Advance Sustainable Science, Engineering and Technology (ASSET)
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
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<![CDATA[Control of ABC Pen Production Raw Materials Using the Material Requirement Planning to Minimize Inventory ]]>
<![CDATA[Melly Herliyati Utami]]>;
<![CDATA[Qurtubi]]>;
<![CDATA[Danang Setiawan]]>;
<![CDATA[Meilinda F. N. Maghfiroh]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.909
<![CDATA[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.]]>
<![CDATA[Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya ]]>
<![CDATA[Ilham Dwi Raharjo]]>;
<![CDATA[Egia Rosi Subhiyakto]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.934
<![CDATA[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.]]>
<![CDATA[Automated Disease Detection in Silkworms Using Machine Learning Techniques ]]>
<![CDATA[Binson V A]]>;
<![CDATA[Manju G]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.965
<![CDATA[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.]]>
<![CDATA[Optimized Stacking Ensemble Classifier for Early Cancer Detection Using Biomarker Data ]]>
<![CDATA[K. Jegadeeswari]]>;
<![CDATA[R. Rathipriya]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.986
<![CDATA[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.]]>
<![CDATA[Low-Cost Wearable Device for Sleepiness Detection Based on Heart Rate Monitoring ]]>
<![CDATA[Marvin Yonathan Hadiyanto]]>;
<![CDATA[Ananda Keshava]]>;
<![CDATA[Budi Harsono]]>;
<![CDATA[Shoaib Aslam]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.992
<![CDATA[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.]]>
<![CDATA[Spatial Analysis of Waste Management Facility Distribution Using GIS ]]>
<![CDATA[Vidyana Arsanti]]>;
<![CDATA[Rizqi Sukma Kharisma]]>;
<![CDATA[Ivan Ardiansyah]]>;
<![CDATA[Bayu Nugroho]]>;
<![CDATA[Muhammad Ihsan Fajruna]]>;
<![CDATA[Luthfia Zahra Deswanti]]>;
<![CDATA[Muhammad Fais Al Qori]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.996
<![CDATA[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.]]>
<![CDATA[Risk Mitigation Strategies for Sustainable Poultry Supply Chain Management ]]>
<![CDATA[Haswika]]>;
<![CDATA[Agus Mansur]]>;
<![CDATA[Meilinda F. N. Maghfiroh]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.997
<![CDATA[The livestock sector is an important pillar in providing animal protein and sustaining the rural economy. However, the sector faces major challenges from environmental and socio-economic issues, such as climate change and environmental degradation, which can threaten its sustainability. Negative impacts such as environmental contamination can reduce production quality and quantity and increase supply chain operational costs. This study aims to identify effective risk mitigation strategies to reduce these negative impacts and improve the sustainability of supply chain management. Data were collected from laying duck farms and analyzed using the House of Risk (HOR) method with a Phase 1 and 2 approach. This approach allows the identification of the most critical risks and risk agents and mapping mitigation priorities. Key findings indicate that providing drugs or vaccines to prevent animal virus outbreaks is the highest priority mitigation strategy, while strategic policy decision-making has the lowest priority. Overall, 15 risks and 21 risk agents were identified. This study implies that the implementation of effective mitigation strategies can significantly reduce operational risks, strengthen the resilience of the livestock sector, and support the sustainability of supply chain management as a whole.]]>
<![CDATA[Assessment of Abiotic Factors for Sea Turtle Nesting Suitability in Coastal Bays ]]>
<![CDATA[Chukwudi Ikegwu]]>;
<![CDATA[Agus Nuryanto]]>;
<![CDATA[Moh. Husein Sastranegara]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.998
<![CDATA[Cilacap Bays, critical nesting areas for sea turtles, face growing habitat disturbances from tourism. However, studies on nesting suitability in these regions remain scarce. This research assesses the abiotic factors influencing sea turtle nesting in Cilacap Regency, Indonesia, across eight observation stations. Key ecological parameters—land surface temperature (28°C - 36.3°C), pH (mean 6.8), sand particle size (0.212-0.500 mm), beach slope (11.50%-20.99%), and beach width (28.8m-81.8m)—were evaluated. The results highlight Sidaurip Beach as the most suitable for nesting due to optimal environmental conditions, with Station (SP1) being particularly favorable for producing male hatchlings due to its suitable 28°C temperature. These findings suggest targeted egg relocation to SP1 could help address gender imbalances, ensuring long-term population sustainability. This research provides valuable insights for sea turtle conservation and supports future policy efforts to protect nesting sites in Cilacap amidst growing environmental pressures]]>
<![CDATA[Advances in Deep Learning for Skin Cancer Diagnosis ]]>
<![CDATA[Maysaa R. Naeemah]]>;
<![CDATA[Mohammed Kamil]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.1002
<![CDATA[The most prevalent type of cancer worldwide is known as skin cancer. Early detection is critical because if left undiagnosed in the primary stage, it might be fatal. Although there are differences within the class and high inter-class similarities, it is too difficult to distinguish with the naked eye. Owing to the disease's global prevalence, a number of deep learning based automated systems were created thus far to help doctors identify skin lesions early on. Using pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks (CNNs), we trained VGG19 on the HAM10000 dataset. The optimal performance was observed with FT. The model that was created, which yielded an accuracy that was greater overall than the one used in transfer learning, was 82.4±1.9 %. By offering a second opinion and supporting the clinician's diagnosis, this performance could lower morbidity and treatment costs.]]>
<![CDATA[Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction ]]>
<![CDATA[Henri Tantyoko]]>;
<![CDATA[Dade Nurjanah]]>;
<![CDATA[Yanti Rusmawati]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
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DOI: 10.26877/asset.v6i4.1003
<![CDATA[Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.]]>
