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Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest Taqiyuddin, Muhammad; Bayu Sasongko, Theopilus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7897

Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.

Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm Setyanto, Joko; Sasongko, Theopilus Bayu
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.v8i1.7772

In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.

Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks Arnandito, Seno; Sasongko, Theopilus Bayu
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.v8i1.7927

This study compares the performance of EfficientNetB7 and MobileNetV2 in classifying herbal plant species using Convolutional Neural Networks (CNNs). The primary objective was to automatically identify herbal plant species with high accuracy. Based on the evaluation results, both EfficientNetB7 and MobileNetV2 achieved approximately 98% accuracy in recognizing herbal plant species. While both models demonstrated excellent performance in precision, recall, and F1-score for most plant species, EfficientNetB7 showed a slight edge in some evaluation metrics. These findings provide valuable insights into the potential implementation of CNN architectures in automatic plant recognition applications, particularly for developing widely applicable web-based systems for herbal plant identification.

Comparison of LSTM Model Performance with Classical Regression in Predicting Gaming Laptop Prices in Indonesia Dewantoro, Agus; Sasongko, Theopilus Bayu
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.v8i1.8137

The demand for gaming laptops has surged in the digital era, appealing to both professional gamers and the general public. Gaming laptops come equipped with advanced features such as powerful graphics, fast processors, and sleek designs, offering a portable solution for gaming enthusiasts. However, the price of gaming laptops varies due to factors like brand, hardware specifications, screen size, and additional features. Accurately predicting these prices can help consumers make informed purchasing decisions and assist manufacturers in setting competitive prices. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm to predict gaming laptop prices, comparing its performance with classic regression algorithms such as Linear Regression and Multi-layer Perceptron. Utilizing a comprehensive dataset of gaming laptop prices and specifications in Indonesia, this study employs robust pre-processing and model optimization techniques. The results show that the LSTM model achieves a Root Mean Squared Error (RMSE) of 0.09011, a Mean Squared Error (MSE) of 0.00812, and an RÂ² Score of 0.90016. In comparison, the Linear Regression model has an RMSE of 0.09075, an MSE of 0.00823, and an RÂ² Score of 0.89873, while the Multi-layer Perceptron model has an RMSE of 0.09891, an MSE of 0.00978, and an RÂ² Score of 0.87971. These results indicate that the Long Short-Term Memory algorithm outperforms other classic regression algorithms in this case. This study highlights the potential of LSTM in developing a robust price prediction model for gaming laptops, particularly in the Indonesian market, providing valuable insights for both consumers and manufacturers.

Pelatihan Desain Grafis Menggunakan Aplikasi Canva Untuk Siswa Di SMK Kesehatan Binatama Mizwar A. Rahim, Abd; Bayu Sasongko, Theopilus; Asrawi, Hannan
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 11 : Desember (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Technological development is an important factor as a driver of growth and progress in a country. Technology today plays a very important role, especially in the field of education. One way to deal with this is utilizing technology, which can be done by starting to introduce technology to students at an early age through graphic design using the Canva app. In the environment of SMK Kesehatan Binatama, the use of technology in teaching activities has not been done effectively. Learning activities are still done normally without frequently using technology, so it is necessary to have Canva training as a form of technology adaptation and a means to channel the creativity of the students. The methods used are training methods that include material delivery, practice, and evaluation. Training activities run smoothly: each student can follow a whole range of activities from start to finish, and each student may create one graphic design independently.

Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest Taqiyuddin, Muhammad; Bayu Sasongko, Theopilus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7897

Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.

Optimasi Performa Random Forest dengan Random Oversampling dan SMOTE pada Dataset Diabetes Hasbi, Hasbi; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7855

Diabetes, or high blood sugar, is a chronic condition that needs careful monitoring. If left untreated, it can lead to severe complications. This research aims to accurately diagnose diabetes, addressing the issue of class imbalance in the dataset, which can affect the model's classification accuracy. The goal is to improve diabetes classification accuracy using balancing methods, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and Random Oversampling. These methods are applied to data from patients diagnosed with diabetes and those who do not have the disease.The initial step in the research involved addressing class imbalance by applying SMOTE and random oversampling to generate synthetic samples for the minority class. This was followed by data normalization using the min-max normalization method. Subsequently, the Random Forest Classifier was used to train the model for classification. The results demonstrate that this approach enhances the model's ability to identify diabetes cases, achieving an accuracy of 96%. This represents a 1% improvement over the accuracy of 95% reported in previous research.

Implementasi Algoritma Transformers BART dan Penggunaan Metode Optimasi Adam Untuk Klasifikasi Judul Berita Palsu Subagyo, Ageng Ramdhan; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7852

Classification is a process of identifying new data provided based on validation of previous data. One classification process that can be used is fake news classification. The classification process requires as little time as possible to get maximum results, so a faster method is needed to classify news. The BART algorithm can be a method that can be used to carry out classification and use Adam optimization to improve the performance of the algorithm. The aim of this research is to classify fake news, whether the BART algorithm and Adam optimization are able to provide good results and to label whether the news is fake or not. The results of this process are based on the use of a dataset of 65% for training, 30% for validation, and 5% to produce 2 BART models. With the additional use of Adam optimization and several other parameters for the training process, the first model was able to provide accuracy performance of 92.88%, training loss reached 12.2%, and validation loss reached 28.4% and the second model produced an accuracy of 92.63 %, training loss 15% and validation loss reaching 20.2%. In the first model, it can predict 105 data labeled negative and 1306 positive data. Meanwhile, the second model was able to predict 128 data labeled negative and 1283 positive data.

Analisis Sentimen Masyarakat Terhadap Presiden dan Calon Presiden Terpilih 2024 Menggunakan NaÃ¯ve Bayes Yusrizal, Muhammad; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7882

It has been more than 4 months since the KPU announced the results of the general election on Wednesday, 20 May 2024. With the election of pair number 2 as President and Vice President of the Republic of Indonesia for the 2024-2029 period, various public opinions, both positive and negative, emerged on Twitter. The main problem faced in this research is knowing the dominant sentiment of society towards Prabowo Subianto and Gibran Rakabuming after their election, whether negative or positive sentiment dominates. If you look at it with the naked eye, there are indeed many negative sentiments written by netizens through their tweets on Twitter. This research aims to analyze the sentiment of the Twitter community towards these two figures. Data collected through data crawling using tweet-harvest was 1520 tweets with several keywords such as Prabowo, Wowo, Minister of Defense, Gibran, and Samsul. The stages carried out on the data include preprocessing, translating, labeling, splitting the data, and applying the Naive Bayes algorithm. The analysis results show that positive sentiment is 39.02% and negative sentiment is 60.98%, with an accuracy value of 75%. With detailed values of 79% precision, 80% recall and 80% f1-score. It is hoped that this research will provide an overview of public opinion towards the elected President and Vice President as well as provide evaluation material for them and their supporting political parties to increase positive sentiment in the future.

Analisis Tingkat Kemiskinan di Indonesia Menggunakan Model Vanilla Long Short-Term Memory dan Stacked Long Short-Term Memory Fajar, Rizqi Al; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Indonesia, as the fourth most populous country in the world and a developing nation, faces significant challenges in addressing widespread poverty. Poverty is a condition where individuals or groups have limited access to adequate economic resources, quality food, healthcare services, and education. Despite government efforts to implement programs aimed at reducing poverty levels in Indonesia, these programs have often been ineffective and poorly targeted. The objective of this research is to compare the performance of two Long Short-Term Memory (LSTM) models, Vanilla LSTM and Stacked LSTM, in analyzing poverty levels in Indonesia. The data used for this study is from the year 2021 and encompasses 514 cities across Indonesia. This data includes variables such as the percentage of the impoverished population, regional gross domestic product, life expectancy, average years of schooling, and per capita expenditure, all of which are relevant to Indonesia's economic and social conditions.The research employs Vanilla LSTM and Stacked LSTM models. Evaluation is conducted using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Error (MAE) as the main metrics to measure the accuracy of the model predictions. The results indicate that Vanilla LSTM consistently outperforms Stacked LSTM, achieving an MSE of 0.0109, RMSE of 0.1046, NRMSE of 0.1334, and MAE of 0.0795. In contrast, Stacked LSTM shows an MSE of 0.0119, RMSE of 0.1091, NRMSE of 0.1391, and MAE of 0.0833. These findings suggest that Vanilla LSTM has lower and more stable prediction errors and is more accurate in estimating poverty levels. Vanilla LSTM is therefore a better choice for analyzing and reducing poverty levels in Indonesia. This model can serve as an effective tool for policymakers to design more efficient and targeted strategies to reduce poverty rates.

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