Jurnal Nasional Teknik Elektro dan Teknologi Informasi

A Self-Adaptive Routing Region in Wireless Sensor Network’s Heterogeneous Traffic

2024-09-11T10:45:56+07:00

The paper presents a new routing scheme using the information on the locations of nodes to create a routing region that controls the region of packet routing to achieve route optimization. The proposed scheme aimed to reduce the occurrence of packet detours or other routing overheads caused by the undirected packet transmission. The strength of this approach is that it can improve the lifetime of nodes in the network while decreasing the time taken for a packet to arrive at its destination or base station (BS). The proposed scheme used a self-adaptive algorithm that dynamically adjusted the routing region based on the BS’s calculation of the network layer parameters to achieve energy efficiency while satisfying data quality. The routing region limits the area of routing and restricts data flooding in the entire network, which potentially will waste resources and cause data redundancy. The simulation showed that the proposed scheme outperformed, the original fitness scheme and SPEED, according to energy consumption, transmission delay, throughput, and reliability (packet delivery ratio) under different congestion levels. The proposed scheme offered double the throughput and shortened packet delay by 20%. Furthermore, it had a longer lifetime, exceeding other schemes by approximately twofold when the traffic was not too congested. However, the gap decreases when the network becomes worse.

Conducted Emission Analysis of Induction Cooker at Frequencies of 150 kHz–30 MHz

2024-09-11T10:45:51+07:00

Induction cooker usage is predicted to replace conventional cookers due to efficiency and energy resilience advantages. These energy conservation efforts are also the government efforts in reducing the energy crisis related to the liquified petroleum gas (LPG) supply. However, household appliances, including induction cooker using inverter technology, have the potential to cause electromagnetic interference (EMI) in the form of conducted emissions, which can be interpreted as noise currents propagating along conduction paths and potentially disrupting other electronic equipments through the voltage source. Regulations related to electromagnetic interference from induction cookers are listed in the Comité International Spécial des Perturbations Radioélectriques (CISPR) 14-1:2020. This research aimed to identify the induction cooker distribution with regard to electromagnetic interference requirements, namely conducted emissions, according to CISPR 14-1:2020. The conduction emission measurement was conducted on four induction cooker brands circulating in the community (A, C, M, and P) in various cooking modes and power levels in the frequency range of 150 kHz–30 MHz, with PLN electric voltage of 220 V and frequency of 50 Hz. Measurements were performed ten times for each stage, and the six highest conduction emission values were obtained. Based on measurements in the frequency range of 150 kHz–30 MHz, it was found that the conducted emission levels in most induction cookers exceeded the CISPR 14-1:2020 standard. In the future, induction cooker components must pay more attention to regulations regarding conducted emissions to ensure that these household appliances are increasingly safe and environmentally comfortable in the electromagnetic environment.

Named Entity Recognition in Statistical Dataset Search Queries

2024-09-11T10:45:47+07:00

Search engines must understand user queries to provide relevant search results. Search engines can enhance their understanding of user intent by employing named entity recognition (NER) to identify the entity in the query. Knowing the types of entities in the query can be the initial step in helping search engines better understand search intent. In this research, a dataset was constructed using search query history from the Statistics Indonesia (Badan Pusat Statistik, BPS) website, and NER in query modeling was employed to extract entities from search queries related to statistical datasets. The research stages included query data collection, query data preprocessing, query data labeling, NER in query modeling, and model evaluation. The conditional random field (CRF) model was employed for NER in query modeling with two scenarios: CRF with basic features and CRF with basic features plus part of speech (POS) features. The CRF model was used due to its well-known effectiveness in natural language processing (NLP), particularly for tasks like NER with sequence labeling. In this research, the basic CRF and the CRF model with POS feature achieved an F1-score of 0.9139 and 0.9110, respectively. A case study on a Linked Open Data (LOD) statistical dataset indicated that searches with synonym query expansion on entities from NER in query produced better search results than regular searches without query expansion. The model's performance incorporating additional POS tagging features did not result in a significant improvement. Therefore, it is recommended that future research will elaborate on deep learning.

GSA With Factor Screening for Performance Evaluation of Transmission Line Protection Relays

2024-09-11T10:45:43+07:00

This paper presents a global sensitivity analysis with factor screening to efficiently test conventional distance relay algorithm models used as transmission line protection devices with series compensators. Various system indeterminacy parameters (factors) may affect the functional performance of the fault impedance measurement algorithm model of intelligent electronic devices, specifically the SEL-421 type distance relays. The purpose of global sensitivity testing is to determine the influence strength of individual and interacting factors on the output of the fault impedance measurement algorithm. Global sensitivity analysis, conducted through variance analysis using quasi-Monte Carlo methods, aims to compute the error in fault impedance measurement results. As an initial step, the Morris method was employed to filter out factors that did not predominantly affect relay performance, thereby reducing the computational burden of the global sensitivity analysis. Several simulated transmission line faults with series compensators and multiple factors were modeled using DIgSILENT PowerFactory. Automatic fault simulations, both before and after compensators, were developed using DIgSILENT Programming Language. The sensitivity of the relay algorithm output was tested for each simulation based on read-out voltage, fault current signals, and the values of sampled factors using both Morris and Sobol methods. The variance of the algorithm output model influenced by several factors was calculated using SIMLAB software. Fault resistance emerged as the dominant factor affecting algorithm performance, with sensitivity indices exceeding 0.9 and 0.7 for faults before and after the compensator, respectively. This technique has effectively tested the SEL-421 distance relay algorithm.

Securing RFID in IoT Networks With Lightweight AES and ECDH Cryptography Approach

2024-09-11T10:45:38+07:00

Radio frequency identification (RFID) technology integrated into the Internet of things (IoT) networks often poses security and privacy concerns due to its attack vulnerability. This research proposed a lightweight cryptographic model tailored for implementation in resource-constrained environments. The objective is to address security challenges while accommodating limited memory, power, and size requirements. A combined modified 126-bit Advanced Encryption Standard (AES) algorithm with a 256-bit elliptic curve Diffie-Hellman (ECDH) cryptographic key was utilized to develop lightweight cryptography for securing RFID data. The implementation used the Python programming language in Jupyter Notebook, with RFID operating at 13.56 Mhz. The methodology involved retrieving RFID data through additional programs and equalizing ECDH keys for encryption and decryption. Encryption and decryption testing demonstrated a high success rate, achieving an accuracy of 99.9%. The first encryption attempt required 85.125 ms, with the second attempt completed faster at 65.95 ms, showcasing improved efficiency. File encryption sizes averaged 29.875 bytes for the initial attempt and 30.1 bytes for the subsequent one. This research was limited to algorithm evaluation and had not been implemented in hardware. However, the proposed hybrid cryptography offers significant benefits for maintaining the confidentiality of RFID data within IoT environments. Rapid, efficient, and compact encryption of unique identifier (UID) data ensures enhanced security, thereby addressing critical concerns associated with RFID-enabled IoT networks.

Enhancing Learning Applications for Clinical Decision Support Through Gamification Design

2024-09-11T10:45:32+07:00

In the learning process, learning media supports the delivery of educational material to students. Implementing gamification enriches educational media and enhances student engagement. This research developed e-learning media for clinical decision support systems (CDSS) material. In the medical field, CDSS is well established and integrated into education as part of the curriculum. CDSS is a computerized system aiding decision-making in diagnosing and treating diseases. In the educational domain, CDSS courses are offered to clinical and nonclinical students. The application was developed using a feature-driven development method and incorporated gamification elements like rewards, challenges, and leaderboards through MDA frameworks. The development process began with the overall design of the application and the determination of learning objectives, followed by the integration of gamification elements aligned with the application’s design. The development included application design, gamification elements, functionality, usability, and user experience testing aspects. The final product is an Android application. Functionality testing using black box testing achieved 100% suitability. User testing was conducted using the system usability scale (SUS) and the user experience questionnaire (UEQ). The results showed an average SUS score of 74.9, indicating good usability, and the UEQ score was rated “Excellent.” These findings demonstrate that incorporating gamification in CDSS learning enhances the application’s supporting features. Gamification elements such as rewards, challenges, and leaderboards are expected to attract learners and encourage active participation in the learning process. CDSS learning applications have the potential to increase motivation and engagement, creating an interesting and effective learning experience for individuals from various backgrounds.

Developing a Low-Cost Syringe Pump as a Support System for Electrospinning

2024-09-11T10:45:29+07:00

Electrospinning is one of the techniques used to fabricate nanofibers. The syringe pump is one of the main parts of electrospinning, responsible for injecting the solution into the chamber with high precision. The syringe pump has a simple operating system, but it has a high price on the market. Its high price has been one of the obstacles for research groups in the fabrication of nanofibers. Hence, this research aimed to solve the problem of expensive syringe pumps by developing a low-cost syringe pump using affordable components. This research utilized methods from a literature study of syringe pump design, including the manufacturing and assembly of both hardware and software components. It also involved testing the calibration, optimization, and performance of the syringe pumps. An analysis of each stage was carried out until a conclusion was obtained. This syringe pump built in this research used a NEMA 17 stepper motor and TB6600 motor driver to control the flow rate. The total cost to develop this low-cost syringe pump was IDR632,300. Test and calibration were measured at a flow rate ranging from 1 mL/h to 5 mL/h using distilled water, resulting in an accuracy value of 96.7% and a precision value of 95.0%. Further research should utilize gear wheels to reduce the load of the motor stepper so as to prevent prolonged heated conditions. The results of this research can also be used as insight for researchers to develop another low-cost tool in other research fields.

The Development of Intelligent Web for Rural Social E-Learning

2024-09-11T10:45:24+07:00

Social media technology affects the learning paradigm change towards social media-based learning, known as social e-learning. Social e-learning regards a person as a center of learning, dubbed people-centered learning. Here, people are encouraged to interact or communicate with others and produce their learning content. This work attempted to provide a solution model for rural e-learning social learning empowered with intelligent web technology. The proposed social e-learning includes several modules for development, such as personal space, collaboration space, and communication space modules. It also leverages intelligent web technologies currently implemented in today’s social media applications, such as article search, article recommendations, friendship recommendations, and document classification. In the searching module, the PageRank method was used to calculate the relevance score to determine the rating of the documents or articles. The similarity-based element calculation method was utilized to create articles’ suggestions and recommendations. The naïve Bayes algorithm, decision tree, and neural network were compared to find the best solution for article classification in agriculture, fisheries, animal husbandry, and plantations. When comparing these three algorithms, the result showed that the neural network was the most accurate classification, reaching 95.2% accuracy. A clustering algorithm, namely robust clustering using links (ROCK), was utilized for rural friendship recommendation. Thus, these algorithms (the PageRank, the similarity-based element, neural network, and the ROCK) were suitable and recommended for supporting intelligent web paradigms in social e-learning applications.

Impact Analysis of NZE Scenarios on National Energy Supply Using LEAP

2024-09-11T10:45:20+07:00

The achievement of the national energy supply target based on new and renewable energy (NRE) by 2025, as stated in the National Energy Policy, is still far below expectations. This shortfall is due to the continued fossil energy dominance in all sectors. To achieve net zero emission (NZE) targets by 2060, systematic and consistent transitions from fossil fuels to NRE are essential. The fossil energy utilization (domestic and imported) is expected to decline, while the substitution with NRE will increase. This study aimed to provide a forecast analysis of national energy supply and utilization across various sectors, including household, industry, power generation, transportation, and commercial sectors, until 2060. The analysis used energy modeling simulations with business as usual (BAU) and NZE scenarios, conducted using the Low Emission Analysis Platform (LEAP) software. LEAP is an integrated, scenario-based energy model used to determine energy demand, production, and resource extraction across all economic sectors. The simulation results for the NZE scenario indicate significant reductions in fossil energy usage across all sectors compared to the BAU scenario, with an increase in NRE utilization, especially in the power generation sector. By 2060, domestic coal, natural gas, fuel oil, and liquefied petroleum gas supplies are projected to decrease by 81%, 74%, 87%, and 84%, respectively; meanwhile, the demand for petroleum remains unchanged. Overall, the supply of NRE under the NZE scenario is expected to grow by an average of 9% per year from 2019 to 2060, amounting to 2.3 times the supply in the BAU scenario.

Factors Affecting Personal Information Sharing: Small-Scale Sample Analysis on Social Media

2024-09-11T10:45:15+07:00

In the contemporary social landscape, the widespread use of social media, such as platforms like TikTok, Instagram, and YouTube, has become a prominent trend in various circles of society, especially in Indonesia. As the number of users on these platforms increases, concerns regarding user security and privacy also increase. Data breaches in 2021 affecting 235 million users on Instagram, TikTok, and YouTube underscored the importance of researching the multifaceted dynamics around privacy concerns, levels of trust, risk awareness, and user behavior patterns related to sharing personal information on social media platforms. This research aimed to address this critical issue by introducing a research model developed based on relevant hypotheses from previous research. The sample used in this research consisted of social media users in Indonesia. Methodologically, this research used sophisticated structural equation modeling (SEM) tools for hypothesis testing and confirmatory factor analysis (CFA) to validate the efficacy of existing research models. These findings indicated that users’ trust, awareness, privacy concerns, and behavioral intentions significantly and positively influence the tendency to share personal data on social media platforms. This research provides valuable insights into the complex interactions between factors influencing user behavior in social media privacy, thereby offering implications for academia and practical applications.

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2024-09-11T13:58:05+07:00

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2024-09-11T14:04:01+07:00