This study aims to measure the accuracy of the sentiment analysis classification model using deep learning and neural networks. This study used the algorithm Recurrent Neural Network (RNN) and Word2vec. No previous research has used this model to analyze sentiments written using Indonesian language so that the level of accuracy is unknown. The research began by making a classification model of sentiment analysis. Then, the model was tested through experiments. In this study, They used two classifications (positive and negative). Experiments are carried out using training data sets and the test used data sets sourced from Traveloka theybsite. The result shows that the model presents outstanding results and reaches about 91.9%.
Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.
Kegiatan pengabdian ini melibatkan mitra dari Desa Guwosari dimana desa ini merupakan desa yang sadar dan memberikan perhatian khusus terhadap sampah. Sebagai bentuk pelaksanaan kewajiban pemerintah desa terhadap pengelolaan sampah, BUMDes membentuk Unit Layanan Kebersihan Lingkungan Desa Guwosari yang disebut dengan Go-Sari. Dengan adanya kegiatan ini diharapkan akan membatu pengelola Go-Sari untuk dapat mensukseskan program Bantul Bersama (Bantul Bersih Sampah 2025). Salah satu alternatif pengolahan sampah organic adalah dengan menjadikannya maggot dan juga kasgot sehingga memiliki nilai tambah. Pengolahan sampah organic melalui media maggot ini menjawab pertanyaan masyarakat cara pemanfaatan sampah bernilai tinggi. Hasil dari pengolahan sampah melalui media maggot yaitu pupuk kompos kasgot, dan budidaya maggot dengan modal pakan sampah sangatlah murah dengan nilai jual maggot yang sangat tinggi.
Yogyakarta merupakan salah satu provinsi penghasil batik di Indonesia. Salah satu batik khas dari Yogyakarta adalah batik tulis. Batik tulis dibuat secara manual menggunakan tangan dengan peralatan tradisional. Ciri khas batik tulis ini terletak pada proses pembuatannya dengan menggunakan canting. Canting digunakan untuk mengambil malam cair yang dipanaskan diatas kompor. Pada umumnya prosen pembuatan batik yang berada di desa-desa Jogjakarta masih menggunakan kompor batik konvensional. Beberapa kelemahan kompor batik konvensional seperti penggunaan bakan bakar yang masih menggunakan kayu atau minyak tanah. Selain itu, hal lain yang perlu diperhatikan oleh para pembatik adalah suhu pada kompor untuk menghasilkan tingkat kekentalan malam yang dibutuhkan. Pengaturan suhu pada kompor batik konvensional masih dilakukan secara manual. Beberapa penelitian telah dilakukan untuk mengembangkan kompor pemanas malam batik, namun masih sedikit yang mengulas tentang kestabilan suhu pada kompor listrik tersebut. Pada penelitian ini akan diterapakan sistem Kalman filter [8] untuk mengurangi noise pada pembacaan sensor suhu NTC pada system kompor listrik malam berbasis fuzzy. Tujuan dari penelitian ini adalah mengurangi noise atau gangguan pada pembacaan sensor suhu NTC pada kompor sehingga menghasilkan pembacaan data sensor yang halus dan stabil. Hasil penelitian menunjukan bahwa Kalman filter bekerja sangat baik untuk mengurangi noise dan gangguan dengan nilai Q (covariance noise process) rendah dan R (covariance noise measurement) tinggi, dimana nilai stabil kalman filter nilai R >= 30 dan nilai Q = 1.
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