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Recursive Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 29866588     DOI : https://doi.org/10.15294/rji
Core Subject : Science,
Computer Science & IT
Recursive Journal of Informatics published by the Department of Computer Science, Universitas Negeri Semarang, a journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. We hereby invite friends to post articles and citation articles in our journals. We appreciate it if you would like to submit your paper for publication in RJI. The RJI publication period is carried out 2 periods in a year, namely in March and September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 40 Documents
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Implementation of Bidirectional Long-Short Term Memory (Bi-LSTM) and Attention to Detect Political Fake News Using IndoBERT and GloVe Embedding Adham Satria Firmansyah; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.159

Abstract

Abstract. Indonesian political news is now increasingly spread through various media, especially social and online media. However, a lot of fake news are spread to bring down political opponents or attract public sympathy in order to find their own supporters. Of course, this news need to be watched out for and preventive measures must be taken so as not to cause misunderstanding in the wider community. Purpose: This study was conducted to detect the political news whether it’s classified as hoax or fact by its narration. Also, understanding how to build the news detector using corresponding architecture and word embeddings. Methods/Study design/approach: The model architecture of Bi-LSTM and attention mechanism is used to reach the goals from this study’s purposes. Many studies have been conducted to detect hoaxes but have not yet paid attention to the context of sentences and the contribution of words in a news text so that this architecture is made to overcome this problem. It uses IndoBERT to optimize the model for Indonesian language and also GloVe to obtain the word weights from pre-trained embedding. Then, the tokenization process is performed with IndoBERT and keras to generate token id and attention mask. After receiving the token id and attention mask as input, the data training process is performed for three architectural scenarios with each configuration of 20 epochs, batch size of 32, and the learning rate is 0.00001. Result/Findings: The results of this study are defined by a confusion matrix which contains accuracy, recall, precision, and F1-score as the evaluation. The combination of Bi-LSTM + Attention + IndoBERT + GloVe obtains the best result of 97,71% of accuracy, 96,33% of precision, 97,93% of recall, and 97,72% of F1-score. Novelty/Originality/Value: This study combines two word embeddings in order to make sure the weight of words is completely defined and optimized into the Bi-LSTM and attention mechanism architecture.
Brain Tumor Classification on Magnetic Resonance Imaging Images using Convolutional Neural Network with Cycle Generative Adversarial Network and Extreme Gradient Boosting Akbar Lintang Aji; Endang Sugiharti
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.486

Abstract

Abstract. With the current advancement in technology, image classification process can be carried out through computer processing. This can also be applied to various fields, one of which is the health sector. The health sector is known for its high complexity in pattern recognizing of diseases. One of the diseases that is difficult to classify is brain tumors.             Purpose: This study aims to improve the accuracy of classification in brain MRI images, which are known to have a small and unbalanced sample. This limitation poses challenges in developing an effective classification model. The classification model is highly dependent on the quantity of data used for training. Therefore, data augmentation techniques play a crucial role in influencing the model's performance. Methods/Study design/approach: In this study, CNN model using VGG-19 architecture was used to learn feature of brain tumor in brain MRI images. Additionally, CycleGAN is used to augment and balance the data, addressing issues related to data scarcity and imbalance, thus improving diversity of the dataset. And then, XGBoost is applied to classify the feature learned by the CNN model. Result/Findings: CycleGAN has the ability to generate new image by transferring characteristics between images with different classes, making it a suitable to replace traditional data augmentation techniques in CNN. Additionally, XGBoost can be used to improve the classification results by classifying the features learned by CNN model during the training process. The proposed combination method achieves a highest accuracy of 97.37%. Novelty/Originality/Value: CNN combined with CycleGAN and XGBoost successfully improved the accuracy of the model and addresed data scarcity and imbalances in the dataset used. This combined method can improve the accuracy of the classification models. This is proven by an accuracy increase of 0.36% when compared to previous research.
Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization Luthfi Ilham Agus Pratama; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.737

Abstract

Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements.             Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection.              Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.
Selection of Trading Indicators Using Machine Learning and Stock Close Price Prediction with the Long Short Term Memory Method Alfandy Himawan Bagus Rafli; Aji Purwinarko
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.945

Abstract

Abstract. Humans have a limit to their physical ability to work, so investment is needed to meet their needs and other goals according to their wants and needs. Investment has many types and risks according to the portion of the return value, such as mutual funds, bonds and stocks. Stocks are a form of investment that has a high risk because of the rapid fluctuations in stock values. Prediction of stock movements is usually assisted by indicators, but predictions using indicators require complex analysis because of the diverse periods and different movements in each stock data case.  Purpose: To predict the closing price of BBCA and BBRI shares in the next 10 days by considering the count of technical indicators in the form of Moving average (MA), Exponential moving average (EMA), Rate Of Change (ROC), Price Momentum, Relative Strength Index (RSI), Stochastic Oscillator in periods 21, 63 and 252. Methods/Study design/approach: This research was conducted by comparing the accuracy of Random Forest, Decision Tree, KNN, SVM using K-fold Cross Validation then the method with the best accuracy was used to find out how much velue from the trading indicators used and predict the closing price of shares per day at BBRI and BBCA companies for the next 10 day period using the LSTM algorithm. Result/Findings: The best accuracy in the k-fold cross validation process is random forest. random forest is used to train indicator data in determining 5 indicators along with the period that has the highest value, in this test it produces values on BBCA data in order, namely ROC63, RSI63, MOM63, MA252, EMA21 while on BBRI data in order, namely ROC63, MOM63, RSI63, MA252, MA21. This indicator is used in the price forecasting process with the LSTM method to determine the closing price in the next 10 days. The LSTM method in this study resulted in 96.8% accuracy for BBCA and 96.4% accuracy for BBRI. Novelty/Originality/Value: The forecasting accuracy on BBCA is 96.8% and the forecasting accuracy on BBRI is 96.4%. This shows that the accuracy results are classified as good because the prediction results are close to the actual results. The data training process is expected to help traders in making stock buying and selling decisions that are adjusted to the fundamental aspects of the company.
Application of Fuzzy Logic in Visual Novel Evaluation System Using Unity 3D Epafraditus Memoriano; Riza Arifudin
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.1158

Abstract

Purpose: Visual novels, narrative-driven games focused on character interaction, commonly employ point-based evaluation systems that struggle to represent the inherent complexity and uncertainty of player choices. This research introduces a novel approach: integrating fuzzy logic into visual novel evaluation systems using Unity 3D. Fuzzy logic addresses the limitations of point-based systems by accounting for the "fuzzy" nature of player choice and its varied impact on story progression and character relationships. Methods/Study design/approach: A visual novel game was developed in Unity 3D, incorporating a fuzzy logic evaluation system for scoring player choices and assessing route progress. Fuzzy sets and membership functions were defined for key aspects like emotional response, character alignment, and plot development. These aspects were dynamically evaluated based on player dialogue selection, and individual scores were aggregated to generate a final route evaluation. Result/Findings: Testing demonstrated seamless integration of the fuzzy logic system within the game engine. Evaluation of conversation choices and route progression yielded accurate and nuanced scores, reflecting the varying weight of each decision based on narrative context and character interaction. Fuzzy logic facilitated the interpretation of "fuzzy" player choices, translating them into meaningful information for story progression and character relationships. Novelty/Originality/Value: This research presents a novel and promising approach to visual novel evaluation by leveraging the strengths of fuzzy logic. It overcomes the limitations of traditional point-based systems, capturing the complexity and dynamism of player choices within the narrative. The dynamic and responsive evaluation results enhance player engagement and provide a more immersive gaming experience.
Random Forest Algorithm Optimization using K-Nearest Neighborand SMOTE on Diabetes Disease Syuja Zhafran Rakha Krishandhie; Aji Purwinarko
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.1576

Abstract

Abstract. Diabetes is a chronic disease that can cause long-term damage, dysfunction and failure of various organs in the body. Diabetes occurs due to an increase in blood sugar (glucose) levels exceeding normal values. Early diagnosis of diseases is crucial for addressing them, especially in the case of diabetes, which is one of the chronic illnesses. Purpose: This study aims to find out how the implementation of the K-Nearest Neighbor algorithm with the Synthetic Minority Oversampling Technique (SMOTE) in optimizing Random Forest algorithm for diabetes disease prediction. Methods/Study design/approach: This study uses the Pima Indian Diabetes Dataset, the random forest algorithm for the classification, k-nearest neighbor for optimization, and SMOTE for the minority class oversampling. Result/Findings: The prediction accuracy of the model using SMOTE and k-nearest neighbor is 92,86%. Meanwhile, the model that does not use SMOTE and k-nearest neighbor obtains an accuracy of 83,03%. Novelty/Originality/Value: This research shows that the use of random forest algorithm with k-nearest neighbor and SMOTE gives better accuracy than without using k-nearest neighbor and SMOTE.
Implementation of the Term Frequency-Inverse Document Frequency Method for Mental Health Classification Using Algorithm Support Vector Machine Ilfa Minatika; Riza Arifudin
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.1921

Abstract

Abstract. Mental health is a person's emotional, social and psychological condition. A person's mental health level can be influenced by emotional experiences, behavior, environment and family educational background. A person's psychological well-being can be influenced by a person's behavior, where they live, the education they receive, and their emotional experiences. It is important not to underestimate the existence of mental health disorders because the number of cases is currently increasing. Purpose: Using SVM algorithm and TF-IDF method can produce good accuracy for classification text. Therefore this research aims to determine the implementation of the use of the TF-IDF method and the SVM algorithm in mental health classification and to determine the accuracy results of using these methods. Study Method/Design/Approach: The methods used in the research this for the mental health classification is Term Frequency-Inverse Document Frequency used in the vectorization process to convert text into a numerical representation, as well as using the Support Vector Machine algorithm in modeling. The dataset used is the Mental Health Corpus dataset obtained from the Kaggle website. This dataset consists of two classes containing text and labels totaling 27,977 data. Before applying the model, preprocessing is carried out first, namely cleaning the text using stopword removal and stemming. After cleaning the text, the next process is vectorization using CountVectorizer and TF-IDF. Results/Findings: In this study the SVM algorithm was used four kernels, namely the linear kernel, the RBF kernel, the polynomial kernel, and the later sigmoid kernel get the best accuracy results on the RBF kernel if compared to with other kernels. Accuracy results obtained _ of 92.62%, value precision of 92.64%, value recall 92.62%, and value f1-score 92.62%. Novelty/Originality/Value: So, it can be concluded that the application of the SVM algorithm and the TF-IDF method is possible used for classification mental health results mark high accuracy.
Combination of Genetic Algorithm and Spiking Neural Network Leaky Integrate-And-Fire Model in Analyzing Brain Ct Scan Image for Stroke Disease Detection Fabian Dominggus Eka Boro; Endang Sugiharti
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.3492

Abstract

Abstract. Stroke is a condition where there is impaired brain function due to lack of oxygen caused by blockage, breakdown, or blood clots inside brain. Diagnosis of stroke is usually based on symptoms, but symptoms are not always the correct measure. In examining a stroke, the most common way to examine a patient is to perform a CT scan of the brain. Purpose: This study was conducted with the aim of predicting brain scan images consisting of normal brain, ischemic stroke brain, and hemorrhagic stroke brain. It is also to understand how an algorithm works to recognize and predict an image. Methods/Study design/approach: The image data is trained using machine learning algorithm of neural network, specifically spiking neural network (SNN) using leaky Integrate-and-Fire (LIF) method, which practices the biological performance of human nerves. SNN offers an alternative way of a computational algorithm that mimics the workings of the human brain, especially the nerves in the brain at a low computational cost. In addition, this research optimizes SNN parameters using genetic algorithm (GA). GA is proven to be a successful optimization algorithm from many sources. GA is performed after going through the process in the SNN LIF algorithm, then the parameters in SNN are entered into the algorithm operations in GA until it reaches the most optimal parameter value. Although it requires a large amount of computational time and cost, combining it with SNN will be precise and less labor-intensive. Result/Findings: Calculation of accuracy results in this study using confusion matrix, conducted on SNN test with LIF method resulted in 90.27%. While with parameter optimization with GA, the final result of the SNN LIF algorithm produces 96.3% accuracy. Novelty/Originality/Value: This study was conducted to predict stroke disease with human brain images as training data, using the SNN LIF model to train the model and identify patterns that help in predicting stroke risk. For comparison, this research also uses optimization of the model using GA which is useful for determining the core parameters in the training process of the SNN LIF model.
QR-Code Based Visual Servoing and Target Recognition to Improve Payload Release Accuracy in Air Delivery Missions using Fully Autonomous Quad-Copter UAV Bondan Eka Nugraha; Zaenal Abidin
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.4430

Abstract

Abstract. Unmanned Aerial Vehicles (UAVs) are increasingly utilized for package delivery due to their efficiency and automation capabilities. UAVs can execute autonomous flight missions using Global Positioning System (GPS)-based navigation. However, challenges arise in the final stage of delivery, known as the last-mile delivery problem. The limitations of GPS-based navigation, the absence of recipient authentication, and shifting drop-off points create reliability and safety concerns. External factors such as varied environmental topography further contribute to delivery inaccuracies, highlighting the need for a more precise approach. Purpose: Many studies have explored UAV navigation and delivery systems, but challenges in last-mile delivery remain unresolved. This research introduces an improved UAV delivery system using computer vision (CV) and image-based visual servoing (IBVS) with QR Codes as location markers. The aim is to enhance UAV navigation accuracy and recipient verification, ensuring more reliable package deliveries. Methods/Study design/approach: The study implements a CV-based navigation system where QR Codes serve as landing markers for UAVs. Image processing is conducted using a companion computer linked to the UAV's flight control system. The IBVS method enables UAVs to adjust their position in real-time, minimizing GPS errors. Recipient verification is performed through QR Code scanning before releasing the package. The system is tested through computer simulations and real flight experiments to assess accuracy and effectiveness. Result/Findings: Experimental results demonstrate that UAVs equipped with the IBVS method can successfully complete package delivery missions with improved accuracy. GPS errors are corrected by aligning the UAV's position with QR Code markers, and recipient authentication is verified before package release. Real-flight tests confirm that this approach significantly enhances UAV delivery reliability compared to conventional GPS-based navigation. Novelty/Originality/Value: This research presents a novel integration of computer vision and UAV navigation for addressing last-mile delivery challenges. By leveraging IBVS and QR Code-based authentication, UAVs can perform fully autonomous, precise, and secure package deliveries. This method offers a viable solution to improve UAV-based logistics, reducing delivery errors and enhancing operational safety.
Implementation of the K-Nearest Neighbor Algorithm (KNN) with Principal Component Analysis to Diagnose Tuberculosis Yuliana Putri; Alamsyah Alamsyah
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.5235

Abstract

Purpose: Tuberculosis (TB) is an infectious disease that attacks the respiratory organs, the lungs, and some can attack organs outside the lungs. Indonesia is one of the largest contributors to TB cases with around 320,000 new cases every year. Delays in diagnosing TB disease can cause a higher number of deaths due to errors in the treatment of sufferers. This makes the early diagnosis of TB disease important as early as possible. The research carried out aims to implement machine learning techniques to help diagnose TB disease. Methods: The research was carried out using the K-Nearest Neighbor (KNN) classification algorithm which was optimized with the Principal Component Analysis (PCA) feature selection technique. The dataset used consists of 577 data with 12 attributes labeled patients with tuberculosis and patients who do not have tuberculosis. Result: From the research that has been conducted, models that implement the KNN algorithm with PCA produce models with better performance than models that only implement KNN. The model that only uses KNN gets an accuracy of 92.528%, while the model that uses KNN and PCA gets an accuracy of 98.85%. This shows that the implementation of KNN and PCA is able to produce a good tuberculosis diagnosis model and can be used to assist in the early diagnosis of tuberculosis. Novelty: Using PCA in the feature selection process can reduce unnecessary attributes. It is a PCA that helps reduce the dimensionality, simplifies the visualization and interpretation of complex data sets. The use of PCA has been proven to be able to optimize the performance of the KNN algorithm for the detection of tuberculosis.  

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