Article Per Year (5 Year)
p-Index From 2021 - 2026
2.024
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Informatika EDUKASI
Rukmi Sari Hartati
Teknik Elektro Unud
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Engineering Library & Information Science Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Classification of Tri Pramana learning activities in virtual reality environment using convolutional neural network Sindu, I Gede Partha; Sudarma, Made; Hartati, Rukmi Sari; Gunantara, Nyoman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2840-2853

Abstract

Tri Pramana as the local genius of Balinese society, is now adopted in the education system. This adaptation results in a Learning Cycle Model which essentially consists of three classes namely Sabda Pramana (theoretical study), Pratyaksa Pramana (direct observation), and Anumana Pramana (practicum). In learning activities, it is difficult for educators to fully observe individuals to find out the most suitable learning model. Through Virtual Environment Technology, educators can observe students more freely through the recording of students' activities. However, in its implementation, manual analysis requires large resources. Deep Learning approach based on Convolutional Neural Network (CNN) is able to automate this analysis process through the classification ability of the image of the recorded learner activity. To produce a robust CNN model, this research compares four of the most commonly used architectures, namely ResNet-50, MobileNetV2, InceptionV3, and Xception. Each architecture is tuned using a combination of learning rate and batch size. Through a 512 x 512 resolution dataset with 70% training subset (4,541 images), 20% validation (1,296 images), and 10% test (652 images), the best ResNet model is obtained with a learning rate configuration of 1e-3 and batch size 64 with an accuracy of 99.39%, precision of 99.37%, and recall of 99.42%.
Rancang Bangun Purwarupa Monitoring Arus Bocor Pada Kabel Grounding Trafo Incoming 20 KV di Gardu Induk Nusa Dua Berbasis Internet Of Things Saputra, Dharma Bagus; Pradana, Aditya; Togatorop, Josua Febrian; Jasa, Lie; Hartati, Rukmi Sari
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.5998

Abstract

Failure of the grounding isolation on the 20 kV transformer incoming cable, resulting in leakage current. The leakage current on the grounding cable can change periodically, so an accurate and real-time monitoring system is required to protect the power transformer equipment and facilitate responsive handling. Therefore, an Internet of Things-based monitoring device is needed that can detect the magnitude of the leakage current present on the 20 kV secondary side of the transformer using an ESP8266 microcontroller and Arduino UNO R3 as the brain of the monitoring system, which controls and processes data from the input to output components. The SCT-013 current sensor is used to measure the AC current on the transformer incoming 20 kV grounding cable without requiring cable cutting, and the Arduino IDE is used to configure the program on the ESP8266 microcontroller to work according to the desired configuration. The results of the prototype testing using the ESP826 and Arduino UNO R3 microcontrollers and the SCT-013 current sensor have shown that the system can work well and the monitoring has been successfully implemented with real-time current monitoring using the Thinkspeak and Blynk platforms. The testing also proved that the SCT-013 current monitoring device can provide a comparison of the test results and measurements with a Tang Ampere, and the data obtained shows that the real-time SCT-013 current monitoring device is accurate, with an average reading error of less than 3% from the SCT-013 non-linearity specification, with a total reading error percentage of 2.0%. Additionally, the current monitoring device is precise, with the lowest standard deviation value of 0.046.
Multi Task Deep Learning with Transformer Encoder Decoder for Semantic Segmentation Indah, Komang Ayu Triana; Darma Putra, I Ketut Gede; Sudarma, Made; Hartati, Rukmi Sari
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.1978

Abstract

Visual understanding is one of the core elements of computer vision consisting of image classification, object detection, and segmentation. The system applies a multilayer process to obtain complex image and video understanding using deep learning methods to convert the images to text. Therefore, this study aimed to extract video in the form of frames followed by the application of Transformer and Inception V3 architectures to the image captioning process. The synchronization was based on Multi-task Deep Learning method developed by combining Convolutional Neural Network (CNN) system in the image area, Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in the sentence area, Caption Content Network (CCN), and Relational Network Context (RCN). Moreover, Transformer Encoder-Decoder architecture was used in the process of labeling and determining the relationships between objects. The results of the image-to-text conversion process were determined by comparing prospective translated text with one or more references. This was achieved using accuracy and loss validation tables to provide graphical comparisons between the number of epochs and losses. The test results showed that the validation data accuracy was 70.166% while the loss was 22,648% and this showed more epoch iterations led to greater validation accuracy.Keywords— Visual Understanding, Transformer, Encoder, Decoder
Development of a Life Story-Based Digital Counseling Model to Detect Student Depression Using LSTM Jiwa Permana, Agus Aan; Sudarma, Made; Sukarsa, I Made; Hartati, Rukmi Sari
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2642

Abstract

This research aims to develop an LSTM-based model to help counselors analyze depressive symptoms in students based on their life stories. Depression often occurs among students, which can affect their lives. However, counselling can overcome these mental problems. In order to support the Indonesian government's programs in the field of mental health, concrete steps are needed. One concrete effort is to prevent children from experiencing depression. Depression can be recognized early through a counselling approach. Currently, counselling can be done using digital counselling technology. Therefore, a reliable model is needed to help counsellors. This research used 2,551 tweets about someone's life story from 2,581 datasets. ANN method with LSTM (Long Short-Term Memory) architecture. This counselling is effective in helping individuals resolve psychological and emotional problems, especially depression. The advantage of LSTM is that it can delete data that is no longer relevant. This method effectively processes, predicts, and classifies data based on a certain time sequence. The dataset was taken from Twitter(X) and then validated by experts before being trained with the model. As a result, the model can recognize the depression levels with a test accuracy of 86%. This research has implications in psychology regarding cases of student mental health in realizing the vision of Indonesia in 2045.
Multi-Document Summarization Using Tuna Swarm Optimization and Markov Clustering Widiartha, I Made; Hartati, Rukmi Sari; Wiharta, Dewa Made; Sastra, Nyoman Putra; Astuti, Luh Gede
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3365

Abstract

The Internet contains a large number of documents from various sources with similar content. The contents of documents that are almost identical will lead to news redundancy, making it difficult for readers to distinguish between factual information and opinions. Multi-document summarization has been designed to enable readers to easily understand the meaning of news documents without needing to read multiple documents. Multi-document summarization aims to extract information from several texts written about the same topic. The resulting summary report enables users to obtain a single piece of information from multiple similar pieces of information sourced from various locations. Various approaches have been used in creating multi-document summaries. Issues regarding accuracy and redundancy are still a significant focus of research. In this paper, a new multi-document summarization model was built using Tuna Swarm Optimization (TSO) and Markov Clustering (MCL) methods. The dataset of this research is Indonesian language news from various online media sources. Based on hyperparameter tuning using training data, the best TSO model performance was obtained at variable values a = 0.7, z = 0.9, and the optimal number of tuna fish > 80. From the research results, it was found that TSO outperformed other swarm intelligence methods. The use of MCL has proven to be effective, as evidenced by the performance results, where TSO achieved an average ROUGE value 7.95% higher when MCL was applied. In this performance test, four standard evaluation metrics of the ROUGE toolkit were used.
Islamic Religious Education and Religious Moderation at University Helmawati, Helmawati; Marzuki, Marzuki; Hartati, Rukmi Sari; Huda, Miftahul
EDUKASI: Jurnal Penelitian Pendidikan Agama dan Keagamaan Vol. 22 No. 1 (2024): EDUKASI: Jurnal Penelitian Pendidikan Agama dan Keagamaan
Publisher : Badan Litbang dan Diklat Kementerian Agama RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32729/edukasi.v22i1.1689

Abstract

This study highlights the ideal ratio between the number of lecturers and students in Islamic Education classes at the Faculty of Economics, Udayana University, Bali. With only one lecturer teaching 200 students, this imbalance is a significant concern, especially amidst worries about the spread of extremism and exclusivism on campus. The research aims to evaluate the learning process, religious moderation attitudes among lecturers and students, and factors influencing religious moderation attitudes among students. Using a qualitative descriptive method and a case study design, data were collected through observation, in-depth interviews, and questionnaires, and analyzed using descriptive analysis techniques. The findings indicate that the ratio of Islamic Education lecturers to students does not meet government standards, despite the implementation of various teaching methods. Nevertheless, attitudes towards religious moderation are generally positive, although there are indications of a lack of moderation among some students. Internal and external factors were identified as influencing factors on religious moderation attitudes. These findings emphasize the need for ongoing efforts to improve the quality of Islamic Education learning through enhancing lecturer competencies and implementing programs to strengthen religious moderation in higher education, aiming to promote tolerance and reinforce religious moderation among students.
Co-Authors Aditya Pradana, Aditya Agus Aan Jiwa Permana Dewa Made Wiharta Hardiansyah, Amien Harist Helmawati Helmawati I GAN Subawa, I GAN I ketut Gede Darma Putra I Made Agus Putrawan, I Made Agus I Made Sukarsa I Made Widiartha I N Satya Kumara I Nyoman Gunantara I Nyoman Setiawan I Nyoman Suprapta Winaya Kapu, Joannes Mario Graciano Komang Ayu Triana Indah Lie Jasa Linawati Linawati Luh Gede Astuti Made Sudarma Marzuki Marzuki Miftahul Huda Nyoman Putra Sastra Pramono, Lukito Putu Lokantara Saputra, Dharma Bagus Sari, Luh Ayu Diah Fernita Servinus, Servinus Sindu, I Gede Partha Togatorop, Josua Febrian Wijaya, Anak Agung Ngurah Mahendra Wiranatha, I Dewa Gde Bayu Yoga Divayana