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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Brahmana : Jurnal Penerapan Kecerdasan Buatan TEPIAN IJISTECH Buletin Poltanesa J-SAKTI (Jurnal Sains Komputer dan Informatika) Informatika Wicida Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Pedagogy and Education Science Journal of Informatics and Electronics Engineering TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi BEduManageRs Journal : Borneo Educational Management and Research Journal Information Technology Studies Journal Research in Education, Technology, and Multiculture
Heny Pratiwi
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Analysis of Visitor Sentiment Towards the Public Facilities at Teras Samarinda Using Naive Bayes Algorithm Alysa Anggelia Y; Muhammad Ibnu Sa'ad; Heny Pratiwi
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3712

Abstract

This study aims to analyze visitor sentiment toward public facilities at Teras Samarinda based on user-generated reviews collected from digital platforms. The increasing number of online reviews provides valuable insights into visitor satisfaction; however, manual analysis is inefficient due to the large volume of data. Therefore, this research applies a text mining approach to automatically classify sentiments into positive, negative, and neutral categories. The dataset consists of 165 comments obtained from YouTube, representing visitor experiences and opinions. The preprocessing stage includes case folding, cleaning, tokenization, stopword removal, and stemming to ensure data quality. Subsequently, Term Frequency–Inverse Document Frequency is used to transform textual data into numerical features. The classification process is performed using the Naive Bayes algorithm. The dataset is divided into training and testing data to evaluate model performance using accuracy, precision, recall, and F1-score metrics. The results show that the model achieves an accuracy of 75.75%, indicating a relatively good performance in classifying sentiments. However, the model demonstrates limitations in distinguishing negative and neutral sentiments due to imbalanced data distribution. The findings reveal that most visitors express positive sentiment toward public facilities at Teras Samarinda, suggesting overall satisfaction. This study contributes to providing insights for improving facility quality and highlights the importance of handling imbalanced datasets in sentiment analysis.
Implementation of Text Mining for Service Quality Classification of Google Maps Reviews at Samarinda Civil Registry Office Using K-Nearest Neighbor Algorithm Abed Nego; Heny Pratiwi; Ivan Haristyawan
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3728

Abstract

This research aims to analyze public sentiment regarding services at the Department of Population and Civil Registration as an objective evaluation instrument for service quality through a series of methodological stages, beginning with text preprocessing including cleansing, folding, tokenizing, filtering, and stemming to reduce noise in the scraped textual data. Feature extraction was performed using Term Frequency-Inverse Document Frequency (TF-IDF) to determine the significance of each term within the documents, while the classification process was implemented using the K-Nearest Neighbor (KNN) algorithm by experimenting with various nearest neighbor (k) values to identify the most optimal model parameters. Based on a comprehensive evaluation using a confusion matrix, the model achieved peak performance at k=3 with an accuracy rate of 92.8%, although significant limitations were identified in predicting negative sentiments, only 6 of the 14 data points were correctly classified. This indicates a classification bias triggered by data imbalance, a common challenge in text mining is that the number of positive reviews far outnumbers the number of negative reviews. Qualitatively, although reviews were dominated by public appreciation, critical complaints were still found regarding perceived slow service duration and overlapping bureaucratic complexities. These findings emphasize that while the model possesses high quantitative accuracy, a thorough evaluation of service quality must focus on strategic transformations that improve system efficiency and information transparency to bridge the gap between community expectations and the reality of public service delivery.
Sentiment Analysis of Akutusocks Store Reviews Using Text Mining and K-Nearest Neighbor (KNN) Ade Maulana Anshari; Heny Pratiwi; Aisyah Fajrianti
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3732

Abstract

Akutusocks is a retail business that utilizes the TikTok platform for digital martaketing and as a vital two way communication channel with its consumers. The high intensity of interaction on this social media platform produces extensive review data containing various customer perceptions, complaints, and appreciation. However, this data remains largely unstructured, making it extremely difficult for the management team to analyze the information manually and efficiently. This study aims to implement advanced text mining techniques to classify the sentiment of Store Akutusocks reviews into positive and negative categories in order to provide an objective basis for evaluating the quality of product and service quality. The methodology applied in this study integrates the K-Nearest Neighbor (KNN) algorithm with a lexicon-based approach to streamline the initial data labeling process for thousands of user comments. The research stages began with rigorous text preprocessing, which is crucial for improving data quality. This process included case folding, cleansing, tokenization, and normalization to correct slang terms and abbreviations specific to TikTok, as well as stopword removal and stemming to reduce words to their base forms. Feature weighting was performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method to extract dominant keywords representing user sentiment. This analysis is vital for Store Akutusocks in mitigating digital reputation risks and understanding market preferences. Through model testing using a Confusion Matrix, this study measures classification accuracy and provides deep insights into the effectiveness of the KNN algorithm
Supply Chain Success Analysis at Makeen Idea Store Muhammad Andrian; Heny Pratiwi; Rizky Zakaryya Rasyad
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3733

Abstract

This study aims to analyze the success of Supply Chain Management (SCM) implementation at Makeen Idea Store in improving operational performance and business competitiveness. This research employed a qualitative approach using a case study design, with data collected through direct observation, interviews with the store owner and employees, and documentation of operational activities. The findings indicate that the success of SCM implementation at Makeen Idea Store is influenced by several key factors, including strong information flow integration, strategic partnerships with suppliers, and demand-based inventory management. In addition, the implementation of an organized inventory recording system helps minimize errors and improve distribution efficiency. Effective SCM implementation has contributed to reducing operational costs, accelerating product distribution, and increasing customer satisfaction through stable product availability. However, several challenges remain, particularly limited digital technology adoption and dependence on certain suppliers, which may hinder sustainable supply chain performance improvement. Therefore, it is recommended that Makeen Idea Store further develop a digital-based SCM system and expand its supplier network to enhance supply chain flexibility and resilience in the future.
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.
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.
Analysis of Customer Perception on Google Maps Reviews of Klinik Kopi Samarinda Using Extreme Gradient Boosting (Xgboost) M.Ariya Parengrengi; Heny Pratiwi; Muhammad Fahmi
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.v7i2.3682

Abstract

This study aims to analyze customer perceptions based on Google Maps reviews of Klinik Kopi Samarinda using the Extreme Gradient Boosting (XGBoost) method. Online customer reviews have become an important source of information for evaluating service quality and customer satisfaction in the food and beverage industry. The data used in this study were collected from Google Maps reviews, consisting of customer comments and ratings. Text preprocessing was conducted through case folding, tokenization, stopword removal, and stemming to prepare the data for analysis. Sentiment labels were classified into positive, negative, and neutral categories. The XGBoost algorithm was applied to perform sentiment classification due to its high performance in handling structured and unstructured data. The results show that the XGBoost model achieved high accuracy in classifying customer sentiment, indicating that most customers have positive perceptions of Klinik Kopi Samarinda. This study demonstrates that machine learning-based sentiment analysis can provide valuable insights for business owners in understanding customer feedback and improving service quality.
Decision Support System for Teacher Decision Following Teacher Professional Education (PPG) SMA / SMK East Kalimantan Province with Web-Based Smart Method Dana Aulia Rahman; Heny Pratiwi; Hanifah Ekawati
TEPIAN Vol. 2 No. 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.339

Abstract

Teacher Professional Education (PPG) for SMA / SMK in East Kalimantan Province is higher education after an undergraduate education program that prepares students to have jobs with special skills requirements to become teachers. The problems in registration that occur at the East Kalimantan Provincial Education and Culture Office are: The calculation of the test data is still calculated manually, so it is necessary to build a Decision Support System for Determination of Participants in the Professional Teacher Education (PPG) for SMA / SMK in East Kalimantan Province using the Web-based SMART Method. The data collection method uses the observation method and the system development method uses the method of the decision support system, namely the intelligence, design, choice, and implementation stages. Because this method has clear, practical stages. Then the system testing is White Box and Beta Testing. With the existence of a Decision Support System for Determining Who Participates in Professional Education for Teachers (PPG) for SMA / SMK in East Kalimantan Province with the Web-based SMART Method, it can handle the calculation process when the test has been implemented. In the test results it can be concluded that the results of testing the questionnaire questions to ten (10) respondents can be concluded that more than 78.2% of respondents answered that the Determination Decision Support System Participating in Teacher Professional Education (PPG) at the High School / Vocational School Level of East Kalimantan Province with the SMART Method Web-based meets the criteria for a good website or web application. With the existence of a Decision Support System for Determining Who Participates in Professional Education for Teachers (PPG) for SMA / SMK in East Kalimantan Province with the Web-based SMART Method, it can handle the calculation process when the test has been implemented. In the testing results it can be concluded that the results of testing the questionnaire questions to ten (10) respondents can be concluded that more than 78.2% of respondents answered that the Determination Decision Support System Participating in Teacher Professional Education (PPG) for SMA / SMK in East Kalimantan Province with the SMART Method Web-based meets the criteria for a good website or web application. With the existence of a Decision Support System for Determining Who Participates in Professional Teacher Education (PPG) at the SMA / SMK in East Kalimantan Province with the Web-based SMART Method, it can handle the calculation process when the test has been implemented. In the testing results it can be concluded that the results of testing the questionnaire questions to ten (10) respondents can be concluded that more than 78.2% of respondents answered that the Determination Decision Support System Participating in Teacher Professional Education (PPG) for SMA / SMK in East Kalimantan Province with the SMART Method Web-based meets the criteria for a good website or web application. can handle the calculation process once the test has been executed. In the testing results it can be concluded that the results of testing the questionnaire questions to ten (10) respondents can be concluded that more than 78.2% of respondents answered that the Determination Decision Support System Participating in Teacher Professional Education (PPG) for SMA / SMK in East Kalimantan Province with the SMART Method Web-based meets the criteria for a good website or web application. can handle the calculation process once the test has been executed. In the testing results it can be concluded that the results of testing the questionnaire questions to ten (10) respondents can be concluded that more than 78.2% of respondents answered that the Determination Decision Support System Participating in Teacher Professional Education (PPG) for SMA / SMK in East Kalimantan Province with the SMART Method Web-based meets the criteria for a good website or web application.
Application of the Finite State Machine Method in the Desktop-Based “Heroes Of Dawn” RPG Turn-Based Game Muhammad Fachri Sanjaya; Heny Pratiwi; Pitrasacha Adytia
TEPIAN Vol. 2 No. 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.348

Abstract

FSM (Finite State Machine) is a method of implementing artificial intelligence that is applied to make a decision on NPC (Non Player Character). The application of FSM that is often encountered is to form an NPC with intelligence, so that the NPC can respond to the player's character so that the NPC seems to be able to think. Games have various types (genres) and are increasingly varied in line with the development of hardware and software technology. Writing will focus on games with the Role Playing Game genre or often called RPG. Games in general use Artifical Intelligence in their systems to make the game more interesting to play. Artifical Intelligence is usually applied to NPC (Non Player Character) / Enemy in the game or opponents who must be defeated, one of the applications of Artifical Intelligence in the game to be used in this research is the Finite State Machine (FSM) method.
Implementation of Multi Objective Optimization on the Basic of Ratio Analysis Method in Decision Support System for Hope Family Program Assistance Recipients in Kelinjau Ulu Village Arsita; Salmon; Heny Pratiwi
TEPIAN Vol. 2 No. 4 (2021): December 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i4.351

Abstract

The research was conducted to be able to create a decision support system for beneficiaries of the Family Hope Program or “Program Keluarga Harapan (PKH)” with the Multi Objective Optimization Method On The Basic Of Ratio Analysis (Moora), which later if this research is successful it can assist aid managers in making decision making for program aid recipients Family Hope “PKH”. This research was conducted at the office of the Family Hope Program “PKH” assistance manager in Kelinjau Ulu Village, Muara Analog District, the data collection method used was interviews which asked questions related to “PKH” beneficiaries. By means of observation, namely making direct observations at the “PKH” Assistance Manager office. In this study, the system development method used is the decision support system development method. The model with the decision support software used is the Visual Basic.NET programming language, the database used by Microsoft Access. The final result of this research is in the form of a Decision Support System for beneficiaries of the Family Hope Program “PKH” Using the Multi Objective Optimization Method on the Basic of Ratio Analysis (Moora) which can facilitate more precise selection of “PKH” aid recipients.
Co-Authors Abed Nego Achmad Sadzali Muftisjar Ade Maulana Anshari Adeputra, James Ahmad Abul Khair Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fajri Ahmad Rofiq Hakim Ahmad Sabirin Aisyah Fajrianti Aisyah Fajriantini Akhmad Rizky Fahrozy Aldianur Fajri Alysa Anggelia Y Amelia Yusnita Ananta Putra, Resifa Andi Yusika Rangan Anggra Prima Angreani, Fadillah Anwar, Rafidan Arsita Ashari Ramadani Atventitus Etwin Loho Azahari Azahari Azahari Azahari Azahari Azahari Bai' Fathur Rayhan Bartolomius Harpad Cembes, Yosefina Chandra Panca Wibawa Cintami Amanda Putri Damaya, Filio Angga Dana Aulia Rahman Daru Caraka Daud Yefkanius Nassa Daud, Jundro Dendy Kurniawan Dessy Purnamasari Dovist Calvino Ekawati, Hanifah Ekawati, Hanifah Eko Junirianto Fadjri Astra Ryan Sinurat Harianto, Kusno Haristyawan, Ivan I Made Borneo Setyawan Ita Arfyanti Julio Enrico Frans Frans Kristian Vandi Hermawan Kristianus Catur Prasetya Ajang Kusno Harianto Kusno Harianto Lamsi, Rahmadiansyah Zain M. Irwan Ukkas Irwan Ukkas Ukkas M.Ariya Parengrengi Muhammad Alamsyah Zakaria Muhammad Andrian Muhammad Fachri Sanjaya Muhammad Fadhilah Muhammad Fahmi Muhammad Fahmi Muhammad Fahriawan Muhammad Ibnu Sa'ad Muhammad Ibnu Sa'ad Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Muhammad Raihan Ramandha Putra Muhammad Rega Praduana Muhammad Sadam Saktia Putra Novandra Satria Winata NUR FITRIANI Nursobah, Nursobah Nurul Hikmah Okvi Marsi Angela Claudia Pahrudin, Pajar Pitrasacha Adytia Putra, Muhammad Sadam Saktia Putri Wulandari Renni Mayasari Resifa Ananta Putra Rifka Karin Afinda Rizky Zakaryya Rasyad Ryan Artanto Halim SA'AD, MUHAMMAD IBNU Saad, Muhammad Ibnu Salmon Salmon Salmon Sarifmata Purnomo Sa’ad, Muhammad Ibnu Shinta Palupi Suhariyadi, Yonatan Sururi, M Za’iem Susi Salviati Syamsuddin Mallala Syamsuddin Mallala Ulfa Nurfadhila W Wahyuni, W Wahyuni - Wahyuni Y Yunita Yunita Yunita Zakaria, Muhammad Alamsyah