Article Per Year (5 Year)
p-Index From 2021 - 2026
2.096
P-Index
This Author published in this journals
All Journal Jurnal Komunikasi Profesional Ultimacomm: Jurnal Ilmu Komunikasi Jurnal Public Relations (J-PR) Jurnal Ekonomi dan Bisnis Digital (MINISTAL) Indonesian Journal of Business Analytics (IJBA) Jurnal Mediakita : Jurnal Komunikasi dan Penyiaran Islam Jurnal Komunikasi Indonesia Journal of Digital Market and Digital Currency
Angelia, Chininta Rizka
Universitas Multimedia Nusantara
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Imaji Kebutuhan Konsumsi Melalui Akun Media Sosial: Studi Semiotika Foto Endorser di Instagram Chininta Rizka Angelia
ULTIMA Comm Vol 13 No 1 (2021): ULTIMACOMM
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ultimacomm.v13i1.1527

Abstract

This study's motivated by competitive rivalry among producers which encourages them to develop a marketing strategy for the sake of absorption abundance of production. The producer created artificial needs through system of signs on social media. This study aimed to dismantle the use of system of signs on social media that carried producers to raise consumption. According to Consumer Society Theory by Jean Baudrillard, this study used a critical paradigm with The Semiotic Analysis Method of Roland Barthes. The result showed that the producer slipped marketing activities through the system of signs in endorser's photos on Instagram. Other findings in this study's there's a mutual relationship between producer and endorser. Keywords: consumer society, endorsement, social media, Instagram, semiotic, roland barthes
Explainable Ensemble Learning for Loan Approval Prediction Using XGBoost, LightGBM, and Random Forest with SHAP Analysis Mikaria Gultom; Chininta Rizka Angelia; Vijayaletchumy Krishnan
Journal of Digital Market and Digital Currency Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i3.74

Abstract

Loan approval prediction is a critical task in financial institutions, as it directly impacts risk management and decision-making processes. However, challenges such as class imbalance and lack of model interpretability often limit the effectiveness and reliability of machine learning approaches. This study proposes an explainable ensemble learning framework for predicting loan approval using Random Forest, XGBoost, and LightGBM models. To address the issue of imbalanced data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training dataset, ensuring a balanced class distribution and improving model performance. The models were evaluated using multiple metrics, including Accuracy, Precision, Recall, F1-score, and ROC-AUC. The results show that all models achieved strong performance, with Random Forest outperforming the others by achieving the highest accuracy of 0.959 and ROC-AUC of 0.9386. Furthermore, SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability by providing both global and local explanations of feature contributions. The analysis revealed that CreditScore, EmploymentType, and Income are the most influential factors in determining loan approval decisions. In addition, SHAP dependence and interaction analysis demonstrated the presence of non-linear relationships and feature interactions, offering deeper insight into the model’s decision-making process.
Co-Authors Amalia, Dinda Amelia Anggraeni Sulistiyo Angelia Heriyanto Daniel Susilo Ernestus Holivil Everd S. R. Daniel Faramita, Aazelia Frederik Masri Gasa Helga Liliani Cakra Dewi Jessica Bong Natasha Juliadi, Rismi Kadek Tri Anggraeni S. C. P Mario Philip Silalahi Masri Gasa, Frederik Mikaria Gultom Mujiono Naomi Tehillah Nurhayati, Kristina Primadini, Intan Silvanus Alvin Vijayaletchumy Krishnan