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All Journal INFORMAL: Informatics Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Kontribusia : Research Dissemination for Community Development Jurnal Computer Science and Information Technology (CoSciTech) Journal of Community Development Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Pengabdian Literasi Digital Indonesia MIMBAR INTEGRITAS National Multidisciplinary Sciences Jurnal Aplikasi Sistem Informasi dan Elektronika Journal Of Humanities Community Empowerment Journal of Digital Literacy and Volunteering Jurnal Pengabdian Masyarakat Manage ABDIMASTEK Journal of Digital Literacy and Volunteering
Ulya Anisatur Rosyidah
Universitas Muhammadiyah Jember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer

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Perancangan Sistem Informasi Keuangan untuk Monitoring dan Evaluasi Koperasi Taufiq Timur Warisaji; Ulya Anisatur Rosyidah
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 3 No 1 (2022): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v3i1.40

Abstract

Cooperatives are business entities consisting of individuals or cooperative legal entities based on their activities based on cooperative principles as well as people's economic movements based on kinship. Based on data from the Department of Cooperatives and MSMEs, in 2020 there were around 32 cooperatives registered and scattered in several areas in Jember. The task of the Cooperative Service is to ensure the implementation of regional affairs tasks in the field of micro, small and medium enterprises cooperatives based on the principle of autonomy and assistance tasks. A Financial Information System is needed for the Jember Cooperative Office as an effort to improve better services by improving the monitoring and evaluation governance of each cooperative and business in Jember district. in English with a distance between sentences of 1 space and the number of words between 150-250. The abstract should contain introductions, methods, results and discussions and conclusions (without citation). Avoid using citations in the abstract.
Implementasi Algoritma Fuzzy C-Means untuk Pengelompokkan Provinsi di Indonesia Berdasarkan Kualitas Perguruan Tinggi Zahro, Ira Halimatuz; Rosyidah, Ulya Anisatur; Handayani, Luluk
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 1 (2024): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i1.102

Abstract

The education system in Indonesia is very large and complex with low quality. The quality of Indonesian education can improve, one way is by having equal distribution of education in every province in Indonesia. This equality can be one solution to improve the quality of graduates in Indonesia. This equality can be done by grouping Indonesian provinces with low quality education. One grouping method that can be used is the Fuzzy C-Means algorithm, which is a clustering technique that is determined by the degree of membership in each data point in one cluster. The grouping process was carried out using 136 data on Higher Education Gross Enrollment Rates in 34 provinces from 2019-2022. The data was processed using the Fuzzy C-Means algorithm and then a search for optimal clusters was carried out using the help of the Partition Coefficient Index. Based on testing from 2 to 10 clusters, the optimum cluster is 2 clusters, with a Partition Coefficinet Index value of 0.83491. In the optimum cluster, we get cluster 1 with 20 provinces and cluster 2 with 14 provincial groups. Characteristics resulting from data from 2019 to 2022, cluster 1 has provincial members with the lowest higher education APK compared to cluster 2, especially in cluster 1 members, namely Kep province. Bangka Belitung which has the lowest higher education APK is 2019 with 14.27 APK, 2020 with 14.73 APK, 2021 with 15.23 APK, 2022 14.85 APK.
Klasifikasi Tingkat Kecemasan Atlet Sebelum Bertanding Menggunakan Algoritma K–Nearest Neighbor (KNN) Berbasis Website Munawaroh, Sulistyowati; Rosyidah, Ulya Anisatur; Yanuarti, Rosita
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.120

Abstract

Anxiety experienced by an athlete before a match often affects their performance, so it is important for the coach to know the athlete's anxiety level before competing in order to provide appropriate mental training and make decisions that will affect the outcome of the match. However, not all coaches can know the level of anxiety of athletes; therefore, it is necessary to build a web-based system to classify the anxiety level of athletes before competing. The system can be built using one of the data mining methods, namely KNN (K-Nearest Neighbour), where this method can be used to classify the anxiety level of athletes based on a dataset of 364 futsal athlete data participating in the Mechanical Futsal Competition, which will be classified into 3 anxiety categories, namely low, medium, and high, from 17 attributes. From the tests carried out on the dataset using the confusion matrix method using the ratio of testing data: 80:20 training data with K = 5, accuracy, precision, and recall values of 100% were obtained. So we successfully built a website that can be used by a coach to classify athletes based on their anxiety level.
Co-Authors Afrian Piesca Firmanda Alif Faiz Risqullah Andriansyah, Moh. Eka Ari Eko Wardoyo Asror, Moh. Miftahul Bakti, Budi Satria Baktiawan Nusanto, Baktiawan Daryanto Daryanto Dermawan, Ichwan Dimas Arya Maulana Fitriyah, Nur Qodariyah Ginanjar Abdurrahman Habibatul Azizah Al Faruq Heni Pujiastuti Hidayani, Nur Lintang Setyo Kurniawati Mochammad Imron Awalludin Moh. Dasuki Muhammad Firdausi Munawaroh, Sulistyowati Oktavianto, Hardian Ramadhani, Raditya Fajar Robbol Hidayatullah Taufiq Timur Warisaji Warisaji, Taufiq Timur Widiyanto, Alfian Wijaya, Guruh Yanuarti, Rosita Zahro, Ira Halimatuz Zakiyyah, Amalina Maryam