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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 212 Documents
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Analisis Sentimen Twitter dan Pengaruhnya Kepada Masyarakat Terhadap Jatuhnya Cryptocurrency Oktasia Nasution, Hafiza; Noviasari, Henni; Nasution, Salhazan; Melinda, Lisa
IT Journal Research and Development Vol. 7 No. 1 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.10599

Abstract

Cryptocurrency has become a global phenomenon nowadays. Based on the results of the GlobalWebIndex Survey, around 10% of internet users in Indonesia have digital currencies. But in recent years, the price of the cryptocurrency has fallen. It has resulted in people starting to hesitate to invest in crypto. This problem causes responses from the community, such as positive, negative, and neutral responses. This study aims to classify public opinion through the response to the fall of crypto by applying sentiment analysis through Twitter social media. Sentiment analysis data collection on Twitter uses the python programming language. There are three stages in the data analysis process: data crawling, preprocessing, and the results of classification and visualization. Based on research from sentiment analysis through social media Twitter, it was obtained that 57.9% produced a neutral value, 35.7% produced a positive value, and 6.4% produced a negative value.
The Concepts, Applications, and Challenges of the Internet of Things Siegfried, Patrick; Amangeldiyev, Nauryzbek
IT Journal Research and Development Vol. 7 No. 1 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.9110

Abstract

The main aim of the study is to give the reader the basic meaning of an “Internet of Things” itself, evaluate its main concepts, types, trends, and areas of application, as well as challenges. The study is a basic and fresh literature review from general sources and researches on the topic that has been done recently by the scientific community. Qualitative and quantitative methods of data collecting have been used. As a result, this paper can offer new interpretations, theoretical approaches, or other ideas. Mendeley referencing application was used to cite and give credits to the authors of a raw material used in this study. This term paper will give an excellent understanding to other researchers who are trying to build basic concepts within the topic, or to those who wish to begin their researches on “IoT” furthermore and will provide effective and accumulation knowledge. Also, can be useful as a raw material to the introductory courses regarding “IoT”.
Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression Mohamad Nurkamal Fauzan; Muhammad Alif; Cahyo Prianto3
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.10437

Abstract

The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The degree of compression speed that each algorithm produces is directly proportional to the file size.
Apriori to Analyze Sales Patterns of Building Tools and Materials Abdul Halim Hasugian; Muhammad Siddik Hasibuan; Siti Nurhaliza Sofyan
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.10544

Abstract

The accumulation of transaction data on the sale of building tools and materials, which is increasing day by day, should be used as information that can support sales in stores. Consumer purchasing patterns are the main source of data processing. In this study, it is necessary to have a system to process the transaction data based on trends that appear simultaneously in one transaction. The data used is 1000 sales data from December 2020 to May 2021 by calculating the minimum value of support and minimum confidence as a benchmark in the apriori algorithm process, results obtained are in the form of output which is an association rule that will be used by the store. Input the data in the application after that obtain results item set combination. After that these are obtained by calculating the minimum support. The results obtained are processed again with minimum confidence then the results are being final results of association rules that can be used by stores. If the minimum support is 0.1 (10%) and the minimum confidence is 0.5 (50%), then 122 association rules are obtained, one of which is in the 100th order, "If a customer buys a VSB Board, the customer also buys Jumbo Furing with a support value of 23, 3% and a confidence value of 82.9%”. Association rules as the final result are to evaluate, carry out sales strategies, alternative decisions in stocking goods and determine the placement of goods close together.
Perbandingan Data Vektor dan Data Raster Untuk Perhitungan Lingkungan Lanskap Menggunakan Pendekatan Sistem Informasi Geografis Eggy Giofandi; Khursatul Munibah; Kraugusteeliana Kraugusteeliana; Amira Novalinda; Cipta Sekarrini
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.10878

Abstract

GIS-based measurements can combine vector and raster data to produce thematic data obtained from remote sensing data. The data used in this study uses data on land use in the city of Pekanbaru. After the data is obtained, the pixel calculation process is carried out using three methods: the cell center method, the maximum area, and the maximum combined area. This data describes information with multiple raster data resolutions and then interprets the level of distortion in the data. The research findings found that in the process of raster data from 8 different resolution levels for the 5x5 meter category, it is able to provide results that are closest to the area of vector data, where PL 1 code produces 404229 pixels, PL 2 code ranges from 225717 pixels, PL 3 code ranges from 160323 pixels, code PL 4 ranges from 92268 pixels, PL 5 code reaches 73384 pixels, PL 6 code reaches 57237 pixels, and PL 7 code reaches 48315 pixels. Meanwhile, of the 3 methods that were compared to determine distortion with vector data, the cell center approach was the closest to raster data by calculation through eight levels of raster resolution compared to the other two methods. In choosing the right pixel resolution for further use in mathematical modeling, it is necessary to pay attention to the level of resolution by generalizing the resolution of satellite imagery data so that the data can have the same resolution. The weakness of the three methods lies in increasing the resolution the greater it will make the data coarser. This research is expected to be used as a consideration in future research to add a more precise process and be able to produce less storage capacity.
Toward Better Analysis of Breast Cancer Diagnosis: Interpretable AI for Breast Cancer Classification Alifia Revan Prananda; Eka Legya Frannita
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.11563

Abstract

Recently, some countries have been distressing with the increasing number of breast cancer cases. Those cases were extremely increased in every year. Practicaly, the increasing number of patients was caused by the manual examination. Recently, some researchers have been done in the development of AI method for solving this problem. However, AI itself still has limitation since it worked in the black-box approach which was difficult to be trusted. Thus, to overcome those problems, we proposed a method that was able to classify breast ultrasound images into two classes (benign and malignant) and able to explain how the prediction was made. Our proposed method consisted of four processes i.e., pre-processing step, development of CNN model, interpretable step and evaluation. In this research work, our proposed method performed into 780 breast ultrasound images divided into three classes (133 normal, 210 malignant, and 437 benign). In the training process, our proposed method obtained training accuracy of 0.9795, training loss of 0.0675. The validation process obtained validation accuracy of 0.8000 and validation loss of 0.5096. While, in the testing process, our proposed method achieved accuracy of 0.7923. In the interpretable process using LIME, the LIME result is covered by doctor visualization. It was indicated that LIME was suitable enough in visualizing the important features of breast cancer severity. Regarding to the results, our proposed method has a potensial to be implemented as an early detection method for classifying malignancy of breast cancer in order to help the doctor in the screening process
English English Afrian F; Okfalisa
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.11224

Abstract

A Decision-Making in determining the project tender winner becomes a significant challenge in the procurement stage, thus it is very vulnerable to administrative errors, corruption, and nepotism. Therefore, a recommendation system becomes a new problem solving in order to increase the information transparency, the company’s opportunity to win, the fraud minimization, and the community complaint on the project tender. The system is developed using the analysis of Fuzzy MOORA to calculate the significant consideration of six criteria, including the administration, the qualifications, the technical experience, the proposed price, the number of projects, and the size of the project based on the winning budget. Herein, 20 companies were acted as alternatives in applying and testing the recommendation tender system. As a result, Blackbox and User Acceptance Test (UAT) of this application from ten staffs of the Working Selection Group (POKJA) at the Bureau of Procurement of Goods and Services (PBJ) of Riau Province found that the entire modules and functions of the system run well. Meanwhile, UAT scores of 87.6% states that this application can assist the POKJA’s staffs in objectively selecting the tender winner. In addition, the sensitivity test analyzes the possible increasing of the weighting criteria, viz., C3 (technical experience) and C4 (price) can improve the quality rankings of alternatives up to 79.16%. Thus, this result enhanced the efficacy of Fuzzy MOORA approach in providing a better recommendation analysis.
Penerapan Gamifikasi Pada Media Pembelajaran Untuk Meningkatkan Aktivitas Dan Hasil Belajar Siswa Kurnia Rahman Agus; Dadang Syarif Sihabuddin Sahid; Syabdan Dalimunthe
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.10068

Abstract

The demands of the learning process during the current pandemic force teachers and students to carry out digital transformation in the learning process. Especially in the new normal where the learning process is done online. Thus, the learning media made by the teacher must also be adapted to online media so that the learning process can be maximized and increase the activeness of students in learning while online. This study aims to create an online learning media by applying the concept of gamification. The method used in this research is R&D (Research and Development) using analysis, design, and development development models. Stages of analysis are carried out to determine the needs of students when learning online. Then in the design stage, the selection of media formats and media design is carried out, and finally at the development stage, the advanced process of making games with programming languages is carried out as a learning medium. The use of gamification learning media is very practical, which can be accessed by students through their devices at home via the internet network. In the learning process, this game can be used to assist teachers in achieving learning objectives so that they can increase learning activity and the value of students in one basic competency.
Sentiment Analysis of Citayam Fashion Week Phenomenon Using Support Vector Machine Muhammad Rosyadi; Erlin
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12188

Abstract

Citayam Fashion Week is a phenomenon that displays a model doing a fashion show using distinctive and unique clothing when crossing a zebra cross as a catwalk. This phenomenon has received extraordinary attention and discussion from various circles and led to numerous pros and cons among the public and observers of society in Indonesia. Therefore, it is great importance to conduct the study on sentiment analysis of this phenomenon to determine society's sentiment tendency to provide government references and help decision-makers improve their policies. Sentiment analysis was performed using the Support Vector Machine based on the polynomial kernel. The results shows that the accuracy, recall, precision and F1-Score value of 95.61%, 95.66%, 96% and 95.55%, respectively. This study proved that the Support Vector Machine classifier with the polynomial kernel provides higher algorithm performance on text classification. Therefore, the government can use the result of this study to evaluate the existence of the citayam fashion week which may be followed by other phenomena.
Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset Untoro, Meida Cahyo; Yusuf, Muhammad Asyroful Nur Maulana
IT Journal Research and Development Vol. 8 No. 1 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12412

Abstract

Classification is a model for making predictions based on existing data. Imbalanced data leads to misclassification or modeling errors where the data is not relevant and results in poor classification modeling. A poor classification model is caused by imbalanced data in the classification label, and there is a need for data balancing as a solution to resolve this issue. The methods used to handle data imbalance are Random Undersampling and MWMOTE. The goal is to see the implementation of Random Undersampling and MWMOTE working well in addressing the imbalanced dataset and to know the performance and accuracy in modeling. The dataset used is an open source dataset from Kaggle consisting of Diabetes data, Bank Turnover data, Stroke data, and Credit Card data with various data ratios, with the goal of addressing the problem of imbalanced data. Model evaluation was performed using the confusion matrix and decision tree algorithm by looking at the precision, recall, f-measure, and accuracy values from the Random Undersampling and MWMOTE methods. Random Undersampling can address the problem of imbalanced data with a precision of 76.28%, recall of 76.74%, f-measure of 76.48%, and accuracy of 76.21%. MWMOTE can address the problem of imbalanced data with a precision of 86.04%, recall of 87.30%, f-measure of 86.66%, and accuracy of 86.61%. It can be concluded that the MWMOTE method is better than the Random Undersampling method because the average evaluation of the confusion matrix of the Random Undersampling method is smaller than the MWMOTE method.

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