International Journal on Advanced Science, Engineering and Information Technology
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Detection of Transposon Gene in Lurik Peanuts (Arachis hypogaea var. lurik L.) with AhMITEs Analysis
Full text linkPeanut (Arachis hypogaea L.) is one of the leading commodities in Indonesia that are consistently growing with high demand. However, its productivity in the current state is relatively few, thus causing dependency on imported products. Developing new varieties is one of the many solutions to these problems. Lurik peanuts are superior local varieties to any other peanuts in terms of productivity and disease resistance. Seed with the purple pattern is this cultivar's special characteristic and main attraction. This study aimed to identify and verify the activity of transposon genes in the seed pattern of Lurik peanuts. This research method was carried out by gene detection and sequencing analysis using PCR-AhMITEs (Arachis hypogaea Miniature Inverted Transposable Elements). The study used the Garuda variety as a comparison due to the absence of seed patterns, and it is a superior variety widely cultivated in Indonesia. Four types of primers used in this study were AhMITE1, AhTE0357, AhTE0391, and AhTE1317. The results revealed that the four primers had a linear relationship that could distinguish Lurik peanuts and Garuda peanuts based on the presence of transposon genes. The sequencing results confirmed that the detected genes were transposons from peanuts, located on chromosome 5 (Arahy.5), chromosome 9 (Arahy.9), chromosome 14 (Arahy.14), and chromosome 19 (Arahy.19). Based on the results of the study, the pattern on Lurik peanuts is an expression of the transposon gene activity
Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology
Full text linkThe Sunda Arc-Indonesia is very vulnerable to tsunamis. There have been at least 55 tsunamis from 416–2018. Tsunami in the Sunda Arc is classified as a near-field tsunami with an arrival time of < 30 minutes after the earthquake. Meanwhile, the BMKG issued a warning within 5 minutes after the earthquake; therefore, speed in giving warnings is very vital. Artificial intelligence is an alternative technology that can quickly predict a tsunami's height and arrival time. For developing this technology, adequate quality and quantity of data and information on tsunamis are needed. Therefore, this study was conducted to build a tsunami database based on the results of simulations and numerical modeling of multiple scenarios from hypothetical and historical earthquake sources. This study used the open-source TUNAMI F1 model. This model simulates the propagation of tsunami waves using a linear equation. This study obtained 465 hypothetical earthquake sources, 534 historical earthquake sources, and 9,990 datasets from tsunami model simulation results. Each dataset contains ten information. Based on the 8.2 magnitude earthquake scenario, the potential tsunami hazard is 3–47 m with an estimated arrival time of < 30 minutes. An earthquake < 7 Mw can trigger a tsunami, especially an earthquake that is shallow and close to the coast, even though the tsunami height is < 0.5m. This data will be used to train an artificial intelligence-based tsunami prediction system. The artificial intelligence-based tsunami prediction system is expected to be used to strengthen the Indonesia tsunami early warning system (InaTEWS)
MikrobatX: The Use of SIFT Feature Extraction in a Deep Learning Approach for Identification and Classification of Microscopic Fragments of Medicinal Leaves
Full text linkDue to a lack of references that contain standard references, it is still difficult to evaluate the accuracy of the raw material for the medicinal plant Simplicia powder based on microscopic testing in the pharmaceutical industry. Furthermore, it takes much time to manually match the findings of microscopic tests with standard reference materials. For these reasons, artificial intelligence must be used so that researchers can rapidly and reliably forecast the kinds of medicinal plants based on microscopic fragments. Deep learning performance in computer vision has demonstrated encouraging outcomes in recent years. Convolutional neural networks (CNN) enhanced by SIFT feature extraction, dubbed "MikrobatX," are used in the proposed work to identify and classify microscopic fragment images of the medicinal plant Simplicia. This technique plays a key role in the microscopic identification and classification of medicinal plant simplicia. Using microscopic photographs of the leaves of medicinal plants, MikrobatX was able to extract essential Simplicia characteristics. Our proposed model may produce the greatest accuracy value of 89.42% for microscopic medicinal leaf Simplicia image problems, according to experimental results utilizing the Mikrobat dataset. Due to the lack of comparable research using the Microbat dataset, these findings cannot be compared to earlier investigations
Synthesis and Characterization of Polymer Electrolyte Membrane Based on Cellulose-Chitosan-Alginate as Li-Ion Battery Separator
Full text linkThe current commercial Gel Polymer Electrolyte (GPE) products are generally made of synthetic and non-biodegradable materials. In addition, some of these polymers require toxic reagents and complex synthesis processes. The purpose of this research is to manufacture GPE membrane products using biodegradable raw materials, a combination of Hydroxy Ethyl Cellulose (HEC), Carboxymethyl Chitosan (CMCs), and Sodium Alginate (SA) with lithium salt as the electrolyte source. The methods start from the fabrication/synthesis of biodegradable GPE membranes in various compositions, then LiOH is added as an electrolyte source and glutaraldehyde as a crosslinking agent using a solution casting technique. The mechanical membrane testing (tensile strength and elongation) and characterization were carried out using XRD, SEM, and FTIR. Based on mechanical tests carried out, variations in HEC 50%: SA 50% has the highest tensile strength value of 81.4255 MPa and the lowest elongation value of 11.68%. The results of XRD analysis in the presence of a typical peak in the HEC: SA variation was 11.56º, which could affect the strength of the electrolyte-polymer gel membrane (GPE). The results of SEM analysis proved that the HEC: SA variation has a porous morphology that can affect the ion absorption capacity in lithium-ion battery applications. The results of FTIR analysis proved that there are functional groups S=O, CH, CO, NH, OH, and COC in the three membranes (SA, CMCs, and HEC)
Multi-Language Program Understanding Tool
Full text linkOpen-source programs have gained popularity due to their decentralized, quick development cycles and accessibility to everyone. Program understanding is vital for open-source software developers to modify or improve the code. However, one problem open-source developers face is the difficulty in understanding the programs as the program grows large and becomes complex. The current program understanding tool is inefficient because it only supports one programming language, while open-source programs are written in various languages. This paper discusses a new program understanding technique that facilitates multi-language program understanding. The proposed technique helps developers to understand open-source programs by supporting two unique features: multimedia and additional comments. We carried out this study in four stages. First, we examined available tools and techniques in software understanding to identify their strengths and weaknesses. Second, we proposed a new technique. Third, we designed a new tool to implement the new technique. Lastly, we evaluated the tool using a survey. We invited twenty users, including students and programmers, to use the system and ask for their feedback. The evaluation of the proposed techniques shows that the respondents have a positive perception as they agree that the technique helped them better understand the program. The multimedia support and an additional comment provided by the tool significantly improve user understanding of the program. For future work, we would like to explore the possibility of utilizing some machine-learning techniques to enhance the process of program understanding
GRU and XGBoost Performance with Hyperparameter Tuning Using GridSearchCV and Bayesian Optimization on an IoT-Based Weather Prediction System
Full text linkWeather is essential to human life, but it is difficult to forecast due to its diverse nature. We evaluated and compared the accuracy of two machine learning algorithms, GRU and XGBoost, in predicting weather patterns. We used GridSearchCV to tune the hyperparameters for the GRU algorithm and Bayesian optimization for the XGBoost algorithm. We used regression to predict weather sensor data and classification to predict rainfall in the following four days. We then deployed the best-performing model to the cloud server and connected it to the local IoT device with weather sensors in Sedati, Sidoarjo Regency, Indonesia. We conducted tests using data from the BMKG Juanda Sidoarjo and data from the local IoT device. The findings indicated that the XGBoost regression model outperformed the GRU model in the first stage, with an average RMSE of 1.2728125. In comparison, the average RMSE for GRU regression was 1.551666667. In the second stage, however, GRU regression performed better, with an average RMSE of 2.23, while the XGBoost regression had 2.28. In the classification tests, the GRU model had a higher F1 score of 0.88 in the first stage, while the XGBoost classification was 0.86. Both models had the same accuracy of 0.75 when tested with IoT data. However, the GRU classification model was better since it considered the context of the prediction, resulting in a lower likelihood of rain when it was not raining
Teacher Performance in Online Learning during the COVID-19 Pandemic: A Systematic Literature Review
Full text linkTeacher performance during the COVID-19 pandemic is an issue in several countries. This article presents a systematic review of literature related to teachers’ performance in facing the COVID-19 pandemic. Teachers’ performance includes pre, process, and post-learning activities. The writing of this article was assisted by Publish or Perish 7, Mendeley, VOSviewer, and NVIVO 12 Plus applications. The search for articles in Scopus-indexed journals is limited to 2020-2022. From the search results on Publish or Perish 7, 200 Scopus-indexed articles were selected according to compatible themes into 50 articles. The findings of the theme/topic are teaching performance, clinical competence, work engagement, schools, teacher attitude, covid-19, digital teaching, online activity, distance learning, distance teaching, online learning, education innovation, online exam, digital competence, teacher training, pedagogical practices, etc. These 50 articles were analyzed according to the topic through the NVIVO 12 Plus application, and the results were described according to the research question. The findings of this article mention that teachers’ performance is facing a pandemic determined by mastery of digital devices, digital competencies, digital management, and administration, and others realized through training in the use of digital technology. Further research is needed to determine what, how, and why teachers should perform well during and after the COVID-19 pandemic in learning in or outside the classroom
Size Identify Local Culture for Developing Sustainability Construction in SEZ Likupang
Full text linkIn the development of modern construction, the sustainable construction approach has grown in importance. Economic, environmental, and social factors have been identified as influencing its implementation. The use of the sustainable construction method is affected by several additional factors. This study aimed to determine how the Likupang SEZ’s local cultural factors affected sustainable construction methods. The method used is a quantitative study with a sample of the Likupang SEZ in North Sulawesi. The results of this study indicate an influence of local cultural factors of 0.264 on the implementation of the sustainable construction approach in the Likupang SEZ. The results of this study also indicate that local cultural factors are important to consider in implementing sustainable construction. Things that focus on local culture in planting trees as materials for construction, local heritage, Work Culture, and the migrant community environment should be of particular concern in implementing a sustainable construction approach in the Likupang SEZ area. Cultural factors have an important role in ensuring development with this sustainable construction approach considering environmental, social, and economic factors as well as cultural factors as new factors identified as having an influence. This study concludes that it is very important to pay attention to local cultural factors that influence the implementation of a sustainable construction approach in the Likupang SEZ project in North Minahasa Regency, North Sulawesi, Indonesia
Non-invasive Frozen Meat Monitoring System Using UHF RFID Tag Antenna-Based Sensing and RSSI
Full text linkThe conditions of frozen meat products must be closely monitored in cold chain logistics (CCL) to maintain their quality and safety. Sensing and monitoring meat products are currently invasive, costly, and lacking tracing capabilities. Therefore, developing a wireless, passive, and cost-effective sensing system capable of tracking and monitoring remains challenging. This work investigates the UHF RFID system performing antenna-based sensing for monitoring frozen meat using the received signal strength indicator (RSSI) data. A commercial off-the-shelf (COTS) UHF RFID reader is programmed through a single-board computer to acquire the RSSI data throughout the RFID 902-926 MHz band. In the experiments, RSSI data from an RFID inlay tag affixed to a defrosted frozen meat sample is acquired for approximately 20 minutes. Then, the RSSI data is recorded periodically during the changes in the sample condition. The experimental results signify that the RSSI data have monotonic relationships with the temperature and hardness of the meat sample. The three-degree polynomial regression models are constructed to show the non-linear relationships between the RSSI and the frozen meat condition. During defrosting, the RSSI lowers as the meat temperature rises and the hardness reduces. Therefore, antenna-based sensing employing the RFID RSSI data can detect changes in frozen meat temperature and hardness, allowing conditional fluctuations in the CCL to be monitored. This work paves the way for low-cost IoT-based sensing systems for improving food safety in cold chain applications
Development of GIS-Based Rumah Gadang Tools as a Tourism Data Integrator for Halal Tourist Villages in West Sumatra, Indonesia
Full text linkTourism is one of the economic pillars of countries, including Indonesia. However, the Covid-19 pandemic has significantly affected the global tourism industry. Nevertheless, the government has taken measures during this pandemic to prepare tourist villages to drive Indonesian tourism. West Sumatra, a province in Indonesia designated as a halal tourist destination, is actively developing halal tourist villages such as the Koto Baru. In this village, there are many Minangkabau traditional cultural houses called Rumah Gadang (RG). However, these houses are yet to attract tourists due to a lack of information dissemination about the uniqueness of the RG. Therefore, GIS-Based RG Tools (GRT) has been developed in web and mobile GIS (Android platform). This research reports on the development of GRT, which is intended to answer the challenges of developing tourist villages using ICT, namely the use of ICT is still limited to data collection and promotional media only, and there is no integrated tourism village management system with tourist attractions. The GRT was developed using a research and development methodology. Additionally, its development included using the waterfall method whereby PostgreSQL/PostGIS, Bootstrap, PHP, B4A, and JavaScript software were used to design and code GRT. After being programmed, the GRT was tested for functionality, and it was concluded that the app is appropriate and meets the needs of tourists during their pre-trips or visitations to tourist sites. Further research should focus on developing tourism app modules following the uniqueness of halal tourism villages