Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal INTI Nusa Mandiri PROFICIO: Jurnal Pengabdian Masyarakat Newton-Maxwell Journal of Physics
Muhammad Nur Rizqi
Unknown Affiliation
Humanities Astronomy Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Mechanical Engineering Physics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
SOLARISLIGHT : SISTEM PENERANGAN BERKELANJUTAN MENGGUNAKAN IOT DAN SOLAR CELL SYSTEM UNTUK EFISIENSI ENERGI PADA DESA WISATA CIASMARA Syifa Nurgaida Yutia; Aisyah Novfitri; Annisa Desianty; Alva Nurvina Sularso; Hakim Giraldi Saputra; Muhammad Nur Rizqi
Jurnal Pengabdian Masyarakat FKIP UTP Vol 7 No 1 (2026): PROFICIO : Jurnal Abdimas FKIP UTP
Publisher : FKIP UNIVERSITAS TUNAS PEMBANGUNAN SURAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/jpf.v7i1.6134

Abstract

Kegiatan Pengabdian Masyarakat yang diadakan di desa Ciasmara di kabupaten Bogor bertujuan untuk meningkatkan infrastruktur berupa fasilitas umum penerangan lampu jalan sebagai bagian dari rencana desa Ciasmara sebagai desa wisata. Fasilitas penerangan lampu jalan berbasis IoT dan solar panel bertujuan untuk memberikan lingkungan yang aman, nyaman bagi para wisatawan, dan menciptakan energi bersih. Penerangan jalan menggunakan teknologi IoT dan Solar Panel menawarkan perawatan yang mudah dan tahan lama, dan tidak membebani biaya listrik desa. Sehingga teknologi ini menjadi solusi terbaik untuk alternatif di daerah terpencil. Tahapan implementasi program SolarisLight dimulai dengan kegiatan observasi untuk melakukan pemetaan titik penanaman tiang lampu. Proses perancangan dan pembuatan alat membutuhkan waktu satu minggu sebelum dilakukan pemasangan solar panel dan sistem IoT. Lima titik lampu penerangan ditenagai dengan kapasitas baterai sebesar 12V 300Ah dalam panel box mampu memberi ketahanan pencahayaan hingga 2 hari di malam hari. Sistem IoT yang diintegrasikan dengan panel surya memungkinkan adanya pemantauan parameter kelistrikan secara real time. Pemberian buku manual dan kegiatan sosialisasi penggunaan SolarisLight dilakukan agar masyarakat desa wisata Ciasmara dapat memonitoring sistem Solarislight melalui aplikasi yang diakses menggunakan smartphone. Hasil survei kepuasan mitra menunjukan adanya peningkatan jumlah wisatawan di desa Ciasmara setelah implementasi sitem SolarisLight. Dengan adanya hasil ini, diharapkan dapat menjadi program pengabdian masyarakat yang berkelanjutan.
Prediksi Trajektori dan Intensitas Siklon Tropis Menggunakan Pendekatan Multi-Task Learning Berbasis Recurrent Neural Network Syahid, Wisnu; Putu Aldi Tusan Pratama; Muhammad Nur Rizqi; Yosik Norman
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.47955

Abstract

The limited ability of Numerical Weather Prediction (NWP) models to capture nonlinear dynamics and atmospheric uncertainty remains a major challenge in improving tropical cyclone forecasts, particularly over the eastern Indian Ocean. This study evaluates a Multi-Task Learning approach based on several Recurrent Neural Network (RNN) variants, namely LSTM, BiLSTM, GRU, and BiGRU, to simultaneously predict three key cyclone components: position (latitude and longitude), wind intensity, and cyclone category. Historical IBTrACS data from 2000 to 2025 with a 3-hour temporal resolution are used as model input, employing 48-hour sequences to forecast cyclone conditions at lead times of 12, 24, 48, and 72 hours. The results show that all models achieve stable convergence during training. At a 12-hour lead time, the BiLSTM model delivers the best performance, with a mean position error of 83.53 km and a Hit Rate of 0.966, outperforming the other models. For longer lead times (24–72 hours), the BiGRU model demonstrates the most stable positional accuracy, exhibiting the lowest error degradation as the forecast horizon increases. In addition, wind intensity predictions remain robust, with a Mean Absolute Error (MAE) below 4.6 knots up to 72 hours. These findings highlight the potential of multi-output RNN-based models to support more adaptive and efficient tropical cyclone forecasting systems.
PREDIKSI KATEGORI CURAH HUJAN BERBASIS MACHINE LEARNING UNTUK MENDUKUNG KETAHANAN PANGAN I Dewa Gede Loka Maheswara; Kanaya Kaizzi Larasati; Muhammad Nur Rizqi; Muhammad Fany Nurwibowo; Yosafat Donni Haryanto
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8601

Abstract

Rainfall variability significantly influences food security in Central Tapanuli Regency, North Sumatra, a region where agriculture is strongly reliant on climatic patterns. This research evaluates and compares the classification performance of Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest (RF) for categorizing daily rainfall as supplementary information to strengthen food security. A total of 3,644 daily meteorological records obtained from FL Tobing Meteorological Station spanning 2015 to 2024 were utilized, encompassing seven predictor variables: minimum temperature, maximum temperature, average temperature, mean relative humidity, sunshine duration, peak wind speed, and average wind speed. To mitigate class imbalance, the original six rainfall categories were consolidated into four classes by merging the minority groups. The data were partitioned into training and testing subsets at an 80:20 ratio using stratified sampling, after which the Synthetic Minority Over-sampling Technique (SMOTE) was employed on the training set. Hyperparameter tuning was conducted through Grid Search combined with 5-fold cross-validation, and classification performance was assessed using accuracy, precision, recall, F1-score, and paired t-test analyses. The experimental results indicated that RF delivered superior performance, attaining an accuracy of 51.44% and a weighted F1-score of 0.5036, significantly outperforming both SVM and K-NN (p-value < 0.05). Feature importance analysis revealed that sunshine duration, average temperature, and maximum temperature were the most influential predictors. These outcomes demonstrate that RF holds considerable promise for advancing machine learning-driven rainfall category prediction systems capable of delivering early-stage information for agricultural planting schedules and preparedness against intense rainfall events in Central Tapanuli Regency
Co-Authors Aisyah Novfitri Alva Nurvina Sularso Annisa Desianty Hakim Giraldi Saputra I Dewa Gede Loka Maheswara Kanaya Kaizzi Larasati Muhammad Fany Nurwibowo Putu Aldi Tusan Pratama Syahid, Wisnu Syifa Nurgaida Yutia Yosafat Donni Haryanto Yosik Norman