Tujuan penelitian ini adalah (1) Untuk mengetahui seberapa besar tingkat pemahaman matematika siswa dengan menggunakan Adobe Flash CS3 pada pokok bahasan limit fungsi di kelas XI IPA SMAN 5 Kota Cirebon. (2) Untuk mengetahui seberapa besar tingkat pemahaman matematika siswa dengan menggunakan software iMindMap pada pokok bahasan limit fungsi di kelas XI IPA SMAN 5 Kota Cirebon. (3) Untuk mengetahui apakah terdapat perbedaan pemahaman matematika siswa antara yang menggunakan Adobe Flash CS3 dengan software iMindMap.Metode penelitian yang digunakan adalah metode eksperimen dengan pendekatan kuantitatif. Populasi dalam penelitian ini adalah seluruh siswa kelas XI IPA di SMAN 5 Kota Cirebon yang terdiri dari 4 kelas IPA yang berjumlah 134 siswa. Sedangkan sampelnya diambil dua kelas secara acak, yaitu kelas XI IPA 3 sebagai kelas eksperimen 1 yang menggunakan Adobe Flash CS3 dan kelas XI IPA 4 sebagai kelas eksperimen 2 yang menggunakan software iMindMap dalam kegiatan pembelajaran. Adapun teknik pengumpulan data dengan menggunakan tes dan observasi. Setelah data diperoleh dari hasil post-test kemudian di analisis menggunakan uji prasyarat yaitu uji normalitas dan uji homogenitas, sedangkan uji hipotesis menggunakan independent sample T-test. Berdasarkan hasil uji hipotesis dengan menggunakan independent sample T-test diketahui bahwa thitung > ttabel yaitu 2.277 > 1.998 maka Ho ditolak. Hal ini menunjukan bahwa terdapat perbedaan yang signifikan pemahaman matematika siswa antara yang menggunakan Adobe Flash CS3 dengan software iMindMap pada pokok bahasan limit fungsi.

It is crucial to know crop growing in order to increase agricultural productivity. In sugarcane's case, monitoring growth can be supported by remote sensing. This research aimed to develop an early warning for sugarcane growth using remote sensing with Landsat 8 satellite at a crucial phenological time. The early warning was developed by identifying regional sugarcane growth patterns by analyzing seasonal trends using linear and harmonic regression models. Identification of growth patterns aims to determine the crucial phenological time by calculating the statistical value of the NDVI spectral index. Finally, monitoring the sugarcane growth conditions with various spectral indices for verification: NDVI, NDBaI, NDWI, and NDDI. All processes used Google Earth Engine (GEE) as a cloud-based platform. The results showed that sugarcane phenology from January to June is crucial for monitoring and assessment. The value of the four corresponding indices indicated the importance of monitoring conditions to ensure a healthy sugarcane region. The results showed that two of the four regions were unhealthy during particular periods; unhealthy vegetation values were below 0.489 and vice versa, one due to excess water and the other due to drought.

Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%

Batik merupakan budaya Indonesia yang menjadi ciri khas Indonesia yang sudah diakui secara internasional. Budaya membatik atau membuat motif batik erat kaitannya dengan pola-pola tertentu. Secara filosofis beberapa motif batik jika dianalisis ada keterkaitannya dengan konsep matematis. Penelitian ini menggunakan metode kualitatif dengan pendekatan etnografi. Penelitian ini bertujuan untuk mendeskripsikan kajian yang mendalam mengenai motif batik dan kaitannya dengan konsep matematis. Teknik pengumpulan data yang digunakan yaitu observasi, pedoman wawancara dan dokumentasi. Sementara untuk subjek penelitian ini adalah pengerajin batik yang berada di Kabupaten Majalengka. Berdasarkan hasil penelitian diperoleh bahwa budaya dan matematika mempunyai keterkaitan satu sama lain, salah-satunya yaitu pada budaya membatik. Pada motif batik khas Majalengka yaitu motif batik Rengginang memiliki keterkaitan dengan konsep matematika yaitu Kongruen pada bidang. Sementara itu pada motif batik Kota Angin memiliki keterkaitan dengan konsep translasi, kemudian pada motif Kopi dan Edelweis memiliki keterkaitan dengan konsep pencerminan. Selain itu pada motif Kopi Gunungwangi memiliki keterkaitan dengan rotasi dan motif Gedong Ginju memiliki keterkaitan dengan konsep dilatasi.

Social media is a place to express or share daily activities. Various new events are often discussed on social media, such as on Twitter. Frequently, the conversations conducted by Twitter users when giving a review or opinion have various emotions, such as anger, sadness, fear, or joy. Emotions are difficult to describe the challenges that occur, sometimes leading to multiple interpretations and misunderstandings leading to debates and reporting to the authorities. So this shows that emotions in reviews and opinions are essential for classification because emotions that come from texts are difficult to understand. In addition, the classification of emotions needs to be done to speed up the identification of emotions. The purpose of this study is to find out which algorithm has optimal performance in the classification of emotions. Machine Learning methods are the Naïve Bayes algorithm, Random Forest, and Support Vector Machines; this is done to determine the dominant algorithm in classifying emotions. The results of the modeling and classification using the Random Forest algorithm obtained a dominant accuracy with an accuracy value of 81.3%, followed by the SVM algorithm with an accuracy value of 76.6% and an accuracy value of 79.1% Naïve Bayes algorithm. In addition, from the speed of time in completing the classification, the Random Forest algorithm has the fastest time of 1.27 seconds