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Signal and Image Processing Letters
Published by Association For Scientific Computing Electrical and Engineering
ISSN : 27146669     EISSN : 27146677     DOI : 10.31763/simple
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The journal invites original, significant, and rigorous inquiry into all subjects within or across disciplines related to signal processing and image processing. It encourages debate and cross-disciplinary exchange across a broad range of approaches.
Arjuna Subject : Ilmu Komputer (semua kategori) - Pemrosesan Sinyal
Articles 10 Documents
 1 
Search results for , issue "Vol 7, No 1 (2025)" : 10 Documents clear
Effectiveness of Various Light Sensors for Indoor Use Guntara, Diki; Sunardi, Sunardi
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.106

Abstract

A light sensor is a type of electronic device that can produce changes in visible light energy or infrared light into electrical energy by utilizing the electrical current and resistance that enters the light sensor. This research objective is to design the light sensor circuit and program that can have a light sensor sensitivity to the light intensity in the room and test the success of indoor light sensors from the cheapest to the most expensive sensors. The sensors used are LDR, BH1750, and Photodiode. The research stage carried out was to prepare the equipment in a boarding room with dimensions of 3x3. The light intensity of windows and lamps for 10 days with three different sessions in the morning, afternoon, and evening are measured. Linear regression calibration is used to obtain more accurate results. The results of the light sensor used are compared with a digital lux meter. The cheapest sensor, namely the LDR, has the slowest response to light and is less accurate with an error value of 23.74%. An affordable sensor, namely a Photodiode sensor, has a fast response to light, but the results are less stable with an error value of 18.20%. The more expensive sensor is the BH1750 with the highest accuracy and stability with an error value of 7.53%.
Design of Rectangular Microstrip Antenna Using Inset Feed and DGS Methods for Digital Television at 598 MHz Frequency Hardiwansyah, Muttaqin; Purnamasari, Dian Neipa; Ms, Achmad Ubaidillah; Haryanto, Haryanto; Ulum, Miftachul
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.102

Abstract

The development of a microstrip antenna for digital TV applications at the center frequency of 598 MHz has been carried out to meet the needs for optimal signal reception in urban areas such as Surabaya. In this research, a microstrip antenna is designed, simulated, and fabricated to verify its performance. The simulation results show that the antenna has a return loss of -12.27 dB, VSWR 1.639, bandwidth 359 MHz, gain 2.26 dBi, and an omnidirectional radiation pattern. After the fabrication process, antenna performance measurements show a return loss of -25.78 dB, VSWR 1.849, and bandwidth 154 MHz. Discrepancies between simulation and fabrication results are mainly due to manufacturing tolerances and material variations. Nevertheless, the results obtained show that the developed microstrip antenna has good performance and is suitable for digital TV applications at the targeted frequency, with the ability to receive signals omnidirectionally which is very important in dense urban environments.
Temperature Monitoring System Internet of Things-based Electric Cars (IoT) Ardana, Regina Olivia Fitri; Ma'arif, Alfian; Marhoon, Hamzah M; Salah, Wael; Sharkawy, Abdel-Nasser
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.107

Abstract

Electric cars are a means of transportation that can meet the mobility needs of the community but are still environmentally friendly because they have no air pollution or exhaust emissions. Electric cars at Ahmad Dahlan University began to be made since 2019. In the effort to develop this electric car, there are several obstacles in monitoring the tools on the electric car during the race. So this research provides an Internet of Things-Based Battery and BLDC Motor Temperature Monitoring System on the ADEV 01 Monalisa Electric Car. which is made using several components including the DS18B20 Sensor, ADEV BLDC Motor, NodeMCU ESP32, LCD (Liquid Crystal Display). This research method develops a temperature monitoring system on an Internet of Things-based electric car using the Thinger.io platform. in this study tested the effectiveness of the DS18B20 temperature sensor in monitoring the temperature of the Battery and BLDC Motor on the ADEV 01 Monalisa electric car. the tests carried out were static testing, dynamic testing, testing data transmission to the Thinger.io platform, and distance testing. The results of testing the battery and BLDC motor on the ADEV 01 Monalisa Electric Car in a static state are good because the reading error is 0.60% and 0.50%. As for testing while running, namely 0.90% and 0.86%. Testing on the Internet of Things is successfully sent with a stable and fixed delay. therefore this parameter is good for monitoring electric vehicles. Researchers conducted distance testing for the Internet of Things using Thinger.io which aims to find out how far the internet connection can send sensor readings to Thinger.io. so the results obtained that a distance of 230 meters the internet connection is disconnected and cannot send data to Thinger.io.
Motorized Vehicle Security System Using SMS with GPS Tracking Method Based on Arduino UNO Prasetio, Andhika Dwi; Aji, Wahyu Sapto
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.108

Abstract

Cases of motorcycle loss are increasingly common in the community. This situation is caused by the lack of security systems installed on motorcycles, which are generally limited to the vehicle's ignition key. In fact, only a few medium-sized motorcycles are equipped with alarms as an additional form of security. A solution is needed to reduce theft or robbery on motorized vehicles, which is realized through a study entitled "Motorized vehicle security system using SMS with GPS Tracking method based on Arduino UNO A system that uses GPS and SMS that can be found on smartphones. This system is able to control the connection and disconnection of electric current in motorized vehicles using SMS, then it will be forwarded to the relay. The motorized vehicle security system using SMS with the Arduino UNO-based GPS Tracking method that has been made can function properly and can detect the position of the motorcycle accurately according to the motorcycle coordinate point. Tests to detect the position of the vehicle were carried out as many as 20 locations and the results were in accordance with the coordinate points of the vehicle. In testing to turn off the motorcycle engine, the results show that the motorcycle can be turned off remotely with an average delay of 6.97 seconds. In testing to turn on and turn off the alarm, the results show that the motorcycle alarm can be turned off remotely with an average delay of 7 seconds. this shows that the motorized vehicle security system using SMS with the Arduino UNO-based GPS Tracking method that has been made can function properly and as it should.
Traditional Herbal Medicine Production Information System Based on Prototyping Method Yunitarini, Rika; Fitrianto, Hambali; Mufarroha, Fifin Ayu; Koeshardianto, Meidya
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.112

Abstract

Indonesia is the country with the second largest biodiversity in the world after Brazil. Indonesia's biodiversity is very rich, both on land and at sea, and is one of the most important in the world. The benefits of Indonesia's biodiversity is as a natural resource that plays an important role one of them in the production of traditional herbal medicine. Madura Island in East Java, Indonesia, is famous for its natural resources and respected Madurese herbal medicine, internationally recognized for its efficacy in addressing health and beauty issues. The increasing demand for traditional herbal medicine products motivates the industry to improve production efficiency, prioritizing effective management and optimal utilization of raw material stocks. This research aims to manage the production needs of traditional herbal medicine by identifying information needs and developing a Production Information System using the Laravel framework to meet industry needs. This research will evaluate the impact of the system on the production process and the management of raw material needs in the traditional herbal medicine sector. The expected results include a positive contribution to the industry, better production performance, and improved handling of raw material stocks. The integration of the Laravel framework is expected to improve production performance and provide features for the traditional herbal medicine industry. In conclusion, this research seeks to offer a customized and effective solution for the traditional herbal medicine industry, addressing the increasing market demand through the optimization of production processes and management practices.
Artificial Neural Network for Corn Quality Classification Based on Seed Damage and Aflatoxin Attributes Maulida, Innayah; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.119

Abstract

Corn plays a critical role in Indonesia’s agricultural sector, functioning as both a staple food for human consumption and a key component of livestock feed. However, its quality is frequently compromised by factors such as mechanical damage during harvesting, fungal contamination, and fluctuating climate conditions, all of which pose challenges to maintaining consistent standards. Traditionally, corn quality classification relies on manual methods, which are not only time-consuming but also prone to human error and inconsistency. To address these limitations, this study employs a Neural Network approach to classify corn into two distinct categories: breeder and commercial grades. The research utilizes a dataset of 2,026 records, meticulously divided into 70% for training, 20% for validation, and 10% for testing, ensuring robust model evaluation. The methodology includes comprehensive data preprocessing, feature standardization to normalize input variables, and hyperparameter optimization, with the model trained over 100 epochs using a batch size of 32 and a learning rate of 0.001. The results demonstrate exceptional performance, achieving an accuracy of 99.5%, precision of 98.3%, recall of 100%, and an F1-score of 99.1%, as validated by a confusion matrix that highlights the model’s classification reliability. This automated system significantly enhances the efficiency and accuracy of corn quality assessment, offering a scalable solution to replace outdated manual techniques. By providing a reliable tool for quality differentiation, this study supports Indonesia’s agricultural and livestock industries, with potential for broader application in optimizing crop management and ensuring food security under varying environmental conditions.
Optimization Day Old Chick Incubator Design to Reduce Mortality Rate Using Fuzzy Logic Saputro, Adi Kurniawan; Ramadhan, Muhammad Fajar; Ibaidilah, Achmad Fiqhi; Haryanto, Haryanto; Sukri, Hanifudin; Hardiwansyah, Muttaqin
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.121

Abstract

In poultry farming, particularly for Day-Old Chicks (DOC), maintaining an ideal environmental condition is a significant challenge due to the limited ability of mother hens to provide adequate warmth and care. This often leads to a high mortality rate among DOC, especially in broiler chickens. The research contribution is the development of an intelligent incubator system based on fuzzy logic to automate environmental control and reduce DOC mortality rates. The system employs a DHT22 sensor to measure temperature and humidity, and an MQ-135 sensor to detect ammonia levels. An ESP32 microcontroller is used for data processing, chosen for its built-in Wi-Fi capability and high processing power. The DHT22 sensor controls a fan and UVA+UVB lamp via an AC dimmer, while the MQ-135 sensor controls a DC motor through the L298N driver. The fuzzy logic method is applied to make more accurate control decisions, and the entire system is connected to an IoT-based monitoring platform that provides a real-time dashboard for farmers. Preliminary results show that the system successfully maintains temperature within the optimal range (30–34?) and humidity (40–70%), and responds efficiently to changes in ammonia concentration. Compared to conventional systems, this intelligent incubator offers better automation, lower energy consumption, and cost efficiency. In conclusion, the proposed system provides a scalable and efficient solution for DOC management. Future work includes AI-based prediction integration, mobile application development, and historical data analysis for smarter poultry farm management.
Protecting South Sulawesi: Combination of Random Forest Regressor and SEIRS Mathematical Model in the Analysis and Prediction of the Spread of Covid-19 Bahar, Nur Qadri; Side, Syafruddin; Abdy, Muhammad
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.126

Abstract

This applied research seeks to examine the dynamics of Covid-19 transmission in South Sulawesi Province. Additionally, this study will also forecast the future spread of Covid-19. This research comprises two phases: the investigation of Covid-19 transmission utilizing the SEIRS mathematical model, incorporating Vaccination and PPKM (Enforcement of Community Activity Restrictions), and the prediction of Covid-19 spread through machine learning techniques. The utilized data is secondary data sourced from the South Sulawesi Provincial Health Office. This study developed a machine learning model utilizing a random forest regressor algorithm due to its proficiency in identifying nonlinear data patterns. This model effectively accounts for the variability of the dataset (target variable) with a R2 score of 95%. The evaluation findings of the random forest regressor model indicated satisfactory performance, with a mean absolute error (MAE) of 29.57 and a root mean square error (RMSE) of 54.42 during training, and an MAE of 58.55 and an RMSE of 98.67 during testing. Given the significant variability in Covid-19 data and the prevalence of zeros in the dataset, the MAE and RMSE values for both training and testing are deemed acceptable. This model is designed to forecast daily Covid-19 instances in the future. This work not only employs machine learning but also analyzes the dissemination of Covid-19 through the SEIRS mathematical model, incorporating vaccination and PPKM factors. The SEIRS model analysis indicates that the disease-free equilibrium point is stable when R01 and unstable when R01. The fundamental reproduction rate derived from the vaccine's efficacy is v=65.3%, and the compliance rate with PPKM is ?=1%. Consequently, R0=1.2407288, indicating that Covid-19 will propagate in South Sulawesi Province. For ?=11%, R0=1.0167230. This indicates that Covid-19 will stabilize, and with ?=69%, R0=0.2338689, suggesting that Covid-19 will vanish from South Sulawesi Province.?
Topic Modelling of Disaster Based on Indonesia Tweet Using Latent Dirichlet Allocation Nuryono, Aninditya Anggari; Iswanto, Iswanto; Ma'arif, Alfian; Putra, Rizal Kusuma; Nugroho H, Yabes Dwi; Hakim, Muhammad Iman Nur
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.132

Abstract

Twitter (now X) is a critical social media platform for disseminating information during crises. This study models disaster-related topics from Indonesian-language tweets using Latent Dirichlet Allocation (LDA). From a dataset of 8,718 tweets collected from official sources like BMKG and BNPB, we performed several preprocessing steps, including case folding, stop word removal, stemming, and normalization of slang and abbreviations. The optimal number of topics was determined using coherence scores, with the model achieving a peak coherence value of approximately 0.57. Keywords such as “banjir”, “kecelakaan”, “tanah longsor,” and others were used to collect data from Twitter accounts like "BMKG" (Meteorology, Climatology, and Geophysical Agency) and "BNPB" (National Disaster Management Agency). The results revealed that the most frequently discussed topics with high coherence values were “angin topan” “topan”, “virus corona”, “kecelakaan”, “tenggelam”, “badai”, “angin puting.” A word cloud was used to visualize these disaster-related topics.
Classification of Road Damage in Sidoarjo Using CNN Based on Inception Resnet-V2 Architecture Zahrah, Fathima; Diyasa, I Gede Susrama Mas; Saputra, Wahyu Syaifullah Jauharis
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.123

Abstract

Road damage is a serious issue in Sidoarjo Regency, posing risks to road users' safety. This study aims to classify road surface conditions using a Convolutional Neural Network (CNN) model based on the Inception ResNet-V2 architecture. The research develops an image-based classification model by combining secondary data from Kaggle and primary data obtained through Google Street View API scraping, along with training strategies such as data augmentation, class balancing, early stopping, and model checkpointing. A total of 885 images were used, categorized into three classes: potholes, cracks, and undamaged roads. The model was trained over 20 epochs with early stopping triggered at epoch 15, when validation accuracy reached 95.95%. Evaluation on the test set showed a test accuracy of 83%. The undamaged road class achieved the highest performance with an F1-score of 0.89, while the pothole class recorded an F1-score of 0.79. The lowest performance was observed in the cracked road class, with an F1-score of 0.65, indicating the model's limited ability to detect fine crack features. This limitation is likely due to class imbalance and visual similarity between classes. Although the model demonstrated good generalization for the two majority classes, the performance gap between validation and test accuracy highlights the need to improve detection for minority classes. Future work is recommended to explore advanced augmentation techniques, increase the representation of minority class data, and consider alternative architectures or ensemble methods to enhance the model’s sensitivity to subtle road damage features.

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