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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 50 Documents
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Search results for , issue "Vol. 6 No. 4 (2024): August-October" : 50 Documents clear
Synthesis of Avocado Seeds Into Biodiesel Using A Catalyst CaO From Blood Cockle Shell Suprihatin; Julian, Alif; Fikri, Muhammad
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 Hadi, Nur Halizah; Muhaimin, M. Hadid; Redjeki, Sri
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 Saputra, Danar Bayu Adi; Sari, Christy Atika; Rachmawanto, Eko Hari
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; Mandailina, Vera; Mehmood, Saba
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; Ristyawan, Aidina; Harini, Dwi; Zaman, Wahid Ibnu; Muzaki, Muhammad Najibulloh ; Abdulnazar, Mohamed Naeem Antharathara
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
Control of ABC Pen Production Raw Materials Using the Material Requirement Planning to Minimize Inventory Utami, Melly Herliyati; Qurtubi; Setiawan, Danang; Maghfiroh, Meilinda F. N.
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 Raharjo, Ilham Dwi; 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.
Automated Disease Detection in Silkworms Using Machine Learning Techniques Binson V A; G, Manju
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 Hadiyanto, Marvin Yonathan; Keshava, Ananda; Harsono, Budi; Aslam, Shoaib
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.

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