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All Journal Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Informasi Cyberku Tedc Jurnal Pengabdian Kesehatan Masyarakat Mulawarman Scientific Journal of Informatics
Guruh Fajar Shidik
Universitas Dian Nuswantoro
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Enhancing Machine Learning Accuracy in Detecting Preventable Diseases using Backward Elimination Method Dliyauddin, Muhammad; Shidik, Guruh Fajar; Affandy, Affandy; Soeleman, M. Arief
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7073

Abstract

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.
Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN) Fafaza, Safira Alya; Rohman, Muhammad Syaifur; Pramunendar, Ricardus Anggi; Sri Winarsih, Nurul Anisa; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Ratmana, Danny Oka; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7098

Abstract

Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
Perbandingan Efektivitas Nave Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube Azzahra, Tarissa Aura; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7186

Abstract

Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.
Peningkatan Deep Neural Network pada Kasus Prediksi Diabetes Menggunakan PSO Firmansyah, Rusmal; Shidik, Guruh Fajar
Techno.Com Vol. 22 No. 4 (2023): November 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i4.9209

Abstract

Diabetes adalah ancaman utama bagi kesehatan penduduk dunia yang saat ini merupakan penyebab utama kematian pada penduduk dunia yang berusia kurang dari 60 tahun. Dengan menggunakan Machine Learning diharapkan mampu memprediksi diabetes. Dengan menggunakan dataset Pima Indians Diabetes (PIMA Dataset). Pada penelitian ini dilakukan pengujian dengan menggunakan 2 Algoritma dan 1 Algoritma yang dioptimasi. Pengujian Pertama Menggunakan Support Vector Machine (SVM), Pengujian Kedua menggunakan Deep Neural Network (DNN)  dan Pengujian Ketiga menggunakan DNN yang dikombinakan dengan Particle Swarm Optimize (PSO). Pemilihan data yang digunakan sebagai training dilakukan dengan menggunakan Non-Random Sampling. Dalam Penelitian ini pengujian pertama dengan menggunakan SVM dengan melakukan pengujian tanpa menggunakan kernel dan menggunakan kernel Linear, Sigmoid, Polynomial dan Radial Basis Function (RBF). Untuk Pengujian Kedua dilakukan dengan menggunakan DNN tanpa menggunakan Optimaliasi atau DNN original dengan dilakukan penggujian dengan jumlah hidden layer 2 dan jumlah neuron 8 sampai 10 pada setiap hidden layer. Pengujian Ketiga dilakukan dengan menggunakan DNN yang dioptimalisasi dengan menggunakan PSO. Pada Pengujian Ketiga dilakukan penggujian dengan jumlah hidden layer 2 dan jumlah neuron 8 sampai 10 pada setiap hidden layer. Hasil yang diperoleh menunjukkan bahwa DNN yang dioptimasi dengan PSO mampu memberikan akurasi tertinggi dengan jumlah hidden layer 1 sebanyak 9 node dan jumlah hidden layer 2 sebanyak 8 node dengan jumlah iterasi pada PSO sebanyak 166 iterasi.
Forecasting Air Quality Indeks Using Long Short Term Memory Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7402

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
Integrating ELECTRA and BERT models in transformer-based mental healthcare chatbot Zeniarja, Junta; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Savicevic, Anamarija Jurcev
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp315-324

Abstract

Over the last decade, the surge in mental health disorders has necessitated innovative support methods, notably artificial intelligent (AI) chatbots. These chatbots provide prompt, tailored conversations, becoming crucial in mental health support. This article delves into the use of sophisticated models like convolutional neural network (CNN), long-short term memory (LSTM), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and bidirectional encoder representation of transformers (BERT) in developing effective mental health chatbots. Despite their importance for emotional assistance, these chatbots struggle with precise and relevant responses to complex mental health issues. BERT, while strong in contextual understanding, lacks in response generation. Conversely, ELECTRA shows promise in text creation but is not fully exploited in mental health contexts. The article investigates merging ELECTRA and BERT to improve chatbot efficiency in mental health situations. By leveraging an extensive mental health dialogue dataset, this integration substantially enhanced chatbot precision, surpassing 99% accuracy in mental health responses. This development is a significant stride in advancing AI chatbot interactions and their contribution to mental health support.
Comparative Study: Flower Classification using Deep Learning, SMOTE and Fine-Tuning Praskatama, Vincentius; Shidik, Guruh Fajar; Ningrum, Amanda Prawita
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8730

Abstract

Deep learning is a technology that can be used to classify flowers. In this research, flower type classification using the CNN method with several existing CNN architectures will be discussed. The data consists of 4317 images in .jpg format, covering 5 classes that is sunflower, dandelion, daisy, tulip and rose. The distribution of data for each class is daisy with 764 pictures, dandelion with 1052 pictures, rose with 784 pictures, sunflower with 733 pictures, and tulip with 984 pictures. With total dataset of 4317 pictures is further split to training data with ratio of 60%, validation with ratio of 10%, and testing with ratio of 30% to process with the CNN method and CNN framework. Due to the imbalance data distribution, the SMOTE method is applied to balancing number of samples in each class. This research compares CNN architectures, including CNN, GoogleNet, DenseNet, and MobileNet, where each transfer learning model undergoes fine-tuning to improve performance. At the classification stage, performance will be measured based on model testing accuracy. The accuracy obtained using CNN is 74.61%, using GoogleNet is 87.45%, DenseNet is 93.92%, and MobileNet is 88.34%.
COMPARATIVE ANALYSIS OF LSTM, BILSTM, GRU, CNN, AND RNN FOR DEPRESSION DETECTION IN SOCIAL MEDIA Muhammad Huda, Alam; Shidik, Guruh Fajar; Praskatama, Vincentius
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4060

Abstract

The prevalence of mental health issues and the increasing use of social media provide an opportunity to leverage technology for early detection of depression. This study evaluates and compares five deep learning models, LSTM, BiLSTM, GRU, CNN, and RNN for detecting depressive tendencies from over 10,000 annotated social media messages. These models were trained on preprocessed data using standard techniques, including cleansing, tokenization, and padding. Evaluation metrics such as accuracy, precision, recall, and F1-score were utilized. BiLSTM emerged as the best-performing model with an accuracy of 98.45% and an F1-score of 96.37%, attributed to its bidirectional architecture for contextual analysis. In contrast, CNN achieved high precision (98.55%) but struggled with recall (15.14%), while RNN and GRU exhibited limitations in capturing complex patterns, with GRU showing no measurable performance. These findings establish BiLSTM as a robust tool for mental health monitoring. Future research could explore transformer-based models such as BERT or multilingual datasets for enhanced applicability.
Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks Prabowo, Dwi Puji; Rohman, Muhammad Syaifur; Megantara, Rama Aria; Pergiwati, Dewi; Saraswati, Galuh Wilujeng; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar; Andono, Pulung Nurtantio
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.9.1.2726

Abstract

Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential due to the unique characteristics of these images. The methodology includes testing different augmentation methods, epoch parameters, and CNN parameters on a coral reef image dataset. Five optimization algorithms (AIWPSO, GA, GWO, PSO, and FOX) are compared. The highest accuracy, 95.64%, is achieved at the 10th epoch. AIWPSO and GA show the highest average accuracies, 93.44%, and 93.50%, respectively, with no significant performance differences among the algorithms. Statistical analysis using the Wilcoxon test indicates a significant difference between training and validation accuracy (p-value = 0.0020). These findings underscore the importance of selecting augmentation methods that align with the characteristics of each optimization algorithm to enhance classification performance. The results provide valuable insights into improving the quality and diversity of input data for classification algorithms in underwater image analysis. They highlight the necessity of matching augmentation methods to specific optimization algorithms to boost accuracy and effectiveness significantly. Future research should explore additional augmentation methods and optimization algorithms further to enhance the robustness and accuracy of underwater image classification.
Co-Authors Abdussalam Abdussalam, Abdussalam Affandy Affandy Aisyatul Karima Andrean, Muhammad Niko Andreas Wilson Setiawan Anggraini, Fitria Anhsori, Khusman Astuti, Yani Parti Azzahra, Tarissa Aura Budi Harjo Cahaya Jatmoko Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Chaerul Umam Christy Atika Sari Dewi Pergiwati Dliyauddin, Muhammad Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dzaky, Azmi Abiyyu Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erlin Dolphina Erna Zuni Astuti Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Firmansyah, Rusmal Harun Al Azies Hayu Wikan Kinasih Heru Lestiawan I Ketut Eddy Purnama Ika Pantiawati Islam, Hussain Md Mehedul Junta Zeniarja Kusuma, Edi Jaya Kusumawati, Yupie L. Budi Handoko Lenci Aryani Megantara, Rama Aria Mochamad Hariadi Muhammad Huda, Alam Muhammad Naufal, Muhammad Ningrum, Amanda Prawita Nurmandhani, Ririn Paramita, Cinantya Pergiwati, Dewi Praskatama, Vincentius Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Putra, Permana Langgeng Wicaksono Ellwid Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Rastri Prathivi Ratmana, Danny Oka Ricardus Anggi Pramunendar Riri Damayanti Apnena Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Sarker, Md. Kamruzzaman Savicevic, Anamarija Jurcev Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Soeleman, M. Arief Sri Winarno Swanny Trikajanti Widyaatmadja Vincent Suhartono Wahyu Adi Nugroho Wellia Shinta Sari Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd Yani Parti Astuti Zainal Arifin Hasibuan Zami, Farrikh Al Zul Azri bin Muhamad Noh