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Triandes Sinaga
Universitas Pelita Harapan
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Klasifikasi Tingkat Kelunturan Warna Kain Menggunakan KNN, SVM, dan Random Forest Romindo Romindo; Triandes Sinaga; Kevin Bastian Sirait; Arosochi Yosua Daeli; Jepronel Saragih
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

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Abstract

The laundry industry faces challenges in maintaining service quality, particularly regarding fabric color fading after washing. Assessments that are still performed manually tend to be subjective and inconsistent, so a more objective automated classification system is required. This study aims to apply and compare three algorithms, namely KNN, SVM, and Random Forest, to classify the level of fabric color fading based on digital images. The features used comprise color in the RGB and HSV spaces as well as shape in the form of area and shape ratio, all extracted automatically. A total of 300 images were divided into 250 training data and 50 testing data, then mapped into three categories, namely not faded, fairly faded, and faded. The testing results show that Random Forest delivers the best performance with an accuracy of 0.96, followed by SVM at 0.94 and KNN at 0.88. All models faced difficulties in recognizing the minority class due to data imbalance. This study proves that the machine learning approach, particularly Random Forest, is able to assess color fading levels more accurately and consistently than manual evaluation, while supporting quality control in the laundry industry.
Pemanfaatan Teknologi IoT dan Aplikasi Android untuk Pengendalian Kadar Amonia pada Peternakan Unggas Aditya Kristianto; Kevin Sirait; Triandes Sinaga
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6910

Abstract

This community service activity aims to implement an Internet of Things (IoT)–based system integrated with an Android application to monitor ammonia gas levels in poultry farms. The activity took place at Ziven Chicken Farm in Stabat, North Sumatra, which faces challenges related to ammonia accumulation from livestock waste. The methodology follows four phases: (1) initiation, involving initial observation and interviews with farm’s owner and workers concerning problems identifications and needs; (2) planning, including the design of the IoT solution and mobile application; (3) implementation, consisting of assembling the IoT device using the MQ-137 and DHT-22 sensors, integrating them with the ESP32 microcontroller, and developing the application connected to a cloud server; and (4) monitoring and control, which includes system performance monitoring, data validation, and evaluation of system use by the partner. The system provides real-time visualization of ammonia, temperature, and humidity levels through tables and graphs. Results show an upward trend of ammonia concentration, reaching 21 ppm on the third day, with humidity positively affecting ammonia levels. A notable spike occurred between 20:00 and 21:00 WIB, indicating the need for improved ventilation and cleaning routines. Overall, the system assists farmers in determining optimal maintenance schedules and supports the adoption of digital technologies in poultry farm management.
Co-Authors Aditya Kristianto Arosochi Yosua Daeli Jepronel Saragih Kevin Bastian Sirait Kevin Sirait Romindo Romindo