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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informasi dan Komputer Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL Journal of Applied Data Sciences Jurnal Sistem Informasi
Achmad Benny Mutiara
Faculty of Computer Science and Information Technology, Gunadarma University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mathematics Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
A Lexicon-Based Long Short-Term Memory (LSTM) Model for Sentiment Analysis to Classify Halodoc Application Reviews on Google Playstore Refianti, Rina; Mutiara, Achmad Benny; Putra, Ryan Arya
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.160

Abstract

The development of information and communication technology is developing very quickly, has made many new breakthroughs. One of these technological advances is in the health sector, the creation of telemedicine applications. During the Covid-19 pandemic, it is difficult for people to get access to health. Therefore, telemedicine applications are needed. Halodoc is one of the telemedicine applications that has successfully become the top health application on the Google PlayStore. The application has been used by more than ten million users throughout Indonesia and received a rating of 4.6. To be able to see ratings and satisfaction from the public, user reviews are needed. The very large number of reviews often contain errors, making them difficult to decipher. Based on this, this research aims to create a web application, which can classify user reviews of the Halodoc application, using a proposed lexicon-based Long Short-Term Memory (LSTM) Model. Application is built using the Flask framework and the Python programming language. Models are created and trained using the TensorFlow library. The results of the model evaluation get an accuracy of 85.3% with an average precision value of 85.3%, a recall value of 85.6% and an f1-score of 85.3%. The proposed LSTM model can be used to classify Halodoc review sentiment classes.
Enhancing Sharia Stock Price Forecasting using a Hybrid ARIMA-LSTM with Locally Weighted Scatterplot Smoothing Regression Approach Gunaryati, Aris; Mutiara, Achmad Benny; Puspitodjati, Sulistyo
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.514

Abstract

Predicting Sharia stock prices is complex because it has high volatility and non-linear data patterns. To improve the accuracy of the forecast, the right technique is needed according to the existing data pattern. One of the techniques currently developing is integrating (hybrid) two forecasting models. This study proposes a hybrid autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) model with the locally weighted scatterplot smoothing (lowess) linear regression technique. This model is designed by creating a linear regression between the actual value and the predicted results of the ARIMA and LSTM models using the Lowess technique. The dataset used here is the closing stock prices of four Indonesian Islamic banking companies. The hybrid ARIMA-LSTM model with lowess linear regression significantly outperforms the individual ARIMA and LSTM models because it produces better performance metrics, namely mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), for training and testing datasets. The proposed hybrid model effectively reduces noise, and the model can capture complex patterns in the Sharia stock price dataset, and the prediction results are more accurate. The accuracy values for training data and data testing datasets were respectively 97.6% and 98.3% (BANK. JK), 98.3% and 98.2% (BRIS. JK), 99.4% and 99.5% (BTPN. JK), and 97.7% and 99.3% (PNBS. JK).
Data Visualization of Climate Patterns in Indonesia Using Python and Looker Studio Dashboard: A Visual Data Mining Approach Refianti, Rina; Mutiara, Achmad Benny; Ariyanto, Ananda Satria
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.420

Abstract

Climate has a significant impact on the lives of Indonesian people. Information about climate patterns, when presented visually and interactively, can greatly enhance understanding of climate conditions in Indonesia. This study aims to produce a visualization of climate pattern data in Indonesia that can be accessed online by the general public, serving as a valuable resource for climate information. The study highlights the ability to display historical trends for a 10-year period (2010-2020) through interactive visuals, which load information according to user-defined filters, enabling diverse presentations of data. The research employs the Visual Data Mining method, encompassing Project Planning, Data Preparation, and Data Analysis phases. Additionally, Exploratory Data Analysis techniques were utilized in the data analysis phase. The data was cleaned and processed using the Python programming language with libraries such as pandas, numpy, seaborn, and matplotlib. Visualizations were created using Looker Studio tools and published on a website, providing accessible climate pattern information in Indonesia via the Internet. The final results of this research indicate that the developed climate visualization dashboard successfully delivers detailed insights into sunlight duration, temperature, humidity, rainfall, and wind speed across various Indonesian regions. Users can effectively monitor climate trends and weather changes. The dashboard also demonstrates significant seasonal variations and differences in climate patterns between provinces. Performance metrics reveal that the dashboard meets Key Performance Indicators, achieving a click-through ratio of 40.1%, the average page position in search engines is 4.8 top positions, and receiving positive user experience scores. Further development and research on the Climate Pattern Dashboard in Indonesia still have room for enhancement. Important aspects include expanding data coverage to include multiple decades for observing significant climate patterns and applying sophisticated prediction methods like machine learning algorithms for future climate change projections.
Generating Image Captions in Indonesian Using a Deep Learning Approach Based on Vision Transformer and IndoBERT Architectures Apandi, Ahmad; Mutiara, Achmad Benny; Dharmayanti, Dharmayanti
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.672

Abstract

The primary objective of this research is to develop an image captioning system in Indonesian by leveraging deep learning architectures, specifically Vision Transformer (ViT) and IndoBERT. This study addresses the challenge of generating accurate and contextually relevant captions for images, which is a crucial task in the fields of computer vision and natural language processing. The main contribution of this research lies in integrating ViT for visual feature extraction and IndoBERT for linguistic representation to enhance the quality of image captions in Indonesian. This approach aims to overcome limitations in existing models by improving semantic understanding and contextual relevance in generated captions. The methodology involves data preprocessing, model training, and evaluation using the Flickr8k dataset, which was translated into Indonesian. The research employs various data augmentation techniques to enhance model performance. The model is trained on a combined architecture where ViT extracts visual features and IndoBERT processes textual information. The experimental procedures include training the model on the Indonesian-translated Flickr8k dataset and evaluating its performance using BLEU and METEOR scores. The training loss and validation loss graphs provide insights into the model’s learning process. The results indicate that the proposed model outperforms traditional CNN+LSTM and Transformer-based models in terms of BLEU and METEOR scores. A detailed analysis of these results highlights the advantages of using ViT and IndoBERT for this task. The findings of this research have significant implications for real-world applications, such as automatic image captioning for visually impaired users, content tagging for multimedia platforms, and improvements in machine translation. Future research can explore the integration of human evaluation metrics and the use of larger datasets to enhance generalizability.
Development of A Deep Learning Model for Mental Health Classification and Early Screening through Draw a Person (DAP) Test Images Nurasiah, Nurasiah; Mutiara, Achmad Benny; Yusnitasari, Tristyanti; Asmarany, Anugriaty Indah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.700

Abstract

Mental health, as defined by the World Health Organization (WHO), is a fundamental aspect of overall well-being. The increasing complexity of modern society, coupled with rising levels of competition and stress, significantly impacts individuals’ mental health. The DAP test is a psychological assessment tool that uses human figure drawings to gain insights into an individual’s personality and mental condition. YOLO (You Only Look Once) is a deep learning algorithm based on Convolutional Neural Networks (CNNs) designed for real-time object detection. This study utilizes a DAP image dataset contributed by adolescents aged 12 to 16 years to develop a model for detecting and classifying objects in DAP images using the YOLOv8 algorithm. Optimal training results were achieved after 150 epochs, yielding a Precision of 0.821, Recall of 0.799, and mAP50 of 0.88. The model evaluation demonstrated an F1-Score of 0.78, indicating a balanced performance between Precision and Recall. Psychological analysis was conducted based on symptoms extracted from the characteristics of DAP images. Mental health conditions were classified according to severity levels consisting of minor, medium, and serious, based on weighted symptomatology derived from DAP image characteristics. The successful development of this model highlights its capability to classify various mental health conditions based on psychological analysis of DAP images. The findings suggest that mental health classification using DAP test images has the potential to support early screening and psychological assessment by providing an innovative and objective approach to identifying psychological indicators.
Co-Authors Anastasia Monika Oktorianti Anugriaty Indah Asmarany Apandi, Ahmad Aries Muslim Aris Gunaryati, Aris Ariyanto, Ananda Satria Avinanta Tarigan Avinanta Tarigan Detty Purnamasari Dharmayanti Dharmayanti Firmansyah Firmansyah HENDRA GUNAWAN B11211055 I Made Wiryana, I Made I Wayan Simri Wicaksana Jayawarsa, A.A. Ketut Lintang Yuniar Banowosari Mahendra Sunt Servanda Nadia R.A. Mukarromah Nasruddin Bin Idris Nurasiah Nurasiah Nuryani Nuryani Parida, Merri Putra, Ryan Arya Putra, Souza Nurafrianto Windiartono Rifky Awaludin Rina Refianti Riya Widayanti Sulistyo Puspitodjati Teddy Oswari Tri Sudinugraha Tristyanti Yusnitasari