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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika dan Multimedia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal ABDINUS : Jurnal Pengabdian Nusantara Prosiding Conference on Research and Community Services Journal Automation Computer Information System (JACIS) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Minfo Polgan (JMP) Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Multimedia Jurnal ELTEK Journal of Applied Business and Technology International Journal of Entrepreneurship, Business, and Creative Economy Journal of Artificial Intelligence and Digital Business Information Technology Education Journal Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Informatika Polinema (JIP) Journal of Information Technology and Cyber Security Jurnal Pengabdian Kepada Masyarakat KOPEMAS
Toga Aldila Cinderatama
Politeknik Negeri Malang
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Deteksi Plat Nomor Kendaraan Angkutan Bus Menggunakan YOLOv11 Benni Agung Nugroho; Toga Aldila Cinderatama; Abidatul Izzah; Ellya Nurfarida
Jurnal Informatika dan Multimedia Vol. 17 No. 2 (2025): Jurnal Informatika dan Multimedia
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jtim.v17i2.9204

Abstract

Penelitian ini mengatasi permasalahan pendataan plat nomor bus secara konvensional di terminal yang rentan terhadap kesalahan manusia, pelaporan yang lambat, dan kurangnya transparansi. Tujuan penelitian ini adalah merancang dan mengimplementasikan sistem deteksi dan pengenalan plat nomor angkutan bus secara otomatis menggunakan kombinasi teknologi Kecerdasan Buatan (AI), khususnya model deep learning YOLOv11, dan Internet of Things (IoT). Penelitian ini menggunakan Raspberry Pi 5 sebagai perangkat edge yang terhubung dengan webcam untuk menangkap video beresolusi 1280x720 , yang kemudian melakukan deteksi plat nomor secara real-time. Model YOLOv11 dilatih menggunakan 682 frame gambar , menunjukkan kinerja yang sangat baik pada dataset validasi dengan nilai mean Average Precision (mAP50) sebesar 0.98674. Meskipun keterbatasan Raspberry Pi 5 tanpa dedicated Neural Processing Unit (NPU) membatasi kecepatan pemrosesan real-time menjadi 10–18 FPS pada resolusi 640x480 , kecepatan ini dinilai mencukupi untuk deteksi bus di lingkungan terminal karena pergerakan kendaraan yang tidak cepat. Teks plat nomor yang berhasil dikenali dan timestamp kemudian dikirimkan ke database Firebase melalui arsitektur IoT. Sistem yang dihasilkan diharapkan dapat membantu dalam pendataan kendaraan bus di terminal dengan lebih cepat, efisien, dan akuntabel
Monitoring of Irrigation Channel Discharge Based on IoT and LoRaWAN Communication with Long Short-Term Memory (LSTM) Predictive Analysis Toga Aldila Cinderatama; Afta Ramadhan Zayn; Rinanza Zulmy Alhamri; Yoppy Yunhasnawa; Kenneth Pinandhito
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16612

Abstract

This study presents the design and implementation of an Internet of Things (IoT)-based irrigation canal monitoring system that integrates Long Range Wide Area Network (LoRaWAN) communication with a Long Short-Term Memory (LSTM) prediction model. The system is designed to provide real-time monitoring and prediction of water discharge in irrigation canals to support efficient water resource management on agricultural land. The proposed system consists of two IoT sensor nodes, each equipped with a YF-B5 flow sensor, an HC-SR04 ultrasonic sensor, and a DS18B20 temperature sensor, all connected to an ESP32 microcontroller. The collected data are transmitted via the LoRaWAN protocol and stored in Firebase Realtime Database, where they are visualized through an Android application. The predictive component employs an LSTM algorithm to forecast future water discharge based on historical time-series data. Experimental results indicate that the LSTM model achieved a Mean Absolute Error (MAE) of 13.1640 and a Root Mean Squared Error (RMSE) of 23.0630, demonstrating high accuracy and stability in predicting water discharge fluctuations. The integration of IoT, LoRaWAN, and LSTM-based prediction enables real-time monitoring and predictive analysis for smart irrigation management.
Optimasi Hyperparameter Convolutional Neural Network untuk Klasifikasi Citra Makanan Selingan Kunti Eliyen; Abidatul Izzah; Toga Aldila Cinderatama; Rinanza Zulmy Alhamri
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16523

Abstract

Optimasi hyperparameter adalah langkah mendasar dalam meningkatkan kinerja model Convolutional Neural Network (CNN). Penelitian ini bertujuan untuk mengoptimalkan hyperparameter dalam arsitektur EfficientNetB0 menggunakan Genetic Algorithm (GA) untuk meningkatkan akurasi klasifikasi gambar snack. Dataset citra makanan selingan terdiri dari 250 citra yang diklasifikasikan dalam 10 kelas, dengan 80% dialokasikan untuk data pelatihan dan 20% untuk data validasi. Proses optimasi dilakukan dengan menentukan nilai terbaik dari learning rate, dropout rate, dan batch size. Metode GA diterapkan untuk menemukan kombinasi optimal melalui proses seleksi, crossover, dan mutasi selama beberapa generasi. Hasilnya menunjukkan bahwa penggunaan GA dapat menentukan kombinasi hyperparameter yang meningkatkan akurasi model dibandingkan dengan seleksi manual. Kombinasi hyperparameter terbaik yang diperoleh adalah learning rate 0,000670, dropout rate 0,34, dan freeze ratio 0,73. Untuk mencegah overfitting, selama pelatihan diterapkan penghentian dini dan pengurangan tingkat training. Model ini dilatih untuk 40 epoh dan memperoleh 92% akurasi validasi. Hasil ini menunjukkan efektivitas GA dalam mempercepat proses menemukan hiperparameter optimal di CNN berbasis EfficientNetB0, terutama untuk kumpulan data citra makanan skala kecil yang rentan terhadap overfitting.
Application for data collection and monitoring of COVID-19 patients in Sukorame Community Health Center Toga Aldila Cinderatama; Rinanza Zulmy Alhamri; Fery Sofian Efendi; Kunti Eliyen; Benni Agung Nugroho
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 12 No. 1 (2022): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v12i1.19-30

Abstract

The significant increase in COVID-19 cases in Indonesia in May-July 2021 overwhelmed health workers. One of the efforts to monitor the spread of COViD-19 disease is collecting data on patients and proper monitoring. For example, the Sukorame Community Health Center, Mojoroto Kediri, does not yet have an application to record and monitor COVID-19 patients. Data collection is currently done manually by writing in books and excel. This study designed and built a data collection and monitoring application for COVID-19 patients to help Puskesmas staff obtain more accurate patient data and monitor the related patient data. This study implements the waterfall method, including system requirements, design, implementation, verification, and maintenance. The results of this study are the applications that can help and facilitate Community Health Center in collecting data on COVID-19 as a form of effort in overcoming and preventing the spread of COVID-19 in the work area of Sukorame Community Health Center, Kediri City. Based on the user satisfaction questionnaire results, 75% of users consisting of staff and heads of community health centers were helped by this application.
Co-Authors Abidatul Izzah Afta Ramadhan Zayn Agustono Heriadi Agustono Heriadi Agustono Heriadi, Agustono Ahmad Saifi Athoillah Amini, Julia Intan Andika Kurnia Adi Pradana Ardiansyah, Mohammad Rifqi Dwi Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta Ashafidz Fauzan Dianta, Ashafidz Fauzan Asmoro, Wiwiek Kusumaning Ata Amrullah Atif Windawati Atik Tri Andari Atik Tri Andari, Atik Tri Atiqah Nurul Asri Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho, Benni Agung Budiprasetyo, Gunawan Candra Bella Vista Devianti, Meryta Dewi, Isyana Wikrama Dharma Tungga Dion Yanuarmawan Eliyen, Kunti Ellya Nurfarida Elok Nur Hamdana Elok Nur Hamdana Fadelis Sukya Fadelis Sukya Fadelis Sukya, Fadelis Fariez Ilham Alviansyah Fery Sofian Efendi Fery Sofian Efendi Fery Sofian Efendi Fery Sofian Efendi Fery Sofian Efendi Fery Sofian Efendi Fery Sofian Effendi Fery Sofian Effendi fitria nur hamidah Fitria Nur Hamidah, Fitria Nur Gunawan Budiprasetyo Hadi Rahmad Hamdani Arif Hendrawan, Muhammad Afif Irfin Sandra Asti Junaedi Adi Prasetyo Kenneth Pinandhito Kenneth Pinandhito Kunti Eliyen Kunti Eliyen Maskur, M Meyti Eka Apriyani Moch Zawaruddin Abdullah Mujahid Wahyu Mungki Astiningrum Nugraha, Girindra Fajar Putranti, Eti Rahmad Aliy Cagar Wahyu Aji Ratna Widyastuti, Ratna Renaldi, Rasyed Rinanza Zulmy Alhamri Rinanza Zulmy Alhamri, Rinanza Zulmy Riswan Eko Wahyu Susanto Riswanda Al Farisi Rudy Ariyanto Setianingsih, Novie Astuti Sofian Efendi, Fery Trisha Alfandi Windawati, Atif Yan Watequlis Syaifudin Yanuarmawan, Dion Yohan Bakhtiar Yoppy Yunhasnawa Yoppy Yunhasnawa Yuri Arianto Yuri Ariyanto Zulmy Alhamri, Rinanza