Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknologi Informasi dan Pendidikan
Fadila Ullul Azmie
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Education Engineering

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

Found 2 Documents
Search

 1 
Sentiment Analysis of Free Nutritious Meal Programs Using Naïve Bayes on Platforms X and TikTok Fadila Ullul Azmie; Yudie Irawan; R.Rhoedy Setiawan
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1112

Abstract

This study analyzes public sentiment toward the Free Nutritious Meal Program (MBG) using the Multinomial Naive Bayes algorithm on data from X (Twitter) and TikTok. A total of 5,173 entries were collected through web scraping and processed with cleaning, normalization, tokenization, stopword removal, and stemming. To address class imbalance, SMOTE was applied, and evaluation employed accuracy, precision, recall, F1-score, and AUC-ROC. Results show that without SMOTE, the model tended to be biased toward the majority class, especially on TikTok, while after SMOTE recall increased significantly and a better balance between precision and recall was achieved. On Twitter, performance was more stable with a moderate class distribution, and SMOTE further improved sensitivity to positive sentiment. Word cloud analysis revealed differences across platforms: TikTok leaned more toward negative sentiment with dominant words such as “racun,” “korupsi,” and “dapur,” while Twitter showed a stronger balance with positive terms like “gizi,” “gratis,” and “program.” These findings highlight the importance of cross-platform analysis to comprehensively understand public perceptions.
Prediksi Pendapatan Penjualan di Indomaret Menggunakan Algoritma Random Forest Regression Moh Adi Kurniawan; Gutti Zaidan Syauqi; Mia Safriyanti; Fadila Ullul Azmie; Arif Setiawan
JSI (Jurnal Sistem Informasi) Universitas Suryadarma Vol. 12 No. 2 (2025): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Fakultas Ilmu Komputer dan Desain - Unsurya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v12i2.1478

Abstract

Penelitian ini bertujuan untuk membangun sistem informasi prediksi pendapatan penjualan di Indomaret menggunakan algoritma Random Forest Regressor. Data yang digunakan merupakan data historis penjualan yang mencakup atribut seperti tanggal transaksi, produk, lokasi toko, metode pembayaran, dan total pendapatan. Model dikembangkan melalui proses pra-pemrosesan data, pelatihan model, dan evaluasi menggunakan metrik Mean Absolute Error (MAE) dan R² Score. Hasil menunjukkan bahwa model memiliki akurasi tinggi dengan MAE sebesar 9.587,48 dan R² sebesar 0,9998, yang menunjukkan kemampuan prediksi yang sangat baik. Visualisasi hasil prediksi juga menunjukkan kesesuaian antara data aktual dan prediksi. Kesimpulan dari penelitian ini adalah bahwa algoritma Random Forest efektif digunakan untuk memprediksi pendapatan penjualan dan dapat dijadikan alat bantu dalam pengambilan keputusan strategis di bidang ritel, khususnya untuk manajemen stok dan perencanaan promosi.
Co-Authors Arif Setiawan Gutti Zaidan Syauqi Mia Safriyanti Moh Adi Kurniawan R.Rhoedy Setiawan Yudie Irawan