1 10 15 https://repository.horizon.ac.id/files/original/5248b08aff1526dfe1dc2961718c63f6.pdf 7b21d2a0de206ba9b03b35996f27b22e 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 8 No 2 (2024) 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 Analysis of Meteorological Datain Sumatra and Nearbyusing Agglomerative Clustering Subject The topic of the resource agglomerative; climate change; clustering; meteorology; sumatra Description An account of the resource Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatrais also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerativeclustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11.The clusters are then analyzed to find their unique pattern.The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The resultscan be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities showsthat there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling Creator An entity primarily responsible for making the resource Teny Handhayani1, Irvan Lewenusa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5663/921 Publisher An entity responsible for making the resource available Teknik Informatika, Fakultas Teknologi Informasi, Universitas Tarumanagara, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-04-2024 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/994cceb938e5dc7aadbc612a0a0411b6.pdf 9d2d1749e9b90bb0ac4d3a00352b5403 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 8 No 2 (2024) 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 Analyzing Reddit Data: HybridModel for Depression Sentiment using FastText Embedding Subject The topic of the resource depression; social media; reddit; biLSTM;fasttext Description An account of the resource Depression, a prevalent mental condition globally, exerts a substantial influence on various aspects of human cognition, emotions, and behaviour. The alarming increase in deaths attributable to depression over the past few years demonstrates the imperative need to address this problem through preventionand treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to convey their emotions and experiences openly. Reddit standsout among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles ondepression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives todetect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings.The BiLSTM-BiGRU model analyzes informationbidirectionally, detecting correlations in sequential data. It's suitable for tasks dependent on input order or addressing data uncertainties. The Reddit dataset, featuring text concerning depression, achieved an accuracy score of 97.03% and an F1 scoreof 97.02%. Creator An entity primarily responsible for making the resource Amrul Faruq1, Merinda Lestandy2*, Adhi Nugraha3, Abdurrahim Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5641/928 Publisher An entity responsible for making the resource available Departmentof Electrial Engineering, Facultyof Engineering, Universitas Muhammadiyah Malang, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-04-2024 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/77e4a2efb9d7b374f24cb3cf41699793.pdf 5450b828bed18af63bb1f131da8b578a 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 8 No 2 (2024) 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 Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data Subject The topic of the resource feature selection;high-dimensional data;recursive feature elimination;genetic algorithm Description An account of the resource The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the wdbc dataset with 30 features and 569 data, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy, and the use of weighted macros on precision, recall and f1 score, using GA selection features has slightly higherresults than RFE so it is concluded that GA selection features are better at solving problems in high-dimensional data Creator An entity primarily responsible for making the resource Yoga Pristyanto1, Dipa Wirantanu Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5375/916 Publisher An entity responsible for making the resource available Departmentof Information System, Facultyof Computer Science, UniversitasAmikom, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-03-2025 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/d0bcbfbbb238f10c910a4d153f63ca47.pdf 7dacd4eed71926060879e8a869963087 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 8 No 2 (2024) 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 Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach Subject The topic of the resource madaline;neural network; perceptron; quantum bit; quantum computing Description An account of the resource The fundamental problem in this research is to explore a more profound understanding regarding both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modelling. This research focuses on developing the Madaline and Perceptron algorithm using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The dataset used in this study is using a lens dataset. Thereare four attributes: 1) patient age: young, pre-presbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. Thisresearch significantly contributes to developing computing and neural networks, with potential applications extending from data processing to more accurate modelling in artificial intelligence, data analysis and understanding complex patterns Creator An entity primarily responsible for making the resource Taufik Baidawi1, Solikhun2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5502/927 Publisher An entity responsible for making the resource available Information System, Universitas Bina Sarana Informatika, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-04-2024 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/b48970b7c2a4a56a6b5da9694c381e42.pdf ba4f6d3719293660e03561fea45dfc66 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 8 No 2 (2024) 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 Design and Implementation of the Shortest Path Navigation in Samosir District using Branch and Bound Algorithm Subject The topic of the resource branch and bound;map;samosir;shortest path;tourist Description An account of the resource Samosir has a wide area and several tourist attractions, making it difficult for visitors to explore the island.Not knowing the route can make the journey less fun and waste time.In general, tourists seek to know the fastest path to a tourist location to save time and money while on vacation. As a result, we require an application that will offer directions to the shortest path. This research aims to develop a web-based application that may display a map of the shortest travel to a tourist site. This website will display a map that marks from the origin point to the destination point. The Branch and Bound algorithm is used to determine the shortest path. The Python libraries OSMnx, Folium, and NetworkX modify paths and show a route map with OpenStreetMap. The error value of the distance between the branch, bound algorithm, and Google Maps is used to get the RMSE accuracy value. The RMSE value is 3.02and MAPE value is 0.0023indicating that the application produced already has a good implementation prototype. Furthermore, there is no significant distinction between the appearance of maps implementing OpenStreetMap and Google Maps Creator An entity primarily responsible for making the resource Rudy Chandra1, Tegar Arifin Prasetyo2,Ricky Ananda Pardomuan Sitorus3,Pakhomios Havel Situmorang4,Yanti Sopia Ruth Hutasoit5,Trito Exaudi Manik Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5585/922 Publisher An entity responsible for making the resource available Department of Information Technology, Faculty of Vocational Studies,Institut Teknologi Del, Sitoluama.Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-04-2024 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/76f1db88ce68af9a5042eb4b57533b4e.pdf 9951e8be719aa4224633e19e6e8125e7 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 8 No 2 (2024) 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 Digital Image Object Detectionwith GLCM Multi-Degrees and Ensemble Learning Subject The topic of the resource Object detection; object rotation; Gray-level Co-occurrence Matrix Multi-Degrees; Ensemble Voting Description An account of the resource Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours(K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours(KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degreesand the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degreesis 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracyfor detecting objects experiencing rotation. Creator An entity primarily responsible for making the resource Florentina TatrinKurniati1, Hindriyanto Dwi Purnomo2, Irwan Sembiring3, Ade Iriani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5597/931 Publisher An entity responsible for making the resource available Facultyof Information Technology, Universitas Kristen Satya Wacana Date A point or period of time associated with an event in the lifecycle of the resource Facultyof Information Technology, Universitas Kristen Satya Wacana 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/91141e3ef6b6030260a30066d631fd72.pdf d68d7f496a6245d27a0ca1d00b9b26d4 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 8 No 2 (2024) 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 Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation Subject The topic of the resource computer vision; blindpeople; YOLOv8; OpenCV Description An account of the resource The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superiorobject detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involveobstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives Creator An entity primarily responsible for making the resource Erwin Syahrudin1, Ema Utami2, Anggit Dwi Hartanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5529/917 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 29-03-2024 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/24c1b04eb68eca7402905f915e64ed8a.pdf a41997475b6aeac981db42898106cbb5 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 8 No 2 (2024) 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 Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification Subject The topic of the resource ensemblelearning;weighted averagingvoting;convolutional neural network;image classification;plant disease Description An account of the resource Deeplearning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant diseaseclassification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble predictiontechnique using a weighted soft-voting method. Instead of assigning a generalized weight to each CNN model, our approach emphasizes giving weights to each label's prediction within every individual model. We employed three esteemed CNN architectures for our experiments: DenseNet201, InceptionV3, and Xceptionfocusing on classifying various diseases affecting grapes. By harnessing transfer learning coupled with end-to-end fine-tuning, we achieved a streamlined and efficient training process. Notably, the f1-score for each grape disease class was used as a parameter for weight determination and as a metric for the final evaluation. In our study, the newly proposed method was tested across various datasets and ensemble scenarios, demonstrating its effectiveness by not only outperforming the conventional soft-voting and prevalent weighted soft-voting methods, which achieved best scores of 95.68% and 95.81% respectively, but also by achieving a remarkable accuracy of 96.56%.This method's efficacy is heightened when ensemble models exhibit distinct characteristics; the more varied the model characteristics, the more enhanced the ensemble results Creator An entity primarily responsible for making the resource Octavian1, Ahmad Badruzzaman2, Muhammand Yusuf Ridho3, Bayu Distiawan Trisedya4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5669/926 Publisher An entity responsible for making the resource available Faculity of Computer Sciences, University ofIndonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 25-04-2024 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/d43a143c47e0c9386ac5605a8384c936.pdf 7b21d2a0de206ba9b03b35996f27b22e 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 8 No 2 (2024) 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 Evaluating the Bibit App: The HEART Framework Approach in UX Design Subject The topic of the resource bibit;HEART framework;investment application;user experience Description An account of the resource With the rise of investment applications like Bibit in Indonesia, evaluating user experience is crucial to improveuser engagement and satisfaction. This study aims to assess the Bibit appuser experience using the HEART framework to identify areas for improvement. A descriptive survey was conducted usingthe Slovin method to determine a sample size of approximately 100 people. The study useda questionnaire distributed throughGoogle Forms that measuresthe five aspects of the HEART frameworkon a Likert scale. Data were analyzed for validity and reliability using IBM SPSS and stored in Google Spreadsheets. Overall, the Bibit app demonstrated high satisfaction, engagement, adoption, retention, and task success among its users, with most categories scoring above the 80% target. However, specific areas, particularlyHappiness 2, Adoption 1, Retention 1, and Task Success 3, requirefurther development attention.The Bibit app provides a generally satisfying user experience, with significant potential for improvement in navigation tooltips, investment product development, and app speed and efficiency. Creator An entity primarily responsible for making the resource Dandi Setya Perdana1, Lintang YuniarBanowosari Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5714/923 Publisher An entity responsible for making the resource available Postgraduate Program, Gunadarma University, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 24-04-2024 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/bf4ea9bf3f4b14631738461d057bb14e.pdf fbbaa07b73f917a044107b2200be38a5 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 8 No 2 (2024) 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 Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives Subject The topic of the resource KSPPS;hybrid data mining;, k-medoids;naïve bayes;k-nearest neighbor (k-NN) Description An account of the resource Syariah Financing Saving And Loan Cooperatives (KSPPS) is an Islamic financial institution aimed at people who are on the lower middle scale to lift the economy of small communities through microfinancingprograms. Problems that often occur in member recommendations to get KSPPS financing are often not on target. In addition, The amount of member financing is often problematic due to a lack of analysis, resulting in poor financing instalments. This research aimsto present an analysis model for clustering and classification using hybrid data mining algorithms.This research method is using hybrid data mining Algorithms, namely K-Medoids, Naïve Bayes, and k-Nearest Neighbors (k-NN). This study uses the historical dataset of the last two years on KSPPS BMT Dadok Tunggul Hitam as a total of 70 data samples. The analysisparameters consist ofincome, business, residence Status, financing application, billing history, and balance amount. The best analysis Model will be obtained by comparing the results between Naïve Bayes with K-Medoids, and K-Nearest Neighbor (k-NN) with K-Medoids. The results of this researchshowed the best performance is using the hybrid Naïve Bayes data mining model with K-Medoids which has an accuracy of 90.91% for data split 70:30, while performance with K-fold cross-validationshows an accuracy of 93.49%using this algorithm. Overall, the results of this study can provide an effective analysis model to determine the status of the loan. Creator An entity primarily responsible for making the resource Ondra Eka Putra1, Randy Permana2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5683/930 Publisher An entity responsible for making the resource available Department InformationSystem, Faculty of Computer Science, Universitas Putra Indonesia YPTK Padang Date A point or period of time associated with an event in the lifecycle of the resource 28-04-2024 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