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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 50 Documents
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Search results for , issue "Vol. 8 No. 2 (2024): December 2024" : 50 Documents clear
Sentiment Analysis of Social Media X in the 2024 Indonesian Presidential Election Using the Naive Bayes Algorithm: Candidates' Backgrounds and Political Promises Prayudani, Santi; Situmorang, Dita Rouli Basa; Hidayah, Rizki; Ginting, Heri Sanjaya
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.7580

Abstract

In 2024, Indonesia holds a presidential election, and the candidates are making promises to each other to attract voters. Many people gave their opinions on X. This study uses the Naïve Bayes algorithm to analyze the sentiment of these tweets, with the aim of understanding the background of the candidates and their campaign promises. Data is collected from X by crawling technique, then data is pre-processed, trained using Naïve Bayes model, and evaluated for accuracy. Sentiments in tweets were classified as positive, negative, or neutral. The results showed that the Prabowo Subianto - Gibran Rakabuming Raka pair was the most talked about with 1005 tweets, followed by Anis Rasyid Baswedan - Muhaimin Iskandar with 707 tweets, and Ganjar Pranowo - Mohammad Mahfud M.D. with 572 tweets. The Prabowo Subianto - Gibran Rakabuming Raka pair received the most positive sentiment, which was 446 more than the other candidates.
Prediction of Basic Commodity Prices at the Cooperative, SME, and Trade Office Using the Least Squares Method Purwani, Desy; Samsudin, Samsudin
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8141

Abstract

The Cooperatives, Small and Medium Enterprises (SME), and Trade Office is responsible for managing national affairs related to maintaining the prices of basic commodities, including the implementation of technical instructions and regulations. A significant issue faced by the office is the lack of accessible information on estimated prices of basic commodities for both the public and government. This gap primarily stems from the absence of an information system in the Pematang Siantar City area capable of publishing these estimates. The purpose of this study is to design and develop a web-based system for predicting basic commodity prices, which will record annual price fluctuations for various basic commodities at the Cooperative, SME, and Trade Office. The findings of this study will provide policymakers with a better understanding of commodity prices in traditional markets within Pematang Siantar City, serving as a foundation for future price estimations. This is particularly relevant for market operations aimed at controlling unreasonable price increases. The Least Squares method was employed to calculate the estimated prices, with the system achieving a Mean Absolute Percentage Error (MAPE) of 14.20%, indicating that the system can predict market prices with a reasonable degree of accuracy.
Bagging Nearest Neighbor and its Enhancement for Machinery Predictive Maintenance Arisani, Muhammad Irfan; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8158

Abstract

K-nearest Neighbor is a simple algorithm in Machine learning for such a prediction classification task which plays in valuable aspects of understanding big data. However, this algorithm sometimes does a lacking job of classification tasks for many different dataset characteristics. Therefore, this study will adopt enhancement methods to create a better performance of the nearest-neighbor model. Thus, this study focused on nearest neighbor enhancement to do a binary classification task from the extremely unbalanced dataset of a machine failure problem. Firstly, this study will create new features from the machinery dataset through the feature engineering processes and transform the chosen numerical features with standardization steps as the proper scaling. Then, the modified under-sampling method will be given which will reduce the amount of the majority class to 4.75 times that of the minority class. Next is the applied grid-search tuning which will find the right parameter combinations for the nearest-neighbor model being applied. Furthermore, the previous pre-processing steps will be combined with an additional bagging method. Finally, the resulting bagged KNN will present a 0.971 rate of accuracy, 0.555 rate of precision, 0.781 rate of recall, 0.649 rate of f1-score, 0.95 auc of ROC curve, and 0.702 auc of precision-recall curve.
DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network Widodo, Rafiq Amalul; Delimayanti, Mera Kartika; Wulandari, Asri
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8242

Abstract

The detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. Based on our study and analysis, CNN has a precision rate of 95%. Its high accuracy and balanced performance across diverse attack types indicate the model's practical application in real-life situations. The model demonstrates promising performance in detecting different network traffic anomalies, offering significant insight into its potential for practical use. Further investigation is necessary to strengthen the resilience of DDoS assault tactics against emerging dangers and to tackle any potential constraints.
Classification of Brain Tumors by Using a Hybrid CNN-SVM Model Nabila, Talitha Safa; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8277

Abstract

Brain tumors are diseases that involve the growth of brain cells, causing abnormalities in the brain region. An MRI scan is a useful tool for tumor detection. Researchers can process the obtained image data to conduct research capable of detecting brain tumor disease. Classifying brain tumors facilitates effort, planning, and accurate diagnosis, enabling the formulation and evaluation of treatment options for a patient with a brain tumor. The research was conducted to classify whether or not there was a tumor in the brain by using a combination of algorithms, namely CNN, to extract features from image data and then use SVM as a classification. CNN is a popular algorithm that deals very effectively with the complexity and variation of image data, whereas SVM is an algorithm for classification that maximizes margins and generalizations to produce accurate classifications. The project's goal is to create a hybrid model that can classify two labels based on image preprocessing processes, feature extraction, and brain tumor image data classification. In this study, the results of the CNN-SVM hybrid were able to obtain the highest score with Adam optimization and learning rate 0.001, accuracy of 98.92%, precision 98.92%, recall 98.92%, and f1-score 98.92%.
Improving Panic Disorder Classification Using SMOTE and Random Forest Nurmalasari, Dini; Yuliantoro, Heri R; Qudsi, Dini Hidayatul
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8315

Abstract

Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life. Early detection and intervention are crucial for managing panic disorder and improving the well-being of those affected. Technology plays a pivotal role in facilitating early detection through data-driven approaches that employ algorithms to identify patterns of behavior or symptoms associated with panic disorder. Accurate classification of panic disorder is crucial for effective diagnosis and treatment. However, machine learning models trained on imbalanced datasets, such as those containing panic disorder patients, are prone to overfitting, leading to poor generalization performance. This study investigates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing overfitting in panic disorder dataset classification using the Random Forest algorithm. The results demonstrate that SMOTE significantly improves the classification performance of Random Forest. By mitigating overfitting and improving generalization to unseen data, SMOTE increases accuracy by 15 percentage points. Before using SMOTE, the accuracy was 82%, and after using SMOTE it is 97%. The findings underscore the promise of SMOTE as a tool for boosting the performance of machine learning algorithms in classifying panic disorder from imbalanced data.
Facial Expression Recognition using Convolutional Neural Networks with Transfer Learning Resnet-50 Istiqomah, Annisa Ayu; Sari, Christy Atika; Susanto, Ajib; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8329

Abstract

Facial expression recognition is important for many applications, including sentiment analysis, human-computer interaction, and interactive systems in areas such as security, healthcare, and entertainment. However, this task is fraught with challenges, mainly due to large differences in lighting conditions, viewing angles, and differences in individual eye structures. These factors can drastically affect the appearance of facial expressions, making it difficult for traditional recognition systems to consistently and accurately identify emotions. Variations in lighting can alter the visibility of facial features, while different angles can obscure critical details necessary for accurate expression detection. This study addresses these issues by employing transfer learning with ResNet-50 and effective pre-processing techniques. The dataset consists of grayscale images with a 48 x 48 pixels resolution. It includes a total of 680 samples categorized into seven classes: anger, contempt, disgust, fear, happy, sadness, and surprise. The dataset was divided so that 80% was allocated for training and 20% for testing to ensure robust model evaluation. The results demonstrate that the model utilizing transfer learning achieved an exceptional performance level, with accuracy at 99.49%, precision at 99.49%, recall at 99.71%, and an F1-score of 99.60%, significantly outperforming the model without transfer learning. Future research will focus on implementing real-time facial recognition systems and exploring other advanced transfer learning models to further enhance accuracy and operational efficiency.
Predicting Startup Success Using Machine Learning Approach Ningrum, Icha Wahyu Kusuma; Ridho, Farid; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8338

Abstract

Predicting startup success is important because it helps investors, entrepreneurs, and stakeholders allocate resources more efficiently, minimize risks, and enhance decision-making in an uncertain and competitive environment. Therefore, investors need to predict whether a startup will succeed or fail. Investors conduct this assessment to determine if a startup is worthy of funding. The company's founders mark success here by receiving a sum of money through the Initial Public Offering (IPO) or Merger and Acquisition (M&A) process. If the startup closes, we will consider it a failure. The data used consists of 923 startup companies in the United States. We carried out the classification using four methods: Random Forest, Support Vector Machines (SVM), Gradient Boosting, and K-Nearest Neighbor (KNN). We then compare the results from the four methods with and without feature selection. We determine the feature selection based on the relative importance of each method. The results of this study indicate that the Random Forest method with feature selection has the best accuracy, precision, recall, and F1 score than the other methods, respectively 81.85%, 80.19%, 87.09%, and 83.44%.
Chat GPT Impact Analysis on API Testing: A Controlled Experiment Setiawan, Yehezkiel David; Yudha, Laurentius Gusti Ontoseno Panata; Mulyono, Yovie Adhisti; Simalango, Veronica Marcella Angela; Karnalim, Oscar
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8182

Abstract

This research examines the impact of ChatGPT as a learning aid for students in API testing. A controlled experiment compared two groups: one utilizing ChatGPT and the other relying on traditional documentation. The findings indicate that participants using ChatGPT scored significantly higher in both exam tests compared to the documentation group, despite taking longer to complete tasks. Statistical analysis using t-tests confirmed these differences as significant. Post-test surveys revealed an increase in participants confidence and effectiveness in understanding and using APIs after interacting with ChatGPT. However, potential downsides, such as over-reliance on ChatGPT and insufficient deep conceptual understanding, were also observed. The results suggest that while ChatGPT can greatly enhance the quality of learning and productivity in API-related tasks, users must balance AI assistance with independent problem-solving skills. This study underscores the potential of ChatGPT as a valuable educational tool, provided it is integrated thoughtfully into the learning process.
Effect of Load Balancing Bonding and Failover on Speed, Latency, Average, and Packet Loss Toriq, Farhan; Santoso, Banu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8276

Abstract

This study compares the performance between using load balancing bonding and not using load balancing bonding. This test was conducted on a virtual environment VMware and applied to an Internet Service Provider (ISP) network. The configuration was carried out on two routers connected with three virtual cables that function as load balancing bonding, for the computer to function as a load balancing bonding test. In this study, the workload given consisted of 1000 package. The results of this study showed better performance with load balancing bonding compared to without load balancing bonding, shown in the default condition, the speed with Balance Round Robin mode being higher with a value of 0,157Mbps (Tx) and 3.4Mbps (Rx). Latency with Balance Round Robin mode is smaller with a value of 729ms. The average with Balance Round Robin mode is higher with a value of 768bps. While the packet loss has the same result, namely 0% no lost packets were found. In failover conditions, the speed with Balance Round Robin mode is still higher with a value of 0,107Mbps (Tx) and 2.2Mbps (Rx). The value is"‹"‹ obtained from testing conducted on Bandwidth Test and Traceroute tools. It can be concluded that the use of load balancing bonding can provide a significant effect on improving network performance both when used in default conditions and in failover conditions based on speed, failover, latency, average, packet loss parameters in the research that has been conducted.

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