1 10 20 https://repository.horizon.ac.id/files/original/67aec3605edc7afd0ea3669c021713bf.pdf 936d17ad477ab3446bbafbff4bd7b7ae Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Feature SelectionUsingPearson Correlation for Ultra-WidebandRangingClassification Subject The topic of the resource ndoor Positioning; Feature selection;Pearson correlation; Machine learning; UWB Ranging Description An account of the resource Indoor positioning plays a crucial role in various applications, including smart homes, healthcare, robotics, and asset tracking. However, achieving high positioning accuracy in indoor environments remains a significant challenge due to obstacles that introduce NLOS conditions and multipath effects. These conditions cause signal attenuation, reflection, and interference, leading to decreased localization precision. This research addresses these challenges by optimizing feature selection LOS, NLOS, and multipath classification within Ultra-Wideband (UWB) ranging systems. A systematic feature selection approach based on Pearson correlation is employed to identify the most relevant features from an open-source dataset,ensuring efficient classification while minimizing computational complexity. The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. Experimental results demonstrate that the proposed feature selection method significantly reduces model training and testing times without compromising accuracy. The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. The reduction in computational overhead makes the proposed approach highly suitable for real-time applications, particularly in edge-computing environments where processing efficiency is critical.These findings highlight the effectiveness of Pearson correlation-based feature selection in improving UWB-based indoor positioning systems. The optimized feature set facilitates robust LOS, NLOS, and multipath classification while reducing resource consumption, making it a promising solution for scalable and real-time indoor localization applications. Creator An entity primarily responsible for making the resource Gita Indah Hapsari1*, Rendy Munadi2, Bayu Erfianto3, Indrarini Dyah Irawati Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6281/1028 Publisher An entity responsible for making the resource available Doctoral of Informatics, School of Computing, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 11-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/30bd6e277afffbd0d722377cd166167e.pdf 5755fd318c8e471d08e6668824f552f0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparing Word Representation BERT and RoBERTa in Keyphrase Extraction using TgGAT Subject The topic of the resource Keyphrase Extraction, BERT, RoBERTa, Pre-Trained Language Models, Topic-Guided Graph Attention Networks Description An account of the resource In this digital era, accessing vast amountsof information from websites and academic papers has become easier. However, efficiently locating relevant content remains challenging due to the overwhelming volume of data. Keyphrase Extraction Systems automatethe process of generating phrases that accurately represent a document’s maintopics. These systems are crucialfor supporting various natural language processing tasks, such as text summarization, information retrieval, and representation.The traditional method of manually selecting key phrasesis still common but often proves inefficient and inconsistent in summarizing the main ideas of a document. This study introduces an approach that integrates pre-trained language models, BERT and RoBERTa, with Topic-Guided Graph Attention Networks (TgGAT) to enhance keyphrase extraction. TgGAT strengthens the extraction process by combining topic modellingwith graph-based structures, providing a more structured and context-aware representation of a document’s key topics. By leveraging the strengths of both graph-based and transformer-based models, this research proposes a framework that improves keyphrase extraction performance. This is the first to apply graph-based and PLM methods for keyphrase extraction in the Indonesian language. Theresults revealed that BERT outperformed RoBERTa, with precision, recall, and F1-scores of 0.058, 0.070, and 0.062, respectively, compared to RoBERTa’s 0.026, 0.030, and 0.027.Theresult showsthat BERT with TgGAT obtained more representative keyphrases than RoBERTa with TgGAT. These findings underline the benefits of integrating graph-based approaches with pre-trained modelsfor capturing both semantic relationships and topic relevance Creator An entity primarily responsible for making the resource Novi Yusliani1*, Aini Nabilah2, Muhammad Raihan Habibullah3, Annisa Darmawahyuni4, Ghita Athalina Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6279/1034 Publisher An entity responsible for making the resource available Department of Informatic Engineering, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/c2d941e2776dbded0e2a91bac19426cd.pdf 506b3dc4f8d86468bc6abe834abe48d1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Hand Sign Recognition of Indonesian Sign Language System (SIBI) Using Inception V3 Image Embedding and Random Forest Subject The topic of the resource hand sign recognition;SIBI; Inception V3; image embedding; random forest Description An account of the resource This paper presents a sign language recognition system for the Indonesian Sign Language System (SIBI) using image embeddings combined with a Random Forest classifier. A dataset comprising 5280 images across 24 classes of SIBI alphabet symbols was utilized.Image features were extracted using the Inception V3 image embedding, and classification was performed using Random Forest. Model evaluation conducted through K-Fold cross-validation demonstrated that the proposed methodachieved an accuracy of 85.40%, anF1score of 85.20%, a precision of 85.30%, and a recall of 85.40%. Moreover, the total computation time required by the proposed method is 1152.85 seconds. While the performance indicates room for improvement, this study lays the groundwork for enhancing sign language recognition systems to support the preservation and broader adoption of SIBI in Indonesia Creator An entity primarily responsible for making the resource Mayang Sari1, Eko Rudiawan Jamzuri2* Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6156/1035 Publisher An entity responsible for making the resource available Departmentof Electrical Engineering, Politeknik Negeri Batam, Batam, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/b2c3613790d07b209ef0ec3eb11ce46a.pdf 36c1206e40711b995f29717c9e001cb2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna Subject The topic of the resource multilayer perceptron;hyperparameter optimization;gridsearch;optuna;SMOTE Description An account of the resource Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning and dataquality. This study optimizes MLP performance using GridSearch and Optuna for hyperparameter tuning while addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset comprises demographic and financial attributes influencing car purchasing power. Initially, the dataset exhibited class imbalance, which could lead to biased predictions; SMOTE was applied to generate synthetic samples, ensuring a balanced class distribution. Two hyperparameter tuning approaches were implemented: GridSearch, which systematically explores a predefined parameter grid, and Optuna, an adaptive optimization framework utilizing a Bayesian approach. The results show that Optuna achieved the highest accuracy of 95.00% using the Adam optimizer, whereas GridSearch obtained the best accuracy of 94.17% with the RMSProp optimizer, demonstrating Optuna's superior ability to identify optimal hyperparameters. Additionally, SMOTE significantly improved model stability and predictive performance by ensuring adequate class representation. These findings offer insights into best practices for optimizing MLP in predictive modeling. The combination of SMOTE and advanced hyperparameter tuning techniques is applicable to various domains requiring accurate predictive analytics, such as finance, healthcare, and marketing. Future research can explore alternative optimization algorithms and data augmentation techniquesto further enhance model robustness and accuracy Creator An entity primarily responsible for making the resource Ginanti Riski1*, Dedy Hartama2, Solikhun3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6328/1037 Publisher An entity responsible for making the resource available Department of Informatics Engineering, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 25-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/22fff4eb74901bead3f439dd270ba38a.pdf 267009a80df30c400c00b73791580ffb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementation of Generative Language Models in Cyber Exercise Secure Coding Using Prompt Engineering Subject The topic of the resource Cyber Exercise;Generative Language Models; OWASP; Prompt Engineering; Secure Coding Description An account of the resource Utilizing Artificial Intelligence (AI) in various fields can open up great opportunities to improve cybersecurity. AI can effectively detect security threats, analyze attack patterns, and respond rapidly to changes in thecyber environment. Overthe times, the need for secure software is becoming increasingly urgent due to increasing vulnerabilities in software products. In 2022, the National Cyber and Crypto Agency (BSSN) recorded 2,348 cases of web defacement. One of theleading causes of these attacks is the need for more attention to secure coding practices during software development. Secure coding is also one of the critical aspects of implementing an Information Security Management System (ISMS), which is regulated in more detail in control 8.28 of ISO 27002:2022, where poor coding practices can trigger cyber-attacks and result in the breach of sensitive information assets. Therefore, a developer needs to have strong coding skills. This research explores the utilization of Large Language Models (LLMs), such as ChatGPT, in secure coding training to improve developer skills. Against the backdrop of increasing cybersecurity threats and a lack of attention to secure coding practices, LLMs are utilized as virtual assistantswith the Prompt Engineering method to provide immediate feedback and exercises to trainees. The LLM implementation was conducted in an ISO 22398-based learning environment, focusing on applying ISO 27001:2022 information security controls and material from OWASP Code Review GuideV2. The research provided a virtual lab Cyber Exercise Secure Coding to enhance developers' skills in secure coding practice Creator An entity primarily responsible for making the resource Jeckson Sidabutar1*, Alfido Osdie2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6012/1044 Publisher An entity responsible for making the resource available Cyber Security Engineering, Cyber Security,National Cyber and Crypto Polytechnic, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 16-04-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/b82bdaf1a3da80095378215be664f903.pdf 38be4103b5c24ca0b8ce71b50c1da728 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Problem-Solving Reliability with Expert Systems and Krulik-Rudnick Indicators Subject The topic of the resource problem-solving;krulik and rudnickmethod; forward chaining;certainty factor; confusion matrix Description An account of the resource Problem-solving is one of the skills needed in the 21st century, but there is a significant gap between the ideal conditions and the reality of students' problem-solving skills. One method that can improve students' problem-solving skills is Krulik and Rudnick, but implementing this method with an expert system to improve problem-solving skills is still limited. This research aims to build an expert system to determine the level of problem-solving using Krulik and Rudnick's problem-solving indicators processed using the forward chaining and certainty factor algorithms. The study had five stages: data analysis, rule generation, certainty measurement, prediction, and testing. The data was processed by developing 5 Krulik and Rudnick problem-solving indicators into 35 statements. Each statement was categorized using Forward Chaining by producing three rules: low, medium, and high. The problem-solving level obtained is calculated using the Certainty Factor for a confidence value. The system's prediction results were evaluated using a confusion matrix, resulting in an accuracy of 80%, a precision of 92%, and a recallof 85%, indicating the system's reliable performance in measuring the level of problem-solving. This research can be used as a reference to support problem-solving in various more advanced educational and professional environments. Creator An entity primarily responsible for making the resource Lita Sari Muchlis1*, Jufriadif Na’am2, Addini Yusmar3, Khairiyah Khadijah4, Sri Wahyuni5, Naufal Ibnu Salam6 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6333/1046 Publisher An entity responsible for making the resource available Universitas Islam Negeri Mahmud Yunus, Batusangkar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 17-04-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/6de3f1d2138f22ebf0415e5c17d401ea.pdf db82907d6debd7f0ca13713d85814f1e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhanced Heart Disease Diagnosis Using Machine Learning Algorithms: A Comparison of Feature Selection Subject The topic of the resource heart disease; feature selection; random forest; hyperparamete Description An account of the resource Heart disease or cardiovascular disease is one of the leading causes of death in the world. Based on WHO data, in 2019,as many as 17.9 million people died from cardiovascular disease. If early prevention is not carried out immediately, of course,the victims will increase every year. Therefore, with the increasingly rapid development of technology, especially in the health sector, it is hoped that it can help medical personnel in treating patients suffering from various diseases, especially heartdisease. Soin this study, it will be more focused on the selection of relevant features or attributes to increase the accuracy value of the Machine Learning algorithm. The algorithms used include Random Forest and SVM. Meanwhile, for feature selection, several feature selection techniques are used, including information gain (IG), Chi-square (Chi2) and correlation feature selection (CFS). The use of these three techniques aims to obtain the main features so that they can minimize irrelevant features that can slow down the machine process. Based on the results of the experiment with a comparison of 70:30, it shows that CFS-SVM is superior by using nine features,which obtain the highest accuracy of 92.19%, while CFS-RF obtains the best value with eight features of 91.88%. By using feature selection and hyperparameter techniques, SVM obtained an increase of 10.88%, and RF obtained an increase of 9.47%. Based on the performance of the model using the selected relevant features, it shows that the proposed CFS-SVM shows goodand efficient performance in diagnosing heart disease. Creator An entity primarily responsible for making the resource Hirmayanti1*, Ema Utami2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6175/1049 Publisher An entity responsible for making the resource available Magister of Informatics Engineering, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-04-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/d20f2aec6ac02238f4f60e6a4cece9a8.pdf 56203289f5b5482b1abc8caccabc75e5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance Subject The topic of the resource Aggregate Data; ANOVA; Bot-IoT; Pearson Correlation; Classification Description An account of the resource Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity used the dataset Bot-IoT UNSW Canberra 2018. Bot-IoTdataset initially showed a significant imbalance, with 2,934,447 entries of attack activity and only 370 entries of normal activity. To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. Feature selection using the ANOVA method identified 10 key features from a total of 17. The classification process utilized Random Forest(RF), k-Nearest Neighbors(kNN), Naïve Bayes(NB), and Decision Tree(DT)algorithms, with 100 iterations and an 80:20 training-testing split. Random Forest showed superior performance, achieving 97.5% accuracy, 97.4% precision, and 97.4% recall, with a total computation time of 11.54 seconds. N IN Conn P DstIP and seq had the highest positive correlation value (+0.937) according to Pearson correlation analysis, whereas N IN Conn P SrcIP and state number had the lowest negative correlation value (-0.224).This research focuses on the implementation of a data aggregation strategy to address class imbalance, greatly enhancing machine learning model performance and optimizing training time, is what makes this research distinctive. These results aid in the creation of strong cybersecurity systems that can identify dangers associated with the Internet of Things Creator An entity primarily responsible for making the resource Firgiawan Faira1*, Dandy Pramana Hostiadi2, Roy Rudolf Huizen Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6332/1053 Publisher An entity responsible for making the resource available Magister Program, Department of Magister Information System, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-04-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/195d70a4065a21121c801416a2cc5359.pdf 2704dff81b2732034e967c3f4a490526 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data Subject The topic of the resource Emergency Departments; Electronic Health Record; Machine Learning; Neural Networks; Patient Care Description An account of the resource very patient who is rushed to the Emergency Department needs fast treatment to determinewhether the patient should be inpatientor outpatient. However, the existing fact is that decidingwhether an inpatientor outpatientmust wait for the diagnosis made by the existing doctor, so if there are many patients, it generally takes quite a long time. So, to predict patient admissions to the emergency unit, a machine learning model that can be fast and accurate is needed.Therefore, this study developed a machine learning and neural network model to determine patient care in Emergency Departments. This study uses publicly available electronic health record (EHR) data, which is 3,309. The model development process uses machine learning methods (SVM, Decision Tree, KNN, AdaBoost, MLPClassifier) and neural networks. The model that has been obtained is then evaluated for its performance using a confusion matrix and several matrices such as accuracy, precision, recall, and F1-Score. The results of the model performance evaluation were compared, and the best model was obtained, namely the MLPClassifier model with an accuracy value = 0.736 and an F1-Score value = 0.635, and the Neural Network model obtained an accuracy value = 0.724 and an F1-Score value = 0.640. The best models obtained in this study, namely the MLPClassifierand Neural Network models, were proven to be able to outperform other models Creator An entity primarily responsible for making the resource Ahmad Abdul Chamid1*, Ratih Nindyasari2, Muhammad Imam Ghozali3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6188/1027 Publisher An entity responsible for making the resource available Department of Informatics Engineering, Faculty of Engineering, Universitas Muria Kudus,Kudus,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/ccc3d2c1637ac75f9f6e042b4c6a7244.pdf e47e5e65966c46a3bfae08bab6d8739d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 2 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches Subject The topic of the resource Sentiment Analysis; Support Vector Machine; Naive Bayes; K-Nearest Neighbor; SMOTE; Sultan Hasanuddin Airport Description An account of the resource Machine learning-based sentiment analysis has become essential for understanding public perceptions of public services, including air transportation. Sultan Hasanuddin Airport, one of the main gateways in eastern Indonesia, faces the challenge of improvingservices amid changing user needs due to the COVID-19 pandemic. This study aims to compare the effectiveness of three machine learning algorithms-SupportVector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN)-in analyzing the sentiment of user reviews related to airport services. The research also explores data splitting techniques, text preprocessing, data balancing using SMOTE, model validation, and method parameterization to ensure optimal results. The review data was retrieved from Google Maps (2021-2024) and underwent manual labelling. Text preprocessing includes normalization, stemming using Sastrawi, and stopword removal. The data-balancingtechnique uses SMOTE, while model evaluation is done with stratified k-fold cross-validation. SVM with a linear kernel showed the best performance, achieving an F1-score of 98.4%. Naive Bayes performed optimally, achieving an F1-score of 93.9%, while KNN recorded the best F1-score of 92.0%. SMOTE was shown to improve Naive Bayes'performance on unbalanced datasets, although it did not significantly impact SVM. The findings of this study provide data-driven recommendations to improve services at Sultan Hasanuddin Airport, such as the management of cleaning facilities, waiting room comfort, and passenger flow efficiency. In addition, this research opens up opportunities for developing real-time sentiment analysis systems that can be applied in other air transportation sectors Creator An entity primarily responsible for making the resource Lilis Nur Hayati1, Fitrah Yusti Randana2*,Herdianti Darwis3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6253/1036 Publisher An entity responsible for making the resource available Department of Information System, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 08-03-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT