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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo Jurnal Sistem Cerdas Indonesian Journal of Electrical Engineering and Computer Science International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Dahlan, Iqbal Ahmad
Universitas Pertahanan RI
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Mathematics Physics Social Sciences

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Perbandingan Metode SVM-Segmentasi Untuk Mendeteksi Kutu Beras Dalam Citra Beras Uvi Desi Fatmawati; Wahyu Hidayat; Dananjaya Ariateja; Iqbal Ahmad Dahlan
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 1 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i1.1479

Abstract

Support Vector Machine (SVM) is a classification method that works by finding the hyperplane with the largest margin. Saturation Value (SV) is a digital image color model consisting of two elements, namely Saturation and Value. SV is taken from HSV, then only two elements are used. Segmentation is the process of separating an image that will be detected with a background image. Rice weevils are small pests that damage the quality of rice in rice storage. The quality and nutrition of rice will be reduced because of that bug. In this study, two methods have been used to detect the rice weevil that placed on a rice in an image. In the first method, feature extraction of the rice weevil texture is taken from RGB images and feature extraction of the SV brightness values ​​is taken from converting RGB images to HSV images. These two parameters are used as the SVM data training. In the second method, SV value in the HSV color model is used to separate between the rice weevil as the object detected and a rice as the background. The results showed that the first method provides an accuracy rate of 78.95% while the second method is 84.78%. Keywords—SVM, HSV, Segmentation, Rice Weevils, Accuracy
Sistem Deteksi Senjata Otomatis Menggunakan Deep Learning Berbasis CCTV Cerdas Iqbal Ahmad Dahlan; Dananjaya Ariateja; Muhammad Abditya Arghanie; Muhammad Azka Versantariqh; Muhammad David; Uvi Desi Fatmawati
Jurnal Sistem Cerdas Vol. 4 No. 2 (2021)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v4i2.172

Abstract

Nowadays, security and safety are big concerns in this modern and cyberwar era. Many countries invest some safety infrastructure to ensure their inhabitants for keeping their lives safely. Indonesia is the country with many problems because of urbanization and other challenges. This problem should be solved with smart city solution and it must be able to face the challenge of ensuring the safety and improving the quality of life regarding network centric warfare era. This problem also should be tackled with CCTV analytics with the ability to implement an automatic weapon detection system. It also can provide the early detection of potentially violent situations that is of paramount importance for citizens security. This paper is using deep Learning techniques based on Convolutional Neural Networks (CNN) can be trained to detect this type of object with YOLOv4 model and it proposes to implement CCTV analytics as a platform to process real-time data for monitoring weapon detection into knowledge displayed in a dashboard with accuracy 0.89, precision 0.82, recall 0.96 dan F1 Score 0.90 result on weapon detection with a real time speed of processing with NVIDIA 2080 Ti around of 35 FPS. It will send an early warning notification if the system detects the weapon detection such as a knife, gun etc.
Oksimeter Militer Pemantau Stres Prajurit TNI Berbasis Internet of Military Things Dananjaya Ariateja; Iqbal Ahmad Dahlan; Uvi Desi Fatmawati
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.174

Abstract

In carrying out their duties, TNI soldiers often experience pressure and threats that attack both physically and psychologically. This can trigger stress. Uncontrolled stress will cause disease disorders such as arrhythmias and hypoxemia. We offer a solution by building an Internet of Military Things (IoMT) based military oximeter for soldier stress monitoring. The proposed tool is real-time and portable, can monitor heart rate (BPM) and blood oxygen saturation (SpO2) when soldiers are on duty in conflict areas. This military oximeter is equipped with notifications and alarms that are integrated with applications installed on smartphones, so commanders can monitor the condition of their soldiers directly and view their health history. Based on the test results, obtained an accuracy of 99.7% and 99.88% for measuring heart rate and oxygen saturation in the blood. This military oximeter can be used as a medical aid to monitor the health condition of soldiers while on duty.
KLASIFIKASI CUACA PROVINSI DKI JAKARTA MENGGUNAKAN ALGORITMA RANDOM FOREST DENGAN TEKNIK OVERSAMPLING faqih hamami; Iqbal Ahmad Dahlan
Jurnal Teknoinfo Vol 16, No 1 (2022): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i1.1533

Abstract

Saat ini Indonesia sering mengalami perubahan cuaca ekstrem yang menyebabkan banyak bencana seperti banjir, kebakaran, longsor dan badai. Jenis cuaca bergantung dari banyak faktor seperti suhu, kelembaban, arah angin dan lainnya. Beberapa kegiatan manusia bergantung terhadap perubahan cuaca seperti di sektor pertanian, perkebunan, penerbangan, daerah tinggi dan pantai. Prediksi cuaca menjadi penting untuk lebih memahami perubahan cuaca ekstrem yang didasarkan dari faktor cuaca. Penelitian ini mengadopsi ensemble learning yang mampu melakukan klasifikasi cuaca dengan baik. Algoritma yang digunakan adalah Random Forest yang dikombinasikan dengan teknik oversampling untuk menangani ketidakmerataan jumlah data dari setiap kelas cuaca. Beberapa kategori cuaca yang diklasifikasikan adalah Cerah, Cerah Berawan, Berawan, Berawan Tebal, Hujan Lokal, Hujan Ringan, Hujan Sedang dan Hujan Petir. Hasil eksperimen diperoleh bahwa model Random Forest mencapai akurasi 70%.  Teknik oversampling yang digunakan adalah metode Synthetic Minority Over-sampling Technique (SMOTE). Dengan kombinasi SMOTE prediksi dari setiap kelas minoritas dapat ditingkatkan dengan rata-rata sebesar 50%
Real-time passenger social distance monitoring with video analytics using deep learning in railway station Iqbal Ahmad Dahlan; Muhammad Bryan Gutomo Putra; Suhono Harso Supangkat; Fadhil Hidayat; Fetty Fitriyanti Lubis; Faqih Hamami
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp773-784

Abstract

Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.
Instrumentasi Pemantauan Perairan Berbasis Telemetri Pada Prototipe Unmanned Surface Vehicle (USV) Dananjaya Ariateja; Uvi Desi Fatmawati; Iqbal Ahmad Dahlan
JTEV (Jurnal Teknik Elektro dan Vokasional) Vol 7, No 2 (2021): JTEV (Jurnal Teknik Elektro dan Vokasional)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (563.147 KB) | DOI: 10.24036/jtev.v7i2.113096

Abstract

Technological advances, especially in the field of remote control, both automatic and non-automatic, are very rapid. This can be seen from the technological capabilities that can work on the land, air, and water. Indonesia as one of the largest archipelagic countries in the world that have borders such as land, air, and vast seas must have this technology to anticipate the potential for problems that endanger citizens and the state. So far, Indonesia has reached this technology, for example, drone technology as a border monitoring mission through the air and aerial photography purposes. Ground robots are used to defuse remote-controlled bombs. To complete this, the researchers conducted research related to a remotely controlled prototype of an unmanned water vehicle. The research conducted discusses the manufacture of prototypes of unmanned surface vehicles and control stations that can communicate with each other. This prototype is controlled by the Arduino Nano microcontroller module, while the main control system uses a desktop-based application that is run via a laptop. Based on the test results, sending sensor data to Arduino and to the control station via the RF-module media went well. The transmission distance of transmitting sensor data and navigation control reaches approximately 250 meters, while the transmission distance of IP camera images reaches approximately 9 meters.
Prototype smart integrated fire detection based on deep learning YOLO v8 and IoT (internet of things) to improve early fire detection Firdaus, Muhammad Azka; Dahlan, Iqbal Ahmad; Rimbawa, H A Danang; Versantariqh, Muhammad Azka; Prakosa, Setya Widyawan
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 2, No 2 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v2i2.437

Abstract

The high incidence of fires in Indonesia in 2018-2023 is 5,336 fire incidents have caused many deaths and enormous material losses. This system is designed to identify early signs of fire through object detection and sensor technology, which is integrated with the Blynk IoT platform for real-time sensor monitoring and Telegram for instant notifications to users. The waterfall prototype method was designed through observation, system design, program code creation, tool testing, and tool implementation. This research uses Deep Learning YOLOv8 technology and IoT using ESP 32 as a microcontroller. Based on the training datasets, it produces precision=0.95872; recall=0.91; mAP50=0.97; mAP50-95 =0.66. The system uses the integration of a multisensor KY-026 flame sensor, DHT 22 temperature and humidity sensor, and MQ-2 sensors can detect CO, LPG, and smoke gas. All these multisensors can be monitored on Blynk IoT and Telegrambot in real time.
Unmanned aerial vehicle classification and detection system based on deep learning, internet of military things, and PID control system Lesmana, Azka Versantariqh; Dahlan, Iqbal Ahmad; Tjahjadi, Hendrana; Prakosa, Setya Widyawan; Firdaus, Muhammad Azka
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 2, No 3 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def..v2i3.439

Abstract

Indonesia is an archipelagic country situated between two continents and two oceans. With numerous islands, it is rich in natural resources but faces various military and non-military threats. One significant threat to maritime nations like Indonesia is from the air, which includes direct attacks from manned and unmanned aircraft and using aerial vehicles for intelligence and surveillance. The primary weapon system is crucial for national defense against such threats. Therefore, developing defense equipment in Indonesia must align with technological advancements to ensure quick and efficient operation. This research focuses on creating a classification and reconnaissance system for flying vehicles to enhance air defense capabilities. In the surveillance system, two servos are used for yaw and pitch axes, controlled by a Proportional, Integrative, and Derivative (PID) system. This PID control significantly improves servo movement both dynamically and statically. The system sends notifications via Telegram for monitoring, with an average FPS of 9.6. Flask is used for the website interface, averaging 6.8 FPS, and MIT App Inventor is used for the smartphone interface, averaging 7.6 FPS. This flying vehicle classification and reconnaissance system enhances Indonesia's air defense, utilizing YOLOv8 for classification, PID control for servo movements, and integrated notifications and interfaces for both web and smartphones.
Anti-kyphosis smart bag detection of overweight bag load and improper spinal posture in army members when using work bags Hirawan, Agung; Renoult, Muhammad Rey; Parwatiningtyas, Diyan; Dahlan, Iqbal Ahmad; Kusumadjati, Adhi; Napitupulu, Jofim Yordaniel
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 2, No 3 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def..v2i3.410

Abstract

Heavy field equipment owned by soldiers such as backpacks unknowingly makes soldiers move with the wrong body posture when in the field. Carrying a bag with a heavy load and the wrong posture (slouching), can cause abnormalities in the spine, such as slouching (kyphosis). Therefore, a posture therapy is needed with the method of habituation using a work bag with a load not exceeding 15 percent of the user's body weight and using it with the correct posture. This therapy / habituation is of course carried out outside of field training hours that do not ignore the use of heavy loads, for example in the office or anywhere else when carrying a bag. Seeing this, researchers are trying to create and develop an anti-kyphosis smart bag that can prevent and treat spinal deformities. After making the system, this smart bag was then tested quantitatively with the aim of seeing the qualification of the system's ability to detect excess bag weight, if the weight of the bag exceeds 15 percent of the user's body weight and the ability of the system to detect improper posture seen from the sensitivity level of the load cell sensor based on its placement position in the back area. The test was conducted on three respondents who were selected based on their height, namely 170 cm, 175 cm, and 182cm. The system calibration results show that the system works accurately, with a measuring error of 0% and effective sensor placement between the thoracic kyphosis and lumbar lordosis areas of the back, with the ability to detect a bend angle of at least 24.7°. Seeing the effectiveness of this anti-kyphosis smart bag, it is hoped that it can be a smart solution for TNI members in maintaining spinal posture to avoid kyphosis due to the habit of carrying excessive loads with the wrong body posture.
Co-Authors Dananjaya Ariateja Dananjaya Ariateja Dananjaya Ariateja Dananjaya Ariateja Fadhil Hidayat Faqih Hamami Fetty Fitriyanti Lubis Firdaus, Muhammad Azka Hirawan, Agung Kusumadjati, Adhi Lesmana, Azka Versantariqh Muhammad Abditya Arghanie Muhammad Azka Versantariqh Muhammad Bryan Gutomo Putra Muhammad David Napitupulu, Jofim Yordaniel Parwatiningtyas, Diyan Prakosa, Setya Widyawan Renoult, Muhammad Rey Rimbawa, H A Danang Suhono Harso Supangkat Tjahjadi, Hendrana Uvi Desi Fatmawati Versantariqh, Muhammad Azka Wahyu Hidayat