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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Sains dan Teknologi SENTRI: Jurnal Riset Ilmiah
Yusril Ihza Tachriri
Institut Teknologi Dan Sains Nahdlatul Ulama Pekalongan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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System Design Of Printer Machine Using Image Classification Method Based On Iot To Optimize Production Output Yusril Ihza Tachriri; Elvinda Bendra Agustina; Dian Arif Rachman; Atika Windra Sari; Muhammad Rofiqul A’la; Muhammad Lutfhi Al Hisyam; Muhammad Irfan
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 3 (2026): SENTRI : Jurnal Riset Ilmiah, Maret 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i3.5986

Abstract

The micro, small, and medium enterprises (MSMEs) industry plays a crucial role in supporting the Indonesian economy, yet faces challenges in production efficiency and cost management. This study presents the design and development of an IoT-based automated screen-printing system that integrates edge-based image classification using a CNN model with microcontroller-driven print actuation, specifically tailored for MSME-scale garment production. The system employs an ESP32/Raspberry Pi as the edge device, enabling local inference without cloud dependency, and utilizes MQTT protocol for IoT connectivity. Quantitative evaluation across 50 test cycles demonstrated a 96% printer success rate, a 60% reduction in production time from 45 to 18 minutes per 10 shirts, a 90% reduction in labor from 10 operators to 1, and an approximately 50% reduction in per-unit production cost from Rp65,000–80,000 to Rp30,000–40,000 per shirt. IoT connectivity testing over 48 continuous hours recorded an average MQTT latency of 120 ms and a system uptime of 98.5%, confirming the reliability of the communication layer for sustained production operations. Grounded in Industry 5.0 principles, this research advances human-machine collaboration in small-scale manufacturing contexts. The proposed system offers a cost-effective, remotely controlled, and semi-autonomous production solution, representing a novel contribution to the field of IoT-based garment manufacturing in Indonesia.
Automatic Air Quality Detection System Design in the Industrial Area Atika Windra Sari; Elvinda Bendra Agustina; Isnaini Almaulida Umami; Yusril Ihza Tachriri; Jessika Artamevira; Elly Safitri
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.16144

Abstract

The industrial sector is an essential component of the economic structure. A large number of current enterprises degrade air quality in the environment as a result of chemical exposure. As a result, an air quality detection system was designed with the purpose of monitoring air pollutant levels, temperature, and humidity. The system is immediately integrated with smartphones via the Internet of Things. Carbon dioxide (CO2) and carbon monoxide (CO) levels are measured using a MQ-135 sensor. Temperature and humidity are measured using DHT-11 sensors. The complete system is controlled by a NodeMCU ESP8266 connected to an Arduino. Data was collected from two industries using an IoT-based system for a continuous 24-hour cycle over seven days to capture real-time daily fluctuation trends. The continuous 7-day monitoring reveals a clear daily fluctuation trend synchronized with factory shifts, peaking between 12:00 and 14:00 WIB. Dutatex exhibits a higher pollutant load (peak CO2: 820–850 ppm; CO: 8–9 ppm) than Bimatex (peak CO2: 620–650 ppm; CO: 5–6 ppm), while both facilities successfully maintain peak temperatures below 33.0°C. These empirical results demonstrate that continuous 24-hour data acquisition provides a highly accurate and comprehensive assessment for monitoring industrial air quality and occupational health.   Keywords - Air Quality, NodeMCU ESP8266, IoT, MQ-135, Arduino.
Rancang Bangun Smart Feeder Terintegrasi Monitoring Kualitas Air Bertenaga Surya Berbasis IoT pada Budidaya Perikanan Elvinda Bendra Agustina; Dian Arif Rachman; Yusril Ihza Tachriri; Jessika Artamevira; Khusfiatul Kholisah
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6658

Abstract

This study presents the design and implementation of a smart feeder system integrated with real-time water quality monitoring powered by a solar photovoltaic system and based on the Internet of Things (IoT). The system automatically dispenses fish feed and monitors key water quality parameters such as pH, temperature, and Total Dissolved Solids (TDS). The ESP32 microcontroller was used as the main controller, integrated with multiple sensors and an MQTT-based monitoring application. The entire system is powered by a 30 Wp solar panel and a 12 V battery, enabling autonomous operation in remote areas without grid access. Experimental results show that motor rotation speed significantly affects feed distribution. The optimal feeding performance was achieved at medium motor speed with a feed distance of 8 m, ensuring uniform feed dispersion and low energy consumption (±14.4 W). Water quality parameters remained within the ideal range for freshwater aquaculture: pH 6.6–6.9, temperature 27.5–28.8 °C, and TDS 380–450 ppm. The photovoltaic system generated around 90–110 Wh/day, meeting the system’s daily energy demand (±45 Wh/day). This integrated IoT–solar solution demonstrates high efficiency and sustainability, suitable for rural aquaculture applications.
Image Histogram Analysis in a Webcam-Based Non-Ionizing Tomographic System for Early Shrimp Disease Detection Yusril Ihza Tachriri; Dian Arif Rachman; Yuni Lestiani; Elvinda Bendra Agustina; Atika Windra Sari; Muhammad Rofiqul A’la; Rif’atun Romadhoni; Azka Nazhan Ristiani
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16203

Abstract

This study presents the development of a non-ionizing tomography imaging system using a webcam to detect diabetes-like metabolic disorders in Litopenaeus vannamei through digital image histogram analysis. The method employs controlled glucose injections (0–10 mL) to simulate metabolic variation, followed by optical image acquisition and RGB histogram characterization using Python-based GUI software. Calibration with a white-screen baseline ensures consistent optical response. Results show significant RGB histogram shifts correlating with glucose concentration changes, indicating optical density variations in shrimp tissues. The RGB mean intensity decreased by approximately 20–35% between 0 and 6 mL glucose concentration. Linear regression analysis within the 0–6 mL range yielded a sensitivity of 11.48 intensity units/mL with R² = 0.987, and an estimated detection limit of 0.4 mL equivalent glucose change. The method demonstrates potential as a non-invasive, low-cost diagnostic tool for early metabolic disorder detection in aquaculture.   Keywords - non-ionizing tomography, RGB histogram analysis, Litopenaeus vannamei, glucose detection, digital image processing
Co-Authors Atika Windra Sari Azka Nazhan Ristiani Dian Arif Rachman Elly Safitri Elvinda Bendra Agustina Isnaini Almaulida Umami Jessika Artamevira Jessika Artamevira Khusfiatul Kholisah Muhammad Irfan Muhammad Lutfhi Al Hisyam Muhammad Rofiqul A’la Rif’atun Romadhoni Yuni Lestiani