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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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Developing a Calorie Requirement Application Based on a BMI Calculator for Android Using User-Centered Design (UCD) Fadjri Astra Ryan Sinurat; Heny Pratiwi; Ahmad Fajri
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i2.3557

Abstract

This study aims to develop an Android-based calorie requirement application using the User-Centered Design (UCD) approach and to evaluate its usability through the System Usability Scale (SUS). The application calculates individualized calorie needs using the Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE) formulas, while the Body Mass Index (BMI) feature functions only as a weight classification tool to support user awareness. Healthcare workers at Puskesmas Loa Ipuh were involved throughout the research, concept development, design, prototyping, and testing stages to ensure that the system aligns with real user needs. The UCD process used in this study consists of five stages: research, concept, design, development, and testing. The final application was built using the Kotlin in Android Studio and includes features such as calorie calculation, BMI calorie calculates, food recommendations, and daily nutritional tracking. Usability evaluation with the System Usability Scale involved 23 respondents and resulted in an average score of 82.60, which falls into the “excellent” category, indicating that the application is easy to use, efficient, and well accepted by target users. These findings demonstrate that integrating UCD with validated calorie estimation formulas can produce a functional and user-centered mobile application that supports users in understanding their daily calorie needs and improves accessibility to basic nutritional information.
Sentiment Analysis Using the Naïve Bayes Method to Improve E-Commerce Customer Satisfaction at the PedagangAksesoris Store Bai' Fathur Rayhan; Heny Pratiwi; Muhammad Fahmi
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i2.3559

Abstract

The rapid development of the e-commerce sector in Indonesia has made customer feedback a very important source of information in assessing the quality of goods and services. However, with so many reviews available, the manual assessment process often becomes complicated. The purpose of this study is to analyze customer sentiment towards PedagangAksesoris store on the Shopee platform using the Naïve Bayes Classifier method to identify positive and negative opinions that can help improve customer satisfaction. The data for this study was collected through web scraping of Shopee user reviews, followed by a preprocessing stage that included cleaning, filtering, removing affixes, and separating words. The data was then divided into training data and testing data to train and test the model. The Naïve Bayes method was applied by calculating word probabilities using Laplace smoothing, while model performance was evaluated using a Confusion Matrix through the RapidMiner application. The results of this study show that the Naïve Bayes model can classify customer reviews with a high degree of accuracy, with precision reaching 100% for the negative category and 80% for the positive category, as well as recall of 87.5% and 100%. These findings confirm that the Naïve Bayes method is an effective and efficient way to perform text-based sentiment analysis on reviews in e-commerce. The results of this sentiment analysis can be used as a basis for strategic decision-making by businesses to improve product quality, services, and customer satisfaction.  
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.
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.
Integrating Artificial Intelligence in Formative Assessment: Connecting Student Engagement, Learning Styles, and Learning Outcomes Heny Pratiwi; Muhammad Ibnu Sa'ad; Nurul Hikmah; Anggra Prima
Journal of Pedagogy and Education Science Vol 5 No 01 (2026): Journal of Pedagogy and Education Science
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.jpes.001513

Abstract

Formative assessment plays an important role in providing continuous feedback that supports the student learning process. However, formative assessment practices in higher education often remain static and insufficiently responsive to individual learner differences. This study examines the integration of artificial intelligence (AI) into formative assessment by exploring patterns of student engagement, learning styles, and academic achievement within a data-informed learning environment. The findings indicate that student engagement is closely associated with academic performance and dropout risk, suggesting its potential function as an early indicator of academic vulnerability. Differences in learning styles are also reflected in formative performance, highlighting the importance of personalized instructional support. These results illustrate how AI-supported analysis can enhance formative assessment by enabling timely feedback, adaptive learning support, and the early identification of students at risk. Beyond confirming established relationships, this study emphasizes the conceptual role of artificial intelligence in reshaping formative assessment practices. AI is positioned as a formative assessment mediator that integrates learning analytics to support personalization, predictive insight, and adaptive feedback. This conceptualization contributes to formative assessment theory by demonstrating how data-driven intelligence can operationalize continuous, student-centered assessment in higher education. Rather than functioning merely as an analytical tool, artificial intelligence is shown to fundamentally reshape formative assessment by enabling continuous, predictive, and adaptive feedback mechanisms that are not achievable through conventional assessment approaches.
The Impact of Artificial Intelligence (AI) on the Future of Jobs in Computer Science and Information Systems: A Systematic Literature Review Heny Pratiwi; Muhammad Ibnu Sa'ad
Information Technology Studies Journal (ITECH) Vol. 1 No. 1 (2024): Information Technology Studies Journal (ITECH)
Publisher : Penelitian dan Pengembangan Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62207/vdebmf42

Abstract

The evolution of artificial intelligence (AI) in the fields of Computer Science and Information Systems has become a major research focus in recent decades. This research aims to investigate the impact of AI on job distribution and employment inequality in the context of Computer Science and Information Systems. The research method used is a systematic literature review, by collecting and analyzing relevant articles from reputable international databases. The results of the discussion show that the evolution of AI has brought significant changes in various aspects of work and influenced the division of tasks between humans and machines. The implication of this research is the importance of paying attention to the impact of AI in designing policies, training programs, and initiatives to minimize employment gaps and ensure fair access to employment opportunities in the AI ​​era.
Perancangan Sistem Informasi Manajemen Kelelahan Driver Berbasis Web pada Honda Amartha Samarinda dengan Metode Self-Assessment dan Penjadwalan Delivery Rifka Karin Afinda; Heny Pratiwi; Pajar Pahrudin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp70-78

Abstract

Kelelahan kerja pada driver merupakan faktor yang memengaruhi keselamatan berkendara, kualitas pelayanan, dan kinerja operasional Perusahaan. Pada Honda Amartha Samarinda, pemantauan kelelahan driver dan penjadwalan delivery masih dilakukan secara manual sehingga berpotensi menimbulkan ketidakefisienan dan kesalahan pencatatan. Penelitian ini bertujuan merancang Sistem Informasi Manajemen Kelelahan Driver berbasis web dengan metode self-assessment dan penjadwalan delivery untuk meningkatkan efektivitas monitoring dan distribusi tugas. Metode penelitian meliputi analisis kebutuhan sistem, perancangan menggunakan Unified Modeling Language (UML), perancangan basis data, dan implementasi sistem berbasis web. Metode self- assessment diterapkan melalui pengisian kuesioner oleh driver sebelum melakukan delivery untuk mengidentifikasi tingkat kelelahan, yang kemudian menjadi parameter dalam penjadwalan tugas secara terintegrasi. Sistem yang dirancang mampu menyajikan informasi tingkat kelelahan secara real-time, sehingga dapat membantu admin logistik dalam menentukan kelayakan penugasan, serta menghasilkan laporan monitoring sebagain bahan evaluasi manajemen. Hasil penelitian menunjukan bahwa sistem ini dapat meningkatkan efisiensi pengelola jadwal, mendukung pengambilan keputusan, serta berpotensi meningkatkan keselamatan dan produktivitas kerja driver.
Media Pembelajaran Interaktif Berbasis Augmented Reality Pengenalan Anggota Tubuh dalam Bahasa Kutai untuk Sekolah Dasar Negeri 008 Samarinda Utara Ahmad Sabirin; Heny Pratiwi; Yunita Yunita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp18-24

Abstract

This study develops markerless Augmented Reality (AR) interactive learning media to introduce human body parts in the Kutai language as local content (Mulok) at SDN 008 Samarinda Utara. The primary issue stems from traditional methods—textbooks, 2D images, and conventional lectures—causing student boredom, low motivation, and shallow vocabulary mastery. This research aims to enhance learning motivation, conceptual understanding, and Kutai language proficiency through immersive experiences. The development follows the six-stage Multimedia Development Life Cycle (MDLC). The application was built on the Assemblr EDU platform (browser-based WebAR) using 3D models from Blender, interactive buttons, and Kutai language explanations. Black box testing was successful, while beta testing with ten students yielded an average score of 76.9% (Good category). This media supports immersive learning without additional installations, facilitating effective Mulok implementation at SDN 008. Future research is recommended to expand the respondent pool and integrate advanced interactive features, such as AR-based quizzes, audio narration, and animations, to further enhance overall media effectiveness. Exploring alternative platforms, including Unity and Zappar, is also encouraged. This tool represents a significant advancement in digitalizing regional language education, ensuring that local indigenous knowledge remains engaging and accessible for the younger generation in East Kalimantan.
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