https://journal.ugm.ac.id/v3/JNTETI/issue/feedJurnal Nasional Teknik Elektro dan Teknologi Informasi2024-12-20T22:15:16+07:00Sekretariat JNTETIjnteti@ugm.ac.idOpen Journal Systems<p><strong><img style="display: block; margin-left: auto; margin-right: auto;" src="/v3/public/site/images/khanifan/HEADER_JNTETI_2020_1200x180_Background_baru_tanpa_list1.jpg" width="600" height="90" align="center"></strong></p> <p><strong>Jurnal Nasional Teknik Elekto dan Teknologi Informasi</strong> is an international journal accommodating research results in electrical engineering and information technology fields.<br><br><strong>Topics cover the fields of:</strong></p> <ul> <li class="show">Information technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Data Communication and Networking, Computer Graphics, Virtual Reality, Data and Cyber Security.</li> <li class="show">Power Systems: Power Generation, Power Distribution, Power Conversion, Protection Systems, Electrical Material.</li> <li class="show">Signal, System and Electronics: Digital Signal Processing Algorithm, Robotic Systems, Image Processing, Biomedical Engineering, Microelectronics, Instrumentation and Control, Artificial Intelligence, Digital and Analog Circuit Design.</li> <li class="show">Communication System: Management and Protocol Network, Telecommunication Systems, Antenna, Radar, High Frequency and Microwave Engineering, Wireless Communications, Optoelectronics, Fuzzy Sensor and Network, Internet of Things.</li> </ul> <p><strong>Jurnal Nasional Teknik Elekto dan Teknologi Informasi is published four times a year: February, May, August, and November.<br></strong><strong><br>Jurnal Nasional Teknik Elektro dan Teknologi Informasi has been accredited by Directorate General of Higher Education, Ministry of Education and Culture, Republic of Indonesia, </strong>Number 28/E/KPT/2019 of September 26, 2019 (<strong>Sinta 2</strong>), <strong>Vol. 8 No. 2 Year 2019 up to Vol. 12 No. 2 Year 2023<br></strong><strong><br>Publisher<br></strong>Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada<br>Jl. Grafika No 2. Kampus UGM Yogyakarta 55281<br>Website : <a href="https://jurnal.ugm.ac.id/v3/JNTETI">https://jurnal.ugm.ac.id/v3/JNTETI</a><br>Email : jnteti@ugm.ac.id<br>Telephone : +62 274 552305</p>https://journal.ugm.ac.id/v3/JNTETI/article/view/10808The Exploration of Student Emotion Experience and Learning Experience in E-learning Platform 2024-11-21T16:11:12+07:00Fitra Bachtiarfitra.bachtiar@ub.ac.idRiza Setiawan Soetedjoriz_stwn@student.ub.ac.idJoseph Ananda Sugihdarmajosephananda88@student.ub.ac.idRetno Indah Rokhmawatiretnoindahr@ub.ac.idLailil Muflikhahlailil@ub.ac.id<p>Previous studies have shown that emotion is crucial in student learning. However, most studies in the e-learning environment have yet to consider emotion as part of learning that could lead to successful learning. Thus, this study explored the relationship between student emotion state, emotion sequences, and student learning experience. A preliminary data collection was conducted to explore the relationship between emotional experience and student learning experience, which involved 16 students. Students were asked to learn a programming subject in an e-learning environment. E-learning is designed to store the students' emotional experience and activity during learning. The sequential pattern mining technique was used to extract the data, exploratory data analysis was conducted to visualize the emotional trajectory during the learning process, and regression analysis was used to explain the relationship between students' emotional learning experiences. The results showed that emotional experience might affect student experience in learning. In one-sequence emotion, all emotion states contributed to the learning experience with p-values < 0.01 except for neutral and disgust with p-values < 0.05. The one-sequence emotion model shows R-squared = 0.585; Adj. R-squared = 0.734; F-statistic = 6.920; Prob (F-statistic) = 0.00702. Meanwhile, in two-sequence emotion, none of the emotion sequences contributed to the student learning experience. Lastly, three-sequence emotion models also showed that most sequences did not influence student learning experience. The only sequence of emotions that influenced the student learning experience was surprise-neutral-surprise. These results suggest that emotion should be considered in learning design as it can influence student experience.</p>2024-11-21T14:34:04+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/12765Optimizing Solar Panel Efficiency: Integration of Dual Axis Solar Tracking and Reflectors2024-11-22T09:41:36+07:00Alvin Rinaldi Wiharja6151801022@student.unpar.ac.idLevin Halimhalimlevin@unpar.ac.idFaisal Wahabfaisal.wahab@unpar.ac.id<p>Solar panels have relatively low efficiency, but their performance can be enhanced by a tracking system directing the panels perpendicular to the light source and adding reflectors to capture more sunlight. The dual-axis solar tracking method, using two linear actuators and optimized by fuzzy logic, efficiently positions solar panels for maximum sunlight exposure. This research aimed to improve the overall efficiency of solar panels by integrating reflectors with a dual-axis solar tracking system optimized by fuzzy logic. Specifically, this research tested various reflectors to determine the most significant efficiency improvement. This research consisted of two tests: a tracking test and a reflector test using a halogen lamp. The tracking test was conducted by positioning the light in four different positions. The light sensor data were obtained before and after the solar tracking, indicating that the tracking was successful. All these tests were conducted with the light source radiation of 1,168 W/m2. This research concluded that the tracking system effectively positioned the solar panels toward the light source, with the tracking time ranging from 12 to 16 s, depending on the position. Aluminum foil is the most cost-effective reflector, priced at IDR5,341 per 1% increase in efficiency, compared to mirrors at IDR20,204 per 1% and reflective tape at IDR48,034 per 1%. In conclusion, the integration of aluminum foil reflectors and a dual-axis solar tracking system, optimized by fuzzy logic, significantly improves the efficiency of solar panels, which is both cost-effective and efficient.</p>2024-11-21T15:30:08+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/9403Improving the Android Geopositioning Accuracy Using Graham Scan Algorithm and Moment Centroid2024-11-22T09:29:53+07:00Rachmat Wahid Saleh Insanirachmat.wahid@unmuhpnk.ac.idSuciptosucipto@unmuhpnk.ac.id<p class="JNTETIIntisari"><span lang="EN-US">Geopositioning is the process of determining or estimating the geographic position of an object through the global positioning system (GPS). The calculations in geopositioning require measurements of distances or angles relative to known reference positions. In Android devices, achieving accuracy, speed, and power efficiency in geopositioning with GPS, cellular networks, and Wi-Fi can be challenging. This research aimed to improve the accuracy of the geopositioning process for cellular networks on Android devices through polygon triangulation using the Graham scan algorithm and determining a moment centroid for the improved estimation of geolocation data. The geolocation data were collected using an Android smartphone with a cellular network and disabled Wi-Fi. A filtering phase on the coordinates was established to obtain the closest distance coordinates from the other. The distances between each pair of coordinates were calculated using the haversine formula, and then the average distance of all pairs was calculated. Then, a polygon was formed by arranging the coordinates in a sequence, which was achieved using the Graham scan algorithm. After obtaining a set of triangles from the polygon triangulation results, the moment centroid of each formed triangle was determined. The centroid, as a result, was compared with another centroid calculation, the Lagrange interpolation polynomial. Based on the results obtained from quantifying the accuracy and precision using average Euclidean error (AEE) and root mean square error (RMSE), the coordinates derived from the moment centroid were more accurate and precise than the Lagrange interpolation polynomial.</span></p>2024-11-22T09:17:15+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/6963Performance of Used and Aged Glass Insulators Against Basic Insulation Level (BIL)2024-11-25T13:42:36+07:00Naufal Hilmi Fauzannaufalhilmifauzan@ugm.ac.idAdhimas Daffa Kurniaadhimasdaffa2019@mail.ugm.ac.idPrayudi Efendiprayudiefendy28@mail.ugm.ac.idPrasetyohadiprasetyohadi@ugm.ac.idDaryadidaryadi@ugm.ac.id<p>High-voltage insulators are crucial for ensuring the reliability and safety of electrical systems operating under high voltage. Their primary function is to electrically separate phase conductors from each other and the ground. In designing electrical power systems, the basic insulation level (BIL) is a key parameter that must not be neglected, representing the maximum voltage the system can endure before a flashover occurs on the insulator. Besides voltage endurance, insulators are required to withstand environmental factors like temperature, humidity, and pollution, which can considerably affect their performance. This research examined the performance of glass insulators used at the Adipala power plant under diverse environmental conditions, comparing the outcomes against the BIL standard. Four testing scenarios were employed: optimal conditions, wet conditions, polluted conditions, and polluted insulators in humid environments. Findings indicate that wet conditions and the combined presence of pollution and humidity exert the most substantial impact on insulator performance. Under clean conditions with exposure to rain, insulator performance degraded by 19% to 25%. In contrast, when subjected to pollutants with an equivalent salt deposit density (ESDD) of 0.113816 mg/cm² and a non-soluble deposit density (NSDD) of 1.309962 mg/cm² at 90% humidity, performance diminished by 41% to 53%, falling significantly below the BIL threshold.</p>2024-11-25T13:41:40+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/12867Comparison of Mobile Transaction Security using NFC and QR Codes2024-12-20T10:26:31+07:00Lucia Nugraheni Harnaningrumningrum@utdi.ac.idKristoforus Nanda Mahardhiankristoforus.nanda@students.utdi.ac.id<p class="JNTETIIntisari"><span lang="EN-US">Mobile device transactions have become commonplace today. Quick-response (QR) codes and near-field communication (NFC) are popular cashless and contactless payment methods. These two payments have their characteristics. NFC payments use secure elements that encrypt credential data to ensure safe transactions. In contrast, QR code payments transmit data in its original form without encryption. Existing data are sent between devices in the form of original data. Given the extensive adoption of these methods, it is imperative to secure transaction data to prevent theft and misuse. It is necessary to know and compare the security level of each transaction and provide the best recommendations. This study undertook a comparative analysis of the security and performance of NFC and QR code-based mobile payment models. The study found that NFC transactions required 1,074 ms for encryption, while QR code transactions took 5.9359 ms. The entropy value, indicating data randomness, was 3.96 for NFC and 3.23 for QR codes. The P value, representing statistical significance, was 0.45 for NFC and 0.069 for QR codes. Both payment methods demonstrated acceptable levels of safety, with processing times and data randomness within satisfactory ranges. However, the analysis concludes that NFC transactions offer superior performance in terms of processing time and data security compared to QR code transactions.</span></p>2024-11-28T10:38:20+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/14440Classification of Emotions in English Texts Using the Ensemble Bagging Approach2024-12-13T19:02:27+07:00Erfian Juniantoerfian.ejn@ars.ac.idMila Puspitasarimileuups14@gmail.comSalman Ilyas Zakariasalmanzakaria38@gmail.comToni Arifintoni.arifin@ars.ac.idIgnatius Wiseto Prasetyo Agungwiseto.agung@ars.ac.id<p>This study highlights the importance of emotion classification in English text, particularly in human interaction on social media, which often involves unstructured data. Emotions play a crucial role in communication; a better understanding of these emotions can aid in analyzing user behavior. The main objective of this research is to enhance accuracy, recall, precision, and F1-score in emotion classification by applying an ensemble bagging approach, combining the naïve Bayes, logistic regression, and k-nearest neighbor (KNN) algorithms. The methodology used included data collection from various sources, followed by data cleaning and analysis using text mining and machine learning techniques. The collected data were then analyzed to detect emotions such as anger, happiness, sadness, surprise, shame, disgust, and fear. Performance evaluation was conducted by comparing the results of the ensemble bagging method with individual algorithms to measure its effectiveness. The findings reveal that the logistic regression method achieved the highest accuracy at 98.76%, followed by naïve Bayes and KNN. This ensemble method overcame the limitations of each individual algorithm, enhancing overall classification stability and reliability. These findings provide valuable insights into text-based emotion analysis techniques and demonstrate the potential of ensemble methods to improve classification accuracy. Future research directions can explore additional ensemble techniques and optimize model complexity for improved performance in emotion analysis across broader datasets.</p>2024-11-28T11:29:58+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/12510Reliability Analysis of 20 kV Electricity Distribution System on CWRU Feeder2024-12-19T10:29:07+07:00Tasma Sucitatasmasucita@upi.eduMaman Somantrimsomantri@upi.eduDiki Fahrizaldiki15@upi.eduMia Agistamiaagista@upi.edu<p>The electricity demand continues to rise alongside population growth, making the reliable distribution of electrical energy to consumers essential. The reliability of the 20 kV electricity distribution system on the CWRU feeder at the National Electricity Company (Perusahaan Listrik Negara, PT PLN) Customer Service Unit (Unit Layanan Pelanggan, ULP) Pelabuhan Ratu Area Surade was evaluated. The primary purpose was to analyze key reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average service availability index (ASAI), and average service unavailability index (ASUI), and to assess the economic impact of disruptions on consumers. Methodologies involved collecting and analyzing field data from PT PLN (Persero) ULP Pelabuhan Ratu’s CWRU feeder, utilizing quantitative reliability calculations and qualitative observations to identify internal and external factors affecting system reliability. The results showed that the SAIFI value reached 52.077 times/customer/year, and the SAIDI value was 99.400 hours/customer/year, classifying the system as unreliable based on PLN standards (<em>standar </em>PLN, SPLN) 68-2:1986. However, the CAIDI value of 1.813 hours/times/year indicated that the system response time was within acceptable limits. The availability of electricity, with an ASAI of 99.828% and an ASUI of 0.172%, was deemed satisfactory. Internal factors contributed to 10.47% disturbances. In contrast, external factors (weather and tree fall) accounted for 48.84%, and the remaining 40.69% were from unknown causes. Economic losses were calculated at Rp52,432.50/customer because these interruptions. More frequent maintenance and the implementation of additional protective measures are recommended to enhance reliability.</p>2024-11-28T13:52:59+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/8753Small-Scale Wind Turbine Selection Based on Wind Energy Potential Analysis Using Windographer2024-12-20T14:13:27+07:00Dwi Risdiantodwir004@brin.go.idNurry Widya Hestyjnteti@ugm.ac.idToha Zakyjnteti@ugm.ac.idRudi Purwo Wijayantojnteti@ugm.ac.idAgustina Putri Mayasarijnteti@ugm.ac.idArio Witjaksojnteti@ugm.ac.id<p>Wind energy is a renewable resource with significant potential for generating electricity, particularly in small islands not connected to the State Electricity Company (Perusahaan Listrik Negara, PLN) grid. This study estimated the electrical energy production of small-scale wind turbines using Windographer software, based on an analysis of wind energy potential utilizing the Weibull distribution. The research focused on selecting small-scale wind turbines tailored to the wind energy potential and electricity needs of Miangas Island, North Sulawesi. The estimation of electrical energy production was conducted using the frequency distribution of wind speeds recorded hourly at a height of 50 m over the 2011–2020 period. The analysis encompasses average wind speed, wind direction distribution, Weibull distribution, average wind power density, and the annual estimation of electrical energy production. The results indicated that Miangas Island had an average annual wind speed of 5.5 m/s, with a wind speed frequency distribution of 15% and an average wind power density of 160.9 W/m². Simulations based on the analyzed wind potential demonstrated that small-scale wind turbines with capacities of 50 kW, 35 kW, and 10 kW could generate 98,434.49 kWh/year, 75,738.78 kWh/year, and 15,875.48 kWh/year, respectively. Considering the energy supply-demand balance, a 35-kW wind turbine is identified as the optimal choice to meet the annual electrical energy demand of Miangas Island, which is approximately 25,550 kWh.</p>2024-11-28T14:30:01+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/13289MULTI-WD: Multilingual Completion Tool for Wikidata Data2024-12-20T22:15:16+07:00Mohammad Yanimyani0703@gmail.comLilyan Arhatia Agustinelilyan0800@gmail.comIryantoiryanto@polindra.ac.id<p>Wikidata, a rapidly expanding knowledge graph (KG), owes its growth to two primary factors. First, Wikidata allows open access and editing by anyone. Second, it offers a multilingual feature that enables data entities to be accessed in various languages worldwide. However, the issue of incomplete information across multiple languages remains a significant challenge. For instance, the description of the entity “bada reuteuk” (ID: Q100606305) is currently available only in Indonesian as “a traditional food in Indonesia,” but it lacks descriptions in other languages. Consequently, these data are not accessible or recognizable in languages other than Indonesian. The system incorporates two primary features: language profiling and data translation. Language profiling, implemented using SPARQL queries via the Wikidata API, provides an overview of the multilingual status of Wikidata entities. For data translation, the system utilized the Translated Labs library, chosen for its open access, cost-free availability, and high-quality translation outputs. The translated results are subsequently saved into Wikidata. System evaluation involved five respondents from the Wikidata community, using a black-box testing approach. Results demonstrated that MULTI-WD’s core functionalities—including category selection, data statistics display, translation, and data updates—achieved 100% operational success. Furthermore, the tool enhanced data translation efficiency by up to 300% compared to manual translation directly through the Wikidata interface.</p>2024-11-28T14:51:17+07:00Copyright (c) https://journal.ugm.ac.id/v3/JNTETI/article/view/12648Regulasi Tegangan Arus Searah Turbin Angin dan Baterai untuk Microgrid2024-11-29T15:40:33+07:00Sidiq Budi Perkasasidiqbudi22@gmail.comMochammad Factajnteti@ugm.ac.idIwan Setiawanjnteti@ugm.ac.id<p>In the future, renewable energy will become a source of electricity to replace fossil energy. Direct current (DC) microgrids can operate independently without being connected to the utility grid. A DC microgrid system configuration can consist of a wind turbine, permanent magnet synchronous generator (PMSG), rectifier, DC-DC boost converter, bi-directional DC-DC converter, battery, and MPPT. Systems with complex components have challenges in maintaining stable DC voltage when there are changes in load or changes in wind speed. The work carried out in this paper focuses on improving the performance of DC microgrids by adding voltage control to meet the load demand and maintain constant DC voltage stability. The tests in this paper were carried out with three test conditions. In the first condition, the wind turbine can supply the load and battery, in the second condition, the wind turbine and battery supply the load, while in the third condition, the load is completely supplied by the battery. The test results show the performance of the system which is designed to meet load demands and to charge the battery. Likewise, when the wind speed is low, the generating capacity of the wind turbine cannot meet the load demand, so the battery and wind turbine can increase the load. The designed system succeeded in maintaining the DC bus working voltage stable at the level of 400 V. There was instability in the DC bus voltage of approximately 1%, this value is still within the tolerance limit that can be accepted by the load.</p>2024-11-29T15:40:32+07:00Copyright (c)