Media online banyak menghasilkan berbagai macam berita, baik ekonomi, politik, kesehatan, olahraga atau ilmu pengetahuan. Di antara itu semua, ekonomi adalah salah satu topik menarik untuk dibahas. Ekonomi memiliki dampak langsung kepada warga negara, perusahaan, bahkan pasar tradisional tergantung pada kondisi ekonomi di suatu negara. Sentimen yang terkandung dalam berita dapat mempengaruhi pandangan masyarakat terhadap suatu hal atau kebijakan pemerintah. Topik ekonomi adalah bahasan yang menarik untuk dilakukan penelitian karena memiliki dampak langsung kepada masyarakat Indonesia. Namun, masih sedikit penelitian yang menerapkan metode deep learning yaitu Long Short-Term Memory dan CNN untuk analisis sentimen pada artikel finance di Indonesia. Penelitian ini bertujuan untuk melakukan pengklasifikasian judul berita berbahasa Indonesia berdasarkan sentimen positif, negatif dengan menggunakan metode LSTM, LSTM-CNN, CNN-LSTM. Dataset yang digunakan adalah data judul artikel berbahasa Indonesia yang diambil dari situs Detik Finance. Berdasarkan hasil pengujian memperlihatkan bahwa metode LSTM, LSTM-CNN, CNN-LSTM memiliki hasil akurasi sebesar, 62%, 65% dan 74%.Kata Kunci — LSTM, sentiment analysis, CNNOnline media produce a lot of various kinds of news, be it economics, politics, health, sports or science. Among them, economics is one interesting topic to discuss. The economy has a direct impact on citizens, companies, and even traditional markets depending on the economic conditions in a country. The sentiment contained in the news can influence people's views on a matter or government policy. The topic of economics is an interesting topic for research because it has a direct impact on Indonesian society. However, there are still few studies that apply deep learning methods, namely Long Short-Term Memory and CNN for sentiment analysis on finance articles in Indonesia. This study aims to classify Indonesian news headlines based on positive and negative sentiments using the LSTM, LSTM-CNN, CNN-LSTM methods. The dataset used is data on Indonesian language article titles taken from the Detik Finance website. Based on the test results, it shows that the LSTM, LSTM-CNN, CNN-LSTM methods have an accuracy of, 62%, 65% and 74%.Keywords — LSTM, sentiment analysis, CNN
The purpose of the study was to determine the marketing strategy of lantung leather craft at the Taste Shop of Bengkulu City in order to obtain maximum sales results. The samples in this study were internal samples (21 people) + external samples (30 people) = 51 people, namely customers from the Sari Rasa Store, Bengkulu City. The analytical method used is a SWOT analysis consisting of an Internal Strategy Factor Matrix (IFAS) and an External Strategic Factor Matrix (EFAS). Based on the results of research on the marketing strategy of latung leather handicrafts, Based on the results of the discussion presented based on the results of previous studies, it can be concluded that the results of the SWOT analysis show that the internal strategic factor analysis summary (IFAS) / strengths and weaknesses of 3.16 and external strategic factor analysis summary (EFAS)/opportunities and threats of the Taste Shop business with a total of 3.41 from these results, it is better for the Taste Shop business to use the SO strategy, namely by utilizing all business strengths in order to seize and take advantage of existing opportunities optimally.
Financing is very important for farmers to manage their farming because capital is very important in lowland rice farming. This study aims to determine the financing pattern of lowland rice farming in the Loea District, East Kolaka Regency. This study aims to identify the financing pattern of lowland rice farming in the Loea District, East Kolaka Regency. This research was conducted from July 2021 to February 2022 in Loea District, East Kolaka Regency, Southeast Sulawesi. The population of this study was 241 people. Using simple random sampling where obtained 36 members of the rice farmers who carried out the financing pattern as the research sample was obtained. The variables in this study are the respondent's identity, including age, education, number of family dependents, and farming experience; Financing patterns, including self-financing and partner financing. Data analysis used descriptive qualitative analysis. The results showed that the financing pattern for lowland rice farming in Loea District, East Kolaka Regency, was a self-financing pattern using capital and costs derived from the farming actors themselves and partner financing patterns using capital and other costs borrowed through formal and non-formal institutions.
Rabbits have their charm for breeding because of the benefits they get from rabbits. However, rabbit farming has a fairly high challenge, namely rabbits die easily because of the cleanliness of the cage. The accumulation of rabbit droppings results in increased levels of ammonia gas which are harmful to rabbits, breeders, and even the environment. In cleaning the cage, rabbit breeders still use manual methods that are ineffective and inefficient. For this reason, a systemwas created to create an automatic feces cleaning system in the rabbit cage based on IoT (Internet of Things) by utilizing loadcell sensors to detect the weight of rabbit droppings that are accommodated on the conveyor belt. Belt conveyor as a medium that transports rabbit droppings. The MQ-135 sensor is used to detect the concentration of ammonia gas and the MCUESP8266 Node as a controller and IoT as a notification and as a system control via a smartphone. Based on testing of the system, the loadcell sensor as a measure of the weight of rabbit droppings has an accuracy rate of 97.43%. The MQ-135 sensor as a measure of theconcentration of ammonia gas in rabbit cages has an accuracy rate of 99.19%. The conveyor belt can move to dispose of rabbit droppings when the weight is > 1000 grams or the ammonia gas content is > 25 ppm. Then the Telegram application bot can 100% control the system by inputting commands and can receive notifications from the system. From testing the system as a whole, the system can work according to the desired function with a 100% success rate.
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