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Journal of Applied Research In Computer Science and Information Systems
Published by PT. BERBAGI TEKNOLOGI SEMESTA
ISSN : -     EISSN : 2988294X     DOI : https://doi.org/10.61098/jarcis
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Applied Research In Computer Science and Information Systems (JARCIS) is dedicated to publishing and disseminating research results and theoretical discussions, applied analysis, and literature studies in the fields of information technology, computer science, and information systems. The scope of the Journal of Applied Research In Computer Science and Information Systems (JARCIS) is as follows: 1. Information Systems 2.Computer Education 3.Adaptive and Self-Organizing Systems 4.Internet of Things 5.Digital Libraries 6. Agents and Multi-Agent Systems 7.Algorithms and Analysis of Algorithms 8. Bioinformatics 9. Robotics 10.Artificial Intelligence 11.Blockchain 12.Cryptocurrency 13.Network Science and Online Social Networks 14.Computer Vision 15.Computational Linguistics 16.Brain-Computer Interface 17.Digital Innovation 18. Information Management 19. Information Security Management 20. ICT for Development 21. E-learning 22. E-Commerce 23. Information Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 23 Documents
 1   2   3 
Development of An Application Transforming Handwriting into Digital Form using CNN Josulin, Claudio; Kurniawati, Yulia Ery
Journal of Applied Research In Computer Science and Information Systems Vol. 1 No. 2 (2023): Desember 2023
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v1i2.87

Abstract

This study aims to develop an application to recognize and predict handwriting using a Convolutional Neural Network (CNN) with ResNet50 architecture. The software development life cycle (SDLC) is an incremental model with two increments. The first increment is used to build the model, and the second increment is used to build the user interface. The data used in this study is handwritten images of Latin uppercase, Latin lowercase, and Arabian numerals with 62 classes. The training data used English Handwritten Characters by Dhruvil Dave from Kaggle Dataset. Data was trained and validated using k-fold cross-validation with tenfold and ten epochs for each fold. The model has an accuracy, precision, recall, and f1-score of 66.33%, 73.4%, 66.2%, and 66%, respectively. The functional application can work as expected based on the black box testing. The developed application can predict handwriting with up to 50% accuracy.
The Current Trend of Culinary Learning from Basically and Self-taught with social media winanti, Winanti; Fernando, Erick; Nurasiah, Nurasiah; Basuki, Sucipto; Hasna, Shofwatun; Riyanto, Riyanto
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.120

Abstract

The large amount of information that can be explored and obtained through social media makes social media one of the media currently most in demand by most internet users because of the ease of access and completeness of the information. The aim of this research is to explore how much social media is used to learn cooking and how effective social media is for learning culinary arts. This research involved 96 respondents and resulted in the most widely used social media being YouTube, namely 62 (65%) apart from Instagram, Facebook, Twitter, TikTok and Facebook. The research results stated that as many as 55 (57%) social media were used to search for information rather than for entertainment, learning, product promotion and status updates. Social media is considered more effective by 67 (70%) for finding information about culinary compared to other electronic media, especially through YouTube social media. No less important, social media is also able to increase user motivation to continue accessing social media compared to other media. In the future, questionnaires will be distributed to more respondents so that results can be maximized. It is possible that trends will change according to the needs of society at large
Spatial Temporal Analysis of Land Surface Temperature Changes in Ambon Island from Landsat 8 Image Data Using Geogle Earth Engine Rakuasa, Heinrich
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.123

Abstract

This study aims to analyze land surface temperature changes on Ambon Island using Landsat 8 imagery data and Google Earth Engine. The research method involves the use of Ambon Island boundary data from the Geospatial Information Agency (BIG) as well as Landsat 8 Collection 2 Tier 1 and Real-Time satellite image data that have been calibrated to reflect the reflectance of the Earth's surface to the atmosphere. The analysis steps include temperature conversion from Kelvin to Celsius, land surface temperature classification, and data export to Arc GIS software. The results showed an increase in land surface temperature associated with urban development in Ambon City, highlighting the importance of sustainable urban planning and good resource management to mitigate the negative impacts of land surface temperature increase and support adaptation to climate change.
Identification of Potential Landslide Areas in Nusaniwe Sub-district using Slope Morphology Method Rakuasa, Heinrich
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.124

Abstract

This study aims to identify potential landslide areas in Nusaniwe Sub-district using the Slope Morphology Method (SMORPH) based on slope and slope shape analysis. The results show that the complex geological and topographical conditions in the area increase the risk of landslides, with areas of high slope and steep slope shapes tending to be landslide hotspots. Interdisciplinary collaboration, community education, and the development of effective mitigation strategies are key in reducing the risk of landslides in Nusaniwe Sub-district and similar areas.
Telemedicine Application Adoption During the COVID-19 Pandemic: The Lens of the UTAUT Framework Model Rahman, Beno; Prawitasari, Agata Pratiwi; Setiawan, Yulius Anggi; wijaya, Lianna
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.148

Abstract

The objective of this study is to analyze the impact of various factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology characteristics, task characteristics, and self-efficacy within the framework of the UTAUT model on the intention to adopt telemedicine applications, particularly in light of the ongoing Covid-19 pandemic. A quantitative approach is employed for data analysis, with data being gathered through the distribution of online questionnaires. The research sample consists of 350 respondents, and the data is examined using the Structural Equation Modeling (SEM) technique facilitated by the SmartPLS software. The findings reveal a positive and significant relationship between task technology fit and technology fit, as well as between technology characteristics and technology fit. Furthermore, task technology fit is shown to positively and significantly influence the intention to adopt telemedicine applications, where the services are anticipated to offer health assistance to users of the application. This study offers valuable insights for application developers to enhance features and services to attract public interest in utilizing telemedicine applications..
Laptop Price Prediction Using Extreme Gradient Boosting Algorithm Adrianty, Syahrani; Maspiyanti, Febri
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.173

Abstract

The laptop is a support for many people in doing all activities. The number of laptop outputs with various models can affect the price of laptops. The presence of various online and offline stores causes different laptop prices and it becomes difficult to compare prices that are close to the low price range. Based on these problems, a system is needed that can predict laptop prices based on laptop specifications that are useful for people in finding a cheap price range. Data collection in this study came from bhinneka.com with 560 data and pemmz.com with 319 data collected by scrapping method. This research uses the Extreme Gradient Boosting method with evaluation techniques in the form of cross-validation resulting in an R2 score at the Bhinneka store of 0.98 and RMSE of 1250363.29 with the best cross-validation of 8. At Pemmz store produces an R2 score of 0.98 and RMSE of 1073090.92 with the best cross-validation of 6. Both results use data with outliers.
Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE) Andiani, Andiani; Simanjuntak, Yoel; Wiliani, Ninuk
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.181

Abstract

Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and LSTM models in predicting the closing price of Solana using RMSE as a testing metric, the aim is to determine the efficiency level of both ARIMA and LSTM models. The research results show that the ARIMA model with an order of (2,1,3) achieves an RMSE of 0.019 (1.9%) with an accuracy of 98.1%, while the LSTM model with a data training ratio of 70:30%, a batch size of 64, and 500 epochs has an RMSE of 0.075 (7.5%) with an accuracy of 92.5%. The conclusion drawn from the conducted experiments is that, in the case of using time series data samples from Solana, the ARIMA method demonstrates higher accuracy compared to the LSTM method.
K-Means Clustering for Identifying Traffic Accident Hotspots in Depok City Wahyono, Herry; Setiaji, Hari; Hartati, Tri; Wiliani, Ninuk
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.182

Abstract

This study applies the K-Means clustering algorithm to support decision-making processes related to identifying traffic accident-prone areas in Depok City over a three-year period (2020-2022). Secondary data was obtained from the Traffic Accident Unit of the Depok Metro Police, encompassing monthly traffic accident recapitulations for each district. The data underwent preprocessing steps, including integration and selection of relevant attributes. Using RapidMiner, the data was clustered into three distinct groups, with the optimal number of clusters determined by the Davies-Bouldin Index (DBI), which yielded a score of 0.896, indicating a satisfactory clustering result. The findings reveal that four districts—Beji, Cimanggis, Pancoran Mas, and Sukmajaya—are identified as high-risk areas for traffic accidents. These results are expected to assist local authorities in implementing targeted safety measures. The study demonstrates that the K-Means clustering method is a viable tool for analyzing traffic accident data and can significantly contribute to improving road safety in urban areas
Comparison of Apriori and Fp-Growth Algorithms in Determining Package Menus at Sate Perawan Restaurant Sawangan Raya Shabrina Putri; Ninuk Wiliani; Maspiyanti, Febri
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.183

Abstract

The culinary creative industry holds promising prospects as it is a necessity for society. However, the variety of menu items and high customer demand lead to slow ordering processes, which hinder service at Rumah Makan Sate Perawan. Additionally, some menu items are less popular among customers. To address these issues, a system is needed to assist in determining food and beverage package menus based on association rules. This system aims to facilitate business owners in organizing packages and improving sales. This study employs the Apriori and FP-Growth algorithms, using sales transaction data collected over a four-month period. The research applies a minimum support of 0.1 for food, 0.01 for beverages, and a minimum confidence of 0.6 for both categories. The results indicate that there is no significant difference between the two algorithms in terms of the generated packages, lift ratio evaluation, and runtime. In the food category, 5 association rules were generated with an average lift ratio of 1.1929, while in the beverage category, 2 rules were generated with an average lift ratio of 1.8990.
Enhancing Text Classification Performance: A Comparative Study of RNN and GRU Architectures with Attention Mechanisms Yulita Ayu Wardani; Mery Oktaviyanti Puspitaningtyas; Happid Ridwan Ilmi; Onesinus Saut Parulian
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.187

Abstract

Text classification plays a crucial role in natural language processing, and enhancing its performance is an ongoing area of research. This study investigates the impact of integrating attention mechanisms into a recurrent neural network (RNN) based architectures, including RNN, LSTM, GRU, and their bidirectional variants (BiLSTM and BiGRU), for text sentiment analysis. Three attention mechanisms Multihead Attention, Self Attention, and Adaptive Attention are applied to evaluate their effectiveness in improving model accuracy. The results reveal that attention mechanisms significantly enhance performance by enabling models to focus on the most relevant parts of the input text. Among the tested configurations, the LSTM model with Multihead Attention achieved the highest accuracy of 68.34%. The findings underscore the critical role of attention mechanisms in overcoming traditional RNN limitations, such as difficulty in capturing long-term dependencies, and highlight the potential for their application in broader text classification tasks.

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