Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy. Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM. This study proposes the integration of BSVM and SMOTEBoost to handle class imbalance problem. Non Support Vector (NSV) sets from negative samples and Support Vector (SV) sets from positive samples will undergo a Weighted-SMOTE process. The results indicate that implementation of Biased Support Vector Machine and Weighted-SMOTE achieve better accuracy and sensitivity.
Stroke is a cardiovascular (CVD) disease caused by the failure of brain cells to get oxygen supply to pose a risk of ischemic damage and result in death. This Disease can detect based on the similarity of symptoms experienced by the sufferer so that early steps can be taking with appropriate counseling and treatment. Stroke detecting requires a machine learning method. In this research, the author used one of the supervised learning classification methods, namely K-Nearest Neighbor (K-NN). K-NN is a classification method based on calculating the distance to training data. This research compares the Euclidean, Minkowski, Manhattan, Chebyshev distance models to obtain optimal results. The distance models have been tested using the stroke dataset sourced from the Kaggle repository. Based on the test results, the Chebyshev model has the highest levels of accuracy compared to the other three distance models with an average accuracy value of 95.49%, the highest accuracy of 96.03%, at K = 10. The Euclidean and Minkowski distance models have the same level of accuracy at each K value with an average accuracy value of 95.45%, the highest accuracy of 95.93% at K = 10. Meanwhile, Manhattan has the lowest average compared to the other distance models, which is 95.42% but has the highest accuracy of 96.03% at the value of K = 6
classification is a process that explains and functions to distinguish data classes or concepts that aim to be able to predictions in classes of objects unknown to the label class. Many popular classification techniques, one of which is K-Nearest Neighbor (KNN). The K-NN algorithm functions to find the closest k neighbors and use the majority class. This study aims to determine the best k value by using Learning Vector Quantization as weight weights. Determination of the Local Mean Based test data class K-Nearest Neighbor uses the measurement of the closest distance to each local model of each data class. In processing Learning Vector Quantization, Cross-Validation and Local K-Fold in the K-Nearest Neighbor classification the lowest k = 4 was 72%, while the highest k value was 9 = 80%. And the highest k value is a good K value that is k = 9 for Iris Data.
In Classification using Support Vector Machine (SVM), each kernel has parameters that affect the classification accuracy results. This study examines the improvement of SVM performance by selecting parameters using Particle Swarm Optimization (PSO) on credit risk classification, the results of which are compared with SVM with random parameter selection. The classification performance is evaluated by applying the SVM classification to the Credit German benchmark credit data set and the private credit data set which is a credit data set issued from a local bank in North Sumatra. Although it requires a longer execution time to achieve optimal accuracy values, the SVM+PSO combination is quite effective and more systematic than trial and error techniques in finding SVM parameter values, so as to produce better accuracy. In general, the test results show that the RBF kernel is able to produce higher accuracy and f1-scores than linear and polynomial kernels. SVM classification with optimization using PSO can produce better accuracy than classification using SVM without optimization, namely the determination of parameters randomly. Credit data classification accuracy increased to 92.31%.
ABSTRAKIndonesia merupakan salah satu negara pengekspor keempat kopi terbesar di dunia, setelah Brazil, Vietnam dan Kolombia. Kopi Arabica Aceh Tengah merupakan salah satu kopi spesialti dari Indonesia yang memiliki nilai ekonomi tinggi yang dibudidayakan di Desa Telagah. Desa Telagah, Kecamatan Sei Bingei, Kabupaten Langkat berlokasi 60.6 km dengan jarak tempuh 1 jam 37 menit dari Kota Medan. Petani kopi di daerah mitra di Desa Telagah, teridentifikasi memiliki pengetahuan rendah, keterampilan sederhana, modal terbatas dalam budidaya dan pengelolaan kopi belum maksimal. Oleh sebab itu, sangat penting dilakukan kegiatan Pengabdian Kemitraan Masyarakat (PKM) yang difokuskan untuk menganalisis faktor-faktor internal, eksternal, strategi pengembangan dan konservasi kopi, minuman penikmat rasa yang trendi masa kini. Kualitas kopi dipengaruhi oleh beberapa faktor seperti varietas kopi, pengendalian hama terpadu kopi, lokasi budidaya kopi, pemanenan kopi serta pengolahan pasca panen kopi. Kelompok petani kopi ‘Perteguhan-Telagah’, menjadi salah satu alternatif pemecahan masalah dengan menginisiasi ‘Model Edukop’, edukasi budidaya dan pengelolaan kopi diharapkan dapat mendukung ekonomi kreatif berkonsepkan ekosistem berkelanjutan, mengenalkan kepada masyarakat budidaya kopi sebagai wahana edukasi dan produksi sebagai minuman dan makanan olahan tepat rasa, tepat gizi, dan tepat guna yang meningkatkan kesejahteraan petani kopi. Rencana kegiatan PKM dilakukan dalam bentuk sosialisasi informasi, pelatihan, bimbingan teknis, dan pendampingan ke kepada mitra petani kopi serta masyarakat. Diprediksikan sumbangan mesin sangrai dan alat grinder pengabdian DRTPM Kemendikbudristek-USU 2022 kepada Poktan Kopi Perteguhan, dalam pengolahan dari kopi menjadi aneka minuman Kopnaco (Kopi-Nata de Coco), Koptel (kopi-Telang), dan Kopcin (Kopi-Cincau) serta makanan dalam bentuk snek Pisang kopi keju (Piskoju), Mikop (mie kopi), Nasi goreng kopi (Naskop) serta sambal kopi. Program PKM Kopi Telagah berbasiskan riset dan sinergitas dengan masyarakat untuk mencapai tujuan khusus mewujudkan Desa Telagah sebagai model harmoni masyarakat Sumatera Utara. Kata kunci: kopi; pengolahan; makanan; minuman; telagah ABSTRACTIndonesia is one of the fourth largest coffee exporting countries in the world, after Brazil, Vietnam and Colombia. Central Aceh Arabica Coffee is one of the specialty coffees from Indonesia has high economic value was cultivated in Telagah Village. Telagah Village, Sei Bingei District, Langkat Regency is located 60.6 km with a distance of 1 hour 37 minutes from Medan City. Coffee farmers in partner areas in Telagah Village determined with low knowledge, simple skills, limited capital in coffee cultivation and management. Therefore, it is important to carry out Community Partnership Service (PKM) activities which are very important to analyze internal and external factors, development and conservation strategies for coffee, a drink that is trendy today. Coffee quality is influenced by several factors such as coffee varieties, integrated coffee pest control, coffee cultivation locations, harvesting, and post-harvest processing of coffee. The 'Perteguhan-Telagah' coffee farmer group, became an alternative problem solving by initiating the 'Edukop Model', education on coffee cultivation and management is expected to support the creative economy with a sustainable ecosystem concept, introduce coffee cultivation to the community as a vehicle and production as a beverage and processed food. right taste, right nutrition, and right use that improve the welfare of coffee farmers. The PKM activity plan is carried out in the form of information dissemination, training, technical guidance, and assistance to coffee farmer partners and the community. It is predicted that the donation of roasting machines and grinding equipment for the Ministry of Education and Culture-USU's 2022 DRTPM service to the Perteguhan Coffee Poktan will increase the use of coffee processing into a variety of drinks,such as: Kopnaco (Kopi-Nata de Coco) drinks, Koptel (Telang-coffee), and Kopcin (Kopi-Cincau) as well as food,such as snacks, Banana coffee cheese (Piskoju), Mikop (coffee noodles), Coffee fried rice (Naskop) and coffee sauce. The PKM coffee of Telagah programme is based on research and synergy with the community to achieve the specific goal of realizing Telagah Village as a model of harmony for the people of North Sumatra. Keywords: coffee; management; food; drink; telagah
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