cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mega Novita
Contact Email
asset@upgris.ac.id
Phone
+6281958990880
Journal Mail Official
asset@upgris.ac.id
Editorial Address
Advance Sustainable Science, Environmental Engineering and Technology (ASSET) 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,
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 50 Documents
 1   2   3   4   5 
Search results for , issue "Vol. 6 No. 4 (2024): August-October" : 50 Documents clear
Enhancing Bus Body Assembly Efficiency: Comparative Analysis of Ranked Positional Weight and Region Approach at PT. ABC Herry Dwi Prasetyo; Joumil Aidil
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.v6i4.675

Abstract

In general line balancing problems occur in assembling industries compared to manufacturing industries. Problems that often occur on a production line can usually be seen from the presence of a high work in process bottleneck. The problem faced by the company in the production process is the level of efficiency of the workforce and production machines which are still less than optimal, due to the imbalance of the workload between work stations caused by delays in materials from the warehouse, then the operator usually waits for directions from the foreman first, then waits for the material processing process from other station. Therefore, in the bus body assembly process, it is necessary to make an analysis or calculation of the balance of the bus body assembly process so that it can run smoothly. In the line balancing study using the ranked positional weight and region approach methods, the results of the ranked positional weight method for the bus body assembly line efficiency were 78.43%, then for the balance delay of the bus body assembly 21.57% and idle time 1189.16 minutes. After data processing using the region approach method for the bus body assembly line efficiency, the results were 64.84%, then for the balance delay of the bus body assembly 35.16% and idle time 2344.75 minutes
Risk Mitigation in Cold Chain Sytem using ANP and FMEA : A Case Study of PT XYZ Lailatul Rohmah; Enny Aryanny
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.v6i4.690

Abstract

PT. XYZ is a company that focuses on exporting marine products, especially products for surimi products, PT. XYZ experienced problems in cold chain system activities such as declining quality of fish raw materials, limited cold storage capacity, diversity of supply quality and the risk of overloud storage, resulting in the inhibition of cold chain system activities. The purpose of this study is to identify the causes and provide appropriate mitigation strategies so that risks can be minimized by the Company. This study uses an integration method between ANP and FMEA, so that a WRPN (Weighted Risk Priority Number) value can be produced. Based on the results of the analysis using the integration of the two methods, WRPN was obtained with the highest priority on quality risk factors of 170.713 and storage risk factors of 153.087 so that both risk factors are classified as high risk and need to be mitigated. The mitigation measures provided include separation of contaminated fish, monitoring, microbiological testing, compliance with standards, SOP training, cold storage maintenance, cold storage forecasting, using the FIFO method, supervision of the use of cold storage.
Synthesis and Characterization of Nanoparticle Calcium Oxide (CaO) from Blood Calm Shell by Precipitation Methods Aisy Aulia Amri; Zahranisa Shorea; Caecilia Puji Astuti
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.v6i4.765

Abstract

Blood Clam (Anadara granosa) contains high calcium carbonate which can be utilized in various fields by being used as nanotechnology. The shell contains 98.7% CaCO3 making it a sustainable material source. The research aims to synthesize and characterize of calcium oxide nanoparticles by precipitation methods. This method begins with crushing the shell into 100 mesh as sample. Each sample is mixed with HCl solute. After mixing, each filtrate is precipitated with KOH solute to multiple pH (7 ; 9 ; 11). The method continues with neutralizing the precipitate with water until it reach pH 7 and drying it with oven in 100oC for 1 hour. The sample will be calcinated for 3 hours in various temperature (300oC ; 500oC ; 900oC). Samples will be analyzed with SEM-EDX and XRF Analysis. Research indicates that The degree of acidity and calcination temperature do not have a significant effect on calcium oxide content. The calcium oxide content is ranged between 82,58% - 87,47% with the sizes being ranged between 550 nm - 20mm.
A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network Salma Shafira Fatya Ardyani; Christy Atika Sari
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.v6i4.771

Abstract

The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.
Classification of Corn Leaf Disease Using Convolutional Neural Network Ratih Ariska; Christy Atika Sari; Eko Hari Rachmawanto
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.v6i4.772

Abstract

Corn is a crop that plays a major role in food supply worldwide. Known as a cereal crop with high economic value, corn is one of the most important raw materials in the agricultural industry in many parts of the world. Leaf blight is characterized by small spots that gradually enlarge and turn brown. It is a decay of foliage caused by the fungus or species Rhizoctonia solani. Leaf spot is caused by the fungus Hel-minthoporium maydis, while stem rot is caused by Fusarium granearum. From these problems, a machine learning-based solution is given to classify corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. CNN are used to classify corn leaf diseases. The selection of CNN is based on its ability to extract local attributes from image data and combine them for a more detailed and abstract representation, which is better. Classification was performed using 2145 datasets for leaf blight and 1574 datasets for leaf spot. The accuracy results obtained from this study reached 99% with the last training accuracy value of 99.06% and the last validation accuracy result of 98.50%. For future research may use more modern architectures such as classification using EfficientNet B3 architecture with transfer learning or MobileNet to improve accuracy results.
Synthesis of Avocado Seeds Into Biodiesel Using A Catalyst CaO From Blood Cockle Shell Suprihatin; Alif Julian; Muhammad Fikri
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.v6i4.779

Abstract

Biodiesel is an environmentally friendly fuel made from oil vegetables that contain triglycerides. Biodiesel from avocado seed vegetable oil in Indonesian agricultural areas which according to BPS 2022 data, Indonesia produces 183,000 tons of avocados per year. This research aims to gained the effect of adding reaction temperature and Oil : Methanol ratio influence on biodiesel production. The avocado seed oil obtained by soxhlet extraction method, where 50 gram avocado seeds powder extracted with n-hexane solvent in 1 hour extraction time and 60℃ temperature giving result 10% yield of avocado seed oil . The CaO catalyst are obtained from Calcination procees of blood cockle shells in 900℃ temperature and 4 hours calcination time giving 98.82% CaO Cotent. The biodiesel is produces with 97% methanol reactant and 98.82% CaO catalyst in various methanol volume (30; 40; 50; 60; and 70 ml) and under different temperature conditions (30; 40, 50, 60, and 70 ℃). The best result of transesterification process biodiesel is obtained in 50℃ and 40ml methanol gets biodiesel yield of 96%, methyl ester content of 99.83%,  density of 865 gr/cm3, viscosity of 2.5 cSt  , mgKOH/gr acid number of 0.56 of, and heating value of 9871.6 kcal/kg. Based on the high result of methyl ester content and heating value of biodiesel obtained from the procees, the avocado seed oil biodiesel potentially used as an sustainable energy.
Preparation of Calcinite Fertilizer from Golden Snail Shells by Calcination and Crystallization Processes Nur Halizah Hadi; M. Hadid Muhaimin; Sri Redjeki
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.v6i4.788

Abstract

The golden apple snail is a significant pest that damages crops and can lead to crop failure because it has a habit of consuming various soft plants, including young rice plants. Golden snail shells have a high calcium carbonate content of around 60.56%. Large calcium content can be used as a source for the production of calcium nitrate fertilizer. The stages in making calcinit fertilizer are washing the shell of gold snails and drying for 1-2 days. After that, size reduction is carried out to 50 mesh. After that, the calcination process was carried out with a variable temperature of 700,750,800,850,900 ℃ for 4 hours. The calcined shell is dissolved with HNO3 with a variable of 1-5 N for 1 hour. After that the solution is filtered from impurities and neutralized to pH 7. After that the solution is crystallized into white crystals. The largest Ca and N content was obtained at a calcining temperature of 900 and HNO3 5 N concentrations, namely Ca of 21.94% and N of 16.52%. The results showed that the higher the calcining temperature and the higher the HNO3 content, the higher the Ca content and N content
Jasmine Flower Classification with CNN Architectures: A Comparative Study of NasNetMobile, VGG16, and Xception in Agricultural Technology Danar Bayu Adi Saputra; Christy Atika Sari; Eko Hari Rachmawanto
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.v6i4.790

Abstract

Jasmine flowers have many benefits and uses such as for traditional medicine, tea, perfume, cosmetics, decoration, and others. in the selection of fresh jasmine flowers for making tea is very important, currently the classification of jasmine flowers for making tea is mostly still using manual methods. Often influenced by individual preferences, opinions, or biases. this causes a lack of objectivity and uncertainty in the classification of jasmine flowers. The manual method is very weak due to human visual limitations and fatigue levels which can result in less than the optimal jasmine flower classification. Therefore, in the research that has been done, a transfer learning system was applied that can classify fresh jasmine flowers with rotten jasmine flowers. This study aims to compare three different Convolutional Neural Network architectures: NasNetMobile, VGG16, and Xception. The results on the three architectures can show maximum results, namely 99.21% for NasNetMobile, 98.69% for VGG16 and 97.91% for Xception. This study provides insight into the classification of good and bad jasmine flowers to encourage further exploration in the field of agriculture.
The Role of Mathematics in Machine Learning for Disease Prediction: An In-Depth Review in the Healthcare Domain Abdillah; Syaharuddin Syaharuddin; Vera Mandailina; Saba Mehmood
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.v6i4.845

Abstract

The rapid advancements in healthcare technologies and the increasing complexity of medical data have made it imperative to explore and optimize predictive models for disease management. This study aims to conduct a systematic literature review to identify advancements, challenges, and opportunities in disease prediction using machine learning (ML) within the healthcare domain. The literature sources include Scopus, DOAJ, and Google Scholar, covering the period from 2013 to 2024. The findings reveal that both machine learning (ML) and deep learning (DL) algorithms have significant potential for disease prediction and treatment outcomes in various clinical contexts. Algorithms such as Random Forest, Logistic Regression, and ensemble techniques like Boosting have demonstrated strong performance in numerous studies. However, the effectiveness of these algorithms is highly context-dependent, including the type of disease, patient characteristics, and available data. Deep learning, particularly Convolutional Neural Networks (CNNs) and hybrid Long Short-Term Memory (LSTM) models, excels in handling complex, high-dimensional data, providing higher prediction accuracy compared to traditional ML models. This research shows that deep learning models, especially CNN and hybrid LSTM, achieve higher accuracy in disease prediction compared to traditional ML models. However, challenges related to data quality, privacy, and the underlying mathematical modeling of these algorithms remain to be overcome for wider applications.
Integrating Cryptographic Security Features in Information System Barcodes for Self-Service Systems Sucipto; Aidina Ristyawan; Dwi Harini; Wahid Ibnu Zaman; Muhammad Najibulloh  Muzaki; Mohamed Naeem Antharathara Abdulnazar
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.v6i4.850

Abstract

Integrating services in an information system is necessary to provide services that can optimize an information system. One of the systems in PKKMB activities that will be combined with information security features is the attendance system. This research uses the Liner Sequential Model (LSM) method to integrate the QR Code attendance system with security features. This research aims to integrate QR Codes by optimizing increased security by combining the Advanced Encryption Standard (AES) algorithm with base64 with a dynamic data model to complicate the QR Code manipulation process. Contribution This study makes optimization of the AES encryption model to improve data security on QR Code. Algorithm testing results include using a Character Error Rate (CER) of 0%, Avalanche Effect (AE) testing with a value of 53.05%, and response time (RT) testing of 10.26ms

Page 1 of 5 | Total Record : 50

 1   2   3   4   5 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): November - January Vol. 7 No. 4 (2025): August-October Vol. 7 No. 3 (2025): May - July Vol. 7 No. 2 (2025): February-April Vol. 7 No. 1 (2025): November-January Vol. 6 No. 4 (2024): August-October Vol. 6 No. 3 (2024): May - July