Computer Science and Information Technologies (E-Journal)
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Classification and similarity detection of Indonesian scientific journal articles
Full text linkThe development of technology is accelerating in finding references to scientific articles or journals related to research topics. One of the sources of national aggregator services to find references is Garba Rujukan Digital (GARUDA), developed by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) of the Republic of Indonesia. The naïve Bayes method classifies articles into several categories based on titles and abstracts. The system achieves an F1-score of 98%, which indicates high classification accuracy, and the classification process takes less than 60 minutes. Article similarity detection is done using the cosine similarity method, and a similarity score of 0.071 reflects the degree of similarity between the title and the abstract that has been concatenated, while a score close to 1 indicates a higher similarity. Searching for similar scientific articles based on title and abstract, sort articles based on the results of the highest similarity score are the most similar articles, and generating article categories. The results of the research show that the proposed method significantly improves the classification and search processes in GARUDA, as well as accurate and efficient similarity detection
Blockchain technology for optimizing security and privacy in distributed systems
Full text linkBlockchain technology is increasingly recognized as an effective solution for addressing security and privacy challenges in distributed systems. Blockchain ensures information security by validating data and defending against cyber threats, while guaranteeing data integrity through transaction validation and reliable storage. The research involves a literature study, problem identification, analysis of blockchain security and privacy, model development, testing, and analysis of trial results. Furthermore, blockchain enables user anonymity and fosters transparency by utilizing a distributed network, reducing the risk of fraudulent activities. Its decentralized nature ensures high reliability and accessibility, even in node failures. Blockchain enhances security and privacy by offering features like data immutability, provenance, and reduced reliance on trust. It decentralizes data storage, making tampering or deletion extremely challenging, and ensures the invalidation of subsequent blocks upon any changes. Blockchain finds applications in various domains, including supply chains, finance, healthcare, and government, enabling enhanced security by tracking data origin and ownership. Despite scalability and security challenges, the potential benefits of reduced costs, increased efficiency, and improved transparency position blockchain as a promising technology for the future. In summary, blockchain technology provides secure transaction recording and data storage, thus enhancing security, privacy, and the integrity of sensitive information in distributed systems
Smart brake pad early warning system: enhancing vehicle safety through real-time monitoring
Full text linkA contributing factor to traffic accidents is brake pad failure, which diminishes braking system performance and extends braking distance. This work develops a prototype utilizing internet of things (IoT) to measure brake pad thickness, hence enhancing driver awareness through real-time monitoring. The system establishes the thickness detection threshold at 75% (3-4 mm) and 50% (5–6 mm) as a cautionary parameter. The thickness parameter employs an American wire gauge (AWG) 18 cable to connect to the ESP32 microcontroller. The web-based IoT monitoring interface employs Laravel. This method enables drivers to get prompt notifications regarding the decrease in brake pad thickness, hence permitting urgent preventative maintenance to mitigate the risk of accidents. The system underwent testing through friction at a rotational speed of 600 to 6,000 rpm. The test findings indicated that the sensor precisely measured the brake pad thickness with a prototype response time of a second. This system is suitable for implementation on old model vehicles that do not have an early warning system. The installation of this technology is anticipated to enhance driver knowledge of the state of the brake pads, hence potentially diminishing the danger of brake system failure caused by unmonitored pad wear
Secure e-voting system using Schorr's zero-knowledge identification protocol
Full text linkIn today's era of technological progress, the electoral system has changed significantly with the introduction of electronic voting (e-voting). The traditional voting system poses many vulnerabilities to manipulation, potential human error, and problems with voter privacy. These limitations can lead to reduced trust and participation in elections. E-voting has emerged to address this issue, aiming to improve the convenience, security, and privacy of voters. E-voting systems are evaluated on accuracy, security, privacy, and transparency; however, ensuring voter privacy while maintaining these principles remains a significant challenge. A potential solution to improving privacy in e-voting is Schorr's zero-knowledge identification protocol. This protocol allows voters to confirm their identity without revealing personal information, maintaining voter privacy throughout the process. By implementing these protocols, the e-voting system can strengthen security and privacy, making elections more transparent and trustworthy. As technology evolves, adopting solutions like Schorr's zero-knowledge identification protocol can help e-voting systems meet the growing demand for safe, fair, and private elections
Optimizing energy distribution efficiency in wireless sensor networks using the hybrid LEACH-DECAR algorithm
Full text linkWireless sensor network (WSN) is a network system consisting of various supporting components that integrate information to the base station. In its operation, delivery is greatly influenced by energy usage because limited battery supply causes variability in energy consumption on node activity factors, communication distance, and environmental conditions. So, in order to increase performance and energy efficiency, a routing protocol is required by selecting the best path through cluster head. The technique of determining the cluster head (CH) based on energy is used to avoid irregularity (randomness). In this study, the hybrid routing protocol selects CH based on the remaining energy, considering distance, coverage radius, and energy metrics. The system test evaluation compares the implementation of low-energy adaptive clustering hierarchy (LEACH) and hybrid LEACH- Distributed, energy and coverage-aware routing (DECAR). The results of 300 rounds show that the hybrid achieves a packet delivery ratio close to 100% and a throughput of 78.22 Kbps, while LEACH achieves a packet delivery ratio of 92.18% and a throughput of 247.15 Kbps. The average energy consumption of LEACH is 99.27%, while the hybrid shows much greater efficiency at 30.55%. This study emphasizes the significance of maintaining equilibrium performance and energy consumption in the development of future routing protocols
A machine learning approach for early prediction of mental health crises
Full text linkThe global mental health crisis, intensified by the COVID-19 pandemic, placed unprecedented strain on healthcare systems and highlighted the urgent need for proactive crisis prevention strategies. This study investigated the effectiveness of various machine learning (ML) models in predicting mental health crises within 28 days post-hospitalization, leveraging an eight-year longitudinal dataset. Multiple data preprocessing techniques, including feature selection (EFSA, RFECV), imputation, and class imbalance handling (SMOTE, Tomek links), were systematically applied to enhance model performance. Six traditional classifiers—logistic regression, support vector machine, k-nearest neighbors, naive Bayes, XGBoost, and AdaBoost—were evaluated alongside ensemble learning (EL) methods (bagging, boosting, stacking). Performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used for comprehensive assessment. Results demonstrated that ensemble methods, particularly boosting and bagging, consistently achieved high predictive accuracy (up to 93%), with XGBoost and AdaBoost emerging as top performers. Feature selection and class imbalance techniques further improved model robustness and generalizability. The findings underscored the potential of ML-driven approaches for early identification of at-risk patients, enabling more effective resource allocation and timely interventions in mental health care. Recommendations for integrating these predictive tools into clinical workflows were discussed to support data-driven decision-making
The smart e-bike ecosystem integrates internet of things and artificial intelligence
Full text linkThe smart e-bike ecosystem, a combination of internet of things (IoT) and artificial intelligence (AI), has transformed urban mobility. This study aims to shed light on the transformative potential of the smart e-bike ecosystem in the context of urban transportation solutions. It includes real-time navigation, crash detection, and a smart electric drive to encourage sustainable practices and reduce reliance on traditional vehicles. The use of smart locks and parking beacon systems creates a safe and efficient urban infrastructure, encouraging e-bike use. This approach reduces traffic congestion and carbon emissions. IoT frameworks in smart e-bikes improve the user experience and contribute to urban mobility solutions. Real-time monitoring of critical parameters, such as battery levels, speed, and maintenance requirements, keeps riders informed and safe at all times. IoT-enabled features, such as navigation assistance, shorten travel times and improve the efficiency of urban transportation systems. The evolution of smart e-bikes is consistent with the anticipated improvements of 6G networks, which promise to transform communication infrastructures. AI-powered features such as real-time navigation and crash detection make rides safer. The use of smart electric drives and cloud server technology promotes a data-driven approach to transportation. Future research and development should look into the use of advanced localization techniques to improve user experience while addressing accuracy and energy consumption issues
Hybrid feature fusion from multiple CNN models with bayesian-optimized machine learning classifiers
Full text linkInformation technology advancements have created big data, necessitating efficient techniques to retrieve helpful information. With its capacity to recognize and categorize patterns in data, especially the growing amount of picture data, deep learning is becoming a viable option. This research aims to develop a medical image classification model using chest X-Ray with four classes, namely Covid-19, Pneumonia, Tuberculosis, and Normal. The proposed method combines the advantages of deep learning and machine learning. Three pre-trained CNN models, VGG16, DenseNet201, and InceptionV3, extract features from images. The features generated from each model are fused to enhance the relevant information. Furthermore, principal component analysis (PCA) was applied to reduce the dimensionality of the features, and Bayesian optimization was used to optimize the hyperparameters of the machine learning algorithms support vector machine (SVM), decision tree (DT), and k-nearest neighbors (k-NN). The resulting classification model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that FF-SVM, which is the proposed model, achieved an accuracy of 98.79% with precision, recall, and F1-score of 98.85%, 98.82%, and 98.84%, respectively. In conclusion, fusing feature extraction from multiple CNN models improved the classification accuracy of each machine-learning model. It provided reliable and accurate predictions for lung image diagnosis using chest X-Ray
Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things
Full text linkWater quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture
Geoinformation system for monitoring forest fires and data encryption for low-orbit vehicles
Full text linkThis article discusses two important aspects of unmanned aerial vehicles (UAVs): forest fire monitoring and data security for low-orbit vehicles. The first part of the article is devoted to the development of a geographic information system (GIS) for monitoring and forecasting the spread of forest fires. The system uses intelligent processing of aerospace data obtained from UAVs to timely detect fires, determine their characteristics and forecast the dynamics of development. The second part of the article focuses on the problem of high-speed encryption of data transmitted from low-orbit aircraft. An effective encryption algorithm is proposed that ensures high data processing speed and reliable protection of information from unauthorized access. The article presents the results of modeling and analysis of the effectiveness of the proposed solutions