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All Journal JUKI : Jurnal Komputer dan Informatika Journal of Computer Networks, Architecture and High Performance Computing
Sunge, Aswan S.
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Computer Science & IT Education

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The Analysis of Product Sales in the Application of Data Mining with Naive Bayes Classification Zahri, M. Hannata; Sunge, Aswan S.; Zy, Ahmad Turmudi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4255

Abstract

H&F Shoe Store is a privately owned Micro, Small, and Medium Enterprises retail store that sells merchandise. The owner serves customers directly and also acts as a cashier. In this store, the business owner is less aware of what types or categories of products are most in demand by customers, making sales operations less than optimal. Because of this, special expertise is needed to handle the problems in the retail store, namely data mining or Data Mining with the aim of digging up information related to sales problems, in this case the author will use the Classification method with the Naive Bayes algorithm. In this study, the author uses secondary data obtained from sales notebooks and re-collected into Microsoft Excel according to research needs. The data that has been collected on the software is 121 data which have 10 attributes, namely “Nama Produk”, “Size Produk”, “Kategori Produk”, “Jenis Produk”, “Gender Produk”, “Merek Produk”, “Stok Awal”, “Stok Terjual”, “Stok Sisa”, and “Penjualan”. The Naive Bayes Classifier method has successfully produced good results in classifying sales on a type or category of marketed products, the results obtained are in the form of product sales analysis and Naive Bayes model evaluation values. The results of the model evaluation values on the Confusion Matrix obtained are accuracy of 86.11%, recall of 84.62% and precision of 84.62%.
Analisis Klasifikasi Keamanan dalam Shorting Malware Android dengan Algoritma K-Nearest Neighbors Majid, Nurcholis; Sunge, Aswan S.; Suherman, Suherman
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 2 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v5i2.378

Abstract

Abstrak. Perkembangan teknologi dan popularitas perangkat berbasis Android telah membuka pintu lebar bagi pengembangan aplikasi yang beragam dan inovatif. Namun, kesuksesan Android juga telah menarik perhatian para penjahat cyber untuk mengembangkan Malware. Berbagai jenis perangkat dapat diinfeksi oleh malware, salah satunya adalah smartphone, dimana kasus malware terbanyak didominasi pada sistem operasi Android. Rentannya serangan malware dan dapat merugikan para pengguna Android sehingga diperlukan analisis lebih lanjut, pada kasus ini digunakan pendekatan Machine Learning untuk melakukan klasifikasi data serangan malware android. Algoritma yang digunakan adalah K-Nearest Neighbor. Mendapatkan hasil accuracy dengan nilai sebesar 98%, precision sebesar 98%, dan recall sebesar 98%. Hasil ini membuktikan bahwa algoritma K-Nearest Neighbor memberikan hasil yang cukup baik dalam mengklasifikasi malware android. Abstract. Technological developments and the popularity of Android-based devices have opened wide doors for the development of diverse and innovative applications. However, Android's success has also attracted the attention of cybercriminals to develop Malware. Various types of devices can be infected by malware, one of which is smartphones, where the majority of malware cases are dominated by the Android operating system. Malware attacks are vulnerable and can harm Android users so further analysis is needed, in this case a Machine Learning approach is used to classify Android malware attack data. The algorithm used is K-Nearest Neighbor. Get accuracy results with a value of 98%, precision of 98%, and recall of 98%. These results prove that the K-Nearest Neighbor algorithm provides quite good results in classifying Android malware.
The Analysis of Product Sales in the Application of Data Mining with Naive Bayes Classification Zahri, M. Hannata; Sunge, Aswan S.; Zy, Ahmad Turmudi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4255

Abstract

H&F Shoe Store is a privately owned Micro, Small, and Medium Enterprises retail store that sells merchandise. The owner serves customers directly and also acts as a cashier. In this store, the business owner is less aware of what types or categories of products are most in demand by customers, making sales operations less than optimal. Because of this, special expertise is needed to handle the problems in the retail store, namely data mining or Data Mining with the aim of digging up information related to sales problems, in this case the author will use the Classification method with the Naive Bayes algorithm. In this study, the author uses secondary data obtained from sales notebooks and re-collected into Microsoft Excel according to research needs. The data that has been collected on the software is 121 data which have 10 attributes, namely “Nama Produk”, “Size Produk”, “Kategori Produk”, “Jenis Produk”, “Gender Produk”, “Merek Produk”, “Stok Awal”, “Stok Terjual”, “Stok Sisa”, and “Penjualan”. The Naive Bayes Classifier method has successfully produced good results in classifying sales on a type or category of marketed products, the results obtained are in the form of product sales analysis and Naive Bayes model evaluation values. The results of the model evaluation values on the Confusion Matrix obtained are accuracy of 86.11%, recall of 84.62% and precision of 84.62%.
Co-Authors Majid, Nurcholis Suherman, Suherman Zahri, M. Hannata Zy, Ahmad Turmudi