Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Journal of Environmental Engineering and Sustainable Technology Pharmascience Jurnal Informatika IJGIE (International Journal of Graduate of Islamic Education)
Nuraini, Nabila
Unknown Affiliation
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Environmental Science Mechanical Engineering Medicine & Pharmacology Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analysis of FastText with Support Vector Machine for Hate Speech Classification on Twitter Social Media Nuraini, Nabila; Latipah, Asslia Johar; Verdikha, Naufal Azmi
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.21107

Abstract

Hate speech refers to sentences or words that aim to demean or insult individuals, groups, or communities based on factors such as ethnicity, religion, race, or social class. In this study, Natural Language Processing (NLP) techniques were employed using FastText feature extraction and SVM algorithm for text classification. The evaluation was conducted using F1 Score as the performance metric. The data was divided using the Cross-Validation method with 10 folds, and the experiment was performed with four SVM kernels: RBF, Linear, Polynomial, and Sigmoid. The results of this research, based on the effectiveness of the FastTextSVM method combination, demonstrate a strong performance in hate speech classification. By adopting FastText parameters from previous studies and involving four SVM kernels, this research achieved a satisfactory average F1 Score. The results obtained for the Polynomial kernel showed the best performance with an F1 Score of 0.813, followed by the Linear kernel with 0.809, the RBF kernel with 0.808, and the Sigmoid kernel with 0.805. This indicates that the F1 Score results do not show significant differences in outcomes.
NUTRITION ESTIMATION OF LEFTOVER USING IMPROVED FOOD IMAGE SEGMENTATION AND CONTOUR BASED CALCULATION ALGORITHM Adinugroho, Sigit; Sari, Yuita Arum; Maligan, Jaya Mahar; Sari, Kartika; Bihanda, Yusuf Gladiensyah; Nuraini, Nabila; Fatchurrahman, Danial
Journal of Environmental Engineering and Sustainable Technology Vol 9, No 01 (2022)
Publisher : Directorate of Research and Community Service (DRPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2022.009.01.5

Abstract

In pandemic conditions, awareness of keeping a healthy balance is necessary. One is considering food consumption and understanding its nutrition content to avert food waste. We have been developing a prototype to estimate the nutrition of leftover food, and the main problem lies in image segmentation. Therefore, we propose the Improved Food Image Segmentation (IFIS) and Contour Based Calculation (CBC) to measure the area of the segmented image instead of pixel-wise. First, the tray box image is acquired and broken down into compartments using an automated cropping algorithm. The first step of this proposed method is tray box image acquisition and dividing the compartment using an automatic cropping algorithm. Then each compartment is treated using IFIS, calculates the result of IFIS by CBC, measures the estimated leftover by Automatic Food Leftover Estimation (AFLE), and then predicts the nutritional content. The evaluation is applied by comparing the actual measurement from the Comstock method and leftover estimation by the proposed algorithm. The result shows that Root Square Means Error (RMSE) reaches 0.48 compared to the actual weighing scale and 96.67% accuracy compared to the Comstock method. Based on the results, the proposed algorithm is sufficient to be applied.
Analisis Kandungan Natrium Siklamat pada Manisan Buah Mangga yang Dijual di Kota Pontianak Menggunakan Metode Spektrofotometri UV Nuraini, Nabila; Kurniawan, Hadi; Nugraha, Fajar
Jurnal Pharmascience Vol 11, No 2 (2024): Jurnal Pharmascience
Publisher : Program Studi Farmasi FMIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jps.v11i2.18074

Abstract

Natrium siklamat umumnya ditambahkan pada manisan buah mangga untuk memperbaiki cita rasa produk. Penggunaan natrium siklamat pada manisan mangga dicurigai tidak sesuai dengan kadar yang tercantum dalam Peraturan BPOM Nomor 11 Tahun 2019. Kondisi ini dapat menimbulkan efek yang berbahaya pada tubuh. Penelitian ini dilakukan untuk mengidentifikasi, menghitung kadar dan mengevaluasi kadar natrium siklamat pada manisan mangga yang dijual di kota Pontianak. Identifikasi natrium siklamat dilakukan secara kualitatif melalui metode pengendapan dengan pereaksi HCl, BaCl2, dan NaNO2. Sedangkan, penentuan kadar natrium siklamat dalam sampel secara kuantitatif dilakukan dengan spektrofotometri UV pada panjang gelombang maksimum 313,4 nm. Hasil uji kualitatif menunjukkan 3 dari 5 sampel manisan mangga dengan kode A, B, dan E mengandung natrium siklamat. Sementara itu, hasil uji kuantitatif menunjukkan kadar sampel A sebesar 255,824 mg/kg, kadar sampel B sebesar 352,49 mg/kg, dan kadar sampel E sebesar 125,743 mg/kg. Hasil ini menunjukkan kadar natrium siklamat yang digunakan masih berada dibawah batas maksimal konsumsi yang tercantum dalam Peraturan BPOM Nomor 11 Tahun 2019 yaitu sebesar 500 mg/kg. Kata Kunci: Keamanan Pangan, Uji Kualitatif, Uji Kuantitatif, Bahan Tambahan Pangan, Metode Pengendapan Sodium cyclamate is generally added to candied mangoes to improve the taste of the product. It is suspected that the use of sodium cyclamate in candied mangoes does not comply with the levels stated in BPOM Regulation Number 11 of 2019. This condition can cause dangerous effects on the body. This research was conducted to identify, calculate levels and evaluate sodium cyclamate levels in mango sweets sold in the city of Pontianak. Sodium cyclamate identification was carried out qualitatively through the precipitation method with HCl, BaCl2 and NaNO2 reagents. Quantitative determination of sodium cyclamate levels in samples was carried out using UV spectrophotometry at a maximum wavelength of 313.4 nm. Qualitative test results showed that 3 out of 5 samples of mango sweets with codes A, B and E contained sodium cyclamate. Meanwhile, quantitative test results showed that sample A levels were 255.824 mg/kg, sample B levels were 352.49 mg/kg, and sample E levels were 125.743 mg/kg. These results show that the levels of sodium cyclamate used are still below the maximum consumption limit stated in BPOM Regulation Number 11 of 2019, namely 500 mg/kg.
Enhancing Fourth Grade Students’ Mathematics Achievement through the Snowball Throwing Model Assisted by the Smart Wheel Nuraini, Nabila; Hidayat, Puput Wahyu; Andriani, Opi
IJGIE (International Journal of Graduate of Islamic Education) Vol. 6 No. 2 (2025): September
Publisher : Master of Islamic Studies Masters Program in the Postgraduate Institute of Islamic Studies Sultan Muhammad Syafiuddin Sambas, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37567/ijgie.v6i2.4192

Abstract

Mathematics education in Indonesia continues to face challenges such as low student engagement and achievement in elementary schools, often due to traditional teacher-centered methods. This study implemented the Snowball Throwing model assisted by Smart Wheel media to improve fourth-grade students’ mathematics achievement at SDN 112/II Purwobakti. The research employed Classroom Action Research (CAR) with two cycles, each consisting of planning, implementation, observation, and reflection stages. Data were collected through teacher and student observations, learning outcome tests, and documentation. The study involved 28 students and utilized quantitative and qualitative analysis to evaluate the effectiveness of the intervention. Results and discussion: he research employed Classroom Action Research (CAR) following Kemmis and McTaggart’s framework, conducted in two cycles to achieve measurable improvement within a practical timeframe. Participants were 28 students. Data were gathered through validated observation sheets, achievement tests, and documentation, with reliability ensured through expert review and inter-rater agreement. Quantitative data were analyzed using percentage and gain score calculations, while qualitative data were triangulated from observations and reflections. This study highlights the effectiveness of active learning models combined with interactive media in improving mathematics education. Teachers are encouraged to adopt such innovative strategies, while schools should provide support through training and resources. Future research could explore the model's applicability to other subjects or digital platforms.
Co-Authors Andriani, Opi Bihanda, Yusuf Gladiensyah Fajar Nugraha Fatchurrahman, Danial Hadi Kurniawan, Hadi Hidayat, Puput Wahyu Jaya Mahar Maligan Jayawarsa, A.A. Ketut Latipah, Asslia Johar Naufal Azmi Verdikha Sigit Adinugroho Yuita Arum Sari