1 10 20 https://repository.horizon.ac.id/files/original/a69f96f80a2bb53f7312b8d9495a93c4.pdf 26caadbbfc264f4817221b4200cf87c9 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 LargeLanguage Model-Based Extraction of Logic Rules from Technical Standards for Automatic Compliance Checking Subject The topic of the resource automatic compliance checking;logic rules;technical standards;large language model;prompting Description An account of the resource n this research, we design logic rules as a representation of technical standards documentsrelated to ship design,which will be used in automatic compliance checking. We present a novel design of logic rulesbased on a general pattern of technical standards’ clausesthat can be produced automatically from text using a large language model (LLM).We also present a method to extract said logic rules fromtext.First, we design data structuresto represent the technical standards and logic rules used to process the data. Second, the representation of technical standardsis produced manually and tested to ensure that it can give the same conclusionas human judgmentregarding compliance. Third, a variation of prompting methods, namely pipeline method and few-shot prompting, isgiven to LLM to instruct it to extract logic rules from text following the design. Evaluation against the logic rules produced shows that the pipelinemethod gives an accuracy score of 0.57, a precision of 0.49,and a recall of 0.62. On the other hand, logic rules extracted using few-shot prompting havean accuracy score of 0.33, precision of 0.43,and recall of 0.5. Theseresults show that LLM is able to extract a logic rulerepresentation of technical standards. Furthermore, the representation resultingfromtheprompting technique that utilizes the pipelinemethodhas a better performance compared to the representation resultingfrom few-shot prompting. Creator An entity primarily responsible for making the resource Rizky Nugroho1*, Adila Krisnadhi2, Ari Saptawijaya3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6285/1045 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Indonesia, Depok, 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/ade6d7c26a3a8842ef9aee1d796c9cf4.pdf 70110c5ed9289a8a6fd787d22af0ae4e 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 Sentiment Analysis of ChatGPTon Indonesian Textusing Hybrid CNN and Bi-LSTM Subject The topic of the resource sentiment analysis;CNN; Bi-LSTM; hybrid model Description An account of the resource This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model thatintegrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stopword removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate of 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness ofhybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability Creator An entity primarily responsible for making the resource Vincentius Riandaru Prasetyo1*, Mohammad Farid Naufal2, Kevin Wijaya3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6334/1043 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Engineering, University of Surabaya, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 11-04-2025 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/60840f73a28c03e45f4ff1421aa66b89.pdf cd7bba6bf8d366a4d8d04833558a4994 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 Measuring Factors of Trust in the Use of E-Government: A Multi-Factor Analysis ofthe E-Governmentin Indonesia Description An account of the resource Research related to trust has been widely conducted, but few studies have comprehensively explored the factors influencing trust, encompassing both technical and non-technical aspects, particularly in the context of the public sector. This gap serves as the important background for this study, which aims to comprehensively analyze the factors shaping trust in e-government adoption. The focus of this research is on the Integrated Dynamic Archival Information System (SRIKANDI)application at the National Civil Service Agency (BKN) in Indonesia, which faces the issue of low adoption rates despite being launched in 2020.This study examines the influence of technical factors, government agencies, public factors, risk factors, social influence, and facilitating conditions on the formation of trust in e-government, as well as explores the impact of trust on satisfaction, intention to use, and e-government adoption. The research method used is quantitative, with data analysis employing partial least squares structural equation modeling (PLS-SEM) through the SmartPLS version 4 application. The results show that technical factors (β=0.083, t=1.702), government agencies (β=0.249, t=3.337), citizen factors (β=0.114, t=2.054), risk factors(β=0.467, t=6.130), social influence (β=0.101, t=1.722), and facilitating conditions (β=0.140, t=2.025) significantly influence trust in e-government. This trust, in turn, positively impacts intention to use (β=0.564, t=8.576), satisfaction (β=0.372, t=4.358), and e-government adoption (β=0.458, t=3.337) & (β=0.503, t=1.702). These findings indicate that users with high trust in e-government systems are more likely to use and adopt the system, highlighting the critical role of trust in enhancing the utilization of e-government services.Keywords: technical factors;government agency factors;citizen factors;risk factors;social influence;facilitating condition; trust in e-government;satisfaction Creator An entity primarily responsible for making the resource Iqbal Caraka Altino1, Reska Nugroho Sudarto2*,Dana Indra Sensuse Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6016/1042 Publisher An entity responsible for making the resource available Sofian Lusa4, Prasetyo Adi Wibowo Putro5, Sofiyanti Indriasari6, Bramanti Brillianto Date A point or period of time associated with an event in the lifecycle of the resource 10-04-2025 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/db5bad12cf8ddcca156653a19ff8359f.pdf ff31e03795a9eb627e467a81b4460147 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 Customer Satisfaction Evaluation in Online Food Delivery Services: A Systematic Literature Review Subject The topic of the resource customer satisfaction;online food delivery;analysis methods;evaluation metrics;datasets; future directions Description An account of the resource The rapid growth of online food delivery services has heightened the need for effective customer satisfaction measurement. This systematicliterature review examines 476 papers, selecting 15 key studies to identify prevailing evaluation approaches. Findings reveal that sentiment analysis and PLS-SEM are the most frequently used analytical methods, each appearing in six studies. Satisfaction measurement relies on sentiment polarity scores in five studies and SERVQUAL frameworks in three studies. Data collection primarily involves surveys in seven studies and user-generated content in six studies, but limited demographic diversity reduces generalizability. Three key future research directions emerge. Advanced analytical techniques appear in 5 of 11 future works in the analysis methods domain. Expanding evaluation metrics is mentioned in 6 of 12 proposalsin the evaluation domain. Exploring demographic context is highlighted in 10 of 25 recommendations in the dataset’s domain, with dataset development receiving twice the attention of methodological advancements. These results provide researchers with a structured framework for customer satisfaction evaluation while guiding food delivery platforms in refining service quality. By systematically mapping current methodologies and future priorities, this study bridges gaps between academia and industry, ensuring more effective customer satisfaction assessments. Creator An entity primarily responsible for making the resource Adimas Fiqri Ramdhansya1*, Shella Maria Vernanda2, Indra Budi3, Prabu Kresna Putra4, Aris Budi Santoso5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6205/1041 Publisher An entity responsible for making the resource available nformation Technology, Computer Science, Universitas Indonesia, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 08-04-2025 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/7b29a064acc7ff38cfeef0b03a9b20ad.pdf 81da0c84ee850446abf816c11a331489 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 Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation Subject The topic of the resource bayesian optimization;deep learning; ECG delineation Description An account of the resource Electrocardiography (ECG)serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical points necessary for observing cardiac abnormalities based on the characteristics and features of the waveform. In this study, we propose a deep learning approach combined with Bayesian Hyperparameter Optimization (BHO) for hyperparameter tuning to delineate the ECG signal. BHO is an optimization method utilized to determine the optimal values of an objective function. BHO allows for efficient and faster parameter search compared to conventional tuning methods, such as grid search. This method focuses on the most promising search areas in the parameter space, iteratively builds a probability model of the objective function, and then uses that model to select new points to test. The used hyperparameters of BHO contain learning rate, batch size, epoch, and total of long short-term memory layers. The study resulted in the development of 40 models, with the best model achieving a 99.285 accuracy, 94.5% sensitivity, 99.6%specificity,and 94.05% precision. The ECG delineation-based deep learning with BHO shows its excellence for localization and position of the onset, peak, and offset of ECG waveforms. The proposed model can be applied in medical applications for ECG delineation Creator An entity primarily responsible for making the resource Annisa Darmawahyuni1*, Winda Kurnia Sari2, Nurul Afifah3, Siti Nurmaini4, Jordan Marcelino5, Rendy Isdwanta6 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6171/1040 Publisher An entity responsible for making the resource available Intelligent Systems Research Group, 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 08-04-2025 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/dbeff4794721140acf85c598cc3126ef.pdf e0d7c7ef9633937577979d9305cdcd3f 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 Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern Subject The topic of the resource Natural Language Processing, Schizophrenia, Speech Pattern, Word2Vec Description An account of the resource Schizophrenia is a chronic brain disorder characterized by symptoms such as delusions, hallucinations, and disorganized speech, posing significant challenges for accurate diagnosis. This research investigates an innovative Natural Language Processing (NLP)framework for classifying the speech patterns of schizophrenia patients using Word2Vec, with the aim of determining whether there are significant differences between the two features. The dataset comprises speech transcriptions from 121 schizophrenia patients and 121 non-schizophrenia participants collected through structured interviews. This study compares two Word2Vec architectures, Continuous Bag-of-Words (CBOW) and Skip-Gram (SG), to determine their effectiveness in classifying schizophrenia speech patterns. The results indicate that the SG architecture, with hyperparameter tuning, produces more detailed word representations, particularly for low-frequency words. This approach yields more accurate classification results, achieving an F1-score of 93.81%.These results emphasize the effectiveness of the framework in handling structured and abstract linguistic patterns. By utilizing the advantages of both static and contextual embedding,this approach offers significant potential for clinical applications, providing a reliable tool for improving schizophrenia diagnosis through automated speech analysis. Creator An entity primarily responsible for making the resource Putri Alysia Azis1*, Andi Tenriola2, Dewi Fatmarani Surianto3, Nur Azizah Eka Budiarti4, Andi Akram Nur Risal5, Zulhajji Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6323/1039 Publisher An entity responsible for making the resource available Department of Informatics and ComputerEngineering, Universitas Negeri Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 26-03-2025 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/e51ccaecdf645268678c2b92a318edd0.pdf 5ee7c293fd6d3cb46ae2ac5a63002a96 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 Multi-Process Data Mining with Clustering and Support Vector Machine for Corporate Recruitment Subject The topic of the resource data mining; clustering algorithm; k-means; support vector machine; employee recruitment Description An account of the resource Having an efficient and accurate recruitment process is very important for a company to attract candidates with professionalism, a high level of loyalty, and motivation. However, the current selection method often faces problems due to the subjectivity of assessing prospective employees and the long process of deciding on the best candidate. Therefore, this research aims to optimize the recruitment process by applying data mining techniques to improve efficiency and accuracy in candidate selection.The method used in this research utilizes a multi-process Data Mining approach, which is a combination of clustering and classification algorithms sequentially. In the initial stage, the K-Means algorithm is applied to cluster candidates based on administrative selection data, such as document completeness and reference support. Next, a classification model was built using a Support Vector Machine (SVM) to categorize the best candidates based on the results of psychological tests, medical tests, and interviews.The experimental results showthat the SVM model produces high evaluation scores, with an AUC of 87%, Classification Accuracy (CA) of 90%, F1-score of 89%, Precision of 91%, and Recall of 90%. With these results, it can be concluded that this model is able to improve accuracy in the employee selection process and help companies make more measurable and data-based recruitment decisions Creator An entity primarily responsible for making the resource Ruri hartika Zain1*, Randy Permana2,Sarjon Defit3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6197/1038 Publisher An entity responsible for making the resource available Fakultas Ilmu Komputer, Universitas Putra Indonesia YPTK Padang 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/79d503812e3722eed332c407bff36729.pdf 9db5f96544d7337dd1bfcde6263d89fd 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 Detecting Alzheimer'sBasedon MRI Medical Imagesby Using External Attention Transformer Subject The topic of the resource alzheimer’s disease; detection; MRI; CNN; external attention transformer Description An account of the resource Alzheimer's disease is one of the major challenges in medical care this century, affecting millions of people worldwide. Alzheimer's damages neurons and connections in brain areas responsible for memory, language, reasoning, and social behavior. Early detection of this disease enables more effective treatment and proper care planning.Unfortunately, the traditional method ofdetecting Alzheimer'shas several limitations, such as subjective analysis and delayeddiagnosis.One commonly used method is visual inspection, which uses magnetic resonance imaging(MRI). The limitations of visual inspection include subjectivity and its time-consuming nature, especially with large or complex MRI datasets, making accurate interpretation a significant challenge. Therefore, an alternative for detecting Alzheimer’s disease is to use deep learning-based MRI image analysis. One promising approach is to implement the External Attention Transformer (EAT) model. It enhances image classification by using two shared external memories and an attention mechanism that filters out redundant information for improved performance and efficiency. The aim of this research is to evaluate and compare the performance of the baseline Convolutional Neural Network (CNN) model, the Vision Transformer (ViT) model,and the EAT model in detecting Alzheimer's using a dataset of 6400 brain MRI images. The EAT model outperforms the baseline CNN model and ViT modelin detecting Alzheimer's, achieving its best results with an accuracy of 0.965 and an F1-score of 0.747 for the test data.Our resultscould be integrated with clinical analysis to assist in the faster diagnosis of Alzheimer's Creator An entity primarily responsible for making the resource Farrel Ardannur Deswanto1*, Isman Kurniawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6257/1033 Publisher An entity responsible for making the resource available School of Computing, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-03-2025 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/bb6a1a6b1eaa3dc6d0a217d012b6327c.pdf 60527398c16e3bbed71b25192f233b7d 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 Comparison of Sugarcane Drought Stress Based on Climatology Data UsingMachine Learning Regression Modelin East Java Subject The topic of the resource crop water stress index;climatological data;machine learning regression; sugarcane Description An account of the resource Crop Water StressIndex (CWSI), derived from vegetation features (NDVI) and canopy thermal temperature (LST), is an effective method to evaluate sugarcane sensitivity to drought using satellite data. However, obtaining CWSI values is complicated. This study introduces a novel approach to estimate CWSI using climatological data, including average air temperature, humidity, rainfall, sunshine duration, and wind speed features obtained from the local weather station BMKG Malang City, East Java,for the period 2021-2023. Before estimating CWSI, we analyzed sugarcane water stress phenology,examined the strength of the correlation between climatological features and CWSI, and looked at the potential for adding lag features. Our proposed prediction model uses climatological features with additional Lag features in a machine learning regression approach and 5-fold cross-validation of the training-testing data split with the help of optimization using hyperparameters. Different machine learning regression models are implemented andcompared. The evaluation results showed that the prediction performance of the SVR model achieved the best accuracy with R2 = 90.45% and MAPE = 9.55%,which outperformed other models. These findings indicate that climatological features with lag effects can effectively predict water stress conditions in rainfed sugarcane if using an appropriate prediction model. The main contribution of this study isthe utilization of local climatological data, which is easier to obtain and collect than sophisticated satellite data, to estimate CWSI. The application of the results shows that climatological data with lag effects can accurately estimate water stress conditions in rainfed sugarcane. In drought-prone areas, this strategy can help sugarcane farmers make better choices about land management and irrigation. Creator An entity primarily responsible for making the resource Aries Suharso1*, Yeni Herdiyeni2, Suria Darma Tarigan3, Yandra Arkeman4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6159/1032 Publisher An entity responsible for making the resource available Computer Science, School of Data Science, Mathematics and Informatics, IPB University,Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-03-2025 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/26afc3a030e6d129b7f70199719666ca.pdf 394602fb2d8d19185d57e543c602d1ae 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 Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments Subject The topic of the resource Customer Hierarchical Relationships;Data-Driven Marketing;Gaussian Mixture Model;K-Means Clustering; RFM Analysis Description An account of the resource Business development cannot be separated from relationships with customers. Understanding customer characteristics is important both for maintaining sales and even for targeting new customers with appropriate strategies. The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. This research implements K-Means and GMM algorithms for performing clustering based on the Transaction data transformed to the Recency, Frequency, and Monetary (RFM) data model, then implements Formal Concept Analysis (FCA) as an approach to analyzing the customer segment after the class labeling. Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. The FCA implementation in this study further analyzes customer segment characteristics by constructing a concept lattice that categorizes segments using combinations of High and Low values across the RFM attributes based on the median values, which are High Recency (HR), Low Recency (LR), High Frequency (HF), Low Frequency (LF), High Monetary (HM), and Low Monetary (LM). This characteristic can determine the customer category;for example, a customer that has HM and HR can be considered a loyal customer and can be the target for a specific marketing program. Overall, this study demonstrates that using the RFM data model, combined with clustering algorithms and FCA, is a potential approach for understanding MSME customer segment behavior. However, special consideration is necessary when determining the FCA concept lattice, as it forms the foundation of the core analytical insights Creator An entity primarily responsible for making the resource I Gede Bintang Arya Budaya1*, I Komang Dharmendra2, Evi Triandini3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6184/1029 Publisher An entity responsible for making the resource available nformation Technology Department, Institute of Technology and Business STIKOM Bali, Denpasar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-03-2025 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