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Advance Sustainable Science, Environmental Engineering and Technology (ASSET) Jl. Sidodadi Timur No.24, Karangtempel, Kec. Semarang Tim., Kota Semarang, Jawa Tengah 50232
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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,
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Advance Sustainable Science, Engineering and Technology (ASSET) is a peer-reviewed open-access international scientific journal dedicated to the latest advancements in sciences, applied sciences and engineering, as well as relating sustainable technology. 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 sciences, engineering, and technology. The Scope of ASSET Journal is: Biology and Application Chemistry and Application Mechanical Engineering Physics and Application Information Technology Electrical Engineering Mathematics Pharmacy Statistics
Arjuna Subject : Teknik Kimia (semua kategori) - Teknik Kimia (Lain-Lain)
Articles 233 Documents
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Sustainable Digital Transformation in Healthcare: Challenges and Directions in the Society 5.0 Era Haryo Kusumo; Dian Marlina; Achmad Solechan
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.876

Abstract

This study conducts a comprehensive literature review on the digital transformation required by health service institutions during the Society 5.0 era. Utilizing articles related to digital transformation and health services, the study presents qualitative data simplified into descriptive narratives to draw meaningful conclusions. The method employed is a qualitative literature review. The review identifies significant challenges, including big data utilization, data security, privacy concerns, and the implementation of cloud computing systems. Furthermore, the research synthesizes current trends and proposes actionable recommendations for overcoming these challenges, such as adopting Health 5.0 and fostering integrated Community 5.0 systems. The study underscores the importance of maintaining the human aspect amidst technological advancements. Future research directions are outlined, focusing on the "big data-based society" within Society 5.0 to explore innovative solutions, mitigate barriers, and ensure sustainable digital transformation in healthcare services.
Comparing Conventional and Modern Methods for The Phycocyanin Extraction from Spirullina sp Dian Marlina; Desi Purwaningsih; Reny Pratiwi; Ryan Werytama Saputra; Widiastuti Setyaningsih; Supriyono Supriyono
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.907

Abstract

Spirulina platensis, a blue-green algae abundant in tropical regions, is rich in minerals, vitamins, fibers, and pigments, with low nucleic acid content. It has unique chromoproteins called phycobiliproteins, notably phycocyanin, used in various applications. This study aims to optimize phycocyanin extraction using different solvents (distilled water and sodium phosphate buffer pH 6.7) and methods (freeze-thaw and sonication). Spirulina platensis biomass was extracted in both solvents, then some of them was freeze for 24 and 48 hours followed by thawing overnight. The other was sonicated for 2.5 minutes, 50 Hz then soaked for 1, 2, and 3 hours. All of the samples were centrifuged at 6000 rpm for 10 minutes and the absorbance was measured using a UV-Vis spectrophotometer at wavelengths of 280, 620, and 650 nm. with freeze-thawing for 48 hours yielded the highest phycocyanin concentration (0.55%), with a yield of 11.07 and purity of 0.21. Sonication improved phycocyanin concentration, yield, and purity significantly, yielding 1.108, 25.85, and 0.26, respectively.
Additive Manufacturing Technology in the Furniture Industry: Future Outlook for Developing Countries Brian J. Tuazon; John Ryan Cortez Dizon
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.908

Abstract

For the past few years, the adoption of 3D printing technology has benefited various manufacturing industries, including the furniture making industry. However, this adoption has been greatly seen in industrialized countries and lacking in developing countries. Therefore, to understand fully the capability of 3D printing and its benefits, this paper review discusses recent applications of 3D printing in the furniture industry and assesses the potential it can bring for developing countries’ furniture making industry, specifically in the Philippines and other developing countries in Asia. In addition, the drawbacks it brought to the industry, and the challenges that needed to be addressed are also discussed in the paper. The paper covers various 3D printing technologies such as material extrusion, sheet lamination, powder bed fusion, and vat photopolymerization, along with different materials currently used in the furniture industry. Numerous notable examples of applications of 3D-printed furniture are also presented. Based on the review paper, it was found that the most common 3D printing technologies used in the furniture industry are Material Extrusion (MEX) and Powder Bed Fusion (PBF) specifically Fused Deposition Modelling (FDM) and Selective Laser Sintering (SLS), respectively. The most common 3D printing materials used are Polyamide (PA), Polylactic acid (PLA), and recycled Polyethylene terephthalate glycol (PETG). The paper also discusses the possible adoption of 3D printing in developing countries and explores its potential to innovate traditional furniture manufacturing processes.
Control of ABC Pen Production Raw Materials Using the Material Requirement Planning to Minimize Inventory Melly Herliyati Utami; Qurtubi; Danang Setiawan; Meilinda F. N. Maghfiroh
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.909

Abstract

This study addresses the issue of controlling the inventory of raw materials for pen production at PT. XYZ uses Material Requirement Planning (MRP) to minimize excess inventory. The MRP calculates net requirements, planned receipts, planned order releases, and projected on-hand inventory based on estimated demand, product structure, lot size, lead time, and safety stock.  The results categorize 20 raw material components for ballpoint pen products into five levels, determining the optimal quantity and timing for ordering and receiving each component to meet production plans while avoiding excess stock or shortages. This study concludes that the MRP method can significantly optimize inventory management, reducing the risks of overstock and stockouts in the pen production process at PT. XYZ. Consistent application of the MRP method and regular evaluation of supplier capabilities are recommended to ensure efficient and effective raw material inventory planning and control.
Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya Ilham Dwi Raharjo; Egia Rosi Subhiyakto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.934

Abstract

The furniture industry is an important sector in Indonesia that supports the economy and provides quality furniture. An in-depth understanding of the furniture business is essential for industry players to improve operational efficiency and customer satisfaction. This research aims to develop a chatbot for Multi Usaha Raya furniture company to improve customer service and operational efficiency. In its development, the Machine Learning Model Development Life Cycle (MDLC) and deep learning approach using the Flask platform are employed. LSTM, a type of recurrent neural network (RNN) architecture capable of handling long-term dependencies, is utilized in this chatbot model. The model training results show an accuracy of 99%, validation accuracy of 96%, loss of 0.1%, and validation loss of 0.2% after 200 epochs, demonstrating the effectiveness of the LSTM algorithm for developing a chatbot in this company.
Improving Lathe Efficiency through Overall Equipment Effectiveness and Automatic Maintenance Methods Bilal Setiyo Pangestu; Joumil Aidil Saifuddin
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i2.963

Abstract

In the current industrial era, operational efficiency is key for companies. Machining service providers can be seen from the number of workshops. At CV. XYZ has a lathe problem, there is no method for maintenance. Therefore, the purpose of this study is to calculate machine maintenance in companies using the Autonomous Maintenance (AM) method. From the calculation results obtained during January-June 2024, the highest average OEE value was in May 88.82%, Availability Rate parameters 88.76%, Performance Rate 99.28%, Rate of Quality 100%. The lowest average OEE value occurred in January 52.11% with Availability Rate parameters 76.44%, Performance Rate 90.66%, Rate of Quality 75%. From these results, it is necessary to have machine maintenance with indications for each component: cleaning the dynamo regularly, ensuring the on-off button is connected which is expected to increase effectiveness, extend machine life, and reduce maintenance costs.
Automated Disease Detection in Silkworms Using Machine Learning Techniques Binson V A; Manju G
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.965

Abstract

Silkworm diseases pose a major threat to the sericulture industry, with early detection remaining a challenge due to limited infrastructure. This study focuses on detecting Grasserie disease, which can rapidly spread in silkworm rearing units, leading to significant economic losses. A novel dataset of 668 healthy and 574 Grasserie-affected silkworm images forms the basis of this research. The study applies machine learning techniques, using the Histogram Oriented Gradient (HOG) feature descriptor combined with Kernel Principal Component Analysis (KPCA) and supervised classifiers. The integration of Support Vector Machines (SVM) with HOG and KPCA achieved high accuracy (93.16%), recall (93.38%), and precision (91.94%), offering a faster, more accurate alternative to manual detection methods. This approach holds great potential for developing real-time, IoT-based diagnostic tools that enable farmers to quickly identify infected silkworms, reducing disease spread and economic losses, and can be extended to other agricultural applications requiring early disease detection.
Optimized Stacking Ensemble Classifier for Early Cancer Detection Using Biomarker Data K. Jegadeeswari; R. Rathipriya
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.986

Abstract

Ovarian cancer ranks sixth globally as a major cause of death among women, with a five-year survival rate below 50%, largely due to late detection. Early detection is crucial to lower mortality rates. This paper introduces an Optimized Stacking Ensemble Classifier (OSEC) for early ovarian cancer detection using biomarkers. The model comprises two layers: the first layer includes base classifiers optimized with Particle Swarm Optimization (PSO), while the second layer is a meta-classifier integrating Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest(RF) models fine-tuned through grid search. Among the three datasets evaluated, the Blood Routine dataset showed the best performance with a stacked RF meta-classifier, achieving: 94.29% accuracy. The Stacked RF model also outperformed others, reaching 92.82% accuracy on the Serum dataset and 92.77% on the Malignant Ovarian Tumor (MOT) dataset, consistently excelling in precision, recall, and f1-score.
Low-Cost Wearable Device for Sleepiness Detection Based on Heart Rate Monitoring Marvin Yonathan Hadiyanto; Ananda Keshava; Budi Harsono; Shoaib Aslam
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.992

Abstract

Driver sleepiness is one of the most contributing factors in car accidents. Preventions to this problem have been made with various types of driver’s sleepiness detection system, such as systems based on face detection and electrocardiography approaches. However, these approaches require sophisticated systems and impractical design that are not suitable for the low- cost wearable device for daily use. Photoplethysmography based sensor is very favorable to be implemented in the low-cost wearable device to monitor the driver’s heart rate due to its reliability in measurement and simplicity in design. In this study we propose a photoplethysmography based wearable device that is low-cost, wearable, simple to build, and good reliability. We have shown that our wearable device exhibits less than 3.12 BPM in average absolute error heart rate with the standard instruments, moreover, our low-cost wearable device is successfully detecting sleepiness based on heart rate reduction of the subjects, which in sleepy condition the heart rate decreases typically ~30 % from the normal condition. Here, we design a sleepiness detection device with 3 levels of sleepiness alarm based on heart rate reduction that is very promising to be implemented as a wearable device in daily use for car drivers to prevent accidents due to sleepiness factor. In the future, this concept can be further improved as a smart driver monitoring system that can monitor physical conditions, mental conditions, and driver’s behavior particularly for the upcoming era of semi-autonomous and autonomous car.
Spatial Analysis of Waste Management Facility Distribution Using GIS Vidyana Arsanti; Rizqi Sukma Kharisma; Ivan Ardiansyah; Bayu Nugroho; Muhammad Ihsan Fajruna; Luthfia Zahra Deswanti; Muhammad Fais Al Qori
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.996

Abstract

Recently, waste has become an extraordinary phenomenon that has attracted the attention of all levels of society: authorities, local governments, environmentalists, and regional stakeholders at the village level. Based on DIY Regional Regulation No. 3 of 2013 concerning the Management of Household Waste and Waste Similar to Household Waste and Sleman Regency Regional Regulation No. 6 of 2023 concerning the Implementation of Waste Management, efforts to minimize the amount of waste are made by each waste bank collaborating with TPS3R in Sleman Regency. Based on temporary data from 178 waste banks, there are 97 active waste banks and 32 TPS3R in Sleman Regency. The objectives of this study are (1) To determine the distribution pattern of active waste banks in Sleman Regency and (2) To determine the accessibility of active waste banks to TPS3R locations. This study uses the nearest neighbour analysis method, and the accessibility of active waste bank locations to TPS3R locations is measured using the buffering method—data processing using a Geographic Information System (GIS). The results of this study indicate (1) the distribution pattern of active waste banks in Sleman Regency based on the nearest neighbour ratio value is 0.861485 (<1), indicating a spatial pattern that tends to be clustered or spread in groups; (2) the accessibility of active waste banks to the TPS3R location has not shown an even pattern, from 32 TPS3R only 10 TPS3R have two waste banks, the rest 0 - 8 waste banks. The buffering distance shows that the closer the two locations are, the more effective and efficient waste management will be, with a maximum accessibility distance of 4.1 km.

Page 5 of 24 | Total Record : 233

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