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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Muhammad Syaifur Rohman
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN) Safira Alya Fafaza; Muhammad Syaifur Rohman; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Galuh Wilujeng Saraswati; Filmada Ocky Saputra; Danny Oka Ratmana; Guruh Fajar Shidik
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 Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting Nirvan Adam Pramudhyta; Muhammad Syaifur Rohman
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.v8i1.6965

Abstract

Stunting is a condition of stunted physical growth in children due to chronic nutritional deficiencies with serious impacts on health and psychological aspects. The impacts include decreased self-esteem, learning difficulties, impaired concentration, critical thinking problems, and lower economic contributions as adults. This study aims to optimize the XGBoost classification model using the Grid Search and Random Search methods, thereby improving the accuracy of detecting stunting and obtaining an accurate and efficient diagnosis. Seeing the danger and alarming prevalence rate of stunting, signaling the urgency of handling this problem for the welfare of future generations, an automatic classification model is needed to avoid subjectivity and potential errors in the manual decision-making process. XGBoost needs optimization because it has parameters that require adjustment to maximize accuracy. Comparison of two optimization models is important to understand the advantages and disadvantages of each because they have different approaches in finding the best combination. The study used 10,000 data from Krobokan Health Center with attributes of gender, age, birth weight, birth height, weight at measurement, height at measurement, and category. The largest increase in accuracy was obtained by the Grid Search model with an increase in XGBoost accuracy of 5.81% from 83.28% to 89.09%. The Random Search model increased the accuracy by 5.43%, reaching an accuracy of 88.71%. The choice of both models depends on time and resource preferences. Random Search provides higher time efficiency than Grid Search. This research can contribute to identifying children at risk of stunting so that intervention actions can be carried out more efficiently.
Perbandingan Efektivitas Nave Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube Tarissa Aura Azzahra; Nurul Anisa Sri Winarsih; Galuh Wilujeng Saraswati; Filmada Ocky Saputra; Muhammad Syaifur Rohman; Danny Oka Ratmana; Ricardus Anggi Pramunendar; Guruh Fajar Shidik
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.
Co-Authors Danny Oka Ratmana Filmada Ocky Saputra Guruh Fajar Shidik Nirvan Adam Pramudhyta Nurul Anisa Sri Winarsih Ricardus Anggi Pramunendar Safira Alya Fafaza Saraswati, Galuh Wilujeng Tarissa Aura Azzahra