Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika SENTRI: Jurnal Riset Ilmiah
Leviany, Fonda
Unknown Affiliation
Aerospace Engineering Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS Fitriana, Ika Nur Laily; Leviany, Fonda; Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.844

Abstract

The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables.
Feature Selection pada Indikator Indeks Ekonomi Hijau di Indonesia dengan Machine Learning Leviany, Fonda; Fitriana, Ika Nur Laily; Amin, Nurul Nisa’a
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 9 (2025): SENTRI : Jurnal Riset Ilmiah, September 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i9.4615

Abstract

Green economy policies are crucial for all countries to ensure that economic activities progress while preserving environmental sustainability. The success of such policies is measured by the Green Economy Index, which in 2020 recorded a national score of 59.17 with 15 indicators, while provincial-level indicators are still being developed. This study analyzes 18 provincial indicators to identify the main factors influencing the Green Economy Index using LASSO regression. This method was chosen for its ability to efficiently perform feature selection, address multicollinearity, and reduce overfitting risks. The dataset includes 18 indicators and index values from 34 provinces. The results show that 15 indicators significantly affect the index. The developed model demonstrates good performance with an RMSE of 1.23 for the training set and 2.29 for the testing set. The R² values of 95.6% (training) and 85.98% (testing) indicate strong predictive capability. Moreover, surface water quality is identified as the most influential indicator. These findings are expected to support data-driven policymaking in strengthening the green economy at the provincial level.
Studi Komparatif IndoBERT dan SVM-TF-IDF untuk Analisis Sentimen Program Makan Bergizi Gratis Nasional Zildjian, Septian Nuno; Nurdiana, Dian; Leviany, Fonda; Taruk, Medi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 2 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i2.28221

Abstract

Program Makan Bergizi Gratis (MBG) merupakan kebijakan sosial yang memicu beragam respons masyarakat, khususnya di media sosial. Penelitian ini bertujuan menganalisis opini publik terhadap Program MBG melalui komentar TikTok, mengidentifikasi distribusi sentimen, serta membandingkan kinerja model IndoBERT dan Support Vector Machine (SVM) berbasis TF-IDF dalam klasifikasi sentimen berbahasa Indonesia. Data komentar diproses melalui tahapan preprocessing, meliputi pembersihan teks, penghapusan duplikasi, normalisasi bahasa, pengolahan emoji, case folding, dan penyesuaian format teks. Hasil penelitian menunjukkan bahwa IndoBERT memberikan performa yang lebih baik dibandingkan SVM-TF-IDF, dengan nilai accuracy sebesar 89,61% dan F1-makro 83,47%, sedangkan SVM-TF-IDF memperoleh accuracy 78,45% dan F1-makro 64,67%. Distribusi sentimen menunjukkan dominasi sentimen negatif (71,7%), diikuti sentimen netral (14,3%) dan positif (14,1%). Temuan ini mengindikasikan adanya kritik dan kekhawatiran masyarakat terhadap pelaksanaan Program MBG, namun tidak dapat dimaknai sebagai penolakan secara menyeluruh. Penelitian ini menunjukkan bahwa analisis sentimen berbasis Natural Language Processing dapat menjadi pendekatan yang efektif untuk memahami dinamika opini publik dan mendukung evaluasi komunikasi kebijakan berbasis data.
Co-Authors Amin, Nurul Nisa’a Faulina, Ria Fitriana, Ika Nur Laily Nuramaliyah, Nuramaliyah Nurdiana, Dian Safitri, Emeylia Taruk, Medi Zildjian, Septian Nuno