cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Reza Andrea
Contact Email
reza.andrea@gmail.com
Phone
+6285388729017
Journal Mail Official
admin.tepian@politanisamarinda.ac.id
Editorial Address
Kampus Sei Keledang Jl. Samratulangi, Samarinda Kode Pos 75131
Location
Kota samarinda,
Kalimantan timur
INDONESIA
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 282 Documents
 21   22   23   24   25 
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.
Analysis of Public Satisfaction with the JKN Mobile Application in Samarinda City Using the SERVQUAL Method and the Customer Satisfaction Index Muhammad Fadhilah; Heny Pratiwi; Ahmad Fahrijal Pukeng
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3661

Abstract

The rapid development of information and communication technology has significantly transformed the delivery of public services, particularly in the healthcare sector. One of the digital innovations introduced to improve healthcare accessibility in Indonesia is the JKN Mobile application developed by BPJS Kesehatan. This application enables participants to access various administrative services, membership information, healthcare facilities, and queue management systems online. As the number of users continues to increase, evaluating service quality becomes essential to ensure that the application meets user expectations and delivers satisfactory performance. This study aims to analyze public satisfaction with the JKN Mobile application in Samarinda City by integrating the Service Quality (Servqual) method and the Customer Satisfaction Index (CSI). Data was collected through a structured questionnaire distributed to active users of the JKN Mobile application, covering five dimensions of service quality: tangibles, reliability, responsiveness, assurance, and empathy. The Servqual method was applied to measure the gap between users’ expectations and perceived performance, while the CSI method was utilized to determine the overall satisfaction level in the form of an index value. The results indicate that negative gaps are primarily found in the reliability and responsiveness dimensions, suggesting that system stability, transaction accuracy, and response time require improvement. In contrast, tangibles, assurance, and empathy dimensions demonstrate relatively positive evaluations. The overall CSI score categorizes user satisfaction as satisfied, although continuous service enhancement remains necessary. The integration of Servqual and CSI provides a comprehensive framework for identifying service weaknesses and supporting strategic improvements in digital healthcare services in Samarinda City.
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.
Fire Detection Alarm and Electric Circuit Breaker based on SIM800L Arduino Kadek Reda Setiawan Suda; Abdillah Aziz Muntashir; I Komang Gede Suka Wijana; Muhdalifah Muhtar; Ida Bagus Putu Widja; I Wayan Nova Eka Ariana
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3510

Abstract

The danger of fire can have fatal consequences such as loss of property and even loss of life. Preventive action is necessary to avoid, prevent and minimize the occurrence of fire disasters. This research aims to design and build a smoke and fire detection system based on sensors, microcontrollers, which can be used to detect potential fire hazards in certain houses or buildings. The research method in designing this system refers to the prototyping model. The components used are MQ-02 sensors, KY-026 sensors, microcontrollers, alarm buzzers, and SMS notification senders and GSM calls using the SIM800L module. The results of the research are a fire detection alarm system and electricity circuit breaker, which functions to provide early warning of potential fires through buzzer alarms and SMS notifications and GSM calls. This system can be used to help detect and avoid potential fire hazards
A Hybrid AI and Computational Linguistics Framework for Thematic and Narrative Analysis in Literary Studies Tohir Zuhdi; Taqwa Hariguna; Berlilana Berlilana
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3621

Abstract

This study aims to examine the integration of Artificial Intelligence (AI) and computational linguistics in thematic and narrative analysis of literary works, particularly within the framework of digital humanities. The background of this research arises from the limitations of manual methods, which tend to be time-consuming, subjective, and less effective when dealing with large text corpora. To address these challenges, the study adopts a quantitative approach using questionnaires tested for validity and reliability, involving 100 respondents consisting of educators and academics. Data were analyzed using the Wilcoxon test and Friedman test with the assistance of SPSS software. The findings reveal that narrative analysis received the highest appreciation compared to other variables, with a mean score of 48.32. The Wilcoxon test indicates that AI makes a significant contribution to supporting narrative analysis, while computational linguistics shows significant differences in both thematic and narrative analysis. The Friedman test further strengthens these findings by demonstrating significant differences among the variables, indicating that the integration of AI and computational linguistics provides a new methodological framework in literary studies. This research concludes that technological integration enriches literary analysis through more efficient, objective, and systematic approaches, although it still requires humanistic interpretation to maintain the validity of meaning. These findings are expected to provide both practical and theoretical contributions to the development of literary studies in the digital era
IoT-Enabled Monitoring and Methane Energy Forecasting in Tofu Waste Biogas Systems for Electricity Generation Fitri Oktafiani; Sigit Kusuma Wijaya; Al Mutaqim; Ferdi Bagus Purnomo; Hamsir Hamsir; Riza Hadi Saputra
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3638

Abstract

This study presents the development of an Internet of Things based monitoring system and methane energy forecasting for biogas production from tofu wastewater in Somber, Balikpapan. A 1000 L anaerobic biodigester was installed at a home scale tofu industry to process liquid tofu waste through anaerobic fermentation. The monitoring system utilized three ESP32 microcontrollers integrated with MQ-4 methane sensors, DHT11 temperature–humidity sensors, BMP180 pressure sensors, and ultrasonic level sensors. Data were transmitted every 8–10 seconds to Google Spreadsheet via Wi-Fi for real time monitoring. Observations were conducted over a 21-day fermentation period. The results show that methane concentration increased from approximately 4,720–4,810 ppm on day 13 to 5,000–5,050 ppm on day 21. Humidity remained relatively stable between 93.6% and 94.2%, while temperature ranged from 31.5°C to 42.6°C. The average methane quantity was estimated at 1.42 × 10⁷ mol, corresponding to a theoretical energy potential of approximately 3.17 × 10⁶ kWh. These results indicate strong potential for small scale biogas based power generation.
Visitor Satisfaction Analysis of Hotel Luminor Services Based on Google Maps Reviews Using Logistic Regression and Support Vector Machine Aldianur Fajri; Heny Pratiwi; Kusno Harianto
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3656

Abstract

Online reviews are widely used as a data source for evaluating hotel service quality because they reflect visitors’ experiences through ratings and textual feedback. Google Maps provides publicly accessible reviews that enable the analysis of visitor satisfaction toward hotel services. This study analyzes visitor satisfaction based on Google Maps reviews using machine learning–based classification methods within a case study framework. Review data were collected through web scraping and processed through data cleaning to remove duplicate, empty, and irrelevant entries. The cleaned reviews were transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Classification was performed using Logistic Regression and Support Vector Machine. Model performance was evaluated using an 80:20 training–testing split and standard metrics, including accuracy, precision, recall, and F1-score. The results indicate that Support Vector Machine achieves higher overall accuracy compared to Logistic Regression under the applied experimental conditions. However, Logistic Regression demonstrates more balanced performance across evaluation metrics, while Support Vector Machine tends to be more biased toward the majority class in identifying visitor satisfaction categories. These findings suggest that simpler linear models can still perform consistently in high-dimensional textual data, particularly in handling imbalanced review distributions. Furthermore, the analysis shows that classification results can be utilized to identify recurring patterns in visitor feedback, supporting a more systematic and data-driven evaluation of hotel service quality.

Page 23 of 29 | Total Record : 282

 21   22   23   24   25 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2026): June 2026 Vol. 7 No. 1 (2026): March 2026 Vol. 6 No. 4 (2025): December 2025 Vol. 6 No. 3 (2025): September 2025 Vol. 6 No. 2 (2025): June 2025 Vol. 6 No. 1 (2025): March 2025 Vol. 5 No. 4 (2024): December 2024 Vol. 5 No. 3 (2024): September 2024 Vol. 5 No. 2 (2024): June 2024 Vol. 5 No. 1 (2024): March 2024 Vol. 4 No. 4 (2023): December 2023 Vol. 4 No. 3 (2023): September 2023 Vol. 4 No. 2 (2023): June 2023 Vol 4 No 2 (2023): June 2023 Vol 4 No 1 (2023): March 2023 Vol. 4 No. 1 (2023): March 2023 Vol 3 No 4 (2022): December 2022 Vol. 3 No. 4 (2022): December 2022 Vol. 3 No. 3 (2022): September 2022 Vol 3 No 3 (2022): September 2022 Vol 3 No 2 (2022): June 2022 Vol. 3 No. 2 (2022): June 2022 Vol 3 No 1 (2022): March 2022 Vol. 3 No. 1 (2022): March 2022 Vol. 2 No. 4 (2021): December 2021 Vol 2 No 4 (2021): December 2021 Vol 2 No 3 (2021): September 2021 Vol. 2 No. 3 (2021): September 2021 Vol 2 No 2 (2021): June 2021 Vol. 2 No. 2 (2021): June 2021 Vol 2 No 1 (2021): March 2021 Vol. 2 No. 1 (2021): March 2021 Vol. 1 No. 4 (2020): December 2020 Vol 1 No 4 (2020): December 2020 Vol. 1 No. 3 (2020): September 2020 Vol 1 No 3 (2020): September 2020 Vol 1 No 2 (2020): June 2020 Vol. 1 No. 2 (2020): June 2020 Vol. 1 No. 1 (2020): March 2020 Vol 1 No 1 (2020): March 2020 More Issue