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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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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.
Message from the Chair of the Committee Anindya Apriliyanti Pravitasari
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19116

Abstract

Atas nama panitia penyelenggara, kami merasa terhormat dan senang menyambut seluruh peserta, pembicara utama (keynote speaker), dan invited speaker, serta peserta dalam Seminar Nasional Statistika XI (SNS XI). Acara ini adalah seminar nasional tahunan yang diselenggarakan oleh Departemen Statistika Universitas Padjadjaran, dengan dukungan dari Forstat dan Jurnal Inferensi ITS. Secara khusus, tema dari SNS XI ini adalah "Machine Learning: Statistics and Lifestyle" yang merupakan penelitian mengenai kemajuan statistika di era machine learning dan kecerdasan buatan. Kami berharap acara ini dapat memfasilitasi semua peserta untuk berinteraksi secara intensif guna memperluas jaringan ilmiah di masa depan.
Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan Annisa Siti Utami; Anindya Apriliyanti Pravitasari; Irlandia Ginanjar
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19128

Abstract

Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students.
Analisis Sentimen Ulasan Pengguna Aplikasi E-Samsat Provinsi Jawa Barat Menggunakan Metode BiGRU Rahma Kania Dewi; Bertho Tantular; Jadi Suprijadi; Anindya Apriliyanti Pravitasari
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19113

Abstract

Organizing the facilitation of local revenue tasks and public services is one of the main tasks, functions, detailed unit tasks, and work procedures of the West Java Provincial Revenue Agency. One of the public services for the community in improving service to the West Java community is to launch an e-samsat innovation in providing annual Motor Vehicle Tax (PKB) payment services and updating ownership status through an Android-based smartphone application called Samsat Mobile Jawa Barat (SAMBARA) and can be downloaded for free on the Google Play Store. Service satisfaction is an important aspect in service development, therefore research was conducted. This study analyzes the sentiment of the Samsat Mobile Jawa Barat (SAMBARA) application on the Google Play Store by categorizing user reviews into three groups: Positive, Negative, and Neutral. The method chosen is the Bidirectional Gated Recurrent Unit (BiGRU). BiGRU is able to predict user reviews with an accuracy of up to 87.37%, which is considered good and can be used to help the development of service applications in West Java.
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.
NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs Najma Rafifah Putri Syallya; Anindya Apriliyanti Pravitasari; Afrida Helen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6276

Abstract

Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missed questions. This study aims to develop a chatbot model capable of addressing questions about the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick, accurate information retrieval. The research focuses on optimizing the IndoBERT model for intent classification and addresses challenges that arose due to imbalance data, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students. To address data imbalance, we tried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. We achieved the best model with hyperparameters batch size of 8, learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer, resulting in 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model in intent classification for academic chatbots, achieving excellent performance.
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