. These findings demonstrate the effectiveness of ANN in handling data complexity and the non-linear relationships between variables involved in forecasting used car prices. Additionally, this contributes to the implementation of more accurate used car price predictions, enabling automotive companies to improve operational efficiency and provide greater benefits to the community.
UI/UX Design of Mobile-Based Environmental Reporting Application Using User-Centered Design Method Sudrajat, Faozan; Lukmana Sardi, Indra; Yulia Puspitasari, Shinta
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.18632

Abstract

The industrial revolution has significantly increased greenhouse gas emissions, leading to global warming and climate change, which burdens producers worldwide. In Indonesia, PT United Tractors Tbk, as a major player in the heavy equipment industry, faces challenges in the environmental reporting process due to the use of separate data collection methods between digital and non-digital (manual paper- based), which results in a lack of proper integration among aspects, making it impossible for environmental reporting to be done in real- time and causing difficulties in tracking the reporting data. This research aims to design an intuitive user interface (UI) and a seamless, satisfying user experience (UX) for a mobile-based environmental reporting application using the User-Centered Design (UCD) method, focusing on integrating all aspects of environmental reporting —including water, hazardous waste, non hazardous waste, and air—into a practical mobile platform for the company's environmental staff. The System Usability Scale (SUS) method was then employed to evaluate user satisfaction and acceptance of the developed application. The SUS results from 12 prospective users among the company's environmental staff showed an average score of 89.4, with the lowest score being 82.5 and the highest score being 100, indicating an excellent score and demonstrating that the UI/UX design is highly satisfactory and well-received by users.

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