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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) The Indonesian Journal of Computer Science
Mahmudah, Haniah
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Computer Science & IT Electrical & Electronics Engineering Engineering Social Sciences

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Pemanfaatan Alat Monitoring Kadar Air Pada Gabah untuk Peningkatkan Kualitas Panen Zainudin, Ahmad; Santoso, Tribudi; Wijayanti, Ari; Pratiarso, Aries; Sudarsono, Amang; Mahmudah, Haniah; Siswandari, Nur Adi; Budikarso, Anang; Syahroni, Nanang; Wahyuningrat S., Hari; Siswanto, Anang; Juliansyah, Farel; Farhan, Donny; Susanti, Tri
DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Vol 4, No 2 (2020)
Publisher : Politeknik Negeri Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32486/jd.v4i2.457

Abstract

Indonesia merupakan salah satu negara  agraris dimana sebagian besar penghasilan masyarakat Indonesia yaitu dari pertanian. Untuk mempertahan harga jual hasil pertanian, maka diperlukan perhatian khusus dalam penyimpanan hasil panen di gudang terutama kadar air hasil panen. Pada penelitian dalam bentuk kegiatan pengabdian kepada masyarakat ini dibuat sebuah alat monitoring kadar air pada gabah dengan memanfaatkan mikrokontroler node MCU dan sensor kadar air yang terhubung ke internet. Data monitoring kadar air disimpan pada database dan pengguna dapat mengaksesnya melalui perangkat smartphone pada halaman website. Sehingga pengguna dapat mengakses kondisi hasil panen mereka yang ada di gudang bisa dilakukan dimana saja. Kegiatan pengabdian masyarakat dilaksanakan di Desa Kampungbaru, Nganjuk. Berdasarkan hasil implementasi dan pengujian, hasil pembacaan sensor kadar air pada gabah sebesar 14% dapat dikirim ke server dan dapat diakses oleh pengguna menggunakan smartphone.   
Implementasi Telemetri dan Evaluasi Performansi Sistem Komunikasi Lora di Daerah Pesisir Pantai PUSPITORINI, OKKIE; MAHMUDAH, HANIAH; WIJAYANTI, ARI; SISWANDARI, NUR ADI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 1: Published January 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i1.180

Abstract

ABSTRAKSistem komunikasi dikembangkan untuk peningkatan ekonomi nelayan. Penelitian yang dikembangkan sistem komunikasi antar nelayan dengan pesisir pantai dengan biaya murah serta mempunyai jangkauan jarak yang terbatas. Untuk mengatasi jarak jangkauan jauh maka pada penelitian ini mengimplementasikan sistem komunikasi Lora multihop menggunakan telemetri dengan daya transmisi 24 dBm atau 250 mW. Sistem ini terdiri blok end device berupa telemetri berada pada kapal nelayan, blok gateway pada pinggir pantai, dan blok server application pada end user serta tampilan berupa website nelayan. Hasil pengujian telemetri sebagai transceiver dengan komunikasi multihop mampu digunakan untuk pengiriman data menghasilkan daerah jangkauan 7,2 km menjauhi pantai. Telemetri mengirimkan data ke gateway di pantai kemudian data diteruskan ke server cloud sebagai database. Perancangan database menggunakan model data relasional dan pengolahan data menggunakan algoritma FIFO. Hasil pengujian performansi sistem pada pengujian aplikasi front-end dan back-end menunjukan bahwa performansi sistem mampu menangani permintaan user secara cepat. Kata kunci: Telemetri, Lora, Multihop, Aplikasi, Pesisir Pantai ABSTRACTCommunication system was created to boost economic of fishing industry. Research created an inexpensive distance-restricted communication system for fishermen and coast. This study uses telemetry with transmission strength of 24 dBm or 250 mW to create multihop Lora communication in order to cross great distances. This system comprises of gateway block on coast, application server block on end user, display in form of fisherman's website, and end device block in form of telemetry on fishing boat. Using findings of testing telemetry as transceiver with multihop connection, coverage area of 7.2 km can be employed for data transfer. Data is transmitted from telemetry to coastal gateway and then sent to cloud server where it is stored as database. FIFO algorithm are used relational data modeling and data processing in database design. System's ability to handle user requests rapidly is demonstrated by results of front-end and backend application testing for system performance.Keywords: Telemetri, Lora, Multihop, Application, Coast
Detecting road damage utilizing retinanet and mobilenet models on edge devices Mahmudah, Haniah; Aisjah, Aulia Siti; Arifin, Syamsul; Prastyanto, Catur Arif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1430-1440

Abstract

A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devices. After tuning hyperparameters in the batch size of the optimizer, the Adam optimizer displayed enhanced performance with mean average precision (mAP), average recall (AR), and F1-score. This implies a positive impact on overall model performance. The MobileNetV2 model's hyperparameter tuning technique significantly improves performance, resulting in faster inference times and overall system performance. This demonstrates that the MobileNetV2 model could be used directly on edge devices to identify road damage. However, the RetinaNet152 model has a lower inference time, which cannot be deployed directly to edge devices. The RetinaNet152 model can be deployed on edge devices; however, a technique for speeding up inference time is essential.
Design and Development of a System for Monitoring Student Attention and Concentration during Learning using CNN Model and Face Landmark Detection Arifin, Syamsul; Aisjaha, Aulia Siti; Fatima, Azzezza Nurul; Mahmudah, Haniah
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.9.1.2897

Abstract

Mobile learning media has been wide and provides a tendency for lecturers to identify students' concentration levels in online classes. To bring the class into active learning, efforts are needed from lecturers and educational institutions to return students' concentration to the ongoing learning process. In this paper, a monitoring and alarm system is designed to increase student concentration and combines two elements of statistical analysis to validate CNN models that recognize face emotions in real time while learning. The research was carried out by recording face data using a camera, extracting digital features, and analyzing facial features. The results of the analysis are used as data input for the decision-making system regarding the level of concentration. The concentration level will be used to activate alarms and send them via chat so that students can focus on learning.The system is created by merging facial expression recognition (FER) and decision-making with a convolutional neural network. The system using a face landmark via camera V2 and a Raspberry Pi 4 performed with the Haar-Cascade classifier, extracting facial features. Face detection via camera is performed using the Haar-Cascade classifier, which extracts facial features. The results of CNN model face detection with landmark features showed good results, with weighted average performance of precision, recall, and F1-score close to 0.99. According to the implementation results, the average number of facial expressions identified in drowsy and neutral states. The device can alert lecturers to how frequently drowsy detects students within a 10-minute interval.
Vehicle Type Classification and Detection System using YOLOv7-tiny Model on Single-Board Computer Nadziroh, Faridatun; Sa’adah, Nihayatus; Widyatra Sudibyo, Rahardita; Mahmudah, Haniah; Imam Rifai, Moch.
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14637

Abstract

Transportation is playing an important role for human civilization, for example transportations is being used as distributing goods and products. Therefore, the total numbers of vehicles as a part of transportation will continue to increase every year. But in Indonesia, the majority of its people is still using their personal transport rather than public transportation. This is supported by the data of total number of vehicles in Indonesia from 2018 – 2020, which is shows that personal transport is still dominant than public transportation. The causes of traffic jams is a result of various factors, such as the roads are not designed to accommodate the increasing number of vehicles, insufficient traffic signs, and poor traffic management. The road traffic data is one of the aspects that could reduce traffic jams. The process of collecting road traffic data which is still done manually has several shortcomings, such as it takes a long time and there may be errors due to human error. This research has a goal to create a vehicle type detection and classification system that have a good detection accuracy and detection speed that can be run on single-board computer devices. YOLOv7-tiny model that performs detection and classification using input from video on the NVIDIA Jetson Nano device gets a True Positive (TP) score of 96.58%, a False Positive (FP) score of 0.98%, and a False Negative (FN) score of 2.44%. YOLOv7-tiny on the NVIDIA Jetson Nano device can run with an average Frame per Second (FPS) of 6 FPS.
Edge Computing-Based Automated Vehicle Classification System Using the MobileNet V2 Model Widyatra Sudibyo, Rahardhita; Mahmudah, Haniah; Hadi , Moch. Zen Samsono; Sa'adah, Nihayatus
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3106

Abstract

The volume of traffic in one day is referred to as the average daily traffic volume. The Average Daily Traffic System (LHR) is also used to detect road damage caused by excessive vehicle loads. In the LHR system, vehicle data is still collected manually, with humans calculating the type and number of vehicles based on observations made and then divided into a time span. As a result, a system with a camera and deep learning data processing is required to automatically calculate the type and number of vehicles. The goal of this research is to develop edge computing systems by improving the system's performance in the calculation and classification of vehicles using the SSD MobileNet V2 model. The results of the MobileNet model scenario 5 have the lowest loss value of the five scenarios. The MobileNet V2 model can better classify vehicle types with a 65 FPS inference process.
Co-Authors Abd. Rasyid Syamsuri Aisjaha, Aulia Siti Amang Sudarsono, Amang Anang Budikarso, Anang Anang Siswanto, Anang Ari Wijayanti Aries Pratiarso, Aries Aulia Siti Aisjah Catur Arif Prastyanto, Catur Arif Farhan, Donny Fatima, Azzezza Nurul Hadi , Moch. Zen Samsono Imam Rifai, Moch. Juliansyah, Farel Nadziroh, Faridatun Nur Adi Siswandari Okkie Puspitorini, Okkie Sa'adah, Nihayatus Santoso, Tribudi Sa’adah, Nihayatus Syahroni, Nanang Syamsul Arifin TRI SUSANTI Wahyuningrat S., Hari Widyatra Sudibyo, Rahardhita Widyatra Sudibyo, Rahardita