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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
andi susilo
Universitas Darma Persada
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences Other

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Webinar Literasi Digital bagi Pendidik & Anak Didik di Era Digital Bagus Tri Mahardika; Eva Novianti; Aji Setiawan; Afri Yudha; Andi Susilo
JEPTIRA Vol 1 No 2 (2023)
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jep.v1i2.18

Abstract

Pandemi telah memaksa berbagai kegiatan, termasuk pendidikan, untuk dilakukan secara daring guna menghindari penyebaran virus. Namun, model pembelajaran daring ini masih tergolong baru dan belum familiar bagi banyak pengajar dan siswa, sehingga menimbulkan kebingungan dalam pelaksanaannya. Salah satu solusi penting yang dapat mendukung proses pembelajaran di era digital adalah literasi digital. Literasi digital bertujuan untuk meningkatkan kemampuan masyarakat dalam menggunakan dan mengakses teknologi dengan bijak. Melalui kegiatan pengabdian masyarakat, informasi tentang literasi digital akan diberikan untuk mendukung dunia pendidikan. Pelatihan ini diharapkan dapat memberikan pengetahuan baru kepada peserta, memudahkan mereka dalam mengelola pembelajaran di masa pasca pandemi, serta mempersiapkan mereka menghadapi tantangan era industri 4.0.
IoT-Based Air Pollution Trend Analysis: A Case Study in Residential Areas of Karawang Industrial Estate Anindya Tara Danendra Alamsyah; Andi Susilo
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.140

Abstract

Air quality degradation in industrial zones poses a significant health risk to surrounding residential communities. Karawang, as a major manufacturing hub in West Java, faces challenges in monitoring particulate matter and gas emissions due to the high cost and limited coverage of standard monitoring stations. This study aims to design a low-cost Internet of Things (IoT) system to monitor and analyze air pollution trends in the residential buffer zones of the Karawang Industrial Estate. The proposed system integrates an ESP32 microcontroller with multispectral sensors, including MQ-135 for hazardous gases, PMS5003T for particulate matter (PM2.5/PM10), and DHT22 for meteorological variables. Data is transmitted in real-time to a web-based server using a non-blocking transmission algorithm to ensure data integrity. The results demonstrate that the system reliably captures diurnal fluctuations in air quality. Trend analysis reveals a consistent pattern of increased pollutant concentration during morning (06:00–09:00) and late-night (19:00–22:00) periods, correlated with anthropogenic activities and meteorological phenomena such as temperature inversion. While the daily average Air Quality Index (AQI) predominantly falls within the "Moderate" category, episodic spikes reaching "Unhealthy for Sensitive Groups" were observed, highlighting the necessity for granular, real-time monitoring for public health mitigation.
Optimalisasi Algoritma YOLOv5 untuk Deteksi Mata Katarak Andi Susilo; Fachri Adryansyah
Journal TIFDA (Technology Information and Data Analytic) Vol 1 No 2 (2024)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v1i2.55

Abstract

Penelitian ini membahas penerapan algoritma YOLOv5 untuk deteksi dini penyakit katarak berdasarkan pengolahan citra, katarak adalah penyakit mata umum yang dapat menyebabkan kebutaan jika tidak segera ditangani. YOLOv5 sebagai metode deteksi objek real-time mampu mengidentifikasi objek dalam satu frame gambar dengan kecepatan tinggi dan akurasi mencapai 85%. Hasil pengujian menunjukkan bahwa algoritma YOLOv5 ini mampu mengidentifikasi katarak dengan baik yang dibuktikan dengan nilai F1-Score sebesar 0,86, Precision 0,894, dan Recall 0,891.
Perancangan Sistem Pemantauan Kualitas Air Berbasis IoT pada Kolam Ikan Hias Air Tawar Shania Bakhtiar Paturusi; Andi Susilo
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.86

Abstract

This study focuses on designing an IoT-enabled monitoring system to enhance water quality management in freshwater ornamental fish ponds, with an emphasis on tracking temperature, pH, and total dissolved solids (TDS). Utilizing an ESP32 microcontroller along with DS18B20, pH, and TDS sensors, the system collects and transmits real-time water quality data via the Blynk platform. The findings demonstrate that the system effectively monitors temperature, pH, and TDS levels, initiating corrective actions like activating a water heater or solenoid valve when needed. By automating these processes, the system minimizes the need for manual checks, improves resource efficiency, and supports optimal guppy fish farming conditions.
SmartENose: Environmental Health Monitoring System for Cattle Sheds Using Fuzzy System Method Pandu Satya Ramadhani; Andi Susilo
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.110

Abstract

The air quality in livestock barns significantly impacts the health and productivity of animals. This study designs an air quality monitoring system based on the Internet of Things (IoT) utilizing the ESP32 microcontroller and three gas sensors: MQ-135 (ammonia), MQ-4 (methane), and MQ-7 (carbon monoxide). The collected data is processed using the Fuzzy Sugeno logic method, which involves fuzzification, rule base, and defuzzification stages to classify the air conditions into Safe, Alert, or Dangerous categories. The classification results are displayed in real-time through an I2C LCD, the Blynk application, and the SmartENose website developed with PHP and MySQL. Additionally, the system is equipped with LED indicators and a buzzer for early warning notifications. Testing results indicate that the system can detect gas concentrations responsively and accurately, providing air status classifications that align with the actual conditions in the cattle barn. This research demonstrates that the application of IoT technology, supported by Fuzzy Sugeno logic, can be effectively utilized for monitoring and early warning of air quality in agricultural environments.
Co-Authors Afri Yudha Aji Setiawan Anindya Tara Danendra Alamsyah Bagus Tri Mahardika Eva Novianti Fachri Adryansyah Pandu Satya Ramadhani Shania Bakhtiar Paturusi