cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rizki Wahyudi
Contact Email
rizki.key@gmail.com
Phone
+6281329125484
Journal Mail Official
jcse@icsejournal.com
Editorial Address
Perum Pasir Indah Blok K. No. 22, Pasir Lor, Kec. Karanglewas, Kabupaten Banyumas, Jawa Tengah 53161, Indonesia
Location
Unknown,
Unknown
INDONESIA
Journal of Computer Science and Engineering (JCSE)
Published by Institute of Computer Science and Engineering
ISSN : -     EISSN : 27210251     DOI : https://doi.org/10.36596/jcse
Core Subject : Science,
Computer Science & IT
Computer Architecture, Processor design, operating systems, high-performance computing, parallel processing, computer networks, embedded systems, theory of computation, design and analysis of algorithms, data structures and database systems, theory of computation, design and analysis of algorithms, data structures and database systems, artificial intelligence, machine learning, data science, Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 61 Documents
 3   4   5   6   7 
Aircraft Recognition in Remote Sensing Images Based on Artificial Neural Networks Abrar, Muhammad Fauzan; Ayumi, Vina
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables computers and systems to obtain data from images, recordings and other visual information sources. Image Recognition, a subcategory of Computer Vision, addresses a bunch of strategies for perceiving and taking apart pictures to engage the automation of a specific task. It is fit for perceiving places, people, objects and various types of parts inside an image, and reaching deductions from them by analyzing them. With these kinds of utilities it is a no-brainer that Computer Vision has its use cases in the military world. Computer Vision can be immensely useful for Intelligence, Surveillance and Reconnaissance (ISR) work. This paper provides on how Computer Vision might be used in ISR work.  This paper utilises Artificial Neural Network (ANN) such as Convolutional Neural Network (CNN) and Residual Neural Network (ResNet) for demonstration purposes. In the end, the ResNet model managed to edge out the CNN model with a final validation accuracy of 90.9% compared to a validation accuracy of 86% on the CNN model. With this, Computer Vision can help enhance the efficiency of human operators in image and video data related work.
Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments Afandi, Marcel; Isnaini, Khairunnisak Nur
Journal of Computer Science and Engineering (JCSE) Vol 5, No 1: February (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the context of democracy in Indonesia, elections play a crucial role, and survey agencies often publish their results on social media. User responses, especially from voters, often express dissatisfaction, including distrust, insults, and negative comments, if the candidate they support receives low survey results. Therefore, this study aims to examine the level of public trust in the survey results of Presidential candidates in 2024 using the Support Vector Machine (SVM) and Logistic Regression algorithms. The study utilized data from 1778 Instagram comments and 985 Twitter tweets. The process involved problem identification, data collection, and system implementation, such as preprocessing, labeling, SMOTE, TF-IDF, data splitting, model classification, and evaluation. The results show that SVM with an 80% training data and 20% test data scenario provides high accuracy, namely 93.19% from Instagram and 91.19% from Twitter. Logistic Regression, with the highest accuracy of 89.79% from Instagram and 88.01% from Twitter in the same scenario. Sentiment analysis using SVM scenario one resulted in 195 positive comments and 216 negative comments. Logistic Regression scenario one shows 180 positive sentiments and 216 negative sentiments. From the classification results, it can be concluded that the level of public trust tends to be negative towards the survey results of the 2024 Presidential candidates, both using SVM and Logistic Regression.
Class-Oriented Text Vectorization for Text Classification: Case Study of Job Offer Classification Wabo Tatchum, Ghislain; Nzekon Nzeko'o, Armel Jacques; Sosso Makembe, Fritz; Youh Djam, Xaviera
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Advances in data science have made it possible to solve many real-life problems using automatic text classification applications. This is the case in e-recruitment, where job offers are classified and recommended to jobseekers. In natural language processing, text classification involves a vectorization step, whereby each document is represented as a vector of coordinates linked to a keyword. Those keywords are obtained by vectorizing the entire corpus, and are used to distinguish one document from another in the corpus. However, it is preferable for each keyword to distinguish one class from another. To obtain these types of keywords, the authors consider the class of documents in the vectorization process. They first create a class-oriented document for each class by merging all documents from the same class, and then apply a vectorization algorithm. Experiments are carried out using datasets from Minajobs, Nigham, and Monster with the classification models Decision Tree, Naive Bayes, Support Vector Machine, and a deep neural network self-attention transformer (TFM). The vectorization methods used on class-oriented documents are Doc2Vec and TF-IDF combined with our class-oriented vectorization strategies, including OC, ZIPF, and OWDC. To evaluate these experiments, we used the precision, MAP, and F1-Score metrics. According to the results, the TFM methods can improve accuracy by 29, 40, and 33% compared to previous work and the traditional way of classifying text documents. The NB methods can improve accuracy by 19, 22, and 20%, while the DT methods can improve accuracy by 34, 37, and 34%. The SVM methods can improve accuracy by 33, 34, and 34% in the Monster, Nigham, and Minajobs datasets. In addition, we validate our contribution by comparing ourselves with three other works in the literature using four datasets (RE'16, Wap, WebKB, and Kla) and obtain improvements in accuracy and F1-score up to 55%.
Color-Based Image Processing for Autonomous Human Following Trolley Robot Navigation with Camera Vision Artono, Budi; Nugroho, Widya; Wahyudi, Rizki
Journal of Computer Science and Engineering (JCSE) Vol 5, No 1: February (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid advancements in the field of robotics have spurred intensive research, particularly in the industrial sector, aiming to develop robots that can assist in simplifying daily human tasks. One emerging area of research involves the design of a cargo-carrying robot trolley. This trolley robot has the capability to follow a person carrying items by recognizing the color of the clothes worn by that person through image processing. The objective of this research is to facilitate the transportation of goods, especially in airport environments, by enabling the robot to identify and follow human objects with a minimum distance of 30 centimeters and a maximum distance of over 3 meters. The design system of this robot trolley utilizes a camera sensor to detect the object to be followed through image processing using OpenCV on the Microsoft Visual Studio 2012 platform. The image processing results in PWM values sent to the Arduino to drive DC motors. Additionally, ultrasonic sensors are employed to restrict the robot's movement in its surroundings, preventing collisions. The robot's speed can adjust according to the walking speed of a person. If the robot is moving too fast, it will be stopped by the ultrasonic sensor when the distance between the robot and the person being followed is less than 30 cm, avoiding collisions between the robot and the person.
Enhancing Credit Card Fraud Detection with Modified Binary Bat Algorithm: A Comparative Study with SVM, RF, and DT Olasupo, Yinusa Ademola; Malgwi, Musa Yusuf; Hambali, Moshood Abiola
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Numerous studies have revealed the problem of irrelevant features, noise, and dimensionality in a dataset, which can inhibit how the classification algorithm performs. In machine learning, feature selection approaches are critical, particularly in the context of credit card fraud detection, where relevant feature selection is critical. We use techniques such as machine learning algorithms, data mining techniques, and data science to stop and detect credit card fraud. These algorithms often classify genuine and fraudulent transactions in credit card datasets. However, the challenge of high dimensionality and irrelevant features persists, hindering improvements in classifier algorithms. This study centered on detecting credit card fraud (CCF) using a Modified Binary Bat Algorithm (MBBA) for feature selection. The MBBA selects the most informative features to improve the classifier algorithm's performance. The classifier algorithms used in this research are Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). We conducted the experiment using the Python programming language, and the results indicate that RF achieves 99.945% accuracy, SVM 99.847%, and DT 99.909%. As a result, RF has the best accuracy. In summary, the optimal performance of a classification algorithm depends on the selection of relevant features for credit card fraud detection. The paper suggests improving the effectiveness of classifier algorithms for credit card fraud detection by employing the Modified Binary Bat algorithm, which outperforms the Genetic Algorithm (GA) in feature selection
Comparison of deep learning models for weather forecasting in different climatic zones Alam, Farjana; Islam, Maidul; Deb, Arnob; Hossain, Sadab Sifar
Journal of Computer Science and Engineering (JCSE) Vol 5, No 1: February (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Weather forecasting has become an integral part of our day-to-day life. Weather holds significant importance in our everyday lives, impacting areas such as how we travel, produce food, and maintain public well-being. Mostly, weather prediction is done with machines learning models, but the use of deep learning techniques in this field in growing. Still, the existing studies are not sufficient to get a clear concept of weather prediction in different climatic zones. Therefore, in this study, selected four deep learning models, RNN, CNN and LSTM, to predict temperature in four climatic zones. We selected four cities, Dhaka, Moscow, Dubai and Brasilia from four different climatic zones. It is seen that the overall accuracy (OA) of LSTM ranged between 85% to 95%, followed by CNN 78% to 91%, and RNN  64% to 94%. Though the OA values of these three models in four climatic zones differs significantly, high AUC values were seen in all scenario. The highest AUC value (0.999) was seen in continental climatic zone for LSTM model and lowest (0.963) in mil climatic zone for RNN.
Building Reliable Loan Approval Systems: Leveraging Feature Engineering and Machine Learning Shoaeinaeini, Maryam; Shoaeinaeini, Milad; Harrison, Brent; Jasemi, Milad
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Automating loan approval system is essential in today's banking system.  Even with the shift to online platforms, the traditional method is still cumbersome and needs a lot of customer-related data. This study proposes a robust solution to overcome these challenges. Despite previous studies, new financial indicators in feature engineering stage are introduces to extract more important client information, thereby improving prediction robustness and accuracy. To implement our integrated approach, an online dataset from a finance company, is utilized. The dataset is preprocessed by various data preparation techniques, including cleaning, transformation, and feature engineering. Subsequently, the preprocessed data undergoes a range of powerful machine learning techniques such as K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes, and Logistic Regression. Additionally, three robust ensemble methods including Random Forest, AdaBoost Classifier, and Gradient Boosting Classifier are employed for further improveness in performance.  The presented approach succeeded to acheive the highest accuracy by AdaBoost Classifier at 88%. A comparison with the original preprocessed model using ROC curve and feature importance analysis demonstrates the superior performance of our approach, with a larger area under the ROC curve and reduced false positive rate. Furthermore, the comparison findings show a stronger reliance of our model on financial features rather than personal customer features, highlighting its robust classification performance. These results indicate the potential strength of our model to replace the current loan approval system in real-world applications.
Forecasting Indonesia's Unemployment Rates Using Moving Average Methods Orisa‬, Mira; Faisol, Ahmad
Journal of Computer Science and Engineering (JCSE) Vol 5, No 1: February (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In general, individuals with higher levels of education tend to have more opportunities for better workplace employment. Unemployment stands as one of the major social and economic issues in Indonesia. Forecasting can aid governments in predicting the annual unemployment rate. One of the methods used for forecasting is the simple moving average. This method is advantageous over other forecasting techniques when processing data with less complex fluctuations. It is utilized to align historical data over a specific time frame to identify underlying trends or patterns in the data. The moving average method involves two crucial stages: selecting the time window and evenly calculating the values within that window. Based on the data decomposition results, two time periods were identified within the dataset: one spanning 6 months and the other 12 months. The mean absolute percentage error (MAPE) associated with the 6-month Period is lower than that of the 12-month window, indicating that predictions derived from a 6-month timeframe are more accurate than those based on a 12-month period. A clear relationship is observed between the volume of data (number of observations) and the accuracy of predictions for the simple moving average.
Accuracy Assessment of Monthly Rainfall Predictions using Seasonal ARIMA and Long Short-Term Memory (LSTM) Akbar, Ahmad Aldizar; Darmawan, Yahya; Wibowo, Arief; Rahmat, Hayatul Khairul
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hydro meteorological disasters are common in Indonesia. Rainfall predictions can help mitigate the impact of these disasters. This research aims to compare the accuracy of monthly rainfall prediction models using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) methods. The input data consists of monthly rainfall records from four locations: Sampali, Kualanamu, Belawan, and Tuntungan, located around Medan, North Sumatra. The dataset spans from 2000 to 2020, with training data from 2000 to 2018 and test data from 2019 to 2020. The accuracy assessment reveals that Belawan has the largest RMSE values for both models, measuring 27.68 mm for LSTM and 28.36 mm for SARIMA. Belawan records the highest MAE values, with LSTM and SARIMA yielding 5.65 mm and 5.79 mm, respectively. SARIMA models effectively capture general trends and seasonality in linear time series data with clear patterns but struggle with extreme changes or sharp fluctuations due to their reliance on linear relationships. In contrast, LSTMs are effective at modeling complex, non-linear relationships, making them suitable for capturing general trends, seasonal patterns, and more complicated variations in the data. Understanding the characteristics of the data is crucial before applying SARIMA or LSTM models.
Android Apps Vulnerability Detection with Static and Dynamic Analysis Approach using MOBSF Kusreynada, Sabrina Uhti; Barkah, Azhari Shouni
Journal of Computer Science and Engineering (JCSE) Vol 5, No 1: February (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ensuring the security of Android applications is paramount, especially for apps like Mobile JKN, launched by the Social Security Agency on Health “BPJS Kesehatan” under the Ministry of Health Republic Indonesia, which contain sensitive participant data. Such information is often targeted by cybercriminals seeking personal gain through data theft by exploiting security vulnerabilities within the application. To address these risks, a thorough analysis was conducted to detect security loopholes in the Mobile JKN application. The study used the Mobile Security Framework (MOBSF) tools and involved static and dynamic analyses. Despite the application’s implementation of secure SSL Pinning and detection of rooted devices, the static analysis revealed potential security loopholes, including dangerous permission access, weak cryptographic methods, and vulnerable hardcoded secrets. Moreover, the application was found vulnerable to Janus, SQL Injection, and padding oracle attacks. While the dynamic analysis showed satisfactory implementation of SSL Pinning and no performance degradation, it also revealed that root detection was lacking, and debugger connections were not detected while the application was running. These findings emphasize the critical need for immediate security enhancements in the Mobile JKN application.

Page 5 of 7 | Total Record : 61

 3   4   5   6   7 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 6, No 2: August (2025) Vol 6, No 1: February (2025) Vol 5, No 2: August (2024) Vol 5, No 1: February (2024) Vol 4, No 2: August (2023) Vol 4, No 1: February (2023) Vol 3, No 2: August (2022) Vol 3, No 1: February (2022) Vol 2, No 2: August (2021) Vol 2, No 1: February (2021) Vol 1, No 2: August (2020) Vol 1, No 1: February (2020) More Issue