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All Journal Jurnal Biogenerasi MATHunesa: Jurnal Ilmiah Matematika
Emon Sejahtera Zendrato
Universitas Negeri Medan
Education Languange, Linguistic, Communication & Media Mathematics Other

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Analisis Perbandingan Ekstrak Kulit Buah Naga (Hylocereus polyrhizus) dan Kunyit (Curcuma longa L.) Sebagai Indikator Identifikasi Boraks Pada Makanan Claudia Nainggolan; Eka Setiawan; Emon Sejahtera Zendrato; Grecia Trinatal Simbolon; Kania Restya Diva; Larasati Arum Utami; Rejeki Sihite
Jurnal Biogenerasi Vol. 11 No. 2 (2026): April - Juni 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/sbjm5j61

Abstract

This study aimed to analyze and compare the effectiveness of red dragon fruit peel (Hylocereus polyrhizus) extract and turmeric (Curcuma longa L.) extract as natural indicators for detecting borax in food samples. The research used a laboratory experimental method with a qualitative descriptive approach. Food samples tested included sausages, ketupat, lontong, white tofu, crackers, siomay, meatballs, cilok, pempek, nuggets, white noodles, and yellow noodles. The testing process was conducted by dripping dragon fruit peel extract and turmeric extract onto each sample and observing the resulting color changes. The results showed that most samples tested negative for borax, while meatballs and yellow noodles indicated positive results. The color change in turmeric to reddish-brown occurred due to the reaction between curcumin and borax, while dragon fruit peel extract changed color because anthocyanin pigments are sensitive to pH changes. The findings indicate that both natural materials can be used as simple, inexpensive, safe, and effective natural indicators for the qualitative preliminary detection of borax in food. However, further quantitative testing is needed to obtain more accurate results.
KLASIFIKASI LAJU PERTUMBUHAN PENDUDUK ANTAR PROVINSI DI INDONESIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Agung Atra Perkasa; Emon Sejahtera Zendrato; Ilham Pratama; Hizkia Simamora
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p271-278

Abstract

Population data plays a crucial role in development planning, yet its utilization has not yet been optimized to generate truly informative insights. This study aims to classify population growth rates across provinces in Indonesia into low, medium, and high categories using the K-Nearest Neighbor (KNN) algorithm. The research process includes data collection and preprocessing, category labeling using the quantile method, data normalization via Min-Max Scaling, and splitting the data into training and test sets with a 70:30 ratio. The KNN model was built using parameter values of k, namely 3, 5, and 7, and the best value of k was selected based on the model evaluation results. Evaluation was performed using a confusion matrix by calculating the accuracy value. The test results showed that the best model was obtained at k = 5 with an accuracy of 75%. These findings indicate that KNN can identify similarities in demographic characteristics across provinces quite well, although there are still classification errors in classes with closely related characteristics. Therefore, the KNN method can serve as a simple and effective approach for population data analysis and has the potential to support data-driven decision-making.
Co-Authors Agung Atra Perkasa Claudia Nainggolan Eka Setiawan Grecia Trinatal Simbolon Hizkia Simamora Ilham Pratama Kania Restya Diva Larasati Arum Utami Rejeki Sihite