Nowadays, the improvement in resources, especially among women, is considered. One of the efforts to empower women in the village can be done through the assistance of Micro, Small, and Medium Enterprises (MSMEs). This research-based community service aims to assist the community, especially women (housewives) in Pojok Village of Magetan Regency, in developing home businesses. This community service is carried out by using ABCD approach, which is an approach to understanding and internalizing assets, potential, strength, and utilization independently and optimally. The results of the community service carried out by researchers have positive impact to the community and it fosters a high desire and enthusiasm to make changes for the better in the development of marketing businesses, both during the mentoring process and post-mentoring so that the economy in Pojok Village, Magetan Regency can increase
The role of technology in an institution is beneficial for any activities, especially in managing documents administration, which is now most of the company used digital archive systems. That's because the manual archive system is considered less effective and efficient, where it takes a lot of space and time to search for document archives. Microsoft Access is a windows-based database processing program with Visual Basic for Application (VBA), commonly used to create a windows-based application. The purpose of this paper is to make an administration letter and archives system based on Microsoft Access. The results of this design have passed the trial process for approximately two weeks and received a positive response. The system design can be run well, and its functions correctly without errors. So it can help the secretary or other employees to manage all documents (letters and archives) more accessible and faster.
Parkinson’s disease is a neurological disorder in which there is a gradual loss of brain cells that make and store dopamine. Researchers estimate that four to six million people worldwide, are living with Parkinson’s. The average age of patients is 60 years old, but some are diagnosed at age 40 or even younger and the worst thing is some patients are late to find out that they have Parkinson's disease. In this paper, we present a diagnosis system based on Fuzzy K-Nearest Neighbor (FKNN) to detect Parkinson’s disease. We use Parkinson’s disease dataset taken from UCI Machine Learning Repository. The first step is normalize the Parkinson’s disease dataset and analyze using Principal Component Analysis (PCA). The result shows that there are four new factors that influence Parkinson’s disease with total variance is 85.719%. In classification step, we use several percentage of training data to classify (detect) the Parkinson's disease i.e. 50%, 60%, 70%, 75%, 80% and 90%. We also use k = 3, 5, 7, and 9. The classification result shows that the highest accuracy obtained for the percentage of training data is 90% and k = 5, where 19 are correctly classified i.e. 14 positive data and 5 negative data, while 1 positive data is classified incorrectly.Keywords: Parkinson's disease; Fuzzy K-Nearest Neighbor; Principal Component Analysis. AbstrakPenyakit Parkinson merupakan kelainan sel saraf pada otak yang menyebabkan hilangnya dopamin pada otak. Para peneliti mengestimasi bahwa, empat sampai enam juta orang di dunia, menderita Parkinson. Penyakit ini rata-rata diderita oleh pasien berusia 60 tahun, namun beberapa orang terdeteksi saat berusia 40 tahun atau lebih muda dan hal terburuk adalah seseorang terlambat untuk mendeteksinya. Di dalam artikel ini, kami menyajikan sistem diagnosa penyakit Parkinson menggunakan metode Fuzzy K-Nearest Neighbor (FKNN). Kami menggunakan Data uji yang diperoleh dari UCI Machine Learning Repository yang telah banyak diterapkan pada masalah klasifikasi. Tahapan pertama yang kami lakukan adalah menormalisasi data kemudian menganalisisnya menggunakan Analisis Komponen Utama (Principal Component Analysis). Hasil Analisis Komponen Utama menunjukkan bahwa terdapat empat factor baru yang mempengaruhi penyakit Parkinson dengan variansi total 87,719%. Pada tahap klasifikasi, kami menggunakan beberapa prosentase data latih untuk mendeteksi penyakit yaitu 50%, 60%, 70%, 75%, 80% and 90%. Selain itu, kami menggunakan beberapa nilai k yaitu 3, 5, 7, and 9. Hasil menunjukkan bahwa klasifikasi dengan akurasi tertinggi diperoleh untuk 90% data latih dengan k = 5, dimana 19 diklasifikasikan secara tepat yaitu 14 data positif dan 5 data negatif, sedangkan satu data positif tidak diklasifikasikan dengan tepat.Keywords: penyakit Parkinson; Fuzzy K-Nearest Neighbor; Analisis Komponen Utama.
Background: Low-cost carrier (LCC) is a popular air transportation service as it offers affordable fares. Many airlines have adopted the LCC system because they need to adapt to the changes in the airline industry. The competition is tight. Despite the low cost, consumers demand quality services. Therefore, LCC airlines need to find their competitive edge. Objective: This study aims to determine the best-performing LCC airlines, the criteria, and the sub-criteria to improve the performance. Methods: This study uses two methods from multi-criteria decision-making (MCDM), namely the analytical hierarchy process (AHP) and elimination et choix traduisant la realite (ELECTRE) II. The MCDM is selected for this study because there are four criteria and 21 sub-criteria to evaluate airline performance. The AHP method selects subcriteria that affect airline customer satisfaction. It solves complex problems by establishing a hierarchy. After being assessed by relevant parties, weights or priorities are developed. The results are used to determine the best-performing airline. Meanwhile, the ELECTRE II method ranks the airline’s alternatives. This method is straightforward and widely used in the MCDM. Results: The results indicate that four criteria and 18 sub-criteria affect the performance of LCC airlines in Indonesia. The LCC airline with the best performance is AirAsia, followed by Citilink, Wings Air, and Lion Air. Conclusion: This research integrates the AHP and ELECTRE II methods in evaluating the performance of LCC airlines. This research also provides information about the criteria and sub-criteria to improve airline performance, hence, the customer experience.
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