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All Journal Jurnal Informatika dan Teknik Elektro Terapan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi To Maega: Jurnal Pengabdian Masyarakat INTI Nusa Mandiri
Marzuki Sinambela
Sekolah Tinggi Meteorologi Klimatologi dan Geofisika
Computer Science & IT Economics, Econometrics & Finance Other

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 1 
DESIGN OF TILT TABLE FOR ABSOLUTE ACCELEROMETER CALIBRATION Moh. Mambaul Ulum; Hapsoro Agung Nugroho; Marzuki Sinambela
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No1.pp118-122

Abstract

An accelerometer is used in applications that require measurements of absolute or relative acceleration. Tilt table (roll table) is a flat plane whose tilt can be changed. In BMKG (Climatology and Geophysics Meteorological Agency) there are two methods to calibrate accelerometer, namely calibration using relative sine waves and absolute calibration using the media acceleration due to gravity. The tilt table is used as an absolute accelerometer calibration aid. The tilt table uses a DC motor and gear ratio as the actuator to move so that the resolution of the angle changes on the axis of the tilt table will be smaller and smoother. The tilt table is able to assist the technician in calibrating the accelerometer with minimal angle movements so that the inclination settings at the time of calibration are more precise proved by the result of the comparison. The horizontal axis correction is 2,27o and the vertical is 2,31o.
Edukasi Peralatan Meteorologi Sebagai Indikator Cuaca dan Polusi Udara di Desa Pasir Tanjung, Lebak, Banten Agustina Rachmawardani; Djoko Prabowo; Kanton Lumban Toruan; Nardi Nardi; Marzuki Sinambela; Abdul Manaf; Maqbul Azis
To Maega : Jurnal Pengabdian Masyarakat Vol 7, No 1 (2024): Februari 2024
Publisher : Universitas Andi Djemma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/tomaega.v7i1.2481

Abstract

Desa Pasirtanjung merupakan salah satu desa di Indonesia yang menghadapi tantangan terkait pemahaman akan cuaca dan dampak pencemaran udara. Perubahan iklim dan perubahan pola hujan telah menjadi isu yang semakin penting bagi kehidupan sehari-hari masyarakat desa ini. Faktor-faktor lingkungan yang mempengaruhi kualitas udara, seperti polusi udara dari industri, kendaraan bermotor, pembakaran sampah, dan aktivitas manusia lainnya serta faktor-faktor yang memengaruhi curah hujan, seperti pola aliran udara, pengaruh lautan, dan topografi. Edukasi dan sosialisasi mengenai peralatan yang dapat digunakan sebagai indikator cuaca, seperti alat pengukur curah hujan, alat pengukur kualitas udara PM2.5 dan PM10, serta kesadaran akan polusi udara, menjadi aspek krusial dalam mendukung kesejahteraan dan pengelolaan lingkungan yang berkelanjutan di Desa Pasirtanjung. Pada kegiatan pengabdian masyarakat ini telah dirancang alat monitoring curah hujan dan kualitas udara serta edukasi peralatan sebagai indikator cuaca dan polusi udara di desa Pasir Tanjung yang akan menjadi landasan penting untuk meningkatkan pemahaman dan merespons terhadap faktor-faktor lingkungan yang berdampak pada kehidupan sehari-hari mereka. Dari hasil post test dan pretest pengetahuan peserta terkait edukasi peralatan cuaca ini naik sekitar 20 % – 60%.
Prediksi Evaporasi Berbasis Mesin: Perbandingan ANN, KNN, Random Forest dan Regresi Linier Muchamad Rizqy Nugraha; Haidar Amru Rusdan; Marzuki Sinambela
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp380-386

Abstract

Evaporation plays a key role in water allocation, yet data limitations are often encountered. This study evaluates four regression models (Linear Regression, K-Nearest Neighbors, Random Forest, and Artificial Neural Network—ANN) to predict evaporation rates at the Banten Climatology Station. Models were assessed using R-squared (R²) and Root Mean Squared Error (RMSE). The results show that the ANN achieved the best accuracy with RMSE = 0.122 and R² = 0.475 (47.5%), followed by Linear Regression (RMSE = 0.123, R² = 0.460), K-Nearest Neighbors (RMSE = 0.126, R² = 0.437), and Random Forest (RMSE = 0.129, R² = 0.406). Other models also provided acceptable predictions, but the ANN stood out as the most accurate and reliable for applications at the Banten Climatology Station. These findings offer valuable insights for water resources management and agricultural planning, highlighting the potential of machine learning techniques to overcome evaporation data limitations.
ANALISIS OPTIMASI MODEL YOLOv8 UNTUK DETEKSI JENIS AWAN PADA JETSON NANO Arfany Dhimas Muftareza Muftareza; Agustina Rachmawardani; Hapsoro Agung Nugroho; Marzuki Sinambela
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.11023

Abstract

Penerapan deteksi jenis awan berbasis deep learning pada perangkat edge menghadapi keterbatasan sumber daya komputasi yang dapat menurunkan efisiensi inferensi. Penelitian ini menganalisis optimasi model YOLOv8 untuk deteksi 10 jenis awan pada NVIDIA Jetson Nano melalui konversi model ke format TorchScript, ONNX, NCNN, dan MNN dengan presisi FP32 dan FP16. Dataset yang digunakan terdiri atas 4.916 citra, kemudian melalui augmentasi menghasilkan 11.791 citra pelatihan. Model YOLOv8s dilatih selama 100 epoch menggunakan batch size 16, learning rate 0,001, optimizer AdamW, dan pre-trained weight YOLOv8s. Model menghasilkan precision 0,86557, recall 0,90721, F1-score 0,887, mAP50 0,92518, dan mAP50-95 0,73288, dengan bobot terbaik diperoleh pada epoch ke-42. Pengujian pada Jetson Nano menunjukkan NCNN FP16 memberikan performa komputasi terbaik dengan processing time 761,99 ms dan FPS 1,31, meningkat sekitar 6,9 kali dibandingkan baseline PyTorch sebesar 5.238,57 ms dan 0,19 FPS. Nilai mAP50 tetap 0,9927, sedangkan mAP50-95 sebesar 0,8683. Hasil tersebut menunjukkan bahwa NCNN FP16 merupakan format yang paling optimal untuk implementasi YOLOv8 pada Jetson Nano karena mampu meningkatkan efisiensi inferensi dengan penurunan akurasi yang relatif kecil.
KLASIFIKASI INTENSITAS HUJAN PER JAM MENGGUNAKAN 1D-CNN BERBASIS TINYML DENGAN KALIBRASI AMBANG PRECISION-RECALL Revangga Kusuma Dhani; Agustina Rachmawardani; Marzuki Sinambela; Adi Widiatmoko Wastumirad
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.v21i1.8794

Abstract

Hydrometeorological disasters dominate annual disaster occurrences in Indonesia, yet local rainfall prediction remains challenging due to atmospheric complexity and limited resolution of numerical weather models. Server-based forecasting systems further depend on high-performance computing infrastructure and stable network connectivity that are not always available in the field. This study develops an hourly rainfall intensity classification model based on One-Dimensional Convolutional Neural Network (1D-CNN) deployable on an ESP32-S3 microcontroller as a proof-of-concept inference component for rainfall early warning systems. The model uses nine meteorological features arranged in an 18×9 sliding window derived from observational data from the BMKG Soekarno-Hatta Meteorological Station AWS from 2018 to 2025. Logarithmic class weighting and Precision-Recall curve threshold calibration were applied to address extreme class imbalance in hourly resolution data. The model was compared against five baseline models under identical configurations. Threshold calibration increased K2 recall from 0.037 to 0.236 and improved Macro-F1 from 0.461 to 0.530, outperforming all baseline models in terms of Macro-F1. Post-training quantization INT8 reduced model size from 185.8 KB to 64.8 KB with 99.02% decision agreement against the Float32 model. On-device inference on ESP32-S3 achieved a total latency of 13.873 ms with 36.5 KB tensor arena and 234.7 KB free heap, confirming real-time operation without dependence on servers or internet connectivity.
Co-Authors Abdul Manaf Adi Widiatmoko Wastumirad Agustina Rachmawardani Arfany Dhimas Muftareza Muftareza Djoko Prabowo Haidar Amru Rusdan Hapsoro Agung Nugroho Kanton Lumban Toruan Maqbul Azis Moh. Mambaul Ulum Muchamad Rizqy Nugraha Nardi Nardi Revangga Kusuma Dhani