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Angger Abdul Razak
Department Of Electrical Engineering, Universitas Brawijaya, Indonesia
Education Engineering

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RANCANG BANGUN SISTEM PALANG GESER KERETA API PADA PERLINTASAN SEBIDANG BERBASIS SENSOR ULTRASONIK DAN SENSOR INFRAMERAH Pangestu, Mochammad Kelvin Yudha; Maulana, Eka; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 11 No. 6 (2023)
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Railway is one of the ground transportation modes that has characteristics and advantages more efficient compared to other ground transportation modes. However, accidents at railway crossings remain a serious issue. To reduce accidents between trains and road users at level crossings due to negligence or errors in the system, or due to road users' lack of discipline, there is a need for automatic crossing barriers, such as the use of automatic railway crossing gates integrated with components, making it safer and minimizing the possibility of road users attempting to pass through railway gates when they are closing. This research employs arduino UNO components, HC-SR04 ultrasonic sensors, FC-51 infrared sensors, DC motors, L2986N motor drivers, 16x2 LCD with I2C module, LEDs, power supply, and an emergency stop. The railway crossing gate system is designed with a working principle using sensors to detect trains and objects around the crossing. Testing of the ultrasonic sensor successfully detected trains at a maximum distance of 5 cm, with angles of 20° and 30° providing optimal initial detection. The infrared sensor showed rapid response to approaching objects, and using PWM duty cycle 27,4% provided optimal performance for the gate's sliding movement. Additionally, testing of the LED, LCD, buzzer, and emergency stop button functioned according to their purposes. The overall testing results of the system concluded that the design of the railway crossing gate system was successfully integrated to enhance safety at railway crossings and optimize train operations. Keywords: Railway, Accidents, Crossing Gate, Ultrasonic Sensor, Infrared Sensor, and Safety
SISTEM PENGHITUNG KENDARAAN DENGAN OPENCV DAN MODEL PEDETEKSI PRA-TERLATIH MOBILENET SSD Fauzi, Maher; Mudjirahardjo, Panca; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 12 No. 1 (2024)
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Accurate vehicle detection and tracking is required in visual-based systems to perform counting, therefore vehicle detection by utilizing artificial intelligence networks to process image data CNN (Convolutional Neural Network) is introduced, but in implementing CNN several problems arise, CNN models that are often used are better in accuracy and precision, but often use excessive processing power and computing resources to work optimally, they are not compatible with low-budget platforms and systems, and not easy to use on computers without graphics support or NON-GPU computers. In this research, a vehicle counter system is built by implementing pre-trained object detection model, MobileNet SSD and object tracking algorithm MOSSE (Minimum Output Sum of Squared Error). The model and algorithm are implemented with a library for processing digital images, namely OpenCV to build a vehicle counter system with a high comparison value of the predicted number of vehicles and the actual value of the number of vehicles, has low computing power, and can be run on a NON-GPU computer. Keywords: Vehicle Counter System, OpenCV, SSD, MobileNet, MOSSE.
IMPLEMENTASI ALGORITMA SENSOR FUSION PADA PEMBACAAN IMU UNTUK MEREDAM GANGGUAN MEKANIS QUADCOPTER Purnomo, Jason Manuel; Djuriatno, Waru; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 12 No. 3 (2024)
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The balance of unmanned aerial vehicles (UAVs) is crucial to maintain, but there are disturbances from motors and propellers during operation. Additionally, there are unique characteristics of the IMU sensor, namely the sensitive accelerometer and thegyroscope which experiences drift from integration. This research is an analysis of the implementation of the Madgwick filter sensor fusion algorithm (SFA) that combines accelerometer and gyroscope sensor values. The Madgwick filter SFA is implemented on a common IMU, the MPU6050, and an Arduino Nano to test the algorithm on a low-computation microcontroller. Testing showed that the Arduino Nano is capable of computing the Madgwick filter Euler angles at 223 Hz.The parameters of the Madgwick filter SFA are tested by calculating the convergence time of the Madgwick filter SFA output angle with the accelerometer output angle by dropping the sensor on the X-axis. The parameter testing also revealed an output error on the Y-axis due to the Madgwick beta parameter value that amplifies the accelerometer output on the Y-axis. Tests on a quadcopterin hover conditions show that the Madgwick filter SFA output can mitigate disturbances from the harmonic frequencies generated by the quadcopter. Index Terms—Sensor Fusion, Madgwick filter, IMU MPU6050, Quadcopter.
RANCANG BANGUN SISTEM MONITORING PRODUKSI MADU DAN HASIL SAMPING PADA FLOW-HIVE SYSTEM BERBASIS INTERNET OF THINGS Naudy, Ayliefia Ramadhan; Nurussa’adah, n/a; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 12 No. 3 (2024)
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Honey is a non-wood forest product derived from bees, with Indonesian honey production in 2022 reaching 189,780 litersannually, while the demand ranges from 5,000 to 15,000 tons per year based on a per capita consumption of 30 g/year. A primar y issue in conventional beekeeping is the alteration in honey quality due to contamination during post-harvest extraction processes. Beekeepers require an effective and flexible solution to address this problem. The Flow-Hive system, an Australian innovation, utilizes partial split cell technology with artificial combs, significantly differing in yield compared to conventional methods. Annually, conventional honey production yields an estimated net profit of 1,649.08 USD. The integration of IoT technology in the Flow-Hive system enhances monitoring efficiency and reduces bee stress by minimizing human contact. The IoT-based Flow-Hive monitoring system via smartphone includes honey weight measurement using a Load Cell HX711 sensor (up to 5 kg) with an accuracy of 99.8%, and by-product volume measurement using an ultrasonic HC SR04 sensor with an accuracy of 98.71%. The ESP32 microcontroller processes sensor data for display on an LCD and transmits it to the Blynk application, enabling real-time monitoring and data collection with 100% accuracy. Linear regression analysis indicated that honey production reached 3 kg on day 550, and by-product production peaked on day 9, with prediction accuracies of 99.90% and 90.11%, respectively. Keywords: Flow-hive system, Monitoring, Sensor, Honey, and Internet of Things
IDENTIFIKASI JENIS TANAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR RESIDUAL NETWORK (RESNET-50) DAN MOBILE NETWORK (MOBILENETV2) Syarifah, Naily; Mudjirahardjo, Panca; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 12 No. 3 (2024)
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The research was conducted to identify soil types using artificial intelligence using the Convolutional Neural Network method.(CNN). This is done to help young farm activists stay up-to-date to get land use information, i.e. by helping in optimizing theidentification of land or land as a growing medium. The study uses the MobileNetV2 and ResNet-50 architectures to identify different soil types. Both architectures compared their performance in identifying soil types through the texture and colour taken on the test image set data. Before doing the training of course the data will be used through pre-processing for the consistency of input and maximize the modeling process. Both were tested by performing several scenarios to obtain each the best performance results of the optimizer, the number of epochs and the learning rate values. Models of both architectures have a high degree of accuracy and precision. 3. For the MobileNet architecture, the V2 produced models with accuracy values of 91.91%, loss of 90.87%, and a prediction time of 0.108 seconds. And for the ResNet-50 architecture the model produced a precision value of 99.08%, precision 99.11%, recall 99.12%, F1-score 99.10%, specification 99.85%, loss 5.02% and forecast time of 0.037 seconds. Keywords: CNN, Soil Classification, MobileNetV2, ResNet-50
PERBANDINGAN PERFORMA PROTOKOL COAP DAN HTTP PADA MIKROKONTROLER ESP32 SEBAGAI PERANGKAT IOT PEMANTAU KUALITAS UDARA Wijayanto, Micko; Muttaqin, Adharul; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 12 No. 4 (2024)
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The development of the Internet of Things (IoT) is driving transformation in various sectors of life. One important aspect of IoT technology is data communication, which has various solutions, including the Internet. With a variety of internet protocol solutions available, appropriate protocol recommendations are needed for IoT needs, such as CoAP or HTTP. This research compares the performance of these two protocols using an ESP32 Microcontroller integrated with MQ7 and DHT11 sensors for air quality monitoring. Performance assessments include data communication network, power consumption, and memory requirements. Implementing the CoAP Protocol requires configuration so that data packets can capture tokens provided by clients. Data communication analysis, using Wireshark software, shows that the average delay of CoAP is 80% higher compared to HTTP, and the average power consumed by CoAP is also 4% greater. However, the average throughput measured is 27% higher in HTTP compared to CoAP, and CoAP's storage efficiency is also 3% better. Meanwhile, both protocols have insignificant differences in data delivery reliability and dynamic memory usage. Keywords: Internet of Things, Microcontroller ESP32, CoAP, HTTP, Performance Comparasion
IMPLEMENTASI YOLO DALAM SISTEM PENGAWASAN UJIAN DENGAN PROSES DETEKSI SECARA REAL-TIME Brahmana, Nigel Shidqy Razendriya; Mudjirahardjo, Panca; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 13 No. 3 (2025)
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Tingkat kecurangan dalam pelaksanaan ujian di Indonesia masih menjadi permasalahan serius, yang semakinkompleks akibat kemajuan teknologi dan kemudahan akses perangkat elektronik seperti ponsel dan laptop. Berdasarkanlaporan Kemendikbud (2019), terdapat 202 aduan kecurangan dalam Ujian Nasional dengan 126 kasus terkonfirmasi, sertasurvei oleh Winardi & Anggraeni (2017) menunjukkan bahwa 77,5% mahasiswa akuntansi mengakui pernah melakukankecurangan akademik. Penelitian ini mengimplementasikan algoritma YOLOv5 dalam sistem pengawasan ujian untukmendeteksi objek terlarang secara real-time. Sistem ini dirancang untuk mengidentifikasi keberadaan barang-barang sepertiponsel, tas, dan buku yang dapat mengindikasikan kecurangan selama ujian. YOLOv5 dipilih karena efisiensi dan akurasinyadalam deteksi objek. Metode penelitian melibatkan pengumpulan data citra, pelabelan objek, pelatihan model YOLOv5, danpengujian kinerja sistem. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi objek terlarang dengan baik,dengan nilai precision 0,84, recall 0,791, dan mAP 0,852. Sistem ini berpotensi untuk diintegrasikan ke dalam kegiatan ujianuntuk meningkatkan pengawasan ujian secara otomatis.Kata Kunci— YOLOv5, Sistem Proctoring, Deteksi Objek, Computer Vision, Real-time.
PERANCANGAN EKSTENSI CHROME PENDETEKSI SITUS WEB PHISHING DENGAN HYPER PARAMETER TUNING PADA MODEL RANDOM FOREST Abrar, Ahmad Akmal; Setyawan, Raden Arief; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 13 No. 4 (2025)
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Phishing adalah sebuah kejahatan digital untuk mencuri informasi rahasia pengguna dengan caramenggunakan email dan situs web palsu yang tampilannya menyerupai tampilan asli web sebenarnya.Informasi yang dicuri dapat berupa informasi pribadi, informasi akun atau informasi keuangan. Salah satu carauntuk mencegah pengguna internet terjebak phishing yaitu dengan pendeteksian.Penelitian ini bertujuan untuk membangun Google Chrome extension yang dapat mendeteksi situsweb phishing dengan menerapkan hyperparameter tuning pada model machine learning Random Forest.Google Chrome extension dibangun dengan HTML, CSS, dan JavaScript. Pendeteksian situs web phishingdilakukan oleh model Random Forest yang terintegrasi dengan extension melalui server. Model Random Forestdibuat dengan mengimplementasikan hyperparameter tuning menggunakan metode Grid Search. Grid Searchdiatur untuk mencari model dengan akurasi terbaik kombinasi parameter yang dipilih.Hasil pengujian model dengan 150 data situs web phishing dan 150 data situs web valid menunjukkanbahwa model hasil hyperparameter tuning memiliki performa yang lebih baik secara keseluruhandibandingkan model yang tidak di-tuning. Model yang tidak di-tuning memiliki akurasi sebesar 68,67%, presisisebesar 67,72%, recall sebesar 71,33%, dan F1-score sebesar 69,48%. Model dengan hyperparameter tuningmemiliki akurasi sebesar 74,67%, presisi sebesar 74,34%, recall sebesar 75,33%, dan F1-score sebesar74,83%.Terjadi peningkatan pada akurasi sebesar 6%, presisi sebesar 6,62%, recall sebesar 4%, dan F1-Scoresebesar 5,35%.Kata Kunci: Google Chrome extension, phishing, hyperparameter tuning, Random Forest
PENGAPLIKASIAN ALGORITMA DYNAMIC ROUTING PADA ESP-NOW UNTUK ESP32 Rahmansyah, Aqil Gama; Setyawan, Raden Arief; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 13 No. 4 (2025)
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ESP-NOW pada ESP32 memiliki keterbatasandalam membentuk jaringan mesh skala besar dan dinamiskarena skema broadcast datarnya dan tidak adanyamekanisme rerouting. Penelitian ini mengusulkan sebuahprotokol routing dinamis ringan yang terinspirasi dariOSPF, yang disesuaikan untuk ESP-NOW. Node ESP32secara periodik mengirim beacon, saling mengenalitetangga, serta mencatat “kill-count” untuk mendeteksidan menghapus node yang offline. Node-node ini salingbertukar tabel tetangga, menyebarkan metrik hop-count,dan menghitung jalur terpendek untuk mengirim datasecara optimal. Pengujian dilakukan di lingkungan rumahdengan banyak tembok dan gangguan sinyal, sertapenempatan node secara acak untuk mensimulasikankondisi nyata. Hasilnya, jaringan mesh dapat terbentuksecara otomatis dan hampir instan tanpa konfigurasimanual. Tingkat keberhasilan pengiriman paket mencapailebih dari 80% dalam kondisi ideal, meskipun performadapat menurun karena hambatan fisik dan keterbatasanantena bawaan ESP32. Meski begitu, protokol ini terbuktiefektif dalam membentuk jaringan mesh secara otomatis,skalabel, dan tangguh terhadap perubahan topologi. Solusiini cocok untuk implementasi jaringan IoT yang dinamis,hemat daya, dan berlatensi rendah.Kata Kunci: ESP32, ESP-NOW, routing dinamis,jaringan mesh, IoT, OSPF, peer-to-peer
KLASIFIKASI KADAR NITRIT PADA AIR MENGGUNAKAN SPEKTROFOTOMETRI KAMERA DAN METODE SUPPORT VECTOR MACHINE (SVM) Panjaitan, Gian Amadea; Mudjirahardjo, Panca; Razak, Angger Abdul
Jurnal Mahasiswa TEUB Vol. 13 No. 4 (2025)
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Peningkatan kadar nitrit dalam air minum, yangsebagian besar berasal dari penggunaan pupuk nitrogen disektor pertanian, telah menjadi isu lingkungan dankesehatan yang signifikan [1]. Meskipun nitrogen pentinguntuk produksi pangan [2], residunya dapat mencemari airtanah dan permukaan, menyebabkan akumulasi nitrat dannitrit yang berpotensi membahayakan kesehatan manusia[3]. Nitrit dalam tubuh dapat membentuk senyawa Nnitroso yang bersifat karsinogenik [4]. WHO telahmenetapkan batas aman nitrit dalam air minum sebesar 10mg/L [5], namun studi menunjukkan banyak wilayah yangmelebihi batas ini, baik di negara maju maupunberkembang [6]. Berdasarkan latar belakang tersebut,penelitian ini mengembangkan sistem klasifikasi kadarnitrit dalam air menggunakan pendekatan spektrofotometriberbasis kamera dan algoritma Support Vector Machine(SVM). Sistem ini dirancang untuk memberikan solusipraktis, portabel, dan terjangkau bagi deteksi nitrit,khususnya di daerah dengan keterbatasan akseslaboratorium.Kata Kunci: Nitrit, Air Minum, Spektrofotometri Kamera,Support Vector Machine, Klasifikasi.I. PENDAHULU
Co-Authors Abrar, Ahmad Akmal Achmad Basuki Adharul Muttaqin Akhmad Zainuri Algi Fari Fahrozi Aloysius Andhika Aryadwuputra Ardiana, Ahmad Shobri Brahmana, Nigel Shidqy Razendriya Erza Pradipta Erza Pradipta, Erza Ezekiel, Yeftha Joshua Fahrizal Hari Utama Faris Aulia Ramadhan Fauzi, Maher Firhan Bagus Adji Kusnadi, Vanny Lastono Risman Masykur Huda Masykur Huda Maulana, Eka Mudjirahardjo, Panca Muhammad Aswin Muhammad Aswin Muslim, ST., MT., Ph.D, Muhammad Aziz n/a Soeprapto Nardo Golan Naudy, Ayliefia Ramadhan Nur Ridho Fuadi Nurussa’adah, n/a Pangestu, Mochammad Kelvin Yudha Panjaitan, Gian Amadea Prastyawan, Hermanto Purnomo, Jason Manuel Putri, Amelia Kartika R. Arief Setyawan Raden Arief Setyawan Rahmadwati Rahmadwati Rahmadwati, n/a Rahmansyah, Aqil Gama Ramadhan, Faris Aulia Rohmatillah, Mahdin Syarifah, Naily Vika Zaky Firnanda Wahyu Suryo Samudro Waru Djuriatno Wijayanto, Micko