THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH

The aimed of this study was to apply the feature extraction method of GLCM and Back-propagation Artificial Neural Network (ANN) to classify Lombok's typical Songket woven cloth by classifying based on the texture of the Songket woven cloth. Songket woven cloth in Lombok in terms of weaving and texture are vary from region to region. For example the songket woven cloth in Pringgasela Village, Sukarara Village and Sade Village has differences in texture and motifs. For this reason, this study focuses on classifying Lombok's typical Songket woven cloth by performing feature extraction on woven cloth using the GLCM method and the classification method uses Back-propagation Artificial Neural Network (ANN). For data collection, the data was taken directly from the Songket weaving centers in Pringgasela, Sade and Sukarara. In the classification stage the training data used were 64 data and 11 test data. Then the epoch used was 41 iterations with a time of 0:00:04, with neurons 80 and 100. The use of neurons 80 generated 18% which was successful in the classification. While using 100 neurons generated 100% successful which was can be classified. Based on the classification results obtained, the use of 100 neurons gained good classification results

Pengembangan Sistem Informasi Geografis Berbasis Android Pada Wisata Daerah Lombok, Nusa Tenggara Barat

Lombok Island is one of the tourist destinations in Indonesia that has the potential to be developed. Based on data from the NTB Provincial Tourism Office at the end of 2019, the number of tourist visits was 3,706,352 tourists, with details of 1,550,751 foreign tourists and 2,155,561 domestic tourists. Based on the 2018-2023 Strategic Plan, the Culture and Tourism Office of West Nusa Tenggara Province, the island of Lombok has potential tourism objects such as beach tourism, waterfalls, mountains, culinary, culture, religion, and others. However, not all existing tourist attractions are known by visiting tourists, both the location of tourist attractions or the distance traveled to tourist sites, so it is necessary to make efforts to increase the number of visitors who come to the island of Lombok through various efforts. One of the efforts that can be done is the application of an Android-based tourism system so that it can be accessed by foreign tourists or domestic tourists who can provide complete information about tourist information on the island of Lombok. The system development method used in this research is the Research and Development (RD) method. Geographic Information Systems with the Research and Development (RD) method can provide good results and can be proven from the limited test results getting an average result of 71.45% with "GOOD" criteria, and field testing with an average result of 72.32 % with “GOOD†Criteri

Identification of virtual plants using bayesian networks based on parametric L-system

Parametric L-System is a method for modelling virtual plants. Virtual plant modelling consists of components of axiom and production rules for alphabets in parametric L-System. Generally, to get the alphabet in parametric L-System, one would guess the production rules and perform a modification on the axiom. The objective of this study was to build virtual plant that was affected by the environment. The use of Bayesian networks was to extract the information structure of the growth of a plant as affected by the environment. The next step was to use the information to generate axiom and production rules for the alphabets in the parametric L-System. The results of program testing showed that among the five treatments, the combination of organic and inorganic fertilizer was the environmental factor for the experiment. The highest result of 6.41 during evaluation of the virtual plant came from the treatment with combination of high level of organic fertilizer and medium level of inorganic fertilizer. Mean error between real plant and virtual plan was 9.45 %

CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN)

The diversity of tribes makes Indonesia rich in culture that characterizes it, one of which is traditional cloth. Through a variety of patterns and motifs that exist in traditional fabrics, reflecting the life, customs, and culture that exist in an area. Lombok is one of the areas that produces a typical songket cloth. The famous songket craft centers in Lombok are located in the Pringgasela area, Pringgasela District, Sade Village is in Pujut District, Central Lombok Regency and Sukarara is in Jonggat District, Central Lombok Regency. Each area of ​​the center for songket craftsmen has their own characteristics both in terms of the name, motif and texture. When viewed with the naked eye, the texture of each songket will look the same, to be able to know the differences in the texture of each songket, it is necessary to do a classification using computers or technology. Today's society still does not know much information about the textures of songket cloth. The method used to classify the typical Lombok songket in this study uses the Convolution Neural Network (CNN) method. The results obtained from the use of 64 datasets, with details of 40 types of Sade songket and 24 types of Pringgasela songket, after the dataset is trained it produces 86.36% accuracy, 87% precision, 86% recall, and 86% F1-Score.   Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth

Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint

This study aims to find out the relations correspondence by using Gray Level Co-occurrence Matrix (GLCM) feature on parents and children finger print. The analysis is conducted by using the finger print of parents and family in one family There are 30 families used as sample with 3 finger print consists of mothers, fathers, and children finger print. Fingerprints data were taken by fingerprint digital persona u are u 4500 SDK. Data analysis is conducted by finding the correlation value between parents and children fingerprint by using correlation coefficient that gained from extract feature GLCM, both for similar family and different family. The study shows that the use of GLCM Extract Feature, normality data, and Correlation Coefficient could identify the correspondence relations between parents and children fingerprint on similar and different family. GLCM with four features (correlation, homogeneity, energy and contrast) are used to give good result. The four sides (0o, 45o, 90o and 135o) are used. It shows that side 0o gives the higher accurate identification compared to other sides

Fingerprint Pattern of Matching Family with GLCM Feature

In this research, fingerprint pattern matching is done to find out whether there is the similarity between parent and child fingerprint pattern. An important step in fingerprint matching is the fingerprint pattern search and matching. Fingerprint data is used by 11 families from various families. The method used in fingerprint feature extraction is GLCM. The GLCM angle used is 0o, and the features used are contrast, homogeneity, correlation, and energy. For fingerprint pattern matching use minutiae score. From the results obtained GLCM has been widely used in fingerprint texture analysis. This study proves that the proposed method for matching fingerprints on parents and children gets the most dominant pattern is the loop pattern

Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based

The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants. &nbsp

DECISION SUPPORT SYSTEM OF REWARDING ON LECTURER PERFORMANCE USING FUZZY TSUKAMOTO METHOD CASE STUDY AT MATARAM UNIVERSITY OF TECHNOLOGY

To prepare quality and character human resources, Mataram Technological University strives to provide the best in carrying out the tridharma activities of higher education, one of which is by giving rewards in the hope that morale and loyalty can continue to be improved. However, the gift-giving system that the Mataram Technological University has implemented has not been able to bring about change because the gift-giving system is incorrect. This is because the applied reward-giving assessment system only refers to the assessment without paying attention to other criteria in the tridharma of higher education. Such as the implementation of learning, Research, and community service. Therefore, to overcome this problem, a decision support information system for awarding lecturer performance is needed, which is built using the fuzzy Tsukamoto method by considering several criteria such as Presence, Research Results, and Community Service Results. Lecturer Performance Index in carrying out the learning process. With this decision support system, the implementation of the Tridharma carried out by lecturers can continue to monitor the system and improve the quality and accreditation of study programs and universities

MAPPING LOCATIONS AND SHORTEST ROUTE OF TOURISM OBJECTS IN CENTRAL LOMBOK USING GIS-BASED A-STAR ALGORITHM

Central Lombok tourism is a tourism that foreign and domestic tourists often visit. There are many tourist objects offered by the Central Lombok Government, such as waterfall tours, beach tours, traditional village tours, cultural tours, and Pertamina Mandalika International Street Circuit. However, there are many tourist objects, and not all tourists know the location of these tourist objects. Tourists often experience constraints, are the location of tourist objects that is not quite right, it is still difficult to determine the shortest route to the location, and the lack of complete information about existing tourist objects, which can hinder the journey of tourists to the destination location. This study aims to map the location and shortest route of tourism objects in Central Lombok using an Android-based Geographic Information System by applying the A-Star algorithm. The results of this study are to develop an Android-based Geographic Information System or GIS by applying the star algorithm to Central Lombok tourism objects. So that the mapping of the location and information of tourist objects and obtain the search for the shortest route to tourist objects. The A-Star algorithm uses heuristic principles to find the shortest route to a tourism object and is optimal in finding the shortest route to tourism object