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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengabdian kepada Masyarakat Nusantara eProceedings of Engineering Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Jurnal Abdimas
Rifqi Muhammad Fikri
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Desain 3D dan Implementasi IoT pada Sistem Pemberian Makanan Kucing Otomatis Berbasis Deep Learning Fadhel, Hafiz Muhammad; Kallista, Meta; Fikri, Rifqi Muhammad
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Pemberian pakan kucing secara teratur dan terukur merupakan salah satu faktor penting dalam menjaga kesehatan hewan peliharaan. Namun, banyak pemilik kucing menghadapi kendala dalam mengatur porsi dan jadwal makan secara konsisten karena keterbatasan waktu. Penelitian ini mengusulkan pengembangan sistem smart feeder berbasis Internet of Things (IoT) yang terintegrasi dengan teknologi deep learning untuk memantau dan mengatur pemberian makanan kucing secara otomatis. Proses perancangan dimulai dengan pembuatan desain 3D perangkat menggunakan kombinasi material akrilik, kayu, dan komponen hasil cetak 3D berbahan PLA. Raspberry Pi 4 digunakan sebagai mikrokontroler utama yang mengendalikan sensor beban untuk pengukuran porsi makanan, kamera untuk deteksi keberadaan kucing, serta aktuator untuk mekanisme distribusi pakan. Sistem IoT memungkinkan pemilik memantau dan mengontrol perangkat melalui aplikasi berbasis web secara real-time. Hasil implementasi menunjukkan bahwa rancangan ini mampu memberikan porsi makanan sesuai pengaturan dan beroperasi secara otomatis sesuai jadwal yang ditentukan. Kesimpulannya, penggabungan desain mekanis, IoT, dan deep learning dapat menghasilkan perangkat pemberian pakan kucing yang efisien, praktis, dan membantu pemilik dalam mengoptimalkan perawatan hewan peliharaan. Kata kunci — Deep Learning, Internet Of Things, Smart Feeder, Desain 3d, Raspberry Pi 4B, Sensor Beban
Impementasi Deep Learning Berbasis YOLOv5 Untuk Identifikasi Kucing Individu pada Pet Feeder Manna, Muhammad Rayyan Aqiilah; Kallista, Meta; Fikri, Rifqi Muhammad
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstrak — Penelitian ini mengembangkan dan mengimplementasikan model deep learning pada sistem Smart Pet Feeder untuk mengenali kucing peliharaan individu berdasarkan foto yang diunggah pengguna melalui aplikasi. Teknologi You Only Look Once (YOLO) dipilih karena memiliki kecepatan deteksi tinggi dan akurasi yang memadai untuk pengenalan secara real-time. Dataset dibuat secara khusus dari foto-foto kucing milik pengguna dengan berbagai sudut dan kondisi pencahayaan guna meningkatkan kemampuan generalisasi model. Proses pelatihan dilakukan dengan pengaturan hyperparameter yang divariasikan, kemudian dievaluasi menggunakan metrik mean Average Precision (mAP) dan confusion matrix untuk mengukur performa prediksi. Hasil pengujian menunjukkan model mampu mengenali kucing dengan akurasi tinggi pada kondisi pencahayaan terang, namun mengalami penurunan performa pada kondisi minim cahaya. Tingkat keakurasian model pada alat sangat bergantung pada kualitas, sudut, dan pencahayaan foto yang diunggah oleh pengguna. Integrasi model ke dalam Smart Pet Feeder memungkinkan sistem mengatur akses makan hanya untuk kucing yang terdaftar, sehingga meningkatkan keamanan, mengurangi risiko makanan diakses kucing lain, dan membantu pemilik memantau aktivitas makan hewan peliharaan secara efektif. Kata kunci— computer vision, deep learning, deteksi realtime, identifikasi kucing, smart pet feeder, YOLO
Research Article EVALUASI EFEKTIVITAS OTOMATISASI IRIGASI DENGAN SENSOR LINGKUNGAN PADA PERTANIAN BROKOLI anggi, anggi amalia; Faisal Candrasyah Hasibuan; Rifqi Muhammad Fikri
Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Vol. 5 No. 1 (2025): Desember 2025
Publisher : Perpustakaan Universitas Telkom

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Abstract

Penelitian ini membahas pengembangan sistem monitoring dan kontrol irigasi otomatis berbasis Internet of Things (IoT) untuk budidaya tanaman brokoli. Sistem ini menggunakan mikrokontroler ESP32 yang terhubung dengan dua sensor kelembapan tanah, sensor pH tanah, dan sensor pH air, yang mengirimkan data secara real-time ke Firebase Realtime Database. Data divisualisasikan melalui aplikasi Android yang dilengkapi fitur dashboard, grafik monitoring harian dan mingguan, kontrol manual dan otomatis katup selenoid, pencatatan riwayat penyiraman, serta notifikasi otomatis. Pengujian lapangan dilakukan pada dua petak tanaman brokoli seluas ±20 m² dari total lahan petani ±2.500 m². Hasil pengujian pada lahan uji menunjukkan bahwa sistem mampu menjaga kelembapan tanah pada kisaran 60–70%, menurunkan potensi tanaman tidak tumbuh optimal dibanding metode penyiraman manual, serta membantu efisiensi penggunaan air pada area yang diuji. Pengujian white-box dan black-box memastikan seluruh fungsi berjalan sesuai rancangan dan dapat diakses dengan baik oleh pengguna. Sistem ini berpotensi menjadi solusi pendukung bagi petani dalam memantau kondisi lahan brokoli dan mengoptimalkan proses penyiraman pada skala kecil yang dapat dikembangkan lebih lanjut untuk skala lahan yang lebih luas.
LSTM Parameter Optimization with Genetic Algorithm for Stunting Prediction Rifqi Muhammad Fikri; Ayi Purbasari; Arief Zulianto; Fedri Ruluwedrata Rinawan; Ari Indra Susanti
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10761

Abstract

Stunting is caused by a lack of nutrients or sickness, and stunted children may have impaired immune systems, increased mortality rates, and are more prone to endure long-term developmental abnormalities. Stunting prevalence in Indonesia remains concerningly high by the end of 2024. Through the use of integrated health posts, or pos pelayanan terpandu (Posyandu), and the technology-based website iPosyandu, attempts are being made to lower the prevalence of stunting. Using toddler data from iPosyandu, this study proposes a Long-Short Term Memory (LSTM) model for predicting stunting based on WHO standards, categorizing children as tall, normal, stunted, or severly stunted. By using a genetic algorithm (GA) for learning rate hyperparameter tuning, the LSTM model is significantly improved. Five generations, each with five populations, were used for the GA-based optimization, which explored learning rates ranging from 5.23E-04 to 8.83E-03. The results show that 7.82E-03 was the optimal learning rate, producing the greatest accuracy of 91.10%, indicating that this range improves the performance of LSTM models.
Smart Drip Irrigation System with Integrated Soil pH and Moisture Sensors Using RS485 Communication Muhammad Rafindha Aslam; Faisal Candrasyah Hasibuan; Rifqi Muhammad Fikri
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11507

Abstract

Agricultural development requires reliable methods for measurable soil condition monitoring to support crop growth. Soil moisture and soil pH are critical parameters that significantly affect plant development. Manual observation of these parameters is inefficient and often produces inconsistent data. This study designed a smart drip irrigation system integrating soil moisture and soil pH sensors with RS485 communication using the MAX485 module. Arduino Nano was applied as the sensor node for data acquisition, and ESP32 was used as the master controller for decision-making in irrigation and fertilization. System validation employed Lavios VT-05 as the reference instrument. Experimental results showed that soil moisture sensors provided consistent readings with minimal deviation across different conditions, while soil pH sensors maintained high accuracy compared to standard buffer solutions. RS485 communication was tested up to 7 meters and demonstrated stable performance. These findings confirm that the system is capable of accurate soil monitoring and reliable communication for smart drip irrigation applications.
Implementation of Web based Civil Administration System for Citizen Unit Level Budhi Irawan; Purba Daru Kusuma; Rifqi Muhammad Fikri; Muhammad Riyadhul Jinan Nasution; Faiq Haqqani; Raenaldi Ardiansyah Sidik
Jurnal Abdimas Vol. 30 No. 2 (2026): December 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/abdimas.v30i2.57468

Abstract

Civil administration system is an important system that supports e-government to improve the governance process. Unfortunately, many citizen-unit level in Indonesia still handle this process manually.  This condition also happens in citizen unit no. 25, Melong Village, Cimahi, West Java. To solve this problem, a community service project by Telkom University is running to develop and implement the civil administration system for this citizen-unit. The community service team consists of lecturers and students. The system is developed based on web so that it can be accessed easily for the citizens. This system is also a mobile-friendly application so that the appearance is appropriate when it is accessed through smartphone. The main features are the demographic and financial data. The socialization was held on June 5, 2026. There are several issues regarding privacy, security, server, and additional features. All these issues have been addressed. To maintain the sustainability of the program, commitment for continuous improvement and implementation has been stated through capstone project, and submission for next funding.
Co-Authors Akbar, Maulidiatama anggi, anggi amalia ANGGUNMEKA LUHUR PRASASTI Annisa, Shavira Nur Anugerah, Rey Rizqi Ari indra Susanti Ariasa, Rafif Arief Zulianto Ashri Dinimaharawati Ayi Purbasari Azhar, Ferikho Fatih Budhi Irawan Burhanuddin Dirgantoro Butar-Butar, Muhammad Raihan Dhesvira Nurseha Putri Dwiyansyah, Muhammad Raflika Dzikra, Iffat Fadhel, Hafiz Muhammad Fadhilah , Muhammad Rizal Faiq Haqqani Faisal Candrasyah Hasibuan Farha S. N, Augyres Fauzaan, Muhammad Rifqi Al Fauzi, Farhan Rizki Fedri Ruluwedrata Rinawan Firdaus, Mohamad Fidi Fussy Mentari Dirgantara Jamila , Andi Cleopatra Maryam Kalista, Meta Khoerul Umam, Khoerul Malikulmulki, Jaisy Manna, Muhammad Rayyan Aqiilah Maulana, Cholasih Ryan Meta Kallista Muhammad Rafindha Aslam Muhammad Riyadhul Jinan Nasution Prayoga, Ivan Fernanda Purba Daru Kusuma Putra, Ibrahim Bintang Rismayadi Putti, Fasya Hanifah R. Yunendah Nur Fu’adah Raenaldi Ardiansyah Sidik Ramadhanti, Tassya Rita Magdalena Roswan Latuconsina Sihaloho, Mutiara Tampubolon, Yosua Gery