Tajweed is a basic knowledge of learning to read the Al-Qur’an correctly. Tajweed has many laws grouped into several parts so that only some people can memorize and implement Tajweed properly. Therefore, it is necessary to have an automatic detection system to facilitate the recognition of Tajweed, which can be used daily. This study presents Tajweed-YOLO, which applies the HSV color augmentation model to detect Tajweed objects in Mushaf images using YOLO. The contribution to this study was to compare the three versions of You Only Look Once (YOLO), i.e., YOLOv5, YOLOv6, and YOLOv7, and usage of the HSV color model augmentation to improve Tajweed detection performance. Comparing the three YOLO versions aims to solve problems in detecting small objects and recognizing various forms of Mushaf writing fonts in Tajweed detection. Meanwhile, the HSV color model aims to recognize Tajweed objects in various Mushaf and handle minority class problems. In this study, we collected four different Al-Qur’an mushaf with 10 Tajweed classes. The augmentation process can increase the detection performance by up to 85% compared to without augmentation 6th Class (Mad Jaiz Munfashil) using YOLOv6. The comparison of three YOLO versions concluded that YOLOv7 was better than YOLOv5 and YOLOv6, seen in data with augmentation and without augmentation. The evaluation results of mAP0.5 on 17 test data on the YOLOv7, YOLOv6, and YOLOv5 models are 80%, 69%, and 71%, respectively. These results prove that this research model’s results are suitable for the real-time detection of Tajweed.

The emergence of various kinds of food and drinks that are packaged and presented in an attractive manner has made a change in the culinary business into a lifestyle part. One of the culinary businesses that is becoming a trend is boba. Boba products with well-known brands can easily be found in urban areas. Karawang as one of the industrial cities in Indonesia is experiencing rapid business development trends. This causes business actors and owners of the boba beverage brand to prepare a strategy to anticipate brand shifting to customers. This study aims to determine the brand shift in customers of  A products bubble tea and  B products bubble tea  which are one of the well-known brands of the boba business in Karawang, and to find out what strategies are appropriate so that business people can remain competitive and maintain customer loyalty. The use of the Markov Chain method to see the brand transfer to customers and the Game Theory method to find out strategies to win the competition. The results showed that the Market Share for  A product bubble tea was 0.401 and the  B product bubble tea was 0.599 with a business strategy that could be done, namely adding flavors at an affordable price.

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Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and precipitation (rainfall). One way to find out the salinity level is by taking seawater samples, which requires a long time and costs a lot. In this study, the salinity level of seawater can be predicted by utilizing time series data patterns from evaporation and precipitation using artificial neural network learning, namely the backpropagation neural network. The evaporation and precipitation data used were derived from the ECMWF dataset, while the salinity data were derived from NOAA where each data was taken at the coordinate point of 9,625 113,625 in the south of Java island. Seawater salinity, evaporation, and precipitation data were formed into a 7-day time series data. This study conducted several backpropagation architectural experiments, that is the learning rate, hidden layer, and the number of nodes in the hidden layer to obtain the best results. The results of the seawater salinity prediction were obtained at a MAPE value of 2.063% with a model architecture using 14 input layers, 2 hidden layers with 10 nodes and 2 nodes, 1 output layer, and a learning rate of 0.7. Predicted sea water salinity data ranging from 33 to 35 ppt. Therefore, the prediction system for seawater salinity using the backpropagation method can be said to be good in providing information about the salinity level of sea water on the island of Java.

Penelitian ini bertujuan untuk mengetahui identitas sosial pada ORMAS Muhammadiyah dan Nahdlatul Ulama. Penelitian ini menggunakan metode kualitatif dengan pendekatan eksploratif. Terdapat empat informan yang telah diwawancari secara mendalam. Analisis dari penelitian ini menggunakan model analisis interaktif dari Miles dan Huberman. Hasil penelitian dari keempat informan memiliki tujuh poin, yaitu : 1) memiliki kesamaan terkait persepsi terhadap organisasi lain, 2) adanya pandangan positif dan negatif oranglain terhadap organisasi mereka, 3) terdapat kerjasama antar pengurus yang saling melengkapi dan membantu, 4) adanya rasa bangga terhadap identitas sosial mereka, 5) adanya tiga hal yang menjadi daya tarik bagi anggota, yaitu kerabat keluarga, kegiatan organisasi, dan cara berbicara pengurus di organisasi yang tegas, sopan dan santun, 6) mereka belum sepenuhnya mematuhi peraturan dan menerapkan nilai-nilai organisasi, serta 7) ditemukan kesamaan dari lingkungan sekolah, tempat tinggal, pola pemikiran dan kesamaan tujuan berorganisasi dengan pengurus lain.