Midwives are one of the health workers who provide child and maternal health (CMH) services and family planning. At present, most of the recording of midwife services is still managed conventionally by manual book keeping. It is less effective and efficient which causes the workload to increase, the information retrieval process is quite long and the risk of missing important data is likely to occur frequently. On the other hand, maternal patients are required to visit the midwife directly if they want to know the information on the progress of the pregnancy and their child. Based on these facts, a CMH information system was built that was accessible to midwives and parents. The information system developed consists of two integrated applications, namely web-based applications for midwives and mobile applications for parents. The web application facilitates midwives to record transactions, make reports, and deliver information to patients. While the mobile application makes it easier for parents to monitor the development of maternal and child health and other information provided by midwives. The system was developed using the water-fall software development model. The test results using the black-box test method indicate that the CMH system has been able to meet the user's functional requirements.

Batik is one of Indonesia's cultural heritages that is recognized around the world and has existed since the colonial era. Indonesia has a variety of different batik pattern in every Indonesia's region. It causes many ordinary people and tourists to become harder to identify and recognize the existing patterns. Banyuwangi regency itself has more than 10 batik patterns, including the Gajah Oling pattern which in the oldest batik pattern. For preserving the culture and supporting the growing tourism aspect in Banyuwangi, this study developed a system for recognizing Banyuwangi batik patterns based on digital image processing. This system is built using python language and is able to recognize three classes of Banyuwangi batik patterns, such as Gajah Oling, Kopi Pecah and other Banyuwangi batik patterns. This system proposes Gray Level Co-occurrence Matrix (GLCM) as feature extraction method and k-Nearest Neighbors (kNN) as classification method. Based on the experiments that have been carried out, the optimal accuracy is 87,5% with the K parameter of kNN is 9.

Batik cloth is one of Indonesia's most valuable cultural heritages and has been recognized by UNESCO as one of the world heritages. Many Indonesian people do not know the batik motifs of each region. Banyuwangi itself has more than 10 batik motifs, among the most famous Banyuwangi batik motifs is the Gajah Oling motif. The Banyuwangi batik motif classification system is a system built using the Python library with Python programming language. This system can recognize 7 types of Banyuwangi batik motifs, including Gajah Oling, Gedegan, Coffee Pecah, Moto Pitik, Kutah Rice, Paras Earthy and Sisikan. This system uses the convolutional neural network method and for evaluation, the confusion matrix method is used to measure the accuracy value. The research uses a CNN model with an architecture named MyCustomModel. The data used in this study were 120 images for each batik motif and the prediction results get an accuracy value of 63%.

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

Image segmentation is one of the analytical processes for digital image recognition, where this process divides the digital image into several unique regions based on homogeneous pixels. The process of homogeneous grouping images is based on several colour, texture and shape features. Colour in digital image processing is very important because colour has many information humans can easily understand. Colour has various features, combining colour intensity and grey (grayscale) and binary (black and white) values. However, the colour feature extraction process has many weaknesses. If the object used has a very small si[1]ze and range area, the use of colour features needs to be combined with extraction, and the segmentation process can be maximized. This study uses colour and texture features in the extraction process. It uses bacterial objects (microbes) from water, with limited image quality and objects that tend to be difficult to identify. The colour space feature extraction process is combined with a Gabor filter so that the segmentation process produces high-quality accuracy. Good. The Gabor filter used in this study is combined with the L*a*b space vector to increase accuracy in the segmentation process. The results showed that the use of texture features resulted in an increase in accuracy of 17.5% by testing the cluster value of 1.2.