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PELATIHAN PENGISIAN BEBAN KERJA DOSEN (BKD) MELALUI SISTER PADA DOSEN FAKULTAS ILMU KOMPUTER UNIVERSITAS AMIKOM PURWOKERTO Rujianto Eko Saputro; Diah Ratna Febrianti; Inka Saputri; Sarmini Sarmini
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 7, No 4 (2023): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v7i4.17792

Abstract

ABSTRAKMasih kurangnya pemahaman dosen terkait pengisian BKD melalui SISTER menyebabkan beberapa dosen merasa kesulitan dalam melakukan pengisian BKD, hal ini berdampak pada ketepatan waktu dosen dalam mengirimkan laporan BKD kepada asesor. Keterlambatan tersebut juga mengakibatkan asesor terlambat dalam memeriksa dan memberikan penilaian, yang pada akhirnya dosen menjadi terlambat untuk melaporkan laporan BKD kepada lembaga terkait. Maka dari itu perlu adanya kegiatan pelatihan untuk menyamakan persepsi dosen dalam pengisian BKD, dengan pelatihan ini diharapkan mampu meningkatkan pengetahuan, kemampuan dan ketelitian dosen pada saat mengisi BKD. Kegiatan pelatihan ini bertujuan untuk membantu mempermudah dosen dalam melakukan pengisian BKD melalui SISTER dan juga meningkatkan ketepatan waktu dosen dalam pengumpulan laporan BKD. Metode pelaksanaan kegiatan terdiri dari tahap perencanaan, tahap pelaksanaan dan tahap evaluasi. Kegiatan pelatihan diberikan kepada dosen dengan memberikan pemaparan materi, tes setelah pelatihan dan tanya jawab kepada dosen sebelum kegiatan pelatihan diakhiri. Berdasarkan hasil evaluasi kegiatan menunjukkan bahwa kegiatan pelatihan dapat diikuti dan dipahami dengan baik oleh peserta dan sebanyak 80% peserta setelah mengiktui kegiatan pelatihan dapat menyelesaikan pengisian BKD dan menyimpan permanen laporan BKD. Kata kunci: pelatihan; pengisian; BKD; SISTER; dosen. ABSTRACTThere is still a lack of understanding regarding filling in the BKD through SISTER, causing some lecturers to find it difficult to fill in the BKD, this has an impact on the tighter time for lecturers in sending BKD reports to assessors. This delay also results in an assessor being late in examining and providing an assessment, which ultimately results in the lecturer being late in reporting the BKD report to the relevant institution. Therefore, there is a need for training activities to equalize lecturers' perceptions in filling out the BKD. With this training, it is hoped that it will be able to increase the knowledge, ability and accuracy of lecturers when filling out the BKD. This training activity aims to help make it easier for lecturers to fill in BKD through SISTER and also increase lecturers' timeliness in collecting BKD reports. The activity implementation method consists of the planning stage, implementation stage and evaluation stage. Training activities provided to lecturers include presentation of material, tests after training and questions and answers to lecturers before the training activities end. Based on the results of the activity evaluation, it shows that the training activities can be followed and understood well by the participants and as many as 80% of participants after participating in the training activities can complete filling in the BKD and keep a permanent BKD report. Keywords: training; filling; BKD; SISTER; lecturer.
K-Means and Fuzzy C-Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment irma darmayanti; Dinar Mustofa; Nurul Hidayati; Inka Saputri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4552

Abstract

Packaged food and beverages often pose a risk of increasing diabetes when consumed regularly. This study aims to classify these products based on their nutritional content listed on the labels, with a focus on identifying diabetes risk. The methods employed include K-Means and Fuzzy C-Means, K-Means is used to determine initial center of cluster, while Fuzzy C-Means enhances the clustering by assigning probabilistic memberships to each data point. These methods are applied to products sold in stores in Banyumas Regency, Central Java, Indonesia. This research is the first to combine these two methods in the context of product clustering based on nutritional labels. The results indicate that packaged food and beverage products can be classified into high-risk and low-risk clusters for diabetes. Consequently, this study provides important guidance for consumers in choosing healthier.