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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Teknomatika: Jurnal Informatika dan Komputer
Taufaldisatya Wijatama Diwangkara
Universitas Jenderal Achmad Yani Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0 Ulfi Saidata Aesyi; Alfirna Rizqi Lahitani; Taufaldisatya Wijatama Diwangkara; Riyanto Tri Kurniawan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 6 No. 2 (2021): Mei 2021
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (227.177 KB) | DOI: 10.14421/jiska.2021.6.2.113-119

Abstract

The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.
Rekomendasi Posting Promosi pada Sosial Media Berdasarkan Pengelompokan Hasil Penjualan Produk (Studi Kasus: Maula Hijab) Taufaldisatya Wijatama Diwangkara; Ulfi Saidata Aesyi; Netania Indi Kusumaningtyas
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1096

Abstract

Maula Hijab is an MSME (Small and Micro Medium Enterprises) located in Sidomoyo, Godean District, Sleman Regency, Yogyakarta Special Region Province that sells Muslim clothing products. Maula Hijab sells its products directly and through marketplace platforms such as Shopee, Lazada, and Tokopedia. In addition, Maula Hijab promotes its products through social media, one of which is Instagram. Social media is used to promote Maula Hijab products, but there is a decrease in the number of viewers reached by the Maula Hijab Instagram account. In addition, a decline in sales of Maula Hijab was found. Therefore, it is necessary to analyze the level of product promotion performance on Instagram on product sales. To analyze the two data, the Data Mining technique used in this study is K-Means Clustering. The K-Means Clustering algorithm is used to group, classify, or group a set of objects based on their attributes or features into a number of similar groups called clusters. This study aims to provide recommendations for promotion of Maula Hijab products using the K-Means Clustering algorithm. This study uses the K-Means Clustering method. The final result of this research is that 3 product clusters are produced, namely product clusters that are recommended to be promoted more often, product clusters that can be re-promoted, and product clusters that have good promotions. The recommendation system built can run to retrieve Instagram data and process the data to produce output in the form of product promotion recommendations.
Co-Authors Lahitani, Alfirna Rizqi Netania Indi Kusumaningtyas Riyanto Tri Kurniawan Ulfi Saidata Aesyi