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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 292 Documents
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Sentiment Analysis of Bakso GLG Using the Naive Bayes Method Lingga Wardhana; Ita Arfyanti; Kusno Harianto
TEPIAN Vol. 7 No. 3 (2026): September 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i3.3975

Abstract

Accelerating digital innovations have vastly reshaped the methods individuals use to express their perspectives on the gastronomic industry about culinary services through customer reviews on Google Maps. This study seeks to examine the sentiments conveyed in reviews of Bakso GLG to assist management in understanding customer perceptions objectively by employing the Naïve Bayes algorithm only after undergoing rigorous preprocessing phases, including cleaning, case folding, normalization, tokenization, stopword removal, stemming, and the generation of TF-IDF vectors. The classification results yielded an overall accuracy of 77%. The data distribution is dominated by positive sentiment, comprising 226 reviews, followed by 25 neutral reviews and 13 negative reviews. Although the model demonstrated optimal performance in classifying positive sentiment, it encountered difficulties in classifying the minority classes due to the imbalanced dataset. Overall, the system proved effective in processing large-scale review data as a source of strategic evaluation for improving product and service quality in the culinary sector, particularly at Bakso GLG.
Self-Calibrated IMU Footpod Development for Virtual Running and Sensor Learning Adytia Darmawan; Didik Setyo Purnomo; Zahra Rizkiyatul Ummah; Afif Nur Syafiq; Hendrik Elvian Gayuh Prasetya; Hendhi Hermawan
TEPIAN Vol. 7 No. 3 (2026): September 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i3.3977

Abstract

Low-cost inertial measurement unit (IMU) sensors can be used as footpods for virtual running and sensor learning, but speed estimation is sensitive to the sensor, mounting position, and user gait. This study develops a self-calibrated IMU footpod for estimating speed and cadence from foot motion. An ESP32-based prototype with a 6-axis IMU was mounted on the instep. The data were processed using quality control, 20-second windowing, gravity compensation, stance detection, zero-velocity update, feature extraction, and regression calibration. Eight recording sessions produced 40 valid windows at approximately 97 Hz, with 0% packet loss and no sensor saturation. Raw ZUPT estimation yielded an MAE of 3.242 km/h, whereas in-sample calibration reduced the MAE to 1.022 km/h. Cross-subject and cross-device transfer errors support the need for personal calibration. The pipeline also provides a practical learning medium for IMU calibration, filtering, drift, and wearable systems.

Page 30 of 30 | Total Record : 292

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