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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) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat
Anindya Apriliyanti Pravitasari
Universitas Padjadjaran
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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ANALISIS OBYEK DAN KARAKTERISTIK DARI MATRIKS INDIKATOR MENGGUNAKAN HYBRID ANALISIS KELAS LATEN DENGAN BIPLOT ANALISIS KOMPONEN UTAMA (BIPLOT AKU) Ginanjar, Irlandia; Pravitasari, Anindya Apriliyanti; Martuah, Aleknaek
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (933.108 KB) | DOI: 10.14710/medstat.6.2.81-90

Abstract

Analysis of the object and the characteristics will be much easier, efficient, and informative when based on a perceptual map, which can display objects and characteristics. Indicator matrix is a matrix where the rows represent objects and the columns is a dummy variable representing characteristics. This article writes about techniques to make perceptual map from indicator matrix, where that can provide information about the similarity between objects, the diversity of each characteristic, correlations between the characteristics, and characteristic values ​​for each object, the techniques we call Hybrid Latent Class Cluster with PCA Biplot, where Latent Class Cluster Analysis is used to transform the indicator matrix to cross section matrix, where rows represent the objects and columns represent the characteristics, the observation cells is the probability of characteristic for each object, next the cross section matrix mapped using Principal Component Analysis Biplot (PCA Biplot).   Key Words: Hybrid Latent Class Cluster with PCA Biplot, Latent Class Cluster Analysis, Biplot Principal Component Analysis, Indicator Matrix.
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation Anindya Apriliyanti Pravitasari; Nur Iriawan; Mawanda Almuhayar; Taufik Azmi; Irhamah Irhamah; Kartika Fithriasari; Santi Wulan Purnami; Widiana Ferriastuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14753

Abstract

A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification NUR Iriawan; Kartika Fithriasari; Brodjol Sutija Suprih Ulama; Wahyuni Suryaningtyas; Irwan Susanto; Anindya Apriliyanti Pravitasari
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.777 KB) | DOI: 10.21609/jiki.v11i2.536

Abstract

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of  the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model
Ergonomics Analysis of Computer Use in Distance Learning during the Pandemic of COVID-19 Anindya Apriliyanti Pravitasari; Mulya Nurmasnsyah Ardisasmita; Fajar Indrayatna; Intan Nurma Yulita
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 3, No 1 (2022): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v3i1.9-19

Abstract

One impact of the COVID-19 pandemic on education is the mandated learning from home or distance learning (DL) in both state and private education institutions to prevent the transmission of COVID-19. DL may require long periods of time in front of a computer screen, which can create ergonomic issues such as eye, shoulder or neck problems, low back pain, and fatigue or stress. This study was structured to look at the ergonomic behavior of students in the statistics department at Padjadjaran University. The data were gathered using questionnaire, and there were 146 respondents who were willing to answer and send back the questionnaire. The results of the analysis show that the majority of students do not have knowledge about ergonomics when using computers. However, students agree that wrong posture can affect health conditions, especially those related to musculoskeletal disorders. The real impact felt by students is the health condition around their neck, shoulders, waist, bottoms, and wrists.
Parents' Understanding of the Safety and Comfort in Using Gadgets for Children Anindya Apriliyanti Pravitasari; Mulya Nurmansyah Ardisasmita; Fajar Indrayatna; Intan Nurma Yulita; Triyani Hendrawati; Gumgum Darmawan
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 4, No 2 (2023): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v4i2.151-160

Abstract

The utilization of technology among children has significantly increased since the outbreak of the Covid 19 pandemic. Therefore, the use of gadgets among children requires special attention from parents, since under incorrect ergonomic circumstances, it could endanger the health of children. This webinar was designed with parents in mind, giving them valuable information on how to use kid-friendly technology. Additionally, a pre- and post-test was assigned to evaluate parents’ knowledge about ergonomic conditions (safety and comfort) when using gadgets, both before and after the webinar. The results indicated a substantial increasement in parental knowledge among the webinar participants as well as the heightened desire and willingness to apply the right ergonomic conditions for their children’s gadget use at home.
Machine Learning Methods for Forecasting Intermittent Tin Ore Production Rahmah, Nabila Dhia Alifa; Handoko, Budhi; Pravitasari, Anindya Apriliyanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5990

Abstract

Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations of machine learning-based classification and forecasting to predict future tin ore production values, which have not been found in previous studies. The presence of zero values in the forecast in the next day's tin ore production forecast is addressed by combining classification and forecasting techniques. Random Forest and CatBoost classification techniques are used to determine the next day's CSD production operating status. Then, for each time point when the CSD is operational, a forecasting model is created using CatBoost and Bi-LSTM. This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). This study demonstrates how a serial combination of classification and forecasting models can improve the accuracy and efficiency of production forecasting for intermittent time series data.
Co-Authors Aleknaek Martua Almuhayar, Mawanda Brodjol Sutija Suprih Ulama Budhi Handoko Fajar Indrayatna Ferriastuti, Widiana Fithriasari, Kartika Gumgum Darmawan Intan Nurma Yulita, Intan Nurma Irhamah - Irlandia Ginanjar Irwan Susanto Mulya Nurmansyah Ardisasmita Mulya Nurmasnsyah Ardisasmita Nur Iriawan Rahmah, Nabila Dhia Alifa Santi Wulan Purnami Taufik Azmi Triyani Hendrawati Wahyuni Suryaningtyas, Wahyuni