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Erfiani
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Study of Small Area Estimation when Nighttime Lights as an Auxiliary Information is Measured with Error: Kajian Pendugaan Area Kecil dengan Kesalahan Pengukuran pada Peubah Penyerta Nighttime Lights Surya, Ardi; Indahwati; Erfiani
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i1p47-57

Abstract

The need for accelerated development requires rapid data collection. In today's increasingly advanced technological landscape, the utilization of big data emerges as a highly reliable solution for data collection. One exemplary form of big data is the daily capture of satellite imagery, particularly nighttime lights (NTL). NTL serves as a valuable product derived from satellite imagery and can be employed as an alternative dataset for analysis. This research utilizes Nighttime lights as an auxiliary variable to estimate the average household per capita expenditure in small areas, namely districts, employing the empirical best linear unbiased prediction Fay Herriot (EBLUP FH) method and small area estimation by incorporating measurement error effects on the covariate (SAE-ME). The study demonstrates that Nighttime lights can be employed as an alternative auxiliary variable for estimating the average per capita expenditure in districts, as evidenced by a lower RRMSE compared to direct estimation results. However, the measurement error effects on the NTL covariate should be considered by employing a model that takes into account measurement errors. The SAE-ME method provides estimated average expenditure values at the district level that closely align with BPS publications, with an average RRMSE per district of 7.5 percent.
Perbandingan Kinerja Hybrid Classification SVM-RF dan SVM-NN Terhadap Faktor Risiko Anemia Ibu Hamil di Indonesia dengan Pendekatan Clustering K-Means Asyifah Qalbi; Erfiani; Budi Susetyo
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i3.8862

Abstract

Klasifikasi merupakan salah satu topik yang paling banyak diteliti oleh para peneliti dari bidang machine learning dan data mining. Metode machine learning yang sering digunakan antara lain Support Vector Machine (SVM), Random Forest (RF) dan Neural Network (NN). Namun, SVM tidak selalu memberikan nilai akurasi yang baik. Sebagai contoh, ketika diterapkan pada data yang sangat tidak seimbang, SVM akan mengalami tantangan. Selain itu, tidak terdapat satu metode terbaik yang bisa diterapkan untuk semua masalah klasifikasi. Saat ini, pendekatan metode hybrid untuk penggunaan data mining menjadi semakin populer seperti metode hybrid SVM-RF, SVM-NN dan KMeans-SVM. Pada penelitian ini, metode hybrid SVM-RF dan SVM-NN digunakan untuk mengklasifikasikan faktor risiko anemia pada ibu hamil di Indonesia dengan pendekatan K-Means untuk mengelompokkan data yang salah klasifikasi oleh SVM. Hasil penelitian menunjukkan bahwa metode hybrid dapat meningkatkan kinerja model SVM. Hybrid SVM-RF memberikan nilai metrik evaluasi yang lebih tinggi dibandingkan dengan SVM-NN. Empat metrik evaluasi yang digunakan, yaitu accuracy, balanced accuracy, sensitivity dan specificity pada SVM-RF masing-masing bernilai sebesar 0,989; 0,989; 0,988; dan 0,989. Peubah yang berkontribusi secara umum berdasarkan SHAP Global terhadap klasifikasi faktor risiko anemia pada ibu hamil secara berurutan adalah Usia, Tablet Fe, Status Bekerja, Pendidikan, Status Gizi dan ANC
Siamese Model-Based Face Verification Using CNN and MobileNetV2 Abd Rahman; Agus Mohamad Soleh; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6996

Abstract

Face verification plays an important role in computer vision, especially in mobile and embedded systems with limited computational capacity. This study proposes a face verification system based on the Siamese Neural Network (SNN) architecture by integrating six embedding models. These models consist of a standard CNN, an L2-normalized CNN, a baseline MobileNetV2, a structurally adjusted MobileNetV2, a pre-trained MobileNetV2, and a fine-tuned MobileNetV2. The dataset includes facial images captured from three webcams and additional samples obtained from the Labeled Faces in the Wild and ImageNet datasets. The experimental procedure includes image preprocessing, construction of balanced positive and negative image pairs, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that the pre-trained MobileNetV2 and the standard CNN achieve the highest verification accuracy, reaching 100 percent and 99.998 percent, respectively. Among all models, the structurally adjusted MobileNetV2 presents the best trade-off by combining high accuracy, computational efficiency, and training stability while successfully avoiding overfitting. The real-time implementation involves only the structurally adjusted MobileNetV2 model due to its lightweight structure and consistent performance. This model produces low embedding distances, low latency, and high throughput during CPU-based inference. The performance outperforms GPU execution in one-by-one image processing. The proposed system offers a practical and efficient face verification solution for deployment in identity authentication applications on resource-constrained platforms. These findings support the development of scalable and adaptive biometric security systems that rely on deep learning.
Co-Authors Abd Rahman Agus Mohamad Soleh Alfa Nugraha ALIU, MUFTIH ALWI Amanda, Nabila Amelia, Reni Anang Kurnia ASEP SAEFUDDIN Asyifah Qalbi Budi Susetyo Fitrianto, Anwar Gustiara, Dela Indahwati Irsyifa Mayzela Afnan Jumansyah, L.M. Risman Dwi L.M. Risman Dwi Jumansyah M. Yunus Muftih Alwi Aliu Nur Hidayah Pardomuan Robinson Sihombing Pika Silvianti Rifda Nida’ul Labibah Riska Yulianti, Riska Rizki, Akbar Setyowati, Silfiana Lis Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Surya, Ardi Waliulu, Megawati Zein