Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal SINTECH (Science and Information Technology) Journal Jurnal Darma Agung
Winahyu, Raden Rara Kartika Kusuma
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PREDIKSI KELAYAKAN KREDIT PEMAKAI PONSEL PINTAR DI INDONESIA MENGGUNAKAN ALGORITMA K NEAREST NEIGHBOR (KNN) PASCA PANDEMI Winahyu, Raden Rara Kartika Kusuma; Eliviani, Rosa; Saputro, Vian Ardiyansyah; Winda, Athar
JURNAL DARMA AGUNG Vol 31 No 6 (2023): DESEMBER
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Darma Agung (LPPM_UDA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46930/ojsuda.v31i6.3897

Abstract

Dalam studi ini, kami bertujuan untuk menggunakan algoritma pembelajaran mesin untuk memprediksi kelayakan kredit pemakai ponsel pintar di Indonesia pasca pandemi COVID-19. Algoritma pembelajaran mesin Principal Component Analysis (PCA) dan algoritma K-means digunakan untuk mengurangi ukuran dimensi dataset dan menggolongkan peringkat kepercayaan dari dataset yang berisi 803 responden, termasuk 12 pertanyaan yang disajikan kepada pemakai ponsel pintar Indonesia pasca pandemi COVID-19. Algoritma klasifikasi KNN diterapkan untuk mengklasifikasikan kepercayaan pemakai ponsel pintar di Indonesia. Tes yang dilakukan termasuk akurasi, presisi, recall, dan F1-score. Hasil penelitian ini menunjukkan bahwa algoritma klasifikasi KNN mencapai tingkat akurasi 0,84, tingkat presisi 0,85, tingkat recall 0,84 dan skor F1 0,84.
Detecting Emotions of Indonesian Songs Based on Plutchik’s Theory using Data Mining Wardani, Deyana Kusuma; Wazaumi, Dwi Diana; Winahyu, Raden Rara Kartika Kusuma
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1509

Abstract

Listening to songs is a daily activity that everyone engages in. Most people choose songs based on their mood, so a system is needed to detect emotions from song lyrics. Previous research only focused on five basic emotions: happy, sad, love, anger, and fear. In this study, we propose a new method to detect emotions from song lyrics using Plutchik's emotion theory. The data used for this research consisted of 250 song lyrics from Indonesian songs. This research categorizes human emotions into eight: joy, trust, surprise, sadness, disgust, anger, and anticipation. Next, the threshold value is calculated. This value is used to determine the dominant emotion. If the frequency value of an emotion is higher than the threshold value, the system considers it as the dominant emotion. The dominant emotions are then classified into positive and negative emotions using cosine similarity calculations. The sampling technique involves using 30% of the test data, resulting in an accuracy of 0.81.
Co-Authors Dwi Diana Wazaumi Eliviani, Rosa Saputro, Vian Ardiyansyah Wardani, Deyana Kusuma Winda, Athar