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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Scientific Journal of Informatics
Rahmalisa Suhartina
Akademi Teknik Elektromedik Andakara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Evaluasi Kinerja Sensor BH1750 pada Sistem Monitoring Iluminansi berbasis Mikrokontroller Rahmalisa Suhartina
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1983

Abstract

Penilaian kualitas pencahayaan pada berbagai lingkungan aplikasi, termasuk fasilitas pelayanan kesehatan, sangat bergantung pada sistem monitoring iluminansi yang efektif. Perkembangan sensor cahaya digital yang dikombinasikan dengan platform mikrokontroler telah memungkinkan dilakukannya pengukuran iluminansi secara otomatis dengan biaya yang relatif rendah serta fleksibilitas implementasi sistem. Sensor BH1750 merupakan salah satu sensor cahaya digital yang banyak digunakan dalam sistem monitoring iluminansi; namun, kinerja pengukurannya perlu dievaluasi secara cermat sebelum diterapkan secara luas. Penelitian ini bertujuan untuk mengevaluasi kinerja sensor BH1750 pada sistem monitoring iluminansi berbasis mikrokontroler melalui analisis akurasi, presisi, dan linearitas hasil pengukuran. Pengujian dilakukan pada tiga tingkat iluminansi, yaitu rendah (≈8.000 lux), sedang (≈15.000 lux), dan tinggi (≈22.000 lux), dengan beberapa kali pengulangan pengukuran pada setiap kondisi menggunakan sumber cahaya terkontrol serta instrumen acuan terkalibrasi. Hasil pengujian menunjukkan bahwa sistem monitoring yang dikembangkan memiliki akurasi pengukuran yang baik dengan nilai rata-rata galat persentase kurang dari 3%, presisi yang memadai dengan simpangan baku kurang dari 8%, serta hubungan linear yang sangat kuat terhadap instrumen acuan (R² = 0,9989). Temuan ini menunjukkan bahwa sensor BH1750 dapat diandalkan sebagai bagian dari sistem monitoring iluminansi digital untuk penilaian kualitas pencahayaan pada fasilitas pelayanan kesehatan maupun lingkungan kritikal lainnya.
Arduino-Based Optical System for Non-Invasive Blood Glucose Estimation Using BPW34 Photodiode Samsul Mujakar; Rahmalisa Suhartina; Nanda Ferdana; Ade Firdaus; M. Arif Herdiansyah
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 8 No. 3 (2026): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v8i3.2712

Abstract

Diabetes mellitus requires regular blood glucose monitoring, while conventional glucometers generally rely on invasive finger-prick procedures. This study developed an Arduino-based non-invasive blood glucose estimation system using a BPW34 photodiode and a green LED. The system acquired optical signals, processed ADC values, and estimated glucose concentration using linear regression. Experimental measurements were compared with a standard invasive glucometer. The results showed a positive linear relationship between ADC values and glucose concentration, with measurement errors ranging from 0% to 2.13%. The findings demonstrate the preliminary feasibility of the proposed system for portable non-invasive glucose estimation.
Optimizing Fall Detection System as an Early Warning System for the Elderly to Enhance Quality of Life Sidiq Syamsul Hidayat; Fadhil; Irfan Mujahidin; Mohammad Faizin Zaini; Rahmalisa Suhartina
Scientific Journal of Informatics Vol. 11 No. 2: Regular Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3716

Abstract

Purpose: The main objective of this research is to develop a fall detection system and improve rapid emergency response or early warning systems for falls in the elderly. Methods: In this research, the waterfall method was used for image analysis to detect falls with high accuracy. We also used Raspberry Pi 3, and OpenCV3 to set up a server to receive fall detection alerts and forward them to email. Results: This system integrated a camera mounted on a Raspberry Pi 3 to continuously monitor the area captured by the camera. In the fall detection system, the results of testing with data showed that the system accuracy was 72.22%, sensitivity 72.72%, and error 27.77%. Novelty: The approach this research adopted can be used in a variety of settings, including home healthcare, elderly care facilities, or places that require safety monitoring. With this system, we hope to improve rapid response in emergency situations, thereby protecting and improving the quality of life for people in need.
Co-Authors Ade Firdaus Fadhil Irfan Mujahidin M. Arif Herdiansyah Mohammad Faizin Zaini Nanda Ferdana Samsul Mujakar Sidiq Syamsul Hidayat, Sidiq Syamsul