cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mega Novita
Contact Email
novita@upgris.ac.id
Phone
+6285867312111
Journal Mail Official
asset@upgris.ac.id
Editorial Address
Jl. Sidodadi Timur No.24, Karangtempel, Kec. Semarang Tim., Kota Semarang, Jawa Tengah 50232
Location
Kota semarang,
Jawa tengah
INDONESIA
Advance Sustainable Science, Engineering and Technology (ASSET)
Published by Universitas PGRI Semarang
ISSN : -     EISSN : 27154211     DOI : https://doi.org/10.26877/asset
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology
This journal aims to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of science, engineering, and technology
Arjuna Subject : Energi (semua kategori) - Teknik Energi dan Teknologi Daya
Articles 25 Documents
 1   2   3 
Search results for , issue "Vol 6, No 1 (2024): November-January" : 25 Documents clear
The Effect of LAB Color Space with NASNetMobile Fine-tuning on Model Performance for Crowd Detection Rafid, Muhammad; Luthfiarta, Ardytha; Naufal, Muhammad; Al Fahreza, Muhammad Daffa; Indrawan, Michael
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17821

Abstract

In the COVID-19 pandemic, computer vision plays a crucial role in crowd detection, supporting crowd restriction policies to mitigate virus spread. This research focuses on analyzing the impact of using the RGB LAB color space on the performance of NASNetMobile for crowd detection. The fine-tuning process, involving freezing layers in various NASNetMobile base model variations, is considered. Results reveal that the model with LAB color space outperforms model with RGB color space, with an average accuracy of 94.68% compared to 94.15%. From all the test iterations, it was found that the highest performance for the NASNetMobile model occurred when freezing 10% of the layers from the back for both model LAB and RGB color spaces, with the LAB color space achieving an accuracy of 95.4% and the RGB color space achieving an accuracy of 95.1%.
Overcoming The Buildup of Queues By Carrying Out the Concept of Self-Service Using Responsive Web-Based Applications Permatasari, Putri Wahyu; Aryanto, Joko
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17440

Abstract

With today's technological advances, all things can be done with the internet, one of which is a web-based food and beverage menu self-order system. This system was created to make it easier for waiters and customers in the ordering process. Because of the problems that occur today, namely the queue in the ordering process and busy waiters who make customers neglected. Therefore, this system was designed to facilitate service, and waiters no longer need to record food menus manually, customers can also order menus directly through the system without having to queue. Before designing the system, an analysis is carried out first, in the design of the system is designed with a quantitative method where researchers take the necessary data and information by conducting direct interviews with related parties. This system will be designed in the form of a responsive website and designed using the PHP programming language and MySQL which is used as a database storage. With the final result, this system can help and facilitate the ordering process and data collection of incoming orders. And the system can run according to the needs of restaurants and customers who use this system
Yogyakarta Batik Image Classification Based on Convolutional Neural Network Susanti, Indah Dwi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.18002

Abstract

This paper studies the efficiency of identifying motifs and patterns in Yogyakarta batik using the Convolutional Neural Network (CNN) algorithm. This research uses the AlexNet architecture on CNN to increase the accuracy of batik image classification. Apart from that, it also involves the use of Canny edge detection techniques and feature extraction using the Gray Level Co-occurrence Matrix (GLCM) to improve the feature extraction process in batik images. There are 6 folders representing 6 types of motifs containing -+20 to 25 data that have been prepared for the training session. Next, the data is processed with 20% of the data used for training and 80% for testing. The accuracy of this research using the SGDM optimizer reached 100%. The evaluation results provide insight into the extent to which edge emphasis can improve the model's ability to recognize and classify batik patterns. It also presents classification test results and evaluation metrics such as precision, recall, and F1 score.
Zonation Method for Efficient Training of Collaborative Multi-Agent Reinforcement Learning in Double Snake Game Hadiyanto, Marvin Yonathan; Harsono, Budi; Karnadi, Indra
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17562

Abstract

This paper proposes a zonation method for training the two reinforcement learning agents. We demonstrate the method's effectiveness in the double snake game. The game consists of two snakes operating in a fully cooperative setting to maximize the score. The problem in this game can be related to real-world problems, namely, coordination in autonomous driving cars and the operation of collaborative mobile robots in warehouse applications. Here, we use a deep Q-network algorithm to train the two agents to play the double snake game collaboratively through a decentralized approach, where distinct state and reward functions are assigned to each agent. To improve training efficiency, we utilize the snake sensory data of the surrounding objects as the input state to reduce the neural network complexity. The obtained result show that the proposed approaches can be used to train collaborative multi-agent efficiently, especially in the limited computing resources and training time environment
A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks Kamila, Izza Putri; Sari, Christy Atika; Rachmawanto, Eko Hari; Cahyo, Nur Ryan Dwi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17330

Abstract

CNN has been widely used to detect a pattern with image classification. This study used CNN to perform a classification analysis of lung abnormality detection on chest X-ray images. The dataset consists of 5,732 2D images with dimensions of 200 x 200 x 1 divided into training data (85%) and testing data (15%). The preprocessing process includes image resizing, enhancement to increase contrast and reduce image complexity, and filtering to improve visibility and reduce noise. CNN is used to classify imagery into three categories, Normal (no abnormalities), Pneumonia, and Tuberculosis. The results showed a good level of accuracy, with an average accuracy of 97.24% in 3 trainings, and a 100% success rate in 6 classification experiments. This research provides insights into the detection of lung disorders and encourages further exploration in medical diagnosis.
XGBoost and Random Forest Optimization using SMOTE to Classify Air Quality Arifianti, Fidela Putri; Salam, Abu
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.18136

Abstract

Air pollution due to the growth of industry and motorized vehicles seriously threatens human health. Clean air is essential, but pollutant contamination can cause acute respiratory illnesses and other illnesses. Several studies have been carried out to anticipate this air pollution. Various algorithms, methods, and data balancing techniques have been implemented, but still need to be done to obtain better accuracy results. Therefore, this study aims to classify heart disease using the XGBoost and Random Forest algorithms and implement the SMOTE technique to overcome data imbalance. This research produces a Random Forest algorithm with SMOTE implementation with splitting 80:20, which has the best accuracy with an accuracy of 92.4%, an average AUC of 0.98, and a log loss of 0.2366, which shows that SMOTE has succeeded in improving model performance in classifying minority classes. Based on the results obtained, the XGBoost and Random Forest algorithms after SMOTE are superior to the model with SMOTE, with accuracy above 90%.
Comparison of Gradient Boosting and Random Forest Models in the Detection System of Rakaat during Prayer Darmawan, Raihan Aris; Hidayat, Erwin Yudi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17886

Abstract

Abstract. Errors in the execution of prayer among Muslims can occur due to a lack of profound understanding of the prayer procedure. This research aims to compare two machine learning models, Random Forest and Gradient Boosting, in classifying prayer movements, subsequently extending to calculate the number of prayer cycles (rakaat). A total of 7220 manually gathered data based on 33 landmark coordinates using Mediapipe Pose Detection were employed. The research findings reveal that the Random Forest model with a 70:30 ratio achieves 99.9% accuracy, precision, and recall, with the fastest training time being 3.8 seconds. Both models exhibit testing results close to 100%, but the Gradient Boosting model faces challenges in classifying specific movements. On the other hand, Random Forest successfully overcomes thesechallenges, enabling accurate prayer cycle calculations. The findings can contribute to the development of tools supporting Muslims in correct prayer execution, positively impacting religious and well-being aspects.
Implementation Of A Web-Based Chatbot Using Machine Learning For Question And Answer Services In Universities Dewantara, Airlangga Satria; Aryanto, Joko
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17590

Abstract

Advances in communication technology in line with information technology, Chatbot is an innovation that combines communication technology and information technology, is an application that can communicate with humans like a virtual assistant who can respond and answer every question asked. A university must already have a website that can be accessed by the general public so that information about the college can be accessed by everyone anywhere and anytime. To make it easier to get information on the website, chatbots can be the solution because most prospective students and students who are on the campus feel reluctant to browse further into the website that has been provided and usually only open the main homepage page of the website. Parents of students also find it difficult to find out what is on campus if a lot of information is provided in certain tabs of the website. In this study, I utilized Chatbot technology which is a Machine Learning that can process every text that inputted then analyze it and conduct machine training using the Neural Network algorithms that have been provided. This research uses a case study methodology, with Yogyakarta University of Technology as the subject, to develop a chatbot website that incorporates machine learning to facilitate the processing of user input questions
Optimizing Biomass Pre-Treatment Technologies for BBJP Plants in Indonesia: A Multi-Criteria Decision Making Approach Salman, Haekal Awliya Muhamamad
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17877

Abstract

The challenges of energy consumption and environmental sustainability are pronounced in the dynamic landscape of contemporary industries driven by Industry 4.0 technologies. Indonesia, heavily reliant on fossil fuels, charts a course toward a clean energy future with a National Energy Transition Roadmap for Net Zero Emission by 2060. This transition involves innovative strategies such as biomass co-firing and waste utilization in Solid Recovered Fuel (SRF) plants, known as Bahan Bakar Jumputan Padat (BBJP) plants. To optimize these BBJP plants, this study employs Multi-Criteria Decision Making (MCDM) methodologies, specifically the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to evaluate and select pre-treatment technologies. Criteria include capacity, conversion process, waste type, electricity consumption, operational ease, land requirement, and investment cost. Comparing bio-drying, thermal drying, and mechanical drying, AHP ensures consistent criterion weights, with TOPSIS ranking bio-drying as the most favorable, followed by thermal and mechanical drying. The study acknowledges global waste management challenges and introduces a mobile-modular containerized BBJP/SRF plant model, addressing installation, maintenance, scalability, and adaptability issues. While recognizing challenges, especially in pre-treatment processes, the research emphasizes the need for efficient and cost-effective solutions. Practical implications include enhanced decision-making in biomass drying, identification of technology advantages and disadvantages, and a commitment to address challenges for sustainable implementation. The study contributes to Indonesia's energy transition discourse, advocating the pivotal role of BBJP plants in balancing Industry 4.0 demands and environmental protection, providing insights for stakeholders and decision-makers in advancing sustainable waste-to-energy initiatives.
Stroke Classification Comparison with KNN through Standardization and Normalization Techniques Firmansyah, Muhammad Raihan; Astuti, Yani Parti
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17685

Abstract

This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios. Demonstrating the importance of data scaling, both z-score and min-max achieve 95.07% accuracy. Notably, normalization averages a higher accuracy (94.25%) than standardization (94.21%), highlighting the critical role of data scaling for robust machine learning performance and informed health decisions.

Page 2 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 6, No 2 (2024): February - April Vol 6, No 1 (2024): November-January Vol 5, No 3 (2023): August-October Vol 5, No 2 (2023): May-July Vol 5, No 1 (2023): November-April Vol 4, No 2 (2022): May-October Vol 4, No 1 (2022) Vol 3, No 2 (2021): May-October Vol 3, No 1 (2021): November-April Vol 2, No 2 (2020): May-October Vol 2, No 1 (2020): November-April Vol 1, No 1 (2019): May-October More Issue