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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat CARADDE: Jurnal Pengabdian Kepada Masyarakat IJAIT (International Journal of Applied Information Technology) Prosiding Konferensi Nasional PKM-CSR Indonesian Community Journal Literasi: Jurnal Pengabdian Masyarakat dan Inovasi eProceedings of Applied Science eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa
Dedy Rahman Wijaya
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : eProceedings of Engineering

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Automated Tuna Freshness Assessment via Gas Sensors and Machine Learning Algorithms Pratama , Nyoman Raflly; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Ensuring the safety and health of fish products is crucial for public health, with tuna being Indonesia's second most popular fishery product. Tuna freshness is a key indicator of seafood safety, directly impacting both nutritional quality and contamination risk. This study compares the K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM) algorithms to assess and classify tuna freshness, offering an accurate and efficient approach. A machine learning model categorized Tuna freshness based on the gases emitted, utilizing a dataset of 58,389 records. Gas changes were detected using the MQ-135, MQ-9, and MQ-2 sensors, which are highly sensitive to gases like ammonia, methane, and alcohol, commonly associated with spoilage. The KNN, Naive Bayes, and SVM algorithms were then applied to classify the sensor data. KNN and SVM achieved an accuracy of 99%, while Naive Bayes reached 90%. The high accuracy of these methods highlights their potential as practical tools for the fishing industry, enabling suppliers and retailers to assess tuna freshness more effectively. This method could significantly improve consumer safety by ensuring only high-quality, fresh products reach the market. Additionally, automation offers substantial time savings, facilitates faster decision-making, and reduces reliance on manual inspections prone to human error. Keywords—tuna, classification, gas sensor, machine learning
Comparison of k-NN and Naive Bayes Algorithms for Classifying Mackerel Tuna Freshness For Real-Time Classification Using Gas Sensors Setyagraha , Muhammad Rafi Mahfuz; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
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The large production and consumption of mackerel tuna in Indonesia reflect its importance as a local staple and a valuable export product contributing to the nation's economy. Mackerel tuna is prized for its nutritional content and affordability, making it a crucial part of the diet for many Indonesians. Ensuring the freshness and quality of this high-demand product is essential. This study introduces a machine-learning approach to detect fish freshness by analyzing gases emitted during spoilage, utilizing MQ-2, MQ-9, and MQ-135 gas sensors. The data were processed using the k-Nearest Neighbors (k-NN) and Naive Bayes algorithms, both achieving accuracy rates near 100%. These findings highlight the system’s potential to enhance quality control in Indonesia’s fishery industry by offering an efficient and reliable method for assessing fish freshness. Keywords—classification, machine learning, tuna, gas sensor
Crab Quality Detection with Gas Sensors Using a Machine Learning Hermawan, Laksamana Mikhail; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
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Crab is a widely recognized and favored seafood product globally. Crab’s delicious taste and high nutritional value, particularly its protein content, make it a desirable food choice. Given the global popularity of seafood, including crabs, maintaining its quality is essential for both economic and consumption purposes. However, seafood products are prone to rapid spoilage due to their high-water content, with spoilage rates varying among different types of seafood. It is crucial for industries to monitor and ensure the quality of their products before they reach the market. Given the high demand for crabs, there is a pressing need for an effective method to assess their quality. This research seeks to establish a method for assessing the freshness and quality of crabs using an electronic nose (e-nose) system, employing machine learning algorithms for classification analyses. Three algorithms will be utilized, along with hyperparameter optimization, to achieve optimal accuracy in evaluating crab quality. These algorithms are K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Naïve Bayes. The highest result is achieved by K-NN methods with 98% accuracy percentage. The proposed method of this research has acquired targets that can contribute to advancing seafoods production for industries. Keywords—crabs, detection, e-nose, machine learning, quality
Co-Authors , Inne Gartina Aditya Permadi, Aditya Aditya, Bayu R Aldilla Iradianty Ardiansyah, Fajri Asniar Asniar Astri Wulandari Bayu Rima Aditya Bethani Suryawardani Damanik, Junia Putri Dendi Gusnadi Elis Hernawati Fathya Ariyani Fazri, Irfan Moch Gartina, Inne Gunadi, Gagah Aji Gunawan, Tedi Guntur Prabawa Kusuma Hermawan, Laksamana Mikhail Heru Nugroho Inne Gartina Irfan Moch Fazri Ledya Novamizanti Marcelino, Dandy Marchiningrum, Anranur Uwaisy Muhammad Akbar Haikal Frasanta Muhammad Nezar Mahardika Muhammad, Rafi Narwin, Agung Isra Pramuko Aji Pratama , Nyoman Raflly Rathimala Kannan Riyanarto Sarno Setyagraha , Muhammad Rafi Mahfuz Sherla Yualinda Sherli Yualinda Siska Komala Sari SRI RAHAYU Sri Rahayu Suryana Suryana Suryatiningsih Tora Fahrudin Wawa Wikusna