Publication Search

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.

Rice is the most important staple food for humans to have energy to carry out activities. The purpose of this study is to provide information to the public in the form of a reference for selecting rice based on consumer interest, so that it can be used as a decision making in buying rice. The Mamdani method is a method that is able to solve problems in the case of rice selection based on consumer references. The work process of the Sugeno method consists of four parts, namely fuzification, inference engine, implication function amplification and the last one is defuzzification. The final result, the Mamdani method has the characteristics of using the AND operator and using the min-max value. This research is in the form of a decision-making system in rice selection based on references from consumers by looking at four aspects of criteria such as price, quality, taste, and shape variables, these four aspects can be used as a reference in rice selection. Sugeno fuzzy logic to get the final value.The Mamdani method is a very effective method in selecting rice according to the needs and interests of consumers so that potential consumers can easily choose rice according to their interests and desired criteria.

The implementation of the 2020 Pilkada in Gunungpati District as a whole has improved in terms of the quality of its implementation. This is the result of the cooperation of all competent parties at the sub-district and village levels. This study aims to analyze the collaboration of the Supervisory Committee for the Election of Governors and Deputy Governors in 2020, Gunungpati District and to understand in depth the flow of information using Social Network Analysis (SNA). The results showed that the overall density of the supervisory committee network was 0.53 or 53%, so the characteristics of the network of members of the Panwaslu Kec.Gunungpati network were in the high category. Panwaslu members with the initials DAP, RV, WPU and M are the most dominant members of the Panwaslu with values of Centrality, Closeness and Betweness Centrality in the network.  

The climatic chamber is a calibration medium for the thermohygrometer. This simple climatic chamber was built using the Arduino uno, DHT22 sensor, heater, and peltier. The climatic chamber was designed and manufactured well and cheaply. Climatic chamber testing of the design results is carried out to determine the success rate of the tool. Tests are carried out using the Madgetech data logger tool through monitoring or monitoring of temperature within a certain period of time. The results of temperature monitoring at two observed values, namely temperatures of 250C to 300C and temperatures of 300C to 350C, showed good results with the corresponding rhythm of temperature increase and temperature decrease. It's just that the decrease in temperature still takes longer than the increase in temperature. This shows that the heater can be said to be working optimally and the peltier is not fully working. So it is necessary to do further studies related to the peltier for the climatic chamber both in terms of number and characteristics.

Arzara company is a company engaged in the garment sector, the development of the company affects the level of production of goods, so that in order to balance the production needs, it must increase the number of employee needs. The problem that often occurs in Arzara's company is the lack of professionalism of the company's employees, resulting in unilateral termination of employment. Termination of employment of employees of course will also affect the amount of production of goods. The SAW method or often called Simple Additive Weighting is an algorithmic method used for decision making, using certain criteria as the weight of the assessment in decision support. In making employee admission decisions using the SAW method using several criteria, which include expertise (skills), work experience, age, gender, education, health, talent, character, temperament, character, general knowledge tests, psychological tests. By using the PHP programming language, it can be used to assist the company to perform valid recruitment of employees . Applications that have been made can be used as a tool for decision makers while remaining based on a decision support system that is more effective in selecting employee admissions using the SAW ( Simple Additive Weighting ) method . 

In all section of inventory management, including inventory management in a hospital, minimize cost and ideal inventory accommodation against demand has always been a primary goal (Varghese, 2012). Nearly a third of the operational success of a hospital is in the field of medical supplies. In its usual way, inventory  management in hospitals using the approach to the demand usage, the characteristics of the item and the estimate of the demand. The model presented in this study accommodates that common approach which use a combination of inventory safety stock limit, Economic Order Quantity, and reorder point in a information system dashboard that capable to provide quick and easy information about the condition of inventory management. The result of the system are used for the hospital pharmaceutical inventory management in integration with the INACBGs  to optimize the inventory performance.  

Classification is one of the most widely used techniques in machine learning. Text classification is the process of classifying data according to pre-determined groups or classes. Where in most cases, text classification uses labeled training data to obtain the rules used to classify test data into predefined groups. In this study, it is proposed to use CountVectorizer for Indonesian text classification which will be compared with TF-IDF Term Weighting and its three feature levels, namely Character Level, Word Level and N-gram Level as feature extraction which is implemented together with Naive Bayes classification and the BPPPTIndToEngCorpusHalfM dataset. To compare the classification performance, this study uses 10-Fold Cross Validation and Split Data using a ratio of 90:10, while to evaluate the accuracy of the authors using the F1-Score and AUC with the hope that this study will get good accuracy results so that it can be used as a reference to be developed using another method. The F1-Score accuracy obtained in this study was 0.93 and the AUC score was 0.95.