Article Per Year (5 Year)
p-Index From 2021 - 2026
1.897
P-Index
This Author published in this journals
All Journal Populer: Jurnal Penelitian Mahasiswa Jurnal Teknik Mesin, Industri, Elektro dan Informatika Applied Information Technology and Computer Science (AICOMS)
Ghufron Ghufron
Universitas Islam Sultan Agung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Implementasi Vision Transformer (VIT) dan K-Means Clustering dalam Klasifikasi Gaya Visual Poster untuk Otomatisasi Ekstraksi Skema Warna Digital Muhammadun Hanif; Bagus Satrio Waluyo Poetro; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5516

Abstract

The purpose of this study is to implement the Vision Transformer (ViT) architecture and K-Means Clustering algorithm to automate fine-grained visual style classification of posters and the extraction of digital color schemes. MSMEs and beginner digital creators often face challenges in determining harmonious color schemes for promotional poster designs, leading to a time-consuming trial-and-error design process. The research utilized a custom dataset of 1,011 poster images scraped from the Behance platform, evenly distributed into three visual style classes: Bauhaus, Swiss Style, and Minimalist. The data preprocessing employed a dual-pipeline approach, where color extraction was optimized through SLIC Superpixel segmentation within the CIELab* perceptual color space to significantly reduce computational complexity without degrading the boundaries of original visual elements. The evaluation results demonstrated that the ViT model performed exceptionally well, achieving an accuracy of 79% and a Macro F1-Score of 0.80. In the color extraction module, the K-Means algorithm successfully formed five highly separated dominant palette clusters with an average Silhouette Score of 0.7698. The perceptual color accuracy validation recorded an average  of 4.7648, proving that the extracted hexadecimal (HEX) palette codes operate well within the acceptable limits of human visual deviation.
Deteksi Cyberbullying pada Twitter menggunakan Distilbert dan (Explainable Ai) Shap Hadana Maulana; Mustafa Mustafa; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5517

Abstract

The prevalence of cyberbullying on Twitter demands a fast and transparent automated moderation system. This study designs an efficient and explainable binary cyberbullying detection system using DistilBERT and the SHAP-based Explainable AI (XAI) method. The dataset, sourced from Kaggle, consists of 13,169 raw data filtered into 12,548 clean tweets. Preprocessing was conducted without stemming and stopword removal to preserve the language's semantic context, setting a max_length of 48 tokens. The pre-trained distilbert-base-multilingual-cased model was evaluated on an 80% training and 20% testing data split, while model transparency was validated using SHAP's PartitionExplainer algorithm. Evaluation results show DistilBERT performed robustly, achieving 84% accuracy, 84% precision, 86% F1-score, and 88% recall in detecting bullying, with an Area Under Curve (AUC) score reaching 0.9207. Locally, SHAP evaluation proved that the model concentrated heavy penalty weights on abusive words like "brengsek". Globally, SHAP revealed a unique phenomenon where the most important features were dominated by neutral vocabularies such as "times. roman" and "pendidikan", which the model cleverly utilized as strong discriminative indicators to validate normal texts. In conclusion, the integration of DistilBERT and SHAP yields a classification system that is not only accurate and efficient but also transparent and logically reasoned.
Implementasi Arsitektur Temporal Fusion Transformer (Tft) Berbasis Interpretable Multi-Horizon Forecasting Prediksi Volatilitas Harga Aset Kripto Solana Mahmudi Husain Hasbullah; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5520

Abstract

The cryptocurrency market, specifically Solana (SOL), exhibits extreme volatility that is difficult for conventional algorithms to predict accurately. Additionally, advanced Deep Learning models often act as black-boxes. This study implements the Temporal Fusion Transformer (TFT) architecture for robust and interpretable multi-horizon volatility prediction of SOL/USDT. High-frequency historical data (1-hour intervals) was acquired via Binance API from January 2023 to December 2024. Data preprocessing included technical indicator feature engineering, Z-Score normalization, and chronological data splitting. Hyperparameters were optimized using Optuna. The TFT architecture performed exceptionally, achieving a Root Mean Squared Error (RMSE) of 2.5191 USDT and a Coefficient of Determination (R2) of 0.9987. Probabilistically, the model successfully mapped risk ranges with a Weighted Mean Quantile Loss of 0.4629. Explainable AI analysis revealed that predictions were heavily influenced by historical closing price, RSI, Volume, and MACD. Innovatively, TFT outputs were integrated into a Decision Support System utilizing Google Gemini 2.5-Flash to automatically generate market analysis narratives. In conclusion, this study produced a robust forecasting model, successfully resolving the black-box problem into a transparent virtual analyst assistant.
Analisis Sentimen Masyarakat Terhadap Program Makan Bergizi Gratis (MBG) di Jawa Tengah Menggunakan Model Bidirectional Encoder Representations From Transformers For Indonesian (Indobert) Ibra Andnan Faiz Sempana; Imam Much Ibnu Subroto; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5530

Abstract

This study analyzes public sentiment toward the Free Nutritious Meal (Makan Bergizi Gratis/MBG) program in Central Java using data from the social media platform X. Data were collected automatically with Python and Selenium WebDriver, yielding 2,000 tweets containing username, date, location, and comment text. The comments were labeled manually, producing a relatively balanced dataset of 1,017 negative and 983 positive comments. After preprocessing cleaning, case folding, slang normalization, and WordPiece tokenization the dataset was split 80:10:10 for training, validation, and testing. A pre-trained IndoBERT model (124,442,882 total parameters, of which 28,943,618 were trainable) was fine-tuned for binary sentiment classification over 8 epochs; training loss fell from 0.6925 to 0.4914 while validation accuracy rose from 71.00% to 76.50%. On the 200-sample held-out test set, the final model achieved 84.50% accuracy, 84.89% precision, 84.50% recall, and an 84.44% F1-score, with a confusion matrix of 92 true negatives, 77 true positives, 21 false negatives, and 10 false positives. As a practical output, the sentiment analysis results were integrated into an interactive Streamlit dashboard to support real-time monitoring of public opinion.
Co-Authors Aditya Bachtiar Ahmad Labib Zainuddin Dedie Nugroho Dedy Kurniadi Hadana Maulana Ibra Andnan Faiz Sempana Imam Much Ibnu Subroto Mahmudi Husain Hasbullah Muhamad Miftahul Huda Muhammad Ridwan Muhammadun Hanif Mustafa, Mustafa Naufal Lathifan Yumna Naufal Muhammad Afif Nurhasan Nurhasan Poetro, Bagus Satrio Waluyo Riansyah, Andi Sabila Wahyu Pratama Tiza Farhani Maulia Trianando Mohammad Dahlan Wirayudha