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All Journal Jurnal Ilmu Komputer dan Informasi Media Statistika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Aplikasi Statistika & Komputasi Statistik Inferensi REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Enthusiastic : International Journal of Applied Statistics and Data Science
Anindya Apriliyanti Pravitasari
Departemen Statistika, Universitas Padjadjaran, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Real-time Emotion Recognition Using the MobileNetV2 Architecture Hendrawati, Triyani; Apriliyanti Pravitasari, Anindya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6158

Abstract

Facial recognition technology is now advancing quickly and is being used extensively in a number of industries, including banking, business, security systems, and human-computer interface. However, existing facial recognition models face significant challenges in real-time emotion classification, particularly in terms of computational efficiency and adaptability to varying environmental conditions such as lighting and occlusion. Addressing these challenges, this research proposes a lightweight, yet effective deep learning model based on MobileNetV2 to predict human facial emotions using a camera in real time. The model is trained on the FER-2013 dataset, which consists of seven emotion classes: anger, disgust, fear, joy, sadness, surprise, and neutral. The methodology includes deep learning-based feature extraction, convolutional neural networks (CNN), and optimization techniques to enhance real-time performance on resource-constrained devices. Experimental results demonstrate that the proposed model achieves a high accuracy of 94.23%, ensuring robust real-time emotion classification with a significantly reduced computational cost. Additionally, the model is validated using real-world camera data, confirming its effectiveness beyond static datasets and its applicability in practical real-time scenarios. The findings of this study contribute to advancing efficient emotion recognition systems, enabling their deployment in interactive AI applications, mental health monitoring, and smart environments. Real-world camera data is also used to evaluate the model, demonstrating its usefulness in real-time applications and its efficacy beyond static datasets. The results of this work advance effective emotion identification systems, making it possible to use them in smart settings, interactive AI applications, and mental health monitoring.
MRI-Based Brain Tumor Classification Using Inception Resnet V2 Azzahra, Thalita Safa; Jessica Jesslyn Cerelia; Farid Azhar Lutfi Nugraha; Anindya Apriliyanti Pravitasari
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art4

Abstract

Brain tumors are one of the most fatal disorders owing to the uncontrolled proliferation of abnormal cells inside the brain. Digital images are obtained using Magnetic Resonance Imaging (MRI), which is a medical instrument that can assist doctors and other medical personnel in assessing and diagnosing the presence and type of brain tumors. However, manual and subjective classification is time-consuming and error prone. Hence, an objective, automatic, and more reliable method is needed to classify MRI images of brain tumors. Artificial intelligence is considered appropriate to determine the type of brain tumor via MRI images to overcome the constraints of conventional testing methods. One method for performing automatic classification is the Convolutional Neural Network (CNN). This work demonstrates how the Inception Resnet v2 architecture in CNN is utilized to classify MRI brain tumors into four categories via transfer learning, namely glioma tumors, meningioma tumors, no tumors, and pituitary tumors. The accuracy value of the generated model reached 93.4% after running for 20 epochs. It infers that artificial intelligence is beneficial in identifying a brain tumor objectively to help doctors and radiologists in the medical field.
Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java Ningtias, Yunita Dwi Ayu; Andriyana, Yudhie; Pravitasari, Anindya Apriliyanti
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20309

Abstract

Poverty is one of the crucial problems that has a negative impact on all sectors. As a developing country, Indonesia has a fairly high poverty rate. The government's efforts to overcome the problem of poverty can be circumvented by detecting the factors that influence it to determine the policies taken by using statistical modeling. There is a spatial effect on poverty in West Java Province. Spatial Data Analysis is the only statistical model that can explain the relationship between an area and the surrounding area. If the response variable contains a lag that correlates with each other, it is called a Spatial Autoregressive with Autoregressive Disturbances (SARAR) model. The Generalized Method of Moment (GMM) approach is used to get an estimator from the model. This method is applied to obtain the factors that influence poverty in West Java Province. The results of this study indicate that the GMM SARAR poverty modeling with customized weights provides relatively better estimation results. In addition, the relationship between locations (spatial lag dependence) is positive and significant. Expected Years of Schooling and Per capita Expenditure have a negative and significant effect on the increase in the percentage of poor people in West Java.
Adding MSNBURR-IIa Distribution to MultiBUGS Ramadani , Eliana Putri; Choir, Achmad Syahrul; Pravitasari , Anindya Apriliyanti; Paraguison, Joynabel
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.804

Abstract

Introduction/Main Objectives: The MSNBurr-IIa distribution is a neo-normal distribution designed to fit right-skewed data better. This article aims to integrate the MSNBurr-IIa distribution into MultiBUGS, thereby enabling Bayesian estimation of its parameters. Background Problems: Markov Chain Monte Carlo (MCMC) is a popular method for Bayesian computations, although its implementation is frequently challenging. MultiBUGS, a statistical tool that uses the BUGS language, is used to make this easier. Novelty: This paper details integrating the MSNBurr-IIa distribution into MultiBUGS, allowing for estimating its parameters. The module's effectiveness is demonstrated through its application on both simulated data and regional economic growth data of Indonesian districts/cities in 2021. Research Methods: The MSNBurr-IIa module was developed using five steps: requirement, design, development, testing, and implementation in simulation and real-world data. It was built with Blackbox Component Builder, an integrated development environment (IDE) for the Component Pascal programming language. Finding/Results: The findings confirm that MultiBUGS, with the MSNBurr-IIa module, successfully estimates the distribution’s parameters across various datasets.
Co-Authors Afrida Helen Aleknaek Martua Almuhayar, Mawanda Annisa Siti Utami Azzahra, Thalita Safa Bertho Tantular Brodjol Sutija Suprih Ulama Budhi Handoko Choir, Achmad Syahrul Fajar Indrayatna Farid Azhar Lutfi Nugraha Ferriastuti, Widiana Fithriasari, Kartika Gumgum Darmawan Intan Nurma Yulita Irhamah - Irlandia Ginanjar Irwan Susanto Jadi Suprijadi Jessica Jesslyn Cerelia Mulya Nurmansyah Ardisasmita Mulya Nurmasnsyah Ardisasmita Najma Rafifah Putri Syallya Ningtias, Yunita Dwi Ayu Nur Iriawan Paraguison, Joynabel Rahma Kania Dewi Rahmah, Nabila Dhia Alifa Ramadani , Eliana Putri Santi Wulan Purnami Taufik Azmi Triyani Hendrawati Wahyuni Suryaningtyas, Wahyuni Yudhie Andriyana