Kondisi rumah menjadi perhatian setiap warga dan dinas kesehatan terkait. Untuk menentukan kondisi rumah dinyatakan sehat atau tidak sehat menggunakan tiga kriteria, diantaranya: komponen rumah, sarana rumah, dan perilaku. Dalam penelitian ini untuk menentukan kondisi rumah digunakan 10 sampel, dari hasil perhitungan didapatkan 2 rumah dinyatakan kondisi sehat dan 8 rumah dinyatakan kondisi tidak sehat. Metode TOPSIS diterapkan untuk menentukan prioritas kondisi rumah tidak sehat, dari hasil perhitungan dinyatakan alternatif ke-2 dengan nilai preferensi 1 merupakan prioritas utama untuk kondisi rumah tidak sehat. Dari hasil perhitungan metode TOPSIS dapat menjadi masukan bagi dinas kesehatan terkait untuk menindaklanjuti prioritas rumah tidak sehat. Hasil perhitungan sistem telah divalidasi dengan perhitungan manual didapatkan hasil yang sama, dan dapat dikatakan bahwa sistem pendukung keputusan yang telah menerapkan metode TOPSIS untuk menentukan prioritas rumah tidak sehat telah berjalan dengan baik dan sesuai. Kata kunci: sistem pendukung keputusan, metode TOPSIS, kondisi rumah.

Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it still has limitations in detecting aspects and sentiment classification and requires labeled data, but obtaining labeled data is very difficult. This research used a graph-based and semi-supervised approach to improve ABSA. GCN and GRN methods are used to detect aspect and opinion relationships. CNN and RNN methods are used to improve sentiment classification. A semi-supervised model was used to overcome the limitations of labeled data. The dataset used is an Indonesian-language review taken from the marketplace. A small part is labeled manually, and most are labeled automatically. The experiment results for the aspect classification by comparing the GCN and GRN methods obtained the best model using the GRN method with an F1 score = 0.97144. The experiment for sentiment classification by comparing the CNN and RNN methods obtained the best model using the CNN method with an F1 score = 0.94020. Our model can label most unlabeled data automatically and outperforms existing advanced models.

Convection Tas Loram Kulon is a Small and Medium Enterprises (UKM) located in the village of Loram Kulon, Jati District, Kudus Regency. The amount of effort makes everyone compete to be the best to get customers. When business owners cannot use information technology, a lot of information is wasted. Forms of manual reporting make it difficult for the owner to see the development of the business, as a result the owner can make mistakes in making decisions, especially for the addition or reduction of the stock. Forecasting is an estimate of the level of demand for one or more products over the coming periods. In forecasting the estimated level of demand in order to avoid excesses and not less inventory quantities based on past sales. Decision support investigated to predict the number of sales using the single moving average method. The Single Moving Average method uses a number of actual data requests to generate forecast values. The calculation is done by taking a group of values or data and then calculating the average and with the average used to calculate the next period forecast.

Batik is the most popular tradisional cloth made using the wax-resist dyeing technique. The fabric is found in various city in Indonesia, one of them is Lasem which popular with hand-drawn batik is called Batik Tulis Lasem. Natural dye selection is one of the most important priority for the batik tulis craftsmen. Natural dyes made from leaves and flowers. Proper selection of natural dye will impact on color, motif, and brightness on batik tulis fabric. AHP and TOPSIS methods can be used together to selecting natural dye especially the batik tulis lasem. AHP method is used in determining the weights of the criteria, and then TOPSIS method is needed for determining the best alternative on natural dye of batik tulis. According to the result of research, TOPSIS method is used to determine the priority of alternative on natural dye. Based on calculation with TOPSIS method , the fourth alternative (A4 is kayu secang) get priority value is 0.8478, so kayu secang is recommended to the craftsmen that will used this material as the natural dye.

Every patient who is rushed to the Emergency Department needs fast treatment to determine whether the patient should be inpatient or outpatient. However, the existing fact is that deciding whether an inpatient or outpatient must wait for the diagnosis made by the existing doctor, so if there are many patients, it generally takes quite a long time. So, to predict patient admissions to the emergency unit, a machine learning model that can be fast and accurate is needed. Therefore, this study developed a machine learning and neural network model to determine patient care in Emergency Departments. This study uses publicly available electronic health record (EHR) data, which is 3,309. The model development process uses machine learning methods (SVM, Decision Tree, KNN, AdaBoost, MLPClassifier) and neural networks. The model that has been obtained is then evaluated for its performance using a confusion matrix and several matrices such as accuracy, precision, recall, and F1-Score. The results of the model performance evaluation were compared, and the best model was obtained, namely the MLPClassifier model with an accuracy value = 0.736 and an F1-Score value = 0.635, and the Neural Network model obtained an accuracy value = 0.724 and an F1-Score value = 0.640. The best models obtained in this study, namely the MLPClassifier and Neural Network models, were proven to be able to outperform other models.