cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. kuantan singingi,
Riau
INDONESIA
JURNAL TEKNOLOGI DAN OPEN SOURCE
Published by Universitas Islam Kuantan Singingi
ISSN : 26557592     EISSN : 26221659     DOI : 10.36378/jtos
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Open Source menerbitkan naskah ilmiah. yang berkaitan dengan sistem informasi, teknologi informasi dan aplikasi open source secara berkala (2 kali setahun). Jurnal ini dikelola dan diterbitkan oleh Program Studi Teknik Informatika Fakultas Teknik, Universitas Islam Kuantan Singingi. Tujuan penerbitan jurnal ini adalah sebagai wadah komunikasi ilmiah antar akademisi, peneliti dan praktisi dalam menyebarluaskan hasil penelitian.
Arjuna Subject : -
Articles 372 Documents
 30   31   32   33   34 
Evaluation of HRMS Success Using DeLone & McLean Setia, Heldha ayu; Faroqi, Asif; Aulia, Virdha Rahma
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4698

Abstract

Digital transformation has become a strategic step to improve operational efficiency, particularly in human resource management. PT XYZ, which has implemented HRMS, faces challenges such as system errors that disrupt the company's mission to enhance employee welfare. This study aims to evaluate the success of HRMS implementation at PT XYZ using the DeLone and McLean (2003) model, which includes six dimensions: system quality, information quality, service quality, usage, user satisfaction, and net benefits. A quantitative approach with PLS-SEM is used, and data is obtained through a survey. The sample consists of 211 respondents selected through simple random sampling. The analysis results show that system quality, information quality, and service quality significantly affect usage and user satisfaction, which contribute to the net benefits experienced by the organization. However, system quality does not significantly affect user satisfaction, and net benefits do not significantly affect usage. These findings provide insights for PT XYZ to improve the quality of HRMS and optimize system usage.
Analysis of Acceptance Factors of Pospay Application Users in Surabaya City Using Modified TAM Berliana, Ni Made; Wulansari, Anita; Aulia, Virdha Rahma
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4705

Abstract

This study aims to analyze the factors that influence user acceptance of the Pospay application in Surabaya by using a modified Technology Acceptance Model (TAM). Pospay is a digital platform owned by PT Pos Indonesia that offers financial transaction services. Although the app has been downloaded over 5 million times, it still faces acceptance issues, as reflected by the high number of negative reviews due to system errors, verification failures, and unstable performance. To address these challenges, the TAM model was extended by incorporating variables such as Facilitating Conditions, Lifestyle Compatibility, Quality of Internet Connection, Perceived Security, Perceived Trust, Perceived Risk, Self-Efficacy, and Satisfaction. Data were collected from 523 active users and analyzed using Structural Equation Modeling–Partial Least Squares (SEM–PLS). The results showed that Facilitating Conditions, Perceived Ease of Use, and Perceived Usefulness significantly influence Attitude. Furthermore, Perceived Ease of Use, Perceived Usefulness, Perceived Security, Self-Efficacy, and Satisfaction have a significant effect on Intention to Use. Meanwhile, variables such as Attitude, Lifestyle Compatibility, Quality of Internet Connection, Perceived Trust, and Perceived Risk did not significantly affect Intention to Use. These findings highlight the importance of improving usability, usefulness, security, satisfaction, and user confidence to enhance the acceptance and usage of the Pospay application more effectively.
Classification and Mapping of Online Gambling Based on News Articles Using NER and SVM Wisnu Mukti Darwansah; Amalia Anjani Arifiyanti; Rizka Hadiwiyanti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4707

Abstract

The phenomenon of online gambling in Indonesia has developed rapidly, posing serious social and economic threats. This thesis aims to classify and map online gambling activities based on digital news using the Support Vector Machine (SVM) algorithm and Named Entity Recognition (NER). Data were collected from the news portals Detik.com, Kompas.com, and Tribunnews from 2017 to 2024 through a web scraping approach. The research process included setup and library import, data upload, data exploration, data labeling according to Law No. 1 of 2023, data preprocessing, data filtering, location normalization and extraction, and location data cleaning. Subsequently, the SVM model was trained for risk classification and followed by prediction. Evaluation was conducted using accuracy and F1-score metrics to assess overall model performance and classification balance. Based on the evaluation results, the Normal SVM model demonstrated the best performance with an accuracy of 96.94% and an F1-score of 0.97. The findings indicate that the combination of NER and SVM effectively identifies the location and risk level of online gambling activities. This research is expected to contribute to law enforcement authorities and policymakers in their efforts to prevent and address online gambling activities in Indonesia.
Analysis of User Acceptance of Bus Ticket Application Using a Modified UTAUT Model (Case Study: RedBus Application) Trinanda Sanni, Bella; Wulansari, Anita; Rahmawati, Rafika
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4712

Abstract

The advancement of digital technology has driven the adoption of mobile-based transportation applications, such as RedBus—an online bus ticket booking platform. Despite experiencing significant growth, RedBus faces challenges in user acceptance, marked by a decline in positive reviews and an increase in user complaints during 2023–2024. Therefore, it is crucial to understand the factors that influence user acceptance and their decision to continue using the application. This study analyzes these factors using a modified UTAUT model by incorporating external variables such as Habit, Perceived Cost, Customer Satisfaction, and Repurchase Intention. A quantitative analysis was conducted using the PLS-SEM method with SmartPLS 3.0 software, involving 413 respondents selected through purposive sampling. The results show that nine hypotheses were accepted while four were rejected. The variables Performance Expectancy and Facilitating Conditions significantly influenced Trust, whereas Effort Expectancy and Social Influence did not. Performance Expectancy, Effort Expectancy, and Facilitating Conditions had a significant impact on Purchase Decision, while Social Influence did not. Trust was found to mediate the relationship between several variables and Purchase Decision, which in turn affected Customer Satisfaction. Perceived Cost and Customer Satisfaction influenced Repurchase Intention, while Habit had no significant effect. These findings highlight the importance of improving service quality, ease of access, and user trust to maintain customer loyalty toward the RedBus application.
Augmented Reality Application for Teaching the History of the Petilasan Damarwulan Cultural Heritage Site Aris Widya, Moh. Anshori; Zamzami Riyadin, Azmi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4750

Abstract

This study aims to develop a historical learning media application utilizing augmented reality technology to introduce the cultural heritage site of Petilasan Damarwulan. The research addresses the declining reading interest among younger generations and the lack of interactive media in preserving local culture. A research and development (R&D) approach was employed, following ten stages from needs analysis to product evaluation. The application was built as a web-based platform to ensure compatibility with mobile devices, offering features such as image scanning, three-dimensional visualization, and narrative descriptions of the cultural site. Implementation results indicate that the application enhances users’ understanding of local history and culture. Functional testing using the black-box method confirmed that all features operated as intended. In conclusion, the application proves to be an effective tool for interactive learning and holds potential as a means of supporting digital cultural preservation within primary education environments. Augmented Reality
Implementing TF-IDF and Logistic Regression for Sentiment Analysis of YouTube Comments on the iPhone 16 Andi Riswawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.4753

Abstract

Sentiment analysis of user opinions on social media has become a crucial aspect in understanding public perception of technological products. This study specifically aims to classify and analyze public sentiment reflected in YouTube comments regarding the iPhone 16 by employing the Term Frequency-Inverse Document Frequency (TF-IDF) approach and the Logistic Regression algorithm. The data was collected from product review videos on the GadgetIn channel using web scraping techniques.The preprocessing stage included cleaning processes such as converting characters to lowercase (case folding), removing common words that do not carry sentiment meaning (stopword removal), and reducing words to their root forms (stemming). The feature extraction results obtained through TF-IDF were used as input for the Logistic Regression model to classify the comments into three categories of emotional expression: positive (supportive), neutral, and negative sentiments toward the discussed topic. The model’s effectiveness was evaluated using accuracy, precision, recall, and F1-score metrics. Based on the evaluation results, the model demonstrated a reasonably optimal performance in classifying user opinions. The findings indicate that the model performs with stability and accuracy in handling high-dimensional sentiment data. This research contributes to the development of text-based sentiment classification systems in the context of technology review analysis.
Design and Development of a Digital Library System Using PHP For Optimizing Library Services at SMK Negeri 1 Luragung Alvian Nazhif, Hafizh; Ripai, Ipan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4759

Abstract

This study aims to develop a PHP-based digital library system as a solution to the conventional library service problems at SMK Negeri 1 Luragung. The existing manual processes for borrowing and returning books make it difficult to track collections accurately and reduce overall operational efficiency. To address these issues, this research adopts a Research and Development (R&D) model using the ADDIE approach, which consists of five systematic stages: Analysis, Design, Development, Implementation, and Evaluation. At the analysis stage, library needs and user problems were identified. The design stage involved creating system flowcharts, database structures, and interface mockups. During development, a web-based application was built using PHP and MySQL with key features such as member management, book collection management, borrowing and returning modules, and QR code integration for faster transactions. The system was then implemented and tested with actual users. The evaluation was carried out using the Usability (USE) Questionnaire, focusing on usefulness, ease of use, ease of learning, and user satisfaction. The results showed significant improvements in accessibility, transaction efficiency, and overall user experience. Based on these findings, the developed system is considered feasible and highly suitable for implementation as a digital solution for school library management.
The The Use of the K-Means Algorithm in Analyzing E-Commerce Consumer Segmentation: A Case Study of the Online Retail Dataset (UK) Kusdaryanto, Ardo; Wijanarko, Christoporus Dimas; Widyantara Usat, Paskalis Dwi; Prabowo , Ary
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4798

Abstract

This study aims to analyze consumer segmentation on e-commerce platforms by employing the K-Means algorithm as the primary clustering method. Using the Online Retail (UK) dataset, which contains comprehensive transaction records from a UK-based online retail company, the research focuses on identifying behavioral patterns among consumers. Several key variables, including purchase frequency, total transaction value, and recency or visit time, are processed to create meaningful clusters that represent different types of consumer behavior. The K-Means algorithm is applied through a series of preprocessing steps, such as data cleaning, feature selection, and normalization to ensure accurate clustering results. Once the clusters are formed, each consumer group is analyzed to determine its characteristics, purchasing tendencies, and potential value to the business. The segmentation results provide valuable insights for businesses in developing targeted marketing strategies and personalized service offerings. By understanding the unique preferences and behaviors within each cluster, companies can optimize promotional efforts, improve customer retention, and enhance overall user experience. The findings indicate that data-driven segmentation using the K-Means algorithm is a highly effective approach for gaining deeper, actionable insights into consumer behavior, thereby supporting more strategic decision-making in the e-commerce environment.
Integration of Machine Learning Models Random Forest and XGBoost for Credit Card Fraud Detection in a Python Flask-Based Application Heri, Herianto; Zupri Henra Hartomi; Rian Ordila; Yuda Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4821

Abstract

Credit card fraud is one of the major challenges in modern digital payment systems. The increasing volume of online transactions raises the potential for unauthorized use of cardholder data. This research aims to develop a robust and accurate fraud detection system by integrating two machine learning algorithms, Random Forest and XGBoost, both of which are known for their high performance in data classification. The research process begins with the collection and preprocessing of credit card transaction data, followed by model training using the selected algorithms. The model’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. To enable real-time application, the model is implemented in a web-based system using the Python Flask framework, allowing direct integration into financial transaction environments. The need for adaptive systems that can respond to emerging fraud patterns serves as a key motivation for this study. By combining two complementary algorithms within a single web application platform, the system is expected to detect fraudulent activities quickly and accurately. The expected outcomes of this research include: (1) an optimized fraud detection model based on Random Forest and XGBoost, (2) a prototype web application developed with Python Flask for system implementation, and (3) a scientific publication describing the development and results of the proposed system. The targeted outputs are a publication in a nationally accredited journal (Sinta 4) and intellectual property registration. This research is expected to provide a significant contribution to preventing credit card fraud through the effective application of machine learning technologies
Utilization of IndoBERT Representation and Random Forest for Sentiment Analysis on User Reviews of Halodoc Pharmacy Services in Google Play Hendry Fonda; Herianto; Yuda Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4822

Abstract

With the growing use of digital healthcare platforms such as Halodoc, maintaining consistent service quality that meets user expectations is essential. User reviews on platforms like Google Play provide valuable insights into user perceptions. This study aims to classify user sentiments toward Halodoc’s pharmacy services based on reviews obtained through web scraping from the Google Play Store. The analysis employs the pre-trained IndoBERT model to extract textual features, followed by sentiment classification using the Random Forest algorithm. This combination was selected for its efficiency with limited hardware resources and small dataset size. To enhance data diversity and minimize overfitting, simple augmentation methods such as random word deletion and synonym substitution were implemented. The expected outcomes include an effective sentiment classification model and visualizations of sentiment distributions (positive, negative, neutral). Furthermore, the study contributes to the development of sentiment analysis techniques for Indonesian-language data through an efficient and contextually relevant approach. The research outputs target publication in a nationally accredited (Sinta 4) journal and Intellectual Property Rights (IPR) registration. Ultimately, this study is expected to support the improvement of technology-based pharmacy services through the strategic application of machine learning.

Page 32 of 38 | Total Record : 372

 30   31   32   33   34 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025 Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025 Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024 Vol. 7 No. 1 (2024): Jurnal Teknologi dan Open Source, June 2024 Vol. 6 No. 2 (2023): Jurnal Teknologi dan Open Source, December 2023 Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023 Vol. 5 No. 2 (2022): Jurnal Teknologi dan Open Source, December 2022 Vol 5 No 2 (2022): Jurnal Teknologi dan Open Source, December 2022 Vol 5 No 1 (2022): Jurnal Teknologi dan Open Source, June 2022 Vol. 5 No. 1 (2022): Jurnal Teknologi dan Open Source, June 2022 Vol 4 No 2 (2021): Jurnal Teknologi dan Open Source, December 2021 Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021 Vol 4 No 1 (2021): Jurnal Teknologi dan Open Source, June 2021 Vol. 4 No. 1 (2021): Jurnal Teknologi dan Open Source, June 2021 Vol 3 No 2 (2020): Jurnal Teknologi dan Open Source, December 2020 Vol 3 No 1 (2020): Jurnal Teknologi dan Open Source, June 2020 Vol 2 No 2 (2019): Jurnal Teknologi dan Open Source, December 2019 Vol 2 No 1 (2019): Jurnal Teknologi dan Open Source, June 2019 Vol 1 No 2 (2018): Jurnal Teknologi dan Open Source, December 2018 Vol 1 No 1 (2018): Jurnal Teknologi dan Open Source, June 2018 More Issue