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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Techno.Com: Jurnal Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika GUYUB: Journal of Community Engagement Jurnal Rekayasa elektrika Jurnal Informatika: Jurnal Pengembangan IT
Setiawardhana, Setiawardhana
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Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Water Quality Control System Based on Web Application for Monitoring Shrimp Cultivation in Sidoarjo, East Java Fariza, Arna; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Barakbah, Aliridho; Pramadihanto, Dadet; Winarno, Idris; Badriyah, Tessy; Harsono, Tri; Syarif, Iwan; Sesulihatien, Wahjoe Tjatur; Susanti, Puspasari; Huda, Achmad Thorikul; Rachmawati, Oktavia Citra Resmi; Afifah, Izza Nur; Kurniawan, Rudi; Hamida, Silfiana Nur
GUYUB: Journal of Community Engagement Vol 4, No 3 (2023)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v4i3.7245

Abstract

Shrimp farming plays a crucial role to the Indonesian economy, but it is facing challenges from shifting weather patterns and global warming. This research focuses on the development and implementation of a web-based water quality monitoring system for shrimp farming to address these concerns. The research, conducted in collaboration with shrimp farmers in Sidoarjo, East Java, introduces PENS Aquaculture program, which is designed to efficiently monitor pH, salinity, and temperature. The system employs Internet ofThings (IoT) technology, which allows farmers to register several ponds, analyze water parameters, and receive real-time data through tables and graphs. The research takes a mixed-methods approach, integrating quantitative data from IoT devices with qualitative insights gathered through surveys and interviews with shrimp farmers. The study aims to evaluate the influence of IoT technology on shrimp pond quality and its contribution to the production. The findings show that PENS Aquaculture application is helpful in increasing shrimp farming efficiency, providing significant insights for the fisheries and cultural sectors.
Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Fakhri, Haidar; Setiawardhana, Setiawardhana; Syarif, Iwan; Sigit, Riyanto
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3908

Abstract

Metode klasifikasi citra MRI otak yang digunakan pada penelitian ini adalah Deep Learning dengan Convolutional Neural Network (CNN) dengan 2 model skema arsitektur CNN. Model skema 1 terdapat 2 max pooling layer dan 2 hidden layer, sedangkan model skema 2 terdapat 3 max pooling layer dan 4 hidden layer.  Dataset yang digunakan memuat citra MRI otak manusia dengan total 7023 citra, dengan rincian 1621 Glioma, 1645 Meningioma, 1757 Pituitary, dan 2000 Notumor. Evaluasi F1-Score model skema 1 dan skema 2 berturut-turut: 96% dan 97%, Sedangkan untuk nilai Accuracy yaitu 98%. Hal ini menunjukkan bahwa nilai F1-Score dan Accuracy, model skema 2 lebih baik. Untuk menguji dataset digunakan 10 fold cross-validation menghasilkan nilai rata-rata Accuracy, F1-Score, Precision, dan Recall berturut-turut 0,8520, 0,8470, 0,8493 dan 0,8504, dengan standar deviasi yang kecil, yaitu berturut-turut 0,0352; 0,0346; 0,0337 dan 0,0353 yang menunjukkan bahwa penyimpangan sebaran nilai semakin mendekati nilai rata-ratanya. nilai metrik F1-score dan accuracy berturut-turut, 97,47% dan 97,39%. Hasil accuracy penelitian ini lebih tinggi dibandingkan dengan beberapa penelitian sebelumnya, yakni dari [1], [2], [3], [5], [7], dan [8], berturut-turut: 94.39%, 97.54%, 97.18%, 96.08%, 96,36%, dan 95.55%.
Algae content estimation utilizing optical density and image processing method Kamaluddin, Muhammad Wafiq; Gunawan, Agus Indra; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Insivitawati, Era; Asmarany, Anja; Pratama, Ariesa Editya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6248-6257

Abstract

One of the factors that influence shrimp cultivation is the presence of algae. Precise knowing algae content in the pond is essential for effective management. Most research in the field of algae species carried out by researchers were observing Chlorella Sp. more than the other algae species, with a particular emphasis on substance concentrations. This study proposed non-invasive techniques for quantifying algae abundance, utilizing optical density (OD) and image processing (IP) methods. Three different algae species are frequently found in Indonesia i.e., Chlorella Sp., Thalassiosira Sp., and Skeletonema Sp. are used as sample. Those samples are cultured and prepared in a certain volume with a certain quantity. For experimental and observation purposes, those samples are then diluted into water based on percentage value. The experimental results provided RGB values, which were then used to establish polynomial equations. To verify these equations, two approaches were employed: synthetic image analysis and evaluation using additional data. The mean average error (MAE) was found to be 3.467 for IP method and 3.513 for OD method. It shows that IP method give better result compared to OD method in this study. However, it is very possible that the two methods will complement each other.
Robot navigation on inclined terrain using social force model Daffa, Muhammad Fariz; Dewantara, Bima Sena Bayu; Setiawardhana, Setiawardhana
IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v13i2.pp131-139

Abstract

This research introduces an innovative approach to address the limitations of the commonly used social force model-based robot navigation method on flat terrain when applied to sloped terrain. The incline of the terrain becomes a crucial factor in calculating the robot’s steering output when navigating from the initial position to the target position while avoiding obstacles. Therefore, we propose a social forced model-based robot navigation system that can adapt to inclined terrain using inertial measurement unit sensor assistance. The system can detect the surface incline in real time and dynamically adjust friction and gravitational forces, ensuring the robot’s speed and heading direction are maintained. Simulation results conducted using CoppeliaSim show a significant improvement in speed adjustment efficiency. With this new navigation system, the robot can reach its destination in 59.935089 seconds, compared to the conventional social forced model which takes 63.506442 seconds, the robot is also able to reduce slip to reduce wasted movement. This method shows the potential of implementing a faster and more efficient navigation system in the context of inclined terrain.
Level Kualitas Air Nutrisi pada Hidroponik Berdasarkan Sistem Klasifikasi Fuzzy Sanaba, Utari; Rokhana, Rika; Setiawardhana, Setiawardhana
Techno.Com Vol. 23 No. 2 (2024): Mei 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i2.10538

Abstract

Tingginya jumlah penduduk telah menyebabkan perubahan lahan pertanian menjadi lahan non-pertanian. Solusi inovatif untuk mengatasi keterbatasan lahan yaitu urban agriculture, khususnya hidroponik. Namun, kondisi nutrisi pada air hidroponik sering kali dalam kondisi buruk sehingga perlu dimonitoring dan dideteksi tingkat kualitasnya untuk menjaga kondisi air nutrisi dalam bak hidroponik dalam keadaan baik. Kondisi air nutrisi yang baik akan mengoptimalkan proses penyerapan akar dan pertumbuhan tanaman. Parameter kualitas air nutrisi dapat dideteksi melalui suhu air nutrisi, kadar TDS (Total Dissolved Solids) di dalam nutrisi, dan tingkat keasamaan atau pH dari air nutrisi di dalam bak hidroponik. Metode fuzzy logic classification memungkinkan dalam mengolah kondisi aktual nutrisi dari ketiga parameter tersebut menjadi sebuah keputusan level kualitas air nutrisi tanaman dalam kondisi baik, sedang, buruk, ataupun sangat buruk. Penelitian ini menggunakan sensor suhu air, TDS, dan pH dalam pengukuran masing-masing parameter yang kemudian ditampilkan pada website. Hasil pengukuran parameter nutrisi mencapai error rendah yaitu ±5%. Hasil klasifikasi kualitas dari kondisi air nutrisi tanaman yang diputuskan dengan fuzzy logic sudah sesuai dengan yang diinginkan oleh petani dan berhasil 100% ditampilkan pada website pengguna. Sistem ini memudahkan pengguna dalam memantau, mengevaluasi, dan meningkatkan kondisi dan kualitas nutrisi tanaman dari jarak jauh.
Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones Huda, Achmad Torikul; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Sigit, Riyanto
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1961

Abstract

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.
Face Recognition for Logging in Using Deep Learning for Liveness Detection on Healthcare Kiosks Ryando, Catoer; Sigit, Riyanto; Setiawardhana, Setiawardhana; Sena Bayu Dewantara, Bima
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2759

Abstract

This study explores the enhancement of healthcare kiosks by integrating facial recognition and liveness detection technologies to address the limitations of healthcare service accessibility for a growing population. Healthcare kiosks increase efficiency, lessen the strain on conventional institutions, and promote accessibility. However, there are issues with conventional authentication methods like passwords and RFID, such as the possibility of them being lost, stolen, or hacked, which raises privacy and data security problems. Although it is more secure, face recognition is susceptible to spoofing attacks. In order to improve security, this study integrates liveness detection with face recognition. Data preparation is done using deep learning algorithms, namely FaceNet and Multi-task Cascaded Convolutional Neural Networks (MTCNN). Real-time authentication of persons is verified by the system, which provides correct identification of them. Techniques for enhancing data help the model become more accurate and robust. The system's usefulness is shown by the outcomes of the experiments. The VGG16 model outperforms alternative designs like MobileNet V2, ResNet-50, and DenseNet-121, achieving 100% accuracy in liveness detection. Face recognition and liveness detection together greatly improve security, which makes it a dependable option for real-world healthcare applications. Through the ability to differentiate between genuine and fake faces and foil spoofing efforts, facial liveness detection may boost security. This study offers insights into building biometric systems for safe and effective identity verification in the healthcare industry.
Optimalisasi Kualitas Air pada Tambak Udang Vannamei Menggunakan Modul IoT Gunawan, Agus Indra; Setiawardhana, Setiawardhana; Gunawan, M Wisnu; Alam, Daffa Syah; Suasono, Zaikhul Sulthon; Hamida, Silfiana Nur
GUYUB: Journal of Community Engagement Vol 6, No 1 (2025): Maret
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v6i1.10581

Abstract

Indonesian has great potential in the fisheries sector, with vaname shrimp as a leading commodity due to its competitive price and efficient cultivation. However, many shrimp farmers in Keputih Village, Surabaya City still lack an understanding of the importance of monitoring and managing pond water quality. In response to this, the Master of Applied Electrical Engineering and Master of Applied Informatics and Computer Engineering teams at Politeknik Elektronika Negeri Surabaya (PENS) introduced an IoT-based Water Quality Meter module. This program not only provides real-time water quality monitoring technology that can be accessed via smartphone or laptop, but also provides training and assistance to pond farmers in adopting this technology. Evaluation results show that pond farmers can operate the module well to monitor water quality parameters, making it easier to monitor ponds accurately and practically. The community service program is expected to increase yields, strengthen collaboration between academics and communities, and encourage the adoption of modern technology in shrimp farming.
Verifikasi Wajah untuk Menghitung Jumlah Transaksi Pengunjung Menggunakan Metode Deep Metric Learning Maulana, Rifqi Affan; Sigit, Riyanto; setiawardhana, setiawardhana
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8922

Abstract

This research carries the theme of facial recognition to detect visitors' faces by counting the number of times visitors make transactions. The objective of this research is to develop and implement a face verification system for public purposes, such as commercial purposes. One potential application of this system is in the realm of promotions, where it could be utilized to track the number of transactions conducted by visitors. The method employed utilizes deep metric learning (DML) to generate a model capable of verifying various facial images through the Convolutional Neural Network (CNN) architecture, which is designed to train human face image data. The triplet loss method is employed in training data due to its recognition as a more flexible approach in utilizing labels (in the form of face images) to facilitate comparison with the detected face images. The model employed for face recognition applications is facenet, a system that has been demonstrated to achieve a high degree of accuracy. The research's output is an application capable of swiftly and precisely verifying facial images of visitors and calculating the number of visitor transactions. The number of visitor transactions can subsequently be utilized as a promotional or discount strategy in commercial services.
Cloud Computing-based Shrimp Pond Water Quality Prediction Intelligent Service System Suasono, Zaikhul Sulthon; Setiawardhana, Setiawardhana; Winarno, Idris; Gunawan, Agus Indra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2862

Abstract

Maintaining water quality is an essential factor in the success of shrimp farming, particularly in conventional and semi-intensive methods in Indonesian. Poor water quality will affect shrimp's survival, reproduction, development, and harvest yield. In order to furnish data regarding future water quality conditions, This research aims to create an intelligent cloud-based water quality prediction system for shrimp ponds that can provide accurate predictions regarding future water quality conditions. The system utilizes the WQI dataset gathered from four different shrimp farming sites, totaling 408 samples, each location exhibiting a different set of values. The model will be trained using four parameters: pH, DO, salinity, and temperature. The WQI dataset will be pre-processed to address missing data, outliers, and standardization. The water quality prediction model uses three machine learning algorithms: SVM, ANN, and MLR. The model's performance results are evaluated using MAE, RMSE, and R². The results indicate that the ANN model is the most effective, achieving an MAE: 0.4023, RMSE: 0.5336, and R²: 0.7178 for temperature predictions, and an MAE: 0.4080, RMSE: 0.5942, and R²: 0.5997 for salinity. The SVM model had mixed results for temperature, with an MAE: 0.3645 and RMSE: 0.4823, but it performed poorly for DO, as evidenced by a negative R² of -0.2428. The MLR model provided reasonable temperature predictions MAE: 0.4953, RMSE: 0.6370, R²: 0.5602. Subsequent research endeavors should prioritize the augmentation of the dataset size and the incorporation of temporal dimensions in order to enhance the precision of predictive outcomes.
Co-Authors Afifah, Izza Nur Alam, Daffa Syah Ali Ridho Barakbah Ardik Wijayanto Arna Fariza Asmarany, Anja Bima Sena Bayu Dewantara Dadet Pramadihanto Daffa, Muhammad Fariz Fakhri, Haidar Gunawan, Agus Indra Gunawan, M Wisnu Hamida, Silfiana Nur Harun, Ahmad Huda, Achmad Thorikul Huda, Achmad Torikul Idris Winarno Insivitawati, Era iwan Syarif Kamaluddin, Muhammad Wafiq Maulana, Rifqi Affan Novie Ayub Windarko Pratama, Ariesa Editya Rachmawati, Oktavia Citra Resmi Riyanto Sigit, Riyanto Rokhana, Rika Rudi Kurniawan Ryando, Catoer Sanaba, Utari Sena Bayu Dewantara, Bima Sesulihatien, Wahjoe Tjatur Suasono, Zaikhul Sulthon Susanti, Puspasari Tessy Badriyah, Tessy Tri Harsono Yusvida, Rizka