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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 7 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2025): March 2025" : 7 Documents clear
Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization Pratama, Luthfi Ilham Agus; Putra, Anggyi Trisnawan
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.
Application of Fuzzy Logic in Visual Novel Evaluation System Using Unity 3D Memoriano, Epafraditus; Arifudin, Riza
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 Krishandhie, Syuja Zhafran Rakha; Purwinarko, Aji
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.
Combination of Genetic Algorithm and Spiking Neural Network Leaky Integrate-And-Fire Model in Analyzing Brain Ct Scan Image for Stroke Disease Detection Boro, Fabian Dominggus Eka; Sugiharti, Endang
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 Nugraha, Bondan Eka; Abidin, Zaenal
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.
Sentiment Analysis of Presidential Candidates in 2024: A Comparison of the Performance of Support Vector Machine and Random Forest with N-Gram Method Ramadhan, Muhammad Rizki; Budiman, Kholiq
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.v3i1.8385

Abstract

Abstract. This paper conducts a sentiment analysis of presidential candidates in Indonesia's 2024 election using Twitter data. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter entries, sentiment was categorized as positive or negative. Preprocessing techniques cleaned and normalized the data, followed by labeling with the VADER lexicon. This study contributes insights into public sentiment towards presidential candidates and the effectiveness of machine learning algorithms for political sentiment analysis. Purpose: This study aims to analyze public sentiment towards presidential candidates in Indonesia's 2024 election using the N-Gram method. By employing Support Vector Machine and Random Forest algorithms, we compare their performance in sentiment analysis. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter data entries, we seek to provide insights into the electorate's perceptions and preferences, contributing to a deeper understanding of the political landscape during this crucial period. Methods/Study design/approach: The study uses Support Vector Machine (SVM) and Random Forest algorithms for sentiment analysis on a dataset of 8555 tweets about Indonesia’s 2024 presidential candidates. SVM, paired with TF-IDF, and Random Forest, paired with N-Gram, are used for feature extraction. The data is labeled using the Vader lexicon. Result/Findings: The study compared Support Vector Machine (SVM) with TF-IDF and Random Forest with N-Gram methods in analyzing public sentiment towards Indonesia's 2024 presidential candidates. Results showed Random Forest with N-Gram achieved 85% accuracy, outperforming SVM with TF-IDF at 82%. Novelty/Originality/Value: This study provides insights into sentiment analysis applied to the 2024 Indonesian presidential election, enhancing understanding of public sentiment dynamics. Comparing SVM with TF-IDF and Random Forest with N-Gram contributes to the field, suggesting avenues for future research such as integrating contextual information or social network analysis for deeper insights into political opinion trends.
Textual Entailment for Non-Disclosure Agreement Contract Using ALBERT Method Azmi, Abdillah; Alamsyah, Alamsyah
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.v3i1.9730

Abstract

Purpose: NDA (Non-Disclosure Agreement) is one type of contract letter. An NDA binds two or more parties who all agree that certain information shared or created by one party is confidential. This type of contract serves to protect sensitive information, maintain patent rights, or control the information shared. Reading and understanding a contract letter is a repetitive, time-consuming, and labor-intensive process. Nevertheless, the activity is still crucial in the business world, as it can bind two or more parties under the law. This problem is perfect for Artificial Intelligence using Deep Learning. Therefore, this research aims to test and develop a pretrained language model that is designed for understanding contract letters through Natural Language Inference task. Method The method used is to train model to perform the language inference task of textual entailment using CNLI (Contract NLI) dataset. ALBERT-base model version that has been tuned to perform textual entailment is used along with LambdaLR for early stopping and AdamW as optimizer. The model is pre-trained with CNLI dataset several times with multiple hyperparameter. Result: As a result, the ALBERT base model that was used showed an accuracy score of 85 and EM score up to 85.04 percent. Although this score is not the State of the Art of the CNLI benchmark, the trained model can outperform other base versions of model that based on BERT and BART, like SpanNLI BERT-base, SCROLLS (BART-base) and Unlimiformer (BART-base). Value: ALBERT is a model that focuses on memory efficiency and small size parameters while maintaining performance. This model is suitable for performing tasks that require long context understanding with minimum hardware requirements. Such a model could be promising for the future of NLP in the legal area.

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