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All Journal Bulletin of Computer Science Research KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Siti Muntari
Institut Teknologi Pagar Alam, Pagar Alam
Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice

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Pelatihan Pembutan Brosur Dengan Adobe Photoshop Pada SMA PGRI Pagar Alam Siti Muntari; Elpita Aisah; Fitria Rahmadaynti
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 3 No. 2 (2023): Juni : Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v3i2.1752

Abstract

An indicator of a good village is having a clean environment and local residents free from all diseases. In addition, local residents have commodities to be more independent in terms of economic development. This is of course not only the responsibility of village officials, but requires the support and cooperation of local residents as well. Therefore it is necessary to make use of the village environment which can realize the indicators previously mentioned. The first step that can be taken is to start with your own yard. Because based on observations, the service team found that in the partner village, namely Balombong village, it had not been used properly and there were empty lands in the residents' yards. So that the purpose of implementing this service is to provide education for residents to be able to take advantage of the yard by planting fruit in pots. This can make the environment more beautiful, healthy and the results can help the local economy
Sistem Prediksi Prestasi Akademik Siswa Menggunakan Algoritma K-Nearest Neighbor (KNN) Febriansyah Febriansyah; Siti Muntari
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1092

Abstract

This study aims to classify student achievement levels based on academic ability in mastering subject matter using the K-Nearest Neighbor (K-NN) method. This study is motivated by the limitations of the student grade data processing system which is still done manually using Microsoft Excel, where the process of adding and grouping grades into low to high categories takes a long time and makes it difficult for teachers to identify student achievement levels, such as Good, Sufficient, and Poor categories. The data used consists of 134 students with 11 subject attributes as input variables in the classification process. The results show that from 134 student data, 90 students are classified into the Good category, 20 students are classified into the Sufficient category, and 24 students are classified into the Poor category. Testing using RapidMiner shows that the K-NN method obtains an accuracy level of 89.55%, which indicates that this method is effective in grouping student achievement levels. Performance evaluation is carried out using a Confusion Matrix to compare the classification results with actual data. Testing was conducted 10 times with a total of 134 student data obtained, the K-Nearest Neighbor (KNN) algorithm produced an accuracy of 89.55% and a kappa value of 0.763. These figures indicate that the model has excellent classification performance and a high level of reliability. The resulting model was then developed into a web-based prediction system that was tested using the expert system method through the Black-box Testing approach and obtained a feasibility level of 83.44%.
Co-Authors Elpita Aisah Febriansyah . Fitria Rahmadayanti