1 10 2 https://repository.horizon.ac.id/files/original/4077a7bfc9569a66b23fba3f2ab387b9.pdf 288a198368c0afc289b5b6e4099bac7e 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. 19 NO. 2 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Intelligent System for Predicting Breast Cancer (ISPBC) using a Novel Feature Selection Technique Subject The topic of the resource breast cancer; enriched feature set; heuristic search techniques; intelligent system; random forest; stochastic hill climbing Description An account of the resource Breast cancer (BC) is becoming a global epidemic, largely affecting women. Breast cancer cases keep climbing steadily. Thus, early detection technologies or systems that notify patients to this disease are essential. Individuals can start treatment for this life-threatening illness, so that patients may be cured or given longer lives. To achieve this, in this study, an expert intelligence system named Intelligent System for Predicting Breast Cancer (ISPBC) was developed. The proposed system utilizes an innovative feature selection technique known as Enriched Feature Set (EFS) in order to identify the most appropriate and significant features. The proposed EFS employs the advantages of heuristic search techniques and stochastic hill climbing to select the most significant and important features. The Decision Tree and Random Forest techniques are employed for breast cancer diagnosis, distinguishing between malignant and benign types. The suggested model’s performance was evaluated by comparing measures such as accuracy, precision, and recall through the utilization of tenfold cross-validation. To measure the efficacy of the suggested model, ISPBC’s performance was compared to that of base classifiers and models published in the literature. A maximum accuracy of 96.09% was attained by ISPBC according to the results. Creator An entity primarily responsible for making the resource Akhil Kumar Das, Saroj Kr. Biswas, Ardhendu Mandal, Arijit Bhattacharya & Debasmita Saha Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.2.2 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 24 November 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2337-5787 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 breast cancer enriched feature set heuristic search techniques intelligent system Random Forest stochastic hill climbing https://repository.horizon.ac.id/files/original/0f027f3932db4cfee8d27ced481c6af8.pdf f36ac740951e18596be80be9bdd69a1e 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. 19 NO. 2 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction Subject The topic of the resource dependency parsing; medical knowledge graph; named entity recognition; part-of-speech tagging; triplets. Description An account of the resource This study presents a new approach for constructing a medical knowledge graph using Named Entity Recognition (NER) to identify entities such as diseases, drugs, or medical procedures, alongside part-of-speech (POS) tagging and dependency parsing to determine wordsthat function as verbs and roots. These extracted words are then used as relations between entities, forming triplets in the format (entity, relation, entity). While the knowledge graph provides a structured representation of medical information, the evaluation primarily reflects the performance of the underlying NLP pipeline (NER, POS tagging, and dependency parsing) used to generate the triplets. Quantitative evaluation was performed using metrics such as precision, recall, and F1-score to assess the accuracy and completeness of entity and relation extraction. The qualitative evaluation involved medical domain experts to assess the relevance and validity of the relationships derived. The results indicate that fine-tuning a pre-trained model for NER and leveraging a pre-trained model for POS tagging and dependency parsing can effectively generate accurate triplets for constructing a medical knowledge graph. This approach demonstrated strong performance, achieving high evaluation scores in both quantitative and qualitative evaluations. Creator An entity primarily responsible for making the resource Richard Reinhart & Masayu Leylia Khodra Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.2.1 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 6 November 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 23375787 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 dependency parsing medical knowledge graph Named entity recognition part-of-speech tagging triplets