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ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
Published by Institut Teknologi Nasional Bandung
ISSN : 23388323     EISSN : 24599638     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering Engineering
Jurnal ELKOMIKA diterbitkan 3 (tiga) kali dalam satu tahun pada bulan Januari, Mei dan September. Jurnal ini berisi tulisan yang diangkat dari hasil penelitian dan kajian analisis di bidang ilmu pengetahuan dan teknologi, khususnya pada Teknik Energi Elektrik, Teknik Telekomunikasi, dan Teknik Elektronika.
Arjuna Subject : -
Articles 826 Documents
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Utilization of PS-InSAR for Analyzing Land Subsidence in the Bandung Basin, Indonesia using Sentinel-1A Data SARI, DEWI KANIA; KUNCORO, HENRI; NURTYAWAN, RIAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 4: Published October 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i4.1116

Abstract

ABSTRAK Cekungan Bandung, yang terletak di Provinsi Jawa Barat, Indonesia, rentan terhadap penurunan muka tanah. Penelitian ini menganalisis laju penurunan muka tanah di Cekungan Bandung selama tahun 2019 menggunakan metode PS-InSAR yang diterapkan pada citra satelit Sentinel-1A. Sebanyak delapan citra Sentinel1A, yang diperoleh antara Januari hingga Desember 2019, diproses menggunakan perangkat lunak SNAP dan STAMPS. Hasil analisis menunjukkan bahwa laju deformasi permukaan tanah di Cekungan Bandung berkisar dari -133 hingga 98 mm/tahun, dengan penurunan paling signifikan terjadi di Kota Bandung dan Kabupaten Bandung. Perbandingan hasil PS-InSAR dengan data survei GPS dari sembilan titik pemantauan menunjukkan korelasi yang kuat (R=0,75), mengonfirmasi keandalan metode PS-InSAR untuk pemantauan penurunan tanah. Temuan ini menegaskan pentingnya pemantauan berkelanjutan dan pengelolaan sumber daya secara bijak guna mengurangi dampak penurunan muka tanah di Cekungan Bandung. Kata kunci: penurunan muka tanah, PS-InSAR, Cekungan Bandung, Sentinel-1A  ABSTRACT The Bandung Basin, located in West Java Province, Indonesia, is highly susceptible to land subsidence. This study analyzes land subsidence rates in the Bandung Basin during 2019 using the PS-InSAR method applied to Sentinel-1A satellite imagery. Eight Sentinel-1A images, acquired between January and December 2019, were processed using SNAP and STAMPS software. The results indicate that deformation rates in the Bandung Basin range from -133 to 98 mm/year, with the most significant subsidence occurring in Bandung City and Bandung Regency. A comparison between PS-InSAR measurements and GPS survey data from nine monitoring points revealed a strong correlation (R=0.75), confirming the reliability of the PS-InSAR method for land subsidence monitoring. These findings underscore the need for continuous monitoring and sustainable resource management to mitigate land subsidence in the Bandung Basin. Keywords: land subsidence, PS-InSAR, Bandung Basin, Sentinel-1A
Analysis of Solar Power Generation Needs for Weather Stations based on ESP32 FIRDAUS, FIRDAUS; VITRIA, RIKKI; RIFKA, SILFIA; LIFWARDA, LIFWARDA; DEWI, RATNA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.15

Abstract

A remote weather station requires solar power system (PLTS), hence making power consumption calculation is very essential. PLTS analysis is applied to an ESP32-based weather station with low-power sensors to reduce costs. The process starts with setting up the station and PLTS without prior analysis, followed by a literature review, data collection of current and voltage from the SCC, conducting an analysis, and finally determining components recommendations. Calculations determine the need for a 12V, 4.375 Ah battery, 14.255 Wp solar panel, and a SCC with 0.46 A battery current and 0.306 A load current. Adjusted to market availability, a VRLA 12V/6Ah battery, 20 Wp solar panel, and 5A SCC module can replace the previously used 12V/7Ah battery, 50 Wp solar panel, and 10A SCC.
Autonomous Navigation onto Autodocking Drone System using Computer Vision KRISTIANA, LISA; MAULANA, KEINDRA BAGAS; FASYA, SHAFIRA KURNIA; DAFY, MUHAMMAD ZUFAR; JANUAR, MUKTIADI AKHMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.70

Abstract

Autodocking is a sophisticated technology that allows drones to land automatically at a predetermined docking station. Computer vision plays an important role in the drone autodocking system, allowing the drone to "see" the intended object. The problem with drone control is the precision of determining and placing objects, in this case docking, where there is still a difference or error between the desired docking set point. The solution proposed in this article is to use the PID Controller (Proportional-Integral-Derivative Controller) algorithm. By using a PID controller, the drone can regulate its movements more precisely, maintain stability, and ensure proper landing. The results achieved using this approach, reached a 90% success rate (precision) with control of several environmental parameters. first page. The abstract contains a summary of backgrounds, methods, and research results, with the maximum number of characters being 150.
Development of a Data Cleaning System for Consumer Master Data using Sorted Neighborhood and N-Gram Methods LESTANTO, YUSUF; MUALIFA, RAHMA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.57

Abstract

This study developed a data cleaning system for master data using the Sorted Neighborhood Method (SNM) and N-gram methods to detect and eliminate duplicates and standardize name and address formats. The proposed SNM algorithm handles precleaning tasks, removes specific characters and titles, and forms tokens for comparison. The N-gram algorithm calculates record similarity using user-defined N-gram values and thresholds. The effectiveness was evaluated using recall, precision, and F-measure metrics on small and large datasets. The optimal threshold, token length, and N-gram values were 0.7, 5, and 2, respectively, yielding the highest F-measure scores. The results confirm the successful implementation and improvement of data quality. Identifying optimal parameters provides a benchmark for future data-cleaning efforts, potentially streamlining processes and reducing resources.
Performance Comparison of 1D-CNN and LSTM Deep Learning Models for Time Series-Based Electric Power Prediction SUKATMO, SUKATMO; NUGROHO, HAPSORO AGUNG; RUSANTO, BENYAMIN HERYANTO; SOEKIRNO, SANTOSO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.44

Abstract

Accurate electrical power prediction is essential for efficient energy management, especially in institutions with dynamic energy needs. This study compares the performance of 1D-CNN and LSTM for time series based electrical power prediction, using a dataset from the Building Automation System (BAS) of STMKG building. The evaluation metrics Mean Squared Error (MSE) and Mean Absolute Error (MAE) are used to measure accuracy. The results show that the LSTM had an average MSE value of 3.35E-04±0.00013 and an MAE of 0.01312±0.0079 across 10 trials. This is slightly better than the 1D-CNN, which had an average MSE value of 4.68E-04±0.0003 and an MAE of 0.01855±0.00586. Despite the marginal difference, 1D-CNN provides a computational time efficiency advantage of 63.08s, 1D-CNN is about 84.19% faster.
Audio Conversion for Music Genre Classification Using Short-Time Fourier Transform and Inception V3 ROSMALA, DEWI; FADHILAH, MOHAMMAD NOER
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.84

Abstract

This research examines the development of music genres and technological applications in music genre recognition through the MIR (Music Information Retrieval) approach. Automatic music genre labeling is expected to help, reduce, and suppress the role of humans in terms of music genre labeling. This research proposes the use of Mel Spectrogram as an audio representation in the frequency domain as well as Convolutional Neural Network (CNN), specifically the Inception V3 architecture. CNN was chosen for its ability to recognize complex and hierarchical patterns, which corresponds to the musical features represented in the spectrogram. Transfer learning techniques and fine-tuning of models trained on large datasets were applied, which allowed to improve accuracy. This study uses a dataset of 1000 audio files in .wav format, with each genre represented by 100 files, to evaluate the performance and effectiveness of the proposed method in the context of music genre classification.
Evaluating a Preprocessing Pipeline for Fetal Phonocardiography Using FIR Filtering DAMARDHI, SAHI RAFAEL; FARADISA, IRMALIA SURYANI; SOTYOHADI, SOTYOHADI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 2: Published April 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i2.155

Abstract

This study evaluates the effectiveness of a preprocessing pipeline consisting of resampling, normalization, and Finite Impulse Response (FIR) filtering to improve signal consistency for further signal analyses such as feature extraction and classification. Resampling standardizes the sampling rate to 16 kHz, ensuring uniform temporal resolution. Normalization adjusts amplitude across recordings, yielding a mean of 0,0015 and a standard deviation 0,0462. FIR filtering reduces noise, eliminating 77,69% of signal energy above 200 Hz while retaining 29,65% of the main signal. Pipeline evaluation shows a Signal-to-Noise Ratio (SNR) of -1,88 dB, indicating a significant power reduction, but normalization ensures amplitude stability. These results demonstrate that this preprocessing combination effectively reduces noise, although balancing noise reduction and signal preservation remains challenging.
Study and Design of Picohydro Power Plant for Low-Head and Low-Flow Application HAMDANI, DENY; EDYPOERWA, MUGNI LABIB
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 2: Published April 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i2.200

Abstract

The global energy transition trend has driven the utilization of renewable energy from the surrounding environment, one of which is the irrigation water flow. Picohydro Power Plant (PLTPH) offers a solution to harvest kinetic and potential energy from water flow with low discharge and head. This study designed a PLTPH based on a classic water wheel equipped with an impulsive nozzle, a gradual intake pipe, a booster converter, an MPPT device, and a battery to ensure optimal and stable power output. With a water flow discharge of 0.4 m/s and a head of 80 cm, the PLTPH can generate up to 1,100 W, demonstrating the effectiveness of this technology in supporting energy transition and energy utilization in remote areas.
FedFA: Firefly Algorithm for Communication Cost Optimization in Federated Learning FATH, NIFTY; PURNAWAN, PEBY WAHYU; KRISTANTO, DIDI; MUFIDA, RIDHA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 2: Published April 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i2.130

Abstract

Federated Learning is a promising communication model to address data security and privacy issues. Each client device engages in a collaborative machine learning model, eliminating the need to send all client data to the server. However, the main obstacles to applying FL to wireless network communication are limited bandwidth and unstable network conditions. Therefore, this research proposes a new FedFA approach integrating the Firefly algorithm to optimize weight initialization and minimize communication costs. The basic principle of FedFA involves parameters in the Firefly algorithm to select the best weight of each client to be trained on the server. Based on the test results, the proposed algorithm produces an accuracy improvement of 12.84% compared to FedAvg. The FedFA model is also more resilient to unstable communication, as seen from the less significant decrease in accuracy compared to the FedAvg algorithm.
Cognitive Successive Interference Cancellation for Multi-User Visible Light Communication Systems MARSUKI, AMINAH INDAHSARI; PAMUKTI, BRIAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 2: Published April 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i2.185

Abstract

Wireless communication technology continues to face challenges in meeting high bandwidth demands and the capability to serve numerous users. One promising solution is the non-orthogonal multiple access (NOMA) scheme, particularly through the use of power domain (PD) in the application of visible light communication (VLC) technology. However, PD-NOMA still faces power allocation and detection issues at the receiver end. We propose the Cognitive-SIC-VLC method, which considers the line of sight (LOS). Using combined multiplexing techniques, variations in user positions within the system can result in low SNR (dB) values when detecting a BER threshold of 10-3. In three power allocation experiment scenarios, the third scenario was the best with an initial power allocation of 0.95 and a power increment for each user of 0.05. This has been proven to have very low SNR requirements for users close to the LED.

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