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International Journal on Advanced Technology, Engineering, and Information System (IJATEIS)
Published by Transpublika Publisher
ISSN : -     EISSN : 28285425     DOI : https://doi.org/10.55047/ijateis
Core Subject : Engineering,
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering
International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) is an international peer-reviewed journal dedicated to interchange for the results of high-quality research in all aspect of technology, science, engineering and information system. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. Scope: The journal scopes include (but not limited to) the followings: Science: Bioscience & Biotechnology, Agriculture, Chemistry & Food Technology, Environmental, Health Science, Mathematics & Statistics, Applied Physics. Engineering: Architecture, Chemical & Process, Civil & structural, Electrical, Electronic & Systems, Geological & Mining Engineering, Mechanical & Materials. Information Science, Artificial Intelligence, Computer Science, E-Learning & Education Learning, Multimedia, Knowledge Technology, Information System, Internet & Mobile Computing, Machine Learning.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 147 Documents
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Evaluation of Economic Feasibility by Using ETLE in Increasing Road User Compliance to Reduce Traffic Accident Rates Safana, Syahara Almas
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.1848

Abstract

This study aims to evaluate the effectiveness, efficiency, and economic benefits of implementing the Electronic Traffic Law Enforcement (ETLE) system in Indonesia, particularly within the context of digital transformation in traffic law enforcement. Using a quantitative approach based on survey instruments, the research model was developed through the integration of Benefit-Cost. Four key variables were examined: the quality of ETLE camera installation, operational efficiency, road user compliance, and supporting implementation factors. The survey instrument was comprehensively constructed and validated through expert judgment, involving both academics and ETLE practitioners. Data were collected from 115 respondents through both online and offline distribution methods, targeting road users and ETLE system administrators. The results of the analysis indicate that all variables significantly influence traffic law compliance. Operational efficiency was found to be a key mediating factor between installation quality and user compliance. Furthermore, the Benefit-Cost Analysis revealed a BCR value of 3.01, indicating that every Rp1 invested in the ETLE system yields a social and economic return of 3.01. This study asserts that the ETLE system not only enhances the accountability and efficiency of law enforcement but also significantly reduces potential state losses resulting from traffic violations and accidents. The practical implications of these findings provide a strong foundation for formulating sustainable digital transportation policies.
Forecasting Tourism Visitor Numbers Using a Recurrent Neural Network with a Long Short-Term Memory Algorithm Rosyadi, Ibnu Fallah; Subandi, Nurul Arifin; Rusdah, Rusdah
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.1881

Abstract

Accurate forecasting of visitor numbers is essential in tourism management to ensure service quality and visitor satisfaction, especially during peak seasons such as holidays and weekends. This study addresses the lack of a predictive tool at PT Taman Impian Jaya Ancol (TIJA), a major recreational destination in Indonesia, by developing a forecasting model for visitor numbers. The research utilized monthly time series data of visitor numbers from January 2012 to December 2022. A Deep Learning approach was applied using the Recurrent Neural Network (RNN) architecture with the Long Short-Term Memory (LSTM) algorithm. The dataset was split with an 80:20 ratio for training and testing, normalized using the RobustScaler technique, and optimized with the ADAM optimizer. The model achieved a minimum Mean Squared Error (MSE) of 0.3095 and a prediction accuracy of 94.85%. These results indicate that the LSTM model can effectively predict visitor trends. The findings are expected to support TIJA and other tourism operators in preparing resources and facilities in advance, improving operational planning, and enhancing the overall visitor experience.
The Contextuality of Urban Park Facilities in Indonesia: A Systematic Review of Social Aspect Identification Wijaya, Ekky Nada; Panjaitan, Tigor Wilfritz Soadoun; Prakasa, Darmansjah Tjahja
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.1977

Abstract

In Indonesia, urban parks face serious challenges in the form of declining functionality due to limited maintenance, changing community needs and a lack of fit with the local context. Parks intended for social interaction, recreation and improving environmental quality often lose their appeal because their facilities cannot meet users' needs. Consequently, many parks have become passive spaces or have been abandoned. This study aims to systematically review the social aspects of urban park facilities in Indonesia through a systematic literature review of various empirical and theoretical studies. The analysis involves examining scientific publications, research reports, and policy documents that discuss the relationship between park facilities, user characteristics, and social behavior. The review focuses on aligning park facilities with the characteristics, behaviors, and needs of the community to support the social sustainability of public spaces. The findings show that the quality of urban parks is determined by five main social aspects: the user's sense of ownership, sense of peace, social satisfaction, local community activities and the social behavior of users. These five aspects complement each other in shaping the experience of the space. Contextual facilities are able to foster emotional bonds, create peace, increase collective satisfaction and encourage community participation. Therefore, context-based urban park planning strengthens ecological functions and ensures that public spaces remain adaptive, inclusive and meaningful to the community amidst ever-evolving urban dynamics.
Environmental Impact Analysis of Biogas Production Using Life Cycle Assessment (LCA) Towards Net Zero Emissions Fatiha, Aulia Putri; Muniroh, Muniroh; Aldhama, Shofa Aulia
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.1974

Abstract

The dairy cattle sector in Indonesia significantly contributes to greenhouse gas (GHG) emissions, particularly methane (CH₄). This study aims to analyze the environmental impacts of biogas production using the Life Cycle Assessment (LCA) approach. Data was collected from several biogas projects in Central Java and Yogyakarta, then analysed using OpenLCA software with cradle-to-grave system boundaries. The analysis was conducted using the Life Cycle Assessment (LCA) approach to assess the environmental impact at each stage of the life cycle, from raw material collection and production processes to utilisation and final disposal. In addition, supporting data was collected through interviews with project managers, field observations, and literature studies to ensure the accuracy and completeness of the analysis results. The results indicate that anaerobic digestion and biogas combustion are the major contributors to emissions but also play a key role in reducing methane release by up to 60%. Optimization of digester management and utilization of digestate as organic fertilizer can further minimize additional impacts. Hence, biogas demonstrates substantial potential as a mitigation strategy to support net zero emissions in the dairy cattle sector.
Online Store Product Recommendation System Using Collaborative Filtering and Content-Based Filtering Algorithms to Increase Sales Afandi, Yosi; Maskur, Maskur; Widyananda, Wahyu; Fiernaningsih, Nilawati; Budiarti, Lina; Az Zuhri, Fahmi Muhammad
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2007

Abstract

This study aims to evaluate and compare the performance of two recommendation system approaches, namely Collaborative Filtering (CF) and Content-Based Filtering (CBF), in providing relevant product recommendations to users in an e-commerce context. The dataset used consists of 120 data including 90 relevant and recommended products (True Positive), 20 recommended but irrelevant products (False Positive), and 10 relevant but not recommended products (False Negative). Based on the calculation results, both methods show a precision value of 0.818 and a recall of 0.900. This means that approximately 81.8% of products recommended by the system are truly relevant, while 90% of the total relevant products are successfully recommended to users. The F1-score value obtained of 0.857 illustrates a good balance between the accuracy and completeness of the recommendations generated by the system. Furthermore, to measure the level of rating prediction error, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics are used. The evaluation results show that the CF method has an MSE value of 0.0784 and an RMSE of 0.28, while the CBF method shows an MSE of 0.0961 and an RMSE of 0.31. The lower RMSE value of CF indicates that this method has better accuracy in predicting user preferences than CBF. Overall, both methods show good performance with a low error rate. However, CF proved slightly superior in providing recommendations that match user preferences, so it can be used as a basis for developing smarter and more personalized recommendation systems on e-commerce platforms.
Development of Semantic-Based Voicebots and Natural Language Processing for E-Commerce Product Searches Maskur, Maskur; Afandi, Yosi; Widyananda, Wahyu; Fauzi, Ahmad; Armayrishtya, Zhulvardyan
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2008

Abstract

Searching for products online is often an inefficient and confusing process, especially when users do not know the exact name of the product or use terms that differ from the search system. Keyword-based searches tend to produce irrelevant results because the system only matches text literally without understanding the meaning. As users increasingly talk to digital devices, voice-based search technology has become a more natural and intuitive alternative. This research aims to develop a semantic-based voicebot supported by Natural Language Processing (NLP) to improve the effectiveness of product searches on e-commerce platforms. The designed system not only recognizes user speech but also understands the context, intent, and semantic meaning of the given commands. The research stages include collecting user voice data, training the Automatic Speech Recognition (ASR) model for voice-to-text conversion, and applying the semantic NLP model for interpreting the context of product searches. The testing was conducted using Indonesian voice commands in a simulated e-commerce scenario. The results showed that the system achieved an average Word Error Rate (WER) of 1.29%, indicating a high level of accuracy in recognizing speech and understanding user intent. The integration between ASR and semantic NLP proved capable of creating a more natural, responsive search experience that resembles the way humans think and communicate when interacting with online search systems.
An Impact Analysis of Retrofit Shading and Double Skin Facade on Building Performance at the Glass Office of PT Pertamina Patra Niaga - ITJ Jakarta Sumarno, Sumarno; Panjaitan, Tigor Wilfritz Soaduon; Rolalisasi, Andarita
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2014

Abstract

Modern office buildings with glass envelopes often encounter challenges in energy efficiency, particularly due to excessive reliance on artificial lighting during daytime and intensive use of cooling systems. This condition is occurred at the PT Pertamina Patra Niaga - ITJ Jakarta office building. The installation of window films and curtain blinds has made interior spaces darker, preventing optimal utilization of natural daylight and consequently increasing electricity consumption. Such conditions not only reduce energy efficiency but also compromise visual comfort for occupants. This study aims to analyze the impact of retrofit shading and the double skin façade (DSF) on daylighting performance, visual comfort, and energy efficiency. The research employed an existing condition analysis using the Sefaira plug in SketchUp software and Sefaira web model - energyplus to simulate and evaluate the effects of retrofitting on the glass office building. The findings reveal that the integration of retrofit shading and DSF significantly improves natural daylighting quality 2% underlit, 42% well lit, and 44% overlit. Visual comfort was enhanced by lowering indoor illuminance levels of ASE from 87% to 43% lux and sDA from 100% to 98% lux, aligning with recommended standards. Furthermore, annual electricity consumption decreased substantially, from 269 to 130 kWh/m² per year. In conclusion, retrofit shading and DSF provide effective passive design strategies that enhance daylight utilization, improve occupant comfort, and support energy conservation. This study serves as a preliminary investigation for future research on integrating multiple passive design strategies in office building retrofits.

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