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All Journal IJoICT (International Journal on Information and Communication Technology) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Community Development Journal: Jurnal Pengabdian Masyarakat REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Intan Nurma Yulita
Universitas Padjadjaran
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Public Health

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Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video Intan Nurma Yulita; Mohamad Ivan Fanany; Aniati Murni Arymurthy
International Journal on Information and Communication Technology (IJoICT) Vol. 2 No. 2 (2016): December 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2016.22.124

Abstract

With the explosion of data on the internet led to the presence of the big data era, so it requires data processing in order to get the useful information. One of the challenges is the gesture recognition the video processing. Therefore, this study proposes Latent-Dynamic Conditional Neural Fields and compares with the other family members of Conditional Random Fields. To improve the accuracy, these methods are combined by using Fuzzy Clustering. From the result, it can be concluded that the performance of Latent-Dynamic Conditional Neural Fields are  lower than Conditional Neural Fields but higher than the Conditional Random Fields and Latent-Dynamic Conditional Random Fields. Also, the combination of Latent-Dynamic Conditional Neural Fields and Fuzzy C-Means Clustering has the highest. This evaluation is tested in a temporal dataset of gesture phase segmentation.
Combining Deep Belief Networks and Bidirectional Long Short-Term Memory Intan Nurma Yulita; Mohamad Ivan Fanany; Aniati Murni Arymurthy
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (307.775 KB) | DOI: 10.11591/eecsi.v4.1051

Abstract

This paper proposes a new combination of Deep Belief Networks (DBN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for Sleep Stage Classification. Tests were performed using sleep stages of 25 patients with sleep disorders. The recording comes from electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) represented in signal form. All three of these signals processed and extracted to produce 28 features. The next stage, DBN Bi-LSTM is applied. The analysis of this combination compared with the DBN, DBN HMM (Hidden Markov Models), and Bi-LSTM. The results obtained that DBN Bi-LSTM is the best based on precision, recall, and F1 score.
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.
Teachers Understanding about Interesting Online Learning Media Intan Nurma Yulita; Yeni Rizka
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 2, No 1 (2021): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v2i1.11-18

Abstract

Implementation of Indonesian Education in the New Normal Era after the global Covid-19 pandemic uses an online learning system in accordance with new policies and regulations from the government to break the chain of virus spread in the community. With this system, teachers and students are required to understand online learning media quickly. However, students feel bored with the online learning system because it tends to be monotonous and many tasks are given. In addition, teachers are not necessarily proficient in using these online learning facilities and media, especially to make online learning interesting so that students do not get bored. Therefore, a webinar was conducted to improve teacher’s understanding of interesting online learning media such as Kahoot, Mentimeter, and Quizizz. Based on the results of quantitative calculations through the participant’s pre and post-tests, the participant’s knowledge changes were 28% on Mentimeter, 23% on Kahoot, and 18% on Quizizz.
Human Activity Recognition Berdasarkan Tangkapan Webcam Menggunakan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNet Fauzan Akmal Hariz; Intan Nurma Yulita; Ino Suryana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 3 No 4 (2022)
Publisher : Jurusan Teknologi Informasi - Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jitsi.3.4.97

Abstract

Manusia tidak bisa terlepas dari aktivitas sehari-hari yang mana merupakan bagian dari kehidupan manusia. Human activity recognition atau pengenalan aktivitas manusia saat ini merupakan salah satu topik yang sedang banyak diteliti seiring dengan pesatnya kemajuan di bidang teknologi yang berkembang saat ini. Hampir semua bidang terdampak dari pandemi COVID-19 yang memengaruhi aktivitas manusia sehingga menjadi lebih terbatas. Salah satu bidang yang paling terdampak yaitu pendidikan, di mana kampus menerapkan sistem pembelajaran daring, yang membuat dosen lebih sulit untuk mengawasi pembelajaran maupun ujian yang dilakukan secara daring karena tidak dapat mengawasi aktivitas yang dilakukan mahasiswa secara langsung. Penelitian ini bertujuan untuk membuat model yang dapat mengenali aktivitas seseorang saat ujian daring berdasarkan tangkapan webcam dengan memanfaatkan model deep learning dengan metode Convolution Neural Network (CNN) menggunakan arsitektur MobileNetV2. Pengujian hyperparameter dilakukan untuk menghasilkan model optimal yang dilakukan pada batch size sebesar 16, 32, dan 64 serta dense layer sebanyak 1, 3, 5, dan 7. Pengujian tersebut menghasilkan model optimal dengan hyperparameter berupa max epoch sebanyak 20, early stopping dengan patience sebesar 10, learning rate sebesar 0,0001, batch size sebesar 16, dan dense layer sebanyak 5. Model tersebut dievaluasi menggunakan cross validation dan confusion matrix yang berhasil memberikan performa F1-score akhir sebesar 84,52%.
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 Prediction of Time Series Covid-19 Data in West Java, Indonesia Intan Nurma Yulita; Afrida Helen; Mira Suryani
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.58505

Abstract

In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.
PENGGUNAAN MACHINE LEARNING UNTUK PREDIKSI HARGA TELUR AYAM RAS DI KOTA BANDUNG Ihda Anwari; Intan Nurma Yulita
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 3 (2023): Volume 4 Nomor 3 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i3.17689

Abstract

Analisis prediksi merupakan salah satu metode analisis pada data time series untuk mendapatkan informasi di masa depan. Analisis prediksi kini dapat dilakukan oleh machine learning. Pemanfaatan teknologi tersebut sudah diterapkan pada berbagai bidang. Penggunaaan analisis prediksi pada harga komoditas dapat menjadi referensi bagi pemerintah untuk mengendalikan harga di pasar. Harga komoditas ini berpengaruh terhadap inflasi dan tentunya penting untuk diperhatikan. Telur sebagai komoditas makanan yang sederhana dan dikonsumsi semua kalangan menjadi objek utama yang diteliti. Pada penelitian ini peneliti menggunakan ARIMA sebagai metode machine learning prediksi untuk melakukan analisis prediksi. Data diambil dari website resmi kemendagri yang bernama Sistem Pemantauan Pasar dan Kebutuhan Pokok. Setelah data didapatkan kemudian data dilakukan preprocessing dengan mengisi nilai nilai yang hilang. Setelah data lengkap kemudian data dibuat pemodelan ARIMA menggunakan aplikasi KNIME. Terakhir model yang sudah dibuat dievaluasi menggunakan metrik statistic Root Mean Squared Error (RMSE) dan R-square. Model menghasilkan RMSE yang rendah dan R-square yang mendekati satu yang artinya model tersebut memiliki kinerja yang baik. Model kemudian diuji untuk melakukan prediksi 30 hari dari data aktual. Terakhir peneliti memberikan rekomendasi tindakan dan perencanaan pemerintah dalam memanfaatkan model machine learning ini.
Co-Authors Afrida Helen Aniati Murni Arymurthy Anindya Apriliyanti Pravitasari Fajar Indrayatna Fauzan Akmal Hariz Gumgum Darmawan Ihda Anwari Ino Suryana Mira Suryani Mohamad Ivan Fanany Mulya Nurmansyah Ardisasmita Mulya Nurmasnsyah Ardisasmita Triyani Hendrawati Yeni Rizka