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p-Index From 2021 - 2026
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All Journal bit-Tech Journal of Innovative and Creativity Journal of Education Transportation and Business
Bambang Irawan
Muhadi Setiabudi University
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Transportation Other

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Pemetaan Stasiun Kereta Api di Kabupaten Brebes Berbasis Web Dwi Angga Fahrezi; Bambang Irawan; Agyztia Premana
Journal of Education Transportation and Business Vol 1, No 2 (2024): Desember 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetbus.v1i2.3386

Abstract

Kabupaten brebes terletak di Provinsi Jawa Tengah. Ibu Kota Kabupaten Brebes terdapat di Kecamatan Brebes. Kabupaten brebes terdapat banyak Stasiun Kereta Api sehingga diperlukan platform yang dapat menyajikan Informasi Geografis yang cepat dan akurat. Metode yang digunakan dalam penelitian ini metode Deskriptif menggunakan pendekatan Sistem Informasi Geografis (SIG) untuk memetakan lokasi stasiun kereta api. Hasil dari penelitian ini adalah peta berbasis web yang menampilkan informasi tentang Stasiun Kereta Api. Sistem ini diharapkan dapat memudahkan masyarakat dalam mengakses informasi perkeraapian.
Penerapan Vision Transformer Untuk Klasifkasi Sampah Rumah Tangga Prasista Dhiyaul Haq; Bambang Irawan
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.7826

Abstract

The increasing volume of Household waste requires an accurate and efficient automatic waste sorting system. This study aims to apply Vision Transformer (ViT) for image-based household waste clasification. The dataset was divided inti training and validation sets and prepared to match the Vision Transformer archtecture. The ViT-Base Patch16-224 model was trained using the AdamW optimizer with a learning rate of 0.0002, batch size of 16, and 15 training epoch. Model performence was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results show that the proposed model achieved an overall accuracy of 95%. The inorganic class obtained a precision of 0.9, recall of 0.96, and F1-score of 0.95, while the organic class achived a precision of 0.94, recall of 0.93, F1-score of 0.94. these result indicate that self-attention mechanism in Vision Transformer effectively extracts global visual features and improves clasification stability. Therefore, Vision Transformer dermonstrates strong potential for implementasi in intelligent automatic waste sorting systems.
Classification of Sentiment of Emina Product Reviews Using the Naive Bayes Algorithm Wiwik Astriani; Otong Saeful Bachri; Bambang Irawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3554

Abstract

The rapid development of e-commerce in Indonesia has led to an increase in the number of consumer reviews containing opinions and experiences of using products. In the cosmetic product category, text reviews have an important role in influencing purchasing decisions. However, the large volume of data and the imbalance of sentiment distribution are the main challenges in conducting manual and accurate sentiment analysis. Therefore, an automated approach based on machine learning is needed that is efficient and capable of handling large-scale and unbalanced data. This study aims to analyze the sentiment of reviews of Emina brand cosmetic products on the Tokopedia platform and evaluate the effectiveness of the Multinomial Naïve Bayes algorithm combined with TF-IDF and SMOTE data balancing techniques in classifying positive, neutral, and negative sentiments. The research data was obtained through web scraping of Emina product reviews, resulting in 446,325 review data. The research stages include text preprocessing, rule-based sentiment labeling, feature extraction using TF-IDF, data balancing using SMOTE, and classification modeling with the Naïve Bayes Multinomial algorithm. Model performance evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The test results showed that the model achieved an accuracy of 94.72% with a stable F1-score value in all sentiment classes, including minority classes, after the implementation of SMOTE. This study proves that the combination of Multinomial Naïve Bayes, TF-IDF, and SMOTE is effective for large-scale analysis of cosmetic product review sentiment and is able to significantly overcome the problem of data imbalance.
Co-Authors Agyztia Premana Dwi Angga Fahrezi Otong Saeful Bachri Prasista Dhiyaul Haq Wiwik Astriani