Article Per Year (5 Year)
p-Index From 2020 - 2025
3.839
P-Index
This Author published in this journals
All Journal Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Informatics for Educators and Professional : Journal of Informatics Jurnal Teknik Informatika STMIK Antar Bangsa Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Mantik Akasia: Artikel Ilmiah Sistem Informasi Akuntansi JUSTIAN - Jurnal Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Insan Journal of Accounting Information System SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Lisda Widiastuti
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jusikom : Jurnal Sistem Komputer Musirawas

 1 
IMPLEMENTASI ALGORITMA APRIORI DAN FP-GROWTH UNTUK PENEMPATAN BUKU PADA PERPUSTAKAAN (STUDI KASUS : UBSI – BOGOR) Nurlaela, Dini; Utami, Lila Dini; Widiastuti, Lisda
Jusikom : Jurnal Sistem Komputer Musirawas Vol 8 No 2 (2023): Jusikom : Jurnal Sistem Komputer Musi Rawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v8i2.2191

Abstract

The library is a storage place for a diverse collection of knowledge books, magazines, and other media that is organized specifically for easy use by users. In arranging the placement of books in the library, the books are grouped according to categories and given specific numbering. Although the grouping of books has been done based on categories, the placement of books has not taken into account the popularity level of books that are frequently borrowed. As a solution to address this issue, the researcher will use data mining methods, specifically the fp-growth algorithm, especially the apriori method, to determine patterns for arranging the layout of books in the library. This system is expected to help simplify the process of determining book arrangements that better suit the needs of users. The implementation results using RapidMiner show that the highest combination pattern of library book layouts is for the book titled Aplikasi Komputer Akuntansi Menggunakan ABSS Premier Versi 20 and Metode Penelitian with a support level of 60% and a confidence level of 100%.
PENERAPAN KOMPARASI ALGORITMA KLASIFIKASI PADA ANALISIS SENTIMEN APLIKASI SPOTIFY Widiastuti, Lisda; Nurlaela, Dini; Surniandari, Artika; Utami, Lila Dini
Jusikom : Jurnal Sistem Komputer Musirawas Vol 9 No 1 (2024): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i1.2324

Abstract

This study compares three popular text classification algorithms—Naive Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbour (k-NN)—for classifying Spotify review data. The significance of this topic lies in applying these algorithms to sentiment analysis, which can help better understand user feedback. The research method involves testing these algorithms on Spotify review data classified into positive and negative categories. Results show that the k-NN algorithm achieves the highest accuracy at 83.67%, while NB and SVM achieve accuracies of 77.67% and 76.50%, respectively. The AUC values are 0.950 for NB, 0.955 for SVM, and 0.914 for k-NN. Despite k-NN showing the highest accuracy, SVM exhibits the highest AUC, indicating very good performance in distinguishing between categories. In conclusion, while k-NN demonstrates superior accuracy, a comprehensive evaluation based on various metrics is crucial for selecting the optimal algorithm for sentiment analysis
Co-Authors Ahmad Al Kaafi Amanah, Delia Arsiadi, Febri Artika Surniandari Dini Nurlaela Ela Nurelasari Elvira Febriliani Ernawati, Siti Farid Shofli Wahnin Fauziah, Alisha Nur Febri Arsiadi Felisha, Angela Putri Hapsari, Amelia Dwi Haryani Hilda Rachmi Lila Dini Utami Lisda Mahmudah, Sintia Muhammad Habili Firdaus Nurfadillah, Siti Dalfa Rifki Azmi - UBSI Siti Ernawati Sri Wasiyanti, Sri Suparni Suparni Wasiyanti , Sri Zakia, Ade Ucu Tri