Garuda - Garba Rujukan Digital

Tajrin, Tajrin

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Author-ID : 1096445

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Klusterisasi Stunting Pada Balita Menggunakan Algoritma K-Medoids Untuk Mengidentifikasi Faktor Dominan Saragih, Leonardo; Pasaribu, Nanda Sabrina; Harefa, Novi Karlianti; Tajrin, Tajrin
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8713

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use.

ANALISIS METODE CLASSIFICATION MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DALAM MENENTUKAN KUALITAS JERUK POMELO Tajrin, Tajrin; Thuan, Steven
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1970

Pomelo (Citrus maxima), one of Indonesia's native citrus fruits, possesses high economic value and is widely cultivated across various regions in diverse varieties such as Bali Merah, Cikoneng, Nambangan, Raja, Ratu, and Pangkep. Despite its potential, quality assessment of pomelo fruits is still mostly conducted manually based on physical characteristics, which may lead to subjective and inconsistent results. This study aims to develop a more objective and efficient method by utilizing the K-Nearest Neighbor (K-NN) classification algorithm within a data mining framework. Six key features were used as classification variables: peel pigmentation, surface smoothness, fruit softness, weight, skin thickness, and overall quality. The research used a dataset collected from the North Sumatra Plantation Office over the past five years (2020–2024), which was processed and analyzed using the Orange application. Evaluation of the classification model achieved promising results, with an accuracy of 86.0%, F1-score of 0.860, precision of 0.861, recall of 0.860, AUC of 0.834, and MCC of 0.720. Additionally, predictions on new data samples confirmed the model’s ability to classify high-quality pomelo fruits effectively. These findings highlight the effectiveness of K-NN as a decision-support tool for improving fruit quality assessment processes and support the integration of data mining in smart agriculture practices.

Analisis Klusterisasi Stunting Pada Balita Menggunakan Algoritma K-Medoids Untuk Mengidentifikasi Faktor Dominan Saragih, Leonardo; Pasaribu, Nanda Sabrina; Harefa, Novi Karlianti; Tajrin, Tajrin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8713

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use.

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