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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 89 Documents
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Sub Controller Design on KRSBI Humanoid R-SCUAD Robot Sub Controller Design on KRSBI Humanoid Robot R-SCUAD Mizan, Bahrul; Satya Widodo, Nuryono
Signal and Image Processing Letters Vol 4, No 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v4i2.27

Abstract

The purpose of this research is to design OpenCM9.04 controllers such as Arbotix (Pro) with MPU-9250 sensor as robot balance, as well as controlling the movement of DYNAMIXEL servo angles based on camera input on the robot, the Design of the OpenCM9.04 controller board on the robot consists of 2 main components namely OpenCM9.04 which works as a mini system and OpenCM 485 Expansion Board that works as a conference to the serial that provides interface to buttons and LEDs as well as a power supply circuit , where OpenCM9.04 as the main controller then sends data to OpenCM 485 which will process the MPU-9250 sensor as well as the DYNAMIXEL servo on the robot. The hardware system design consists of an MPU-9250 sensor to maintain balance in the robot so that the robot does not fall when walking or running and servo DYNAMIXEL to move the corners on the robot. The results obtained by the OpenCM9.04 controller have been successfully developed and tested on robots in the KRSBI-H racetrack and the robot has been able to maximize in the game well without any constraints on the microcontroller used.
Internet Based Control of Room Lights Using Wemos D1 Rahmatulloh, Rizqi; Sunardi, Sunardi
Signal and Image Processing Letters Vol 3, No 3 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i2.65

Abstract

The Internet of Things (IoT) has made things easier and cheaper because all devices are connected to the internet. Electronic equipment at home can be monitored and controlled remotely via internet so that it can be effective, easy, and automatic. This study builds lights as part of a smarthome that is used for lighting automatically using IoT. This study uses lamp automation method using equipment including the Wemos D1, LDR sensor, LCD, relay, and two lamps which are controlled using the Blynk application. This research has succeeded in building a remote home light monitoring system based on IoT in real time. The test has been carried out 24 times with 100% success. The house is no longer dark when the light intensity is low because the lights will automatically turn on, while if the light intensity is high, the lights will automatically turn off.
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.
Vehicle Detection and Tracking using Coarse-to-Fine Module and Spatial Pyramid Pooling–Fast with Deep Sort Saputri, Anita Nur Widdia; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.118

Abstract

Semarang City, a rapidly growing urban area in Indonesia, faces significant traffic challenges stemming from the widespread use of motorcycles, an inefficient public transportation system, and accelerated urban development. These factors contribute to congestion and complicate traffic management efforts. To address this issue and enhance monitoring capabilities, this study develops an automatic vehicle detection system utilizing the YOLOv8 algorithm, applied to CCTV footage obtained from TILIK SEMAR, a local traffic surveillance initiative. The research methodology encompasses several key stages: data collection from real-world traffic scenarios, meticulous annotation of vehicle types, model training using the YOLOv8 framework, and performance evaluation conducted at two distinct locations in Semarang—Banyumanik and Thamrin Pandanaran. The trained model achieved an impressive average accuracy, measured as mean Average Precision (mAP50), exceeding 97%, with a rapid processing time of 4.2 milliseconds per image, making it suitable for real-time applications. Among vehicle categories, the highest detection accuracies were recorded for buses at 99.3% and box trucks at 99.5%, reflecting the model’s robustness for larger vehicles. However, motorcycles presented a challenge, with a lower mAP50-95 score of 64.3%, attributed to variations in shape, size, and lighting conditions. Overall, the system successfully identified 96.77% of 3,036 vehicles across the test dataset, demonstrating strong generalization across diverse traffic conditions. These findings validate YOLOv8 as an effective tool for real-time traffic monitoring in urban settings. Future enhancements will focus on expanding dataset diversity and improving performance under challenging environmental factors, such as adverse weather or low-light scenarios, to further refine the system’s reliability.
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.
Agricultural Mechatronics: Orange Sorting System Using Image Segmentation Fathurrahman, Haris Imam Karim; Aulia, Imam Haris
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.142

Abstract

Sorting oranges after harvest is a critical step. It requires separating ripe fruit from unripe. Traditionally, this is done by hand. This method is inefficient and subjective. It is not suitable for modern agriculture. This study creates an automated system to solve this problem. The system uses mechatronics and image processing. Its core uses the HSV color space for image analysis. This method is effective for assessing the peel's color, which indicates maturity. The mechatronic system performs the physical sorting using a servo motor. It includes a conveyor belt, a digital camera, a processing unit, and an actuator. This research was tested on 30 sample oranges. The results show 90% accuracy in mechatronics sorting. This proves the system is a reliable and effective tool for quality control.

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