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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 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): December 2024" : 5 Documents clear
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.
Customer Segmentation of Cash Management System Using K-Means Clustering Hesananda, Rizki; Apriliga, Patri
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.v2i2.188

Abstract

The effective financial management is essential for running successful business operations. In the banking context, the Cash Management System (CMS) facilitates real-time, automated transaction management. PT Bank Rakyat Indonesia (Persero) Tbk., as one of Indonesia’s largest banks, has implemented CMS since 2009. Despite its benefits, challenges persist, such as customer transactions outside regular working hours and difficulties in segmenting customers based on transaction volume and frequency. This study aims to address these issues by clustering BRI CMS users using the K-Means Clustering method, following the CRISP-DM framework. The research utilized transaction data of 2,727 users from January 2021 to April 2022. Data preparation involved cleaning anomalies and converting non-numeric values to numeric formats. Using the Elbow method, the optimal number of clusters was determined, resulting in three distinct user segments. The clustering revealed actionable insights, such as identifying high-value customers for targeted marketing and improving service strategies. This research offers a novel application of K-Means Clustering and CRISP-DM to CMS data management, contributing to better customer segmentation and strategic decision-making. These findings can help banks optimize resources, improve customer satisfaction, and enhance overall transaction efficiency.
Fruit and Vegetable Classification using Convolutional Neural Network with MobileNetV2 Khoiruddin, Muhammad; Tena, Silvester
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.v2i2.197

Abstract

Fruits are parts of plants that originate from the plant's pistils and usually contain seeds. Meanwhile, vegetables are leaves, legumes, or seeds that can be cooked. Fruits and vegetables have many variations that can be distinguished based on color, shape, and texture. However, the development of Artificial Intelligence (AI) technology has become pervasive in everyday life, one aspect of which is demonstrated through deep learning, a method of AI learning. Therefore, developing deep learning for tasks such as automatically detecting surrounding objects is necessary. This study aims to classify types of fruits and vegetables by applying a Convolutional Neural Network (CNN) with the MobileNetV2 architecture. In this study, fruits and vegetables encompassing 36 categories, including significant types in daily life, were considered. The results show that the classification system achieved an excellent accuracy rate of 97.31%, demonstrating the effectiveness of using deep learning techniques for this application
Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes Wiliani, Ninuk; Abdul Rahman, Titik Khawa; Ramli, Suzaimah
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.v2i2.200

Abstract

The growing utilization of solar panels as a renewable energy source requires efficient maintenance solutions to guarantee their best functioning. Identifying and categorizing faults on solar panel surfaces is essential for maintenance, as these defects considerably affect energy output and system efficiency. This study investigates the utilization of statistical feature extraction methods alongside Bernoulli Naive Bayes (BNB) and Gaussian Naive Bayes (GNB) algorithms to categorize different defect types, such as cracks, scratches, spots, and non-defective surfaces, through digital image analysis. Statistical criteria, including recall, specificity, and area under the curve (AUC), are employed to assess model performance. The findings indicate that the GNB algorithm surpasses BNB, with a mean average precision (mAP) of 39.83% with an 85:15 training-test ratio, whereas BNB reaches a maximum mAP of 29.25% at a 90:10 ratio. Nonetheless, both models demonstrate constraints in precision, as indicated by a total AUC of 0.644. This work illustrates the potential of statistical feature extraction approaches for defect classification, while emphasizing the necessity for future improvements to boost the efficacy of feature extraction and classification techniques in practical applications

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