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TEPIAN
Published by Politeknik Pertanian Negeri Samarinda
ISSN : 27215350     EISSN : 27215369     DOI : -
Core Subject : Science,
Computer Science & IT
The purpose of TEPIAN is to publish original research studies directly relevant to computer science. TEPIAN encompasses the full spectrum of information technology and computer science, including information system, hardware technology, intelligent system, and multimedia applications. TEPIAN welcomes original papers, reviews and commentaries. Suggestions for special issues covering selected topics may be considered. TEPIAN is devoted to publish manuscripts that advance the knowledge of information technology and communication beyond state-of-the-art. Authors may contact the Editor-in-Chief in advance to inquire about whether their research topic is suitable for consideration by TEPIAN. Through an Open Access publishing model, TEPIAN provides an important forum where computer science researchers in academic, public and private arenas can present the latest results from research on information technology and communication in a broad sense.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 225 Documents
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Analysis of Customer Reviews of Fren.co Coffee & Eatery on Google Maps Using Logistic Regression and Random Forest Methods Julio Enrico Frans Frans; Heny Pratiwi; Ahmad Fahrijal Pukeng
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3660

Abstract

Online review platforms provide valuable data for evaluating customer perceptions and service quality in food and beverage businesses; however, such data are typically unstructured and frequently exhibit naturally imbalanced sentiment distributions that may influence classification outcomes. This study analyzes customer reviews of Fren.co Coffee & Eatery on Google Maps using Logistic Regression and Random Forest within a controlled comparative framework. A total of 225 valid textual reviews were collected and labeled into positive, neutral, and negative categories based on rating scores. The data were preprocessed through case normalization, cleansing, tokenization, stop word removal, and stemming, and subsequently transformed into numerical feature vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting scheme. To preserve the original sentiment distribution, an 80:20 stratified sampling strategy was implemented during model evaluation. Experimental results indicate that Logistic Regression achieved higher overall accuracy of 0.89 (89%) and demonstrated more balanced precision and recall across sentiment classes compared to Random Forest, which achieved an accuracy of 0.87 (87%) and showed stronger bias toward the majority class. These findings suggest that, in small-scale and naturally imbalanced Google Maps review datasets, linear classification models may provide more stable and consistent predictive performance than ensemble-based approaches. The study contributes empirical evidence on model behavior under realistic imbalance conditions and strengthens methodological understanding of classical machine learning applications for sentiment analysis in regional hospitality businesses.
Sentiment Analysis of Public Satisfaction Toward Banjar Grilled Chicken Restaurant Using Random Forest Muhammad Raihan Ramandha Putra; Heny Pratiwi; Kusno Harianto
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3681

Abstract

This study aims to explore the level of public satisfaction with the Banjar grilled chicken restaurant by utilizing customer reviews on the Google Maps platform. These reviews serve as a primary source of information that reflects public perceptions regarding the quality of food, service standards, pricing, and the overall atmosphere of the restaurant environment. In the digital era, online reviews have become an essential factor influencing consumer decisions, as many potential customers rely on shared experiences before visiting a restaurant. However, the large volume of reviews available on Google Maps makes manual analysis inefficient, impractical, and excessively time-consuming, especially when the data continues to grow over time. Therefore, this study adopts a text mining–based analytical approach combined with the Random Forest algorithm to automatically classify customer sentiment in a structured and systematic manner. The data used in this research consist of Indonesian-language comments collected from Google Maps, which are then categorized into two main sentiment classes: positive and negative. The research process involves several stages, including data collection, text preprocessing such as cleaning and normalization, word weighting using the TF-IDF method, and sentiment classification using the Random Forest algorithm, followed by model evaluation through a confusion matrix to measure performance accuracy. The final results are expected to provide a comprehensive overview of customer satisfaction levels and offer valuable insights that can assist restaurant management in improving service quality, enhancing customer experience, and developing more effective business strategies in the future.
Analysis of Scholarship Website Users Using the End-User Computing Satisfaction Model and Importance Performance Analysis Model Ramadiani Ramadiani; Muhammad Reyhan Setiawan; Muhammad Labib Jundillah
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3685

Abstract

The East Kalimantan Scholarship Website is a facility provided by the government of the East Kalimantan Provincial Education and Culture Office. This study aims to assess the user satisfaction of individuals using a scholarship website by applying two well-established models: End-User Computing Satisfaction (EUCS) and Importance-Performance Analysis (IPA). The EUCS model evaluates users’ satisfaction with key aspects of the website. The IPA model is employed to assess the relative importance and performance of these factors from the user’s perspective, enabling the identification of areas for improvement. The combined insights from these models can guide the enhancement of scholarship website services and user experience. Data was collected through questionnaires to respondents, who registered on the BKT site with the Complete category from various universities. The East Kalimantan Scholarship website evaluation system calculates the results of questionnaires from students with various study programs. This system uses EUCS statements in the categories of Content, Accuracy, Format, Ease of Use, Timelines, and User Statistics. The results of this study indicate that the hypotheses designed are all accepted and have a significant influence. Users are satisfied with the website's ease-of-use aspect, which is the strongest aspect in supporting user satisfaction. Conversely, the accuracy aspect shows the weakest relationship among other variables.
Sentiment Classification of Google Maps Reviews for Tepian Pandan Restaurant Using Support Vector Machine I Made Borneo Setyawan; Heny Pratiwi; Kusno Harianto
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3688

Abstract

The rapid development of information technology has driven the increasing use of online review platforms as a means of sharing consumer experiences. Customer reviews now serve not only as a medium for expressing opinions but also as a valuable source of data in measuring the level of public satisfaction with a business, particularly in the culinary field. One of the most widely used platforms is Google Maps, which allows customers to provide ratings and comments regarding food quality, service, price, and the atmosphere of the place. The information presented in text form can be further analyzed to obtain a general overview of consumer perceptions. This study aims to analyze public satisfaction sentiment towards Tepian Pandan Restaurant based on reviews found on Google Maps by applying the Support Vector Machine (SVM). The method used refers to the text approach. mining which includes several stages, namely collecting review data, text preprocessing (such as case folding, tokenizing, and data cleaning), feature extraction using the Term Frequency – Inverse method Document Frequency (TF-IDF), and sentiment classification using the SVM model. The processed reviews were then grouped into two main categories: positive sentiment and negative sentiment. To assess model performance, this study used evaluation metrics such as accuracy, precision, recall, and F1-score. The test results showed that the Support Vector Machine (SVM) model was able to classify review sentiment with good and consistent performance. Therefore, this approach is considered effective in identifying customer satisfaction levels based on online review data. The findings of this study are expected to inform restaurant management's efforts to improve service and product quality based on customer feedback.  
Public Sentiment Analysis on the Free Nutritious Meal Program Using Logistic Regression and Support Vector Machine Algorithms Cintami Amanda Putri; Heny Pratiwi; Ulfa Nurfadhila
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3690

Abstract

The Free Nutritious Meal Program is a national policy initiated by the Indonesian government to improve the nutritional status of school-aged children and support long-term human resource development. The implementation of this policy has generated diverse public responses expressed through social media platforms, particularly YouTube. This study aims to analyze public sentiment toward the Free Nutritious Meal Program and to compare the performance of Logistic Regression and Support Vector Machine algorithms in multiclass sentiment classification. A total of 3,920 Indonesian-language YouTube comments were collected and processed through text preprocessing stages, including case folding, tokenization, stop word removal, and stemming. Sentiment labeling was conducted using a lexicon-based approach, and feature representation was generated using the Term Frequency–Inverse Document Frequency method. The dataset was divided into training and testing sets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicate that positive sentiment dominates public opinion. Although both algorithms achieved similar accuracy (0.79), Support Vector Machine demonstrated more balanced recall and F1-score across minority classes, indicating stronger robustness in handling imbalanced high-dimensional text data. These findings highlight the effectiveness of the Support Vector Machine algorithm in digital public policy evaluation through social media–based sentiment analysis.

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