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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA
Bambang Irawan
Universitas Muhadi Setiabudi, Brebes
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Analisis Sentimen Ulasan Wisata Alun-Alun Brebes pada Google Maps Menggunakan Support Vector Machine Azkiyatul Maulida; Bambang Irawan; Nur Ariesanto Ramdhan
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8883

Abstract

The rapid development of information technology has encouraged the use of digital platforms as media for sharing opinions, including tourism reviews on Google Maps. Alun-Alun Brebes, as one of the most frequently visited public spaces, has generated thousands of reviews with diverse textual characteristics, making manual analysis inefficient and impractical. This study aims to analyze visitor sentiment toward Alun-Alun Brebes by applying a text mining approach using the Support Vector Machine algorithm. The dataset consists of 1,000 Google Maps reviews, including 327 reviews manually labeled as positive and negative sentiments and 673 unlabeled reviews. The research stages include data collection, text preprocessing, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, Support Vector Machine model training and testing, and automatic labeling of unlabeled data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics based on the manually labeled data. The results show that the Support Vector Machine model with a linear kernel achieved an accuracy of 100% with an F1-score of 1.00, indicating excellent sentiment classification performance. Furthermore, word cloud visualization reveals that positive sentiment is dominated by aspects related to comfort and facilities, while negative sentiment is associated with cleanliness, crowd density, and environmental management. These findings provide data-driven insights into key aspects that should be maintained and improved in managing Alun-Alun Brebes as a public space.
Penerapan Algoritma Regresi Linear Berganda untuk Prediksi Produksi Rokok pada Pabrik Rokok CARI santowi azis; Otong Saeful Bachri; Bambang Irawan
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6903

Abstract

Production planning is an essential aspect of the manufacturing industry as it determines the appropriate production quantity to meet market demand and operational conditions. At Pabrik Rokok CARI, production planning is still primarily based on available inventory without optimal utilization of historical data, which may lead to discrepancies between production output and actual demand. This study aims to apply the Multiple Linear Regression method to predict cigarette production using historical data on sales, inventory, and working hours. The dataset consisted of 352 daily records, which were divided into 281 training data (80%) and 71 testing data (20%). The resulting regression model was Y = -24.0255 + (0.3838 × X₁) + (0.0015 × X₂) + (78.0807 × X₃), where X₁ represents sales, X₂ represents inventory, and X₃ represents working hours. Model evaluation produced a coefficient of determination (R²) of 87.51%, a Mean Squared Error (MSE) of 10,688.72, a Root Mean Squared Error (RMSE) of 103.39, and a Mean Absolute Error (MAE) of 73.22. These results indicate that the Multiple Linear Regression method provides good predictive performance for estimating cigarette production and can serve as a reliable basis for supporting production planning. The prediction model was further implemented in a web-based application to facilitate historical data processing and provide production prediction results for management decision support.
Co-Authors Azkiyatul Maulida Nur Ariesanto Ramdhan Otong Saeful Bachri santowi azis