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All Journal JOIV : International Journal on Informatics Visualization Publikasi Hasil Pengabdian Kepada Masyarakat.
Hadi, Mochammad Zen Samsono
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Humanities Computer Science & IT Other

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Sistem Sirkulasi Air Kolam Otomatis Berdasarkan Nilai pH Ramadhani, Afifah Dwi; Sudarsono, Amang; Pratiarso, Aries; Yuliana, Mike; Ningsih, Norma; Hadi, Mochammad Zen Samsono; Kristalina, Prima; Satiti, Rini; Astawa, I Gede Puja; Siswanto, Anang
PUBLIKASI PENGABDIAN KEPADA MASYARAKAT Vol 4 No 1 (2024)
Publisher : Fakultas Ekonomi dan Bisnis Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/padimas.v4i1.6714

Abstract

Banyak petani dari berbagai lapisan masyarakat, baik itu dari kalangan menengah atas maupun kalangan bawah yang memiliki lahan terbatas, beralih ke praktik pertanian modern seperti aquaponik. Pendekatan ini tidak hanya efisien dalam penggunaan ruang dan waktu, tetapi juga mampu menghasilkan panen yang optimal melalui perawatan yang cermat. Dengan memonitoring pH air menjadi langkah penting dalam menilai kualitas air yang dapat memberikan informasi mengenai kondisi baik atau buruknya. Air yang memiliki kualitas buruk dapat menimbulkan dampak negatif terhadap kesehatan ikan, seperti munculnya berbagai penyakit. Perubahan pH air juga dapat mengakibatkan perubahan aroma, rasa, dan warna air. Oleh karena itu, penelitian ini bertujuan untuk merancang dan menerapkan suatu sistem yang berfungsi untuk memantau tingkat keasaman (pH) dalam air, dengan memanfaatkan kemajuan teknologi saat ini. Pengukuran pH air dapat dilakukan secara manual menggunakan pH meter pada mikrokontroler. Oleh karena itu, dikembangkan sistem pemantauan untuk mempermudah pengendalian pH air, sehingga proses pembenihan ikan dapat ditingkatkan dan disederhanakan. Pada pengujian sensor pH, ketika terdeteksi nilai pH di luar kisaran netral misalnya 7,71 maka sistem sirkulasi air akan aktif untuk mengoreksi pH kolam agar mencapai kondisi netral. Hal ini bermanfaat untuk menjaga kualitas air kolam.
Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification Darmawan, Hendri; Yuliana, Mike; Hadi, Mochammad Zen Samsono; Sangaiah, Arun Kumar
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3104

Abstract

The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to watch these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to find pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, keeps an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms SoftMax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system can provide biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also proved efficiency, enabling farmers to identify pests and disease without relying on internet connectivity. 
Integrated Paddy Pest Detection System Using Hybrid Model and Edge Computing with LoRa Communication and GIS Interface Lazuardi, Mochamad Riswandha; Hadi, Mochammad Zen Samsono; Kristalina, Prima; Uehara, Hideyuki
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3529

Abstract

There is an emerging requirement for early detection of pests in the field concerning agricultural yield and quality improvement. Traditional methods often result in a loss of the desired outcome due to delayed intervention and increased crop losses. This work focuses on establishing an integrated pest detection system using a hybrid model that combines MobileNet and Faster R-CNN, optimized for real-time performance at the edge. Additionally, LoRa-based data transmission was employed, along with a GIS interface for monitoring. The system is further tested with the diverse dataset of 4,736 images representing common rice pests. It included lightweight feature extraction with precision object detection, as it produced the lowest loss among other models tested. Further implementation is made on a Raspberry Pi, which shows optimal performance in detecting at a distance of 15 cm and with 100 lux of lighting. LoRa communication was adopted for effective data transmission with low power consumption and extensive coverage, while the GIS interface enabled real-time monitoring of pests in space and time. Field tests demonstrated that this system achieved very high accuracy, rapid response, and was applicable in the field for pest control, offering the potential to increase yields and improve farmer welfare. Further research could focus on adapting the system to a wide range of environmental conditions and scaling it up for more extensive agricultural use. The integral approach forms necessary steps toward smart farming. However, it also provides a scalable, low-cost solution for early pest detection.
Co-Authors Amang Sudarsono, Amang Anang Siswanto, Anang Aries Pratiarso, Aries Darmawan, Hendri I Gede Puja Astawa Lazuardi, Mochamad Riswandha Mike Yuliana Ningsih, Norma Prima Kristalina Ramadhani, Afifah Dwi Sangaiah, Arun Kumar Satiti, Rini Uehara, Hideyuki