Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Pijar : Studi Manajemen dan Bisnis
Felix Fernando
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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

Found 2 Documents
Search

 1 
ANALISIS MANAJEMEN STRATEGI PADA UKM XYZ: MODEL MATRIKS IFE EFE DAN BALANCED SCORECARD Felix Fernando; Amberly Anadya Prames; Mandy Aileen Lius; Muhammad Ihsan Almunawar; Steven Gilbert; Lindawati Kartika
Jurnal Pijar Vol 2 No 03 (2024): Jurnal Pijar : Studi Manajemen dan Bisnis
Publisher : PT Naureen Digital Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65096/pmb.v2i03.1350

Abstract

Small and Medium Enterprises (SMEs) play a significant role in Indonesia's economic growth, contributing 60.5 percent to the Gross Domestic Product (GDP) and 96.9 percent to national employment. However, SMEs in Indonesia face strategic management challenges, particularly in formulating ideal goals and missions, influenced by both internal and external conditions. This study aims to analyze and determine the ideal strategic management for XYZ SME, which is one of the SMEs experiencing such issues. Data collection was conducted through interviews with the owner of XYZ SME in February 2024. The data processing methods used in this study include the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices, as well as the BalancedScorecard. The results of the study indicate that: (1) the strategic management of XYZ SME is not yet ideal based on the determined Key Performance Indicators (KPIs); (2) the appropriate and ef ective strategic management improvement method for XYZ SME involves using the IFE and EFE matrices and the Balanced Scorecard, resulting in a strategic map as the output.
KLASIFIKASI TWEET CYBERBULLYING DENGAN MENGGUNAKAN ALGORITMA SVM DAN XGBOOST Felix Fernando
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32857

Abstract

Penelitian ini bertujuan untuk melakukan klasifikasi tweet cyberbullying dengan menggunakan dua pendekatan machine learning yaitu algoritma SVM dan XGBoost. Data input yang digunakan untuk analisis merupakan hasil pengambilan data tweet secara acak yang telah dilabelkan, dilakukan ekstraksi fitur dan pelatihan model menggunakan kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa XGBoost memberikan kinerja yang sedikit lebih unggul dalam mengklasifikasikan tweet cyberbullying dibandingkan SVM. Temuan ini memberikan kontribusi dalam pengembangan metode klasifikasi untuk mendeteksi cyberbullying di media sosial sehingga dapat membantu dalam memitigasi dampak negatif dari cyberbullying. Penelitian ini juga menunjukkan potensi penggunaan algoritma XGBoost dalam konteks deteksi cyberbullying di platform media sosial seperti Twitter.
Co-Authors Amberly Anadya Prames Lindawati Kartika Mandy Aileen Lius Muhammad Ihsan Almunawar Steven Gilbert