3 10 26 https://repository.horizon.ac.id/files/original/28c93018ca4cb465b3c5a775b77708bb.pdf fbc21f99f677713cdefe4920001ad02f 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 7 NO 6 (2023) 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 Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Subject The topic of the resource pap smear; cervical cancer; nucleus; GLCM otsu; GLCM polynomial Description An account of the resource Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification. Creator An entity primarily responsible for making the resource Dwiza Riana, Jufriadif Na'am, Daniati Uki Eka Saputri, Sri Hadianti, Faruq Aziz, Suryadi Putra Liawatimena, Alya Shafra Hewiz, Dika Putri Metalica, , Teguh Herwanto Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-076 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 cervical cancer GLCM otsu GLCM polynomial nucleus Pap smear https://repository.horizon.ac.id/files/original/cabb629237b3183da132f75aff6eef21.pdf 21871ac6c60fe92613c688f7db5f9d2a 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 7 NO 6 (2023) 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 Comparison of the RFM Model's Actual Value and Score Value for Clustering Subject The topic of the resource RFM model; RFM actual value; RFM core value; clustering Description An account of the resource Clustering algorithms and Recency-Frequency-Monetery (RFM) models are widely implemented in various sectors of e�commerce, banking, telecommunications, and other industries to obtain customer segmentation. The RFM model will assess a line of data which includes the recency and frequency of data appearance as well as the monetary value of a transaction made by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental approach, this research aims to find the best dataset transformation model between actual RFM values and RFM scores. The method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator to get the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-commerce application, where data was taken in the Wiradesa area, Central Java. The resulting dataset consisted of 273,454 rows with 18 attributes from January 2022 to December 2022 through collecting historical data from shopping outlets to wholesalers. Analysis of the dataset was carried out by transforming the dataset using the RFM method into actual values and score values, then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time (time series) can be transformed into data in the RFM model where the RFM model's actual value is better than the RFM score model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a series of data with a cluster label, thus forming supervised learning data. Creator An entity primarily responsible for making the resource Samidi, Ronal Yulyanto Suladi, Dewi Kusumaningsih Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 Clustering RFM actual value RFM core value RFM Model https://repository.horizon.ac.id/files/original/8fad5a552c09969c157edd795c7eb8b6.pdf 7388924759e26c146a9539be68d23934 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 7 NO 6 (2023) 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 Forecasting the Magnitude Category Based on The Flores Sea Earthquake Subject The topic of the resource gaussiannb; random forest; support vector machine; earthquake; forecasting Description An account of the resource Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude forecasting, the application of GaussianNB, Random Forest and SVM has the potential to reveal these patterns and relationships in the data. With the six main phases of this research, namely data acquisition, data preprocessing, feature selection, model training, forecasting result evaluation, and performance analysis. From these results we obtain, firstly that the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the assumption of a Gaussian distribution which may not always suit the complex and diverse characteristics of earthquake data. Based on GaussianNB model, the model accurately predicts magnitude category 1 for 421 observations and magnitude category 2 for 33 observations. Meanwhile, the magnitude 3 and magnitude 4 categories did not produce accurate predictions from the model. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and require more time to compute. In our findings, we obtained an MSE value of 0.12 with an R2 score of -0.10, this indicates conditions that are less effective in explaining differences in test data. The third option is SVM, which has both benefits and drawbacks that must be taken into account. The capacity of SVM to separate data that has both linear and non-linear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments. It is clear from the results of the algorithm comparison that SVM has more potential for earthquake forecasting, especially the linear SVM and polynomial SVM model. The accuracy of the standard SVM is 0.587, which indicates relatively low performance. Linear SVM obtained a very high accuracy of 0.998. Meanwhile, Polynomial SVM achieves perfect accuracy of 1.0. while RBF SVM has the same accuracy as standard SVM, namely 0.587. Creator An entity primarily responsible for making the resource Adi Jufriansah, Azmi Khusnani, Sabarudin Saputra, Dedi Suwandi Wahab Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 Earthquake Forecasting gaussiannb Random Forest Support Vector Machine https://repository.horizon.ac.id/files/original/92c32207384f3ee8b9a89f2e38b33baf.pdf 77ba3850d3e75a0d489e463176f34460 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 7 NO 6 (2023) 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 The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm Subject The topic of the resource oil palm; maturity classification; HSV; k-NN; confusion matrix Description An account of the resource The oil palm is one of the monocot oil-producing plants in Indonesia. Sorting errors in oil palm fruit is caused by a sorter error when distinguishing the color of ripe and immature oil palm fruit. In addition to inefficient time, the area of oil palm plantations is also a factor causing the sorter to make mistakes in sorting. This study aims to produce a system that can classify oil palm maturity based on feature extraction of hue, saturation, and value (HSV) color features. The HSV method is used to produce color characteristics from the image of the oil palm fruit. The classification of oil palm fruit maturity is classified using the K�Nearest Neighbor (KNN) algorithm with a dataset of 400 oil palm fruit image data with a data sharing ratio of 70% training data and 30% test data. 280 image data were used as training data which is divided into 140 image data of ripe oil palm fruit 140 image data of immature oil palm fruit and 120 image data of oil palm used as test data which is divided into 60 image data of ripe oil palm and 45 image data unripe palm oil. Based on the result of tests that have been carried out using a confusion matrix with varied k values, namely, 5 and 7, the average accuracy is 94.16% Creator An entity primarily responsible for making the resource Mohammad Yazdi Pusadan, Indah Safitri, Wirdayanti Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 Confusion Matrix HSV K-NN maturity classification oil palm https://repository.horizon.ac.id/files/original/d2fd7059fc3bed5c7404f00470fba43a.pdf a8e87d174d9dc9c12969dd00fcba5f47 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 7 NO 6 (2023) 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 Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization Subject The topic of the resource histogram of orientation gradients; principal component analysis; k-nearest neighbor; binary particle swarm optimization Description An account of the resource Various cultivars of date fruit distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally may lack variability in date fruits, as various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit based on their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest Neighbor (KNN) classifier. Considering the parameters applied in developing the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN. Creator An entity primarily responsible for making the resource Wikky Fawwaz Al Maki, Khaidir Mauladan, Indra Bayu Muktyas Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Englis Type The nature or genre of the resource Text binary particle swarm optimization histogram of orientation gradients K-Nearest Neighbor Principal component analysis https://repository.horizon.ac.id/files/original/cf2d2ff3c171843b86be8278128d7888.pdf 87877fa6e0688e92ca99a38c12a6b122 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 7 NO 6 (2023) 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 Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Subject The topic of the resource abstractive; extractive; summarization; bert; gpt2; clustering; sentiment; keyword Description An account of the resource Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multi-document, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT�EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive are higher performance than partial approach Creator An entity primarily responsible for making the resource Narandha Arya Ranggianto, Diana Purwitasari, Chastine Fatichah, Rizka Wakhidatus Sholikah Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 abstractive BERT Clustering extractive gpt2 keyword sentiment summarization