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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 5 Documents
 1 
Search results for , issue "Vol 5, No 1: February (2024)" : 5 Documents clear
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)

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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.
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)

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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.
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)

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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.
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)

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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.
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)

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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.

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