Jurnal Nasional Teknik Elektro
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The Effect of Electricity Supply Interruptions on Small Business Productivity in West Sumatra
Full text linkThis research examines the impact of interruptions in electricity supply on the production of small and medium enterprises in West Sumatra from 2014 to 2021. The data used in the research was obtained from the Ministry of Trade and Industry of West Sumatra, including the production variables, employment, investment, and other variables that influence the production activities. A regression equation connecting production factors and production levels is formulated. Furthermore, another regression equation is also formulated by considering the electricity interruption factor, namely the SAIDI index on production levels. The effect of electrical power interruptions is then evaluated by comparing the two equations. The research results show that the most significant production loss occurred in 2019, 16.07 hours/year, while the most negligible loss occurred in 2015, 6.53 hours/year. Trend data collected during the research period regarding loss conditions and interruption parameters shows that electricity disturbances do not have a linear impact on production losses. The research also shows that electric power does not significantly impact the production activities of small and medium enterprises in West Sumatra.Perlu adanya analisis hubungan antara jumlah produksi usaha kecil dan menengah dengan gangguan. Dalam penelitian ini metode analisis dalam menentukan hubungan jumlah produksi terhadap gangguan adalah dengan metode regresi linier. Metode regresi linier sebagai alat untuk menganalisis sejauh mana hubungan antara jumlah produksi UKM dan faktor-faktor yang harus diperhitungkan seperti rata-rata investasi, tenaga kerja dan rata-rata bahan baku terhadap indikator gangguan. Metode regresi menghasilkan perkiraan jumlah produksi dengan dua kondisi yang dialami yaitu tanpa gangguan dan gangguan. Perbedaan antara kedua kondisi tersebut mengakibatkan perubahan jumlah produksi ketika terjadi interupsi. Hal ini terlihat dari peningkatan jumlah produksi pada saat terjadi gangguan yang terlihat pada tahun 2014 mengalami peningkatan produksi sebesar Rp 54.452.000/tahun dengan durasi gangguan 9,43 jam/tahun, pada tahun 2015. terjadi peningkatan produksi sebesar Rp13.813.000/tahun dengan durasi interupsi 6,53 jam/tahun, tahun 2017 terjadi peningkatan produksi sebesar Rp163.621.000/tahun dengan durasi interupsi 19,91 jam/tahun dan tahun 2019 terjadi peningkatan di produksi sebesar Rp 195.655.000/tahun dengan durasi interupsi 16,07 jam/tahun. Dan mengalami penurunan jumlah produksi pada saat terjadi gangguan dapat dilihat pada tahun 2016 terjadi penurunan produksi sebesar Rp48.994.000/tahun dengan durasi gangguan 5,48 jam/tahun, pada tahun 2018 terjadi penurunan produksi sebesar Rp167.096.000/tahun dengan durasi sebesar gangguan sebesar 13,21 jam/tahun, pada tahun 2020 terjadi penurunan produksi sebesar Rp 100.454.000/tahun dengan durasi gangguan sebesar 9,18 jam/tahun dan pada tahun 2021 terjadi penurunan produksi sebesar Rp 110.997.000/tahun dengan durasi gangguan sebesar 8,95 jam/tahun. Berdasarkan perhitungan, hubungan antara gangguan dengan produktivitas usaha kecil dan menengah mengalami hubungan yang tidak signifikan. Hal ini dikarenakan selain terganggunya energi listrik terhadap produktivitas usaha kecil menengah, terdapat beberapa faktor penunjang produksi yang mempengaruhi produktivitas usaha kecil menengah
A GSM-Based Fault Detection on Overhead Distribution Lines
Full text linkPower distribution in Ghana is managed by the Electricity Company of Ghana (ECG) which is responsible for ensuring accessibility of electricity to consumers. One of the challenges that affect the effective operation of ECG is the slow response to faults on the overhead distribution lines. Fault detection on the distribution lines is a very tedious activity but a necessary procedure to ensure efficient power distribution to consumers. This paper seeks to design a system that can detect faults, the type of faults and their location before they cause any casualties to transformers and other power system equipment. This would replace the primitive method of patrolling and manual inspection of faults currently done by the Electricity Company of Ghana (ECG). This objective was achieved using a GSM-based system on an Arduino platform and ATmega 328P microcontroller to locate the occurrence of faults efficiently. Faults are introduced into the system by triggering the type of fault on the Arduino platform which opens the corresponding relay of the line fault. The opening of this relay sends a signal to the microcontroller and a corresponding LED which switches to display the type of fault. The microcontroller then communicates to the GSM module which displays the said fault and location on a display screen with the help of a virtual terminal. This system was tested under the various unsymmetrical faults to show the efficiency of the system using C++ programming. The simulation shows that the system offers a fast fault response time
“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD
Full text linkA home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.Sebuah sistem keamanan rumah adalah sesuatu yang setiap pemilik rumah harus memperhatikan. Kejahatan seperti pencurian seringkali membahayakan pemilik rumah. Oleh karena itu diperlukan suatu alat yang dapat memonitoring secara otomatis dari jarak jauh untuk melindungi rumah. Sistem yang dikerjakan pada artikel ini adalah sistem penginderaan jauh berbasis webcam dan raspberry PI 3. Metode yang digunakan pada sistem penginderaan ini menggunakan metode haar cascade classifier. Hasil yang diperoleh dari sistem penginderaan jauh ini adalah untuk implementasi sistem pada dataset pemilik rumah diperoleh persentase sebesar 98%, sedangkan untuk dataset citra non pemilik rumah didapatkan persentase sebesar 96%. Dari hasil penggunaan sistem penginderaan jauh berbasis webcam dan raspberry pi3 menggunakan metode Haar Cascade Classifier dapat diterapkan dengan baik dan rata-rata error sebesar 97%
English
No full textEvery year as the world's population increases, land is getting full and not enough to be used in agriculture. Various types of technological developments have been abused to grow crops. The purpose of this research is to design a smart farm agriculture system by planting without soil and utilizing technological advances in the city. Smart farming is a technology in agriculture with the Internet of Things (IoT) to help farmers public people get data easy from the garden. Hydroponics is soil-less farming that uses minerals or fertilizers dissolved in water. Nutrient Film Technique (NFT) is one of the hydroponic methods using a thin layer on the flow of nutrients through pipe installations. Pumps that require continuous electrical power can harness solar power plants as an energy source. Off-grid system on solar panel plan produce electrical energy according to the required power without being connected to state electricity network. The design applies a multi-sensor system that includes nutrient sensors and pH sensors, as well as automatic solution pumps, temperature PZEM-004T sensors, and a data logger that collects data and connected to internet server with visual on app Thinger.io as monitoring platform. The results, pH is ranging from 6.5 to 7.5. The TDS sensor testing resulted in a 0.313% pH sensor error with an accuracy of 99.69%, and the TDS sensor testing resulted in a 1.18% TDS sensor error with an accuracy of 98.82% also the agriculture farm system testing, the testing in 1 until 2 weeks showed an error percentage of 38% in the pH solution and 38.73% in the nutrient solution. In addition, the solar panels generated a total power output of 1700.56 W, while the total load demand was 1165.74 W. Based on the testing results, the smart farming system can monitor nutrient and PH solution levels, the automatic pump controls a stable solution, and the power sourced from PLTS can supply the pump properl
Studi Autoencoder Deep Learning pada Sinyal EKG
No full textArrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram (ECG) is commonly employed, as it can record the heart's electrical signals. However, ECGs may encounter interference from sources like electromagnetic waves and electrode motion. Several researchers have investigated the denoising of electrocardiogram signals for arrhythmia detection using deep autoencoder models. Unfortunately, these studies have yielded suboptimal results, indicated by low Signal-to-Noise Ratio (SNR) values and relatively large Root Mean Square Error (RMSE). This study addresses these limitations by proposing the utilization of a Deep LSTM Autoencoder to effectively denoise ECG signals for arrhythmia detection. The model's denoising performance is evaluated based on achieved SNR and RMSE values. The results of the denoising evaluations using the Deep LSTM Autoencoder on the AFDB dataset show SNR and RMSE values of 56.16 and 0.00037, respectively. Meanwhile, for the MITDB dataset, the corresponding values are 65.22 and 0.00018. These findings demonstrate significant improvement compared to previous research. However, it's important to note a limitation in this study—the restricted availability of arrhythmia datasets from MITDB and AFDB. Future researchers are encouraged to explore and acquire a more extensive collection of arrhythmia data to further enhance denoising performance.Aritmia merujuk pada ketidakaturan dalam irama jantung, melibatkan gangguan dalam asal, frekuensi, dan regulasi impuls listrik. Elektrokardiogram (EKG) adalah metode yang banyak digunakan untuk mengukur aktivitas listrik jantung dan menganalisis kondisi jantung pasien. Mendeteksi dan memprediksi penyakit jantung, terutama aritmia, dibantu dengan mengidentifikasi perubahan morfologis dalam sinyal EKG, yang mengindikasikan detak jantung yang tidak teratur. Bahkan perubahan kecil dalam pola EKG dapat menyebabkan aritmia, yang mengakibatkan irama jantung yang tidak teratur, gangguan pada konduksi otot jantung, nyeri dada, kelelahan, dan bahkan kehilangan kesadaran. Penelitian tentang denoising aritmia menggunakan model deep autoencoder menghadapi tantangan dalam mengidentifikasi model Deep Autoencoder yang paling efektif yang dapat mencapai nilai Signal-to-Noise Ratio (SNR) dan Root Mean Square Error (RMSE) yang optimal dalam denoising sinyal aritmia. Studi ini menggunakan model denoising autoencoder dengan lapisan, khususnya Deep LSTM Autoencoder, untuk efektif denoising sinyal EKG. Kinerja model dalam denoising sinyal EKG dievaluasi berdasarkan nilai SNR dan RMSE yang dicapai. Hasil pengujian denoising menggunakan algoritma Deep LSTM Autoencoder, nilai SNR dan RMSE terbaik untuk dataset AFDB adalah 56,16 dan 0,00037, masing-masing. Sedangkan untuk dataset MITDB, nilai-nilai yang sesuai adalah 65,22 dan 0,00018. Keunggulan dari penelitian ini adalah nilai SNR dan RMSE yang dicapai secara signifikan lebih baik dibandingkan dengan penelitian lain. Namun, batasan dari penelitian ini adalah keterbatasan jumlah dataset aritmia yang tersedia dari MITDB dan AFDB. Saran penulis untuk peneliti masa depan adalah untuk menjelajahi dan mendapatkan kumpulan data aritmia yang lebih besar untuk lebih meningkatkan kinerja denoising
Solar Panel Efficiency Improvement through Dual-Axis Solar Tracking with Fuzzy Logic and Water Treatment Techniques
Full text linkIndonesia's heavy reliance on non-renewable energy sources, such as fossil fuels and other resources obtained from mining, poses sustainability challenges. Solar panels, which are environmentally friendly and renewable energy alternatives, are designed to convert solar energy into electricity, and they have shown room for improvement in their efficiency. One method to enhance its efficiency is the utilization of dual-axis solar tracking, employing linear actuators for control over both horizontal and vertical panel movements. In addition, solar panels frequently experience efficiency losses as a result of high working temperatures when exposed to sunlight. The use of water treatment techniques may help address this problem. In this research, the two-axis solar tracking approach with water treatment methods were combined to achieve greater efficiency and boost energy production. A notable increase in solar panel efficiency was seen subsequent to the design, implementation, and testing of the proposed system, resulting in a notable rise in power output. Combining the two-axis solar tracking approach with water treatment methods produced solar panels with a 7.46% efficiency and a 17.77% power increment. Dual-axis solar tracking and combined with water treatment could significantly increase solar panel efficiency, which will ultimately lead to environtmentally clean renewable energy production increment
Enabling Guardian Angels: Designing and Constructing a Wireless Nurse CallSystem with IMU-Based Fall Detection for Enhanced Patient Safety
Full text linkFalling poses a significant health concern across all age groups, with particular severityamong the elderly. Hospitalized patients, in particular, are vulnerable to injuries andevendeath due to falls. While patient supervision is essential for fall prevention, constant proximitybetween patients and healthcare staff is not always feasible. To tackle this challenge, thisstudy aimed to develop a solution that enables immediate assistance for patients who aredistant from the nurse call button when a fall occurs.The study employed the IMU sensor,which combines an accelerometer and a gyroscope. This sensor served as a transmitter todetect gravity acceleration and magnitude when afall event takes place. Thedata obtainedfrom the IMU sensor were further processed using an Arduino Uno microcontroller. Thesensor was integrated into a belt worn around the waist of the participants, who performedvarious movements such as falling facing down, falling up, falling to the right, falling to theleft, standing then sitting, and sitting then standing.The experimental tests yielded compellingresults, with all trials achieving an accuracy rate of 81.7%. The accuracy was determined byanalyzing the confusion matrix, which enabled accurate calculations.The utilization of thisinnovative tool significantly reduces the risk of patients experiencing detrimental outcomesfollowing falls by promptly notifying medical personnel, even when they aredistant from thenurse call button. Moreover, the implementation of this tool enhances overall safety forhospitalized patients, especially those at a high risk of falling. Future research can explore theintegration of additional sensors or the development of more sophisticated algorithms tofurther enhancethe accuracy and efficacy of this tool
Infaq Sterilization Box with UV and Ozone (BIUZ)
Full text linkThe COVID-19 pandemic that has occurred to date has resulted in the loss of many lives. This is due to the ease with which the COVID-19 virus spreads. According to the latest research published by the WHO, the virus can spread through the medium of objects, one of the easies object to spread virus is money. The spread of the COVID-19 virus can be done through money transactions that have previously been used by people infected by the virus. This is because COVID-19 virus can survive for more than 72 hours. To prevent this, it is necessary to sterilize so that the virus in the money can be neutralized. The technology that can be used for disinfection in this tool is Ultra Violet (UV) light and Ozone Generator. Many studies have shown that UV rays and ozone gas (O3) are able to kill viruses that are on the surface of objects. The ability of UV rays and ozone gas (O3) can kill viruses in money because UV rays and ozone gas (O3) have radiation that is quite harsh, so that if exposed to human skin continuously it can cause damage to skin tissue. In this study, to overcome this problem, a device that is automatically able to carry out the disinfection process in the room is made by utilizing UV light. Infaq Sterilization Box with UV and Ozone (BIUZ) can kill viruses in money, it is also easy to operate and safe. The size of the tool made is adjusted to the object or partner of the research activity, namely the Central Java Great Mosque Manager (PP MAJT). The need for partners is that the tool is able to carry out the sterilization process of infaq money provided by the congregation, both in the form of paper and coins effectively
Optimized Multiple-Bit-Flip Soft-Errors-Tolerant TCAM using Machine Learning
Full text linkSoft errors from radiations can change the data in electronic devices especially memory cells such as in TCAMs. The soft errors cause bit-flip errors that makes the data are corrupted in the network. This paper presents a novel machine learning for a multiple-bit-flip-tolerant TCAM that address soft errors problem using partial don't-care keys (X-keys). The general methodology is classified into two steps, i.e., statistical training and X-keys matching. First, we train the machine by collecting match probability of a filter by using X-keys that match the same locations as the search key. This method uses statistical training to determine the most efficient of number of don't cares. Moreover, in the statistical training, we also explore the maximum number of don't cares that produce best performance in covering the soft errors. Finally, the X-keys are implemented in the TCAM to correct bit-flip errors. The suitable number of don't cares in X-key is determined from the distribution of match probability of the X-keys so that the best degree of tolerance of the TCAM against soft errors is found. Match probabilities for various filters are shown. Experimental results demonstrate that the soft-error tolerance using statistical data has better soft-error tolerance than other methods. The proposed method is useful for soft-error tolerant TCAMs in routers and firewalls for robust networks
The Effect of Using Array Technique on Semi-Circular Patch Microstrip Antenna with 2.4 GHz Frequency in Supporting Wireless Body Area Network Technology
Full text linkThis paper aims to design a semi-circular patch microstrip antenna that can work at a frequency of 2.4 GHz (band 2360 MHz - 2400 MHz) to support Wireless Body Area Network technology (WBAN). One of the devices connected to WBAN technology is a Holter monitor and medical data recorder that forms a medical network for post-operative or monitoring ICU patients (Intensive Care Unit). To support one of the WBAN technologies, an antenna is needed that has considerable gain and bandwidth characteristics. To increase the gain and bandwidth, the array method is used on antennas with inset feed unification. The antenna design was simulated using CST Studio Suite 2019. The use of array methods on microstrip antennas can increase the gain by 132.9%, which is 5.73 dB. The simulation results obtained a return loss of -17.223 dB with a bandwidth of 88.3 MHz in the frequency range of 2357.6 MHz - 2445.9 MH