Sistem Pendukung Keputusan untuk Meningkatkan Kualitas Pelayanan di Bidang Kesehatan

Sulitnya pengukuran terhadap kualitas pelayanan menyebabkan para manajer perusahaan atau pimpinan instansi sulit mengambil kebijakan yang berhubungan dengan peningkatan kualitas pelayanan. Padahal, jika kualitas pelayanan meningkat, maka kepuasan pelanggan akan meningkat. Pada akhirnya, jika kepuasan pelanggan tinggi, akan lebih banyak pelanggan yang membeli jasa pelayanan perusahaan/instansi tersebut sehingga keuntungan akan bertambah. Penelitian ini mencoba mengatasi kesulitan yang dihadapi para manajer atau pimpinan instansi dalam permasalahan yang berkaitan dengan pengukuran kualitas produk pelayanannya. Bidang pelayanan yang menjadi pokok penelitian adalah kesehatan. Sistem pendukung keputusan yang dibuat dapat digunakan untuk menentukan dimensi pelayanan yang kualitasnya paling lemah. Sistem pendukung keputusan yang dibangun berbasis web. Dimensi pelayanan yang relevan dengan bidang kesehatan ditinjau dari data yang didapatkan melalui kuesioner. Metode yang dipakai dalam peninjauan tersebut adalah metode SERVQUAL. Selanjutnya, optimisasi diterapkan untuk memaksimumkan kualitas dimensi pelayanan yang lemah sesuai dengan atribut‐atribut untuk dimensi pelayanan yang bersangkutan. Karena atribut‐atribut pada masing‐masing dimensi merupakan parameter yang tidak tentu, maka perumusan optimisasi fuzzy akan lebih efektif. Optimisasi fuzzy dengan pendekatan fungsi ketidakpuasan diselesaikan dengan algoritma genetika. Sistem pendukung keputusan yang dihasilkan diharapkan dapat membantu para pimpinan rumah sakit dalam menentukan strategi yang harus diambil untuk meningkatkan kualitas pelayanannya. Meningkatnya kualitas pelayanan akan meningkatkan kepercayaan masyarakat kepada rumah sakit, sehingga mereka akan memilih rumah sakit tersebut untuk mendapatkan pelayanan di bidang kesehatan. Selain itu, sistem ini diharapkan dapat menjadi model SPK untuk mengukur kualitas pelayanan di bidang lain dengan metode SERVQUAL. Hanya saja, perlu dilakukan penyesuaian terhadap dimensi, kuesioner, dan model optimisasi. Kata kunci: Sistem Pendukung Keputusan, pelayanan kesehatan, metode SERVQUAL, optimisasi fuzzy, algoritma genetik

Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%

Optimization of ARIMA Forecasting Model using Firefly Algorithm

 Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization

Steganographic Model for encrypted messages based on DNA Encoding

Information has become an inseparable part of human life. Some information that is considered important, such as state or company documents, require more security to ensure its confidentiality. One way of securing information is by hiding the information in certain media using steganography techniques. Steganography is a method of hiding information into other files to make it invisible. One of the most frequently used steganographic methods is Least Significant Bit (LSB).In this study, the LSB method will be modified using DNA Encoding and Chargaff's Rule. Chargaff's Rule or complementary base pairing rule is used to construct a complementary strand. The modification of the LSB method using DNA encoding and Chargaff's Rule is expected to increase the security of the information.The MSE test results show the average value of the LSB method is 0.000236368, while the average value for the DNA Encoding-based Steganography method is 0.000770917. The average PSNR value for the LSB method was 76.82 dB while the DNA Encoding-based Steganography method had an average value of 70.88 dB. The time of inserting and extracting messages using the Steganography method based on DNA Encoding is relatively longer than the LSB method because of its higher algorithmic complexity. The message security of the DNA Encoding-based Steganography method is better because there is encryption in the algorithm compared to the LSB method which does not have encryption

Ontology-based Complementary Breastfeeding Search Model

Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5

Challenges of Sarcasm Detection for Social Network : A Literature Review

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance

The K-Means Clustering Algorithm With Semantic Similarity To Estimate The Cost of Hospitalization

 The cost of hospitalization from a patient can be estimated by performing a cluster of patient. One of the algorithms that is widely used for clustering is K-means. K-means algorithm, based on distance still has weaknesses in terms of measuring the proximity of meaning or semantics between data. To overcome this problem, semantic similarity can be used to measure the similarity between objects in clustering, so that, semantic proximity can be calculated. This study aims to conduct clustering of patient data by paying attention to the similarity of the patient’s disease. ICD code is used as a guide in determining a patient’s disease. The K-means method is combined with semantic similarity to measure the proximity of the patient’s ICD code. The method used to measure the semantic similarity between data, in this study, is the semantic similarity of Girardi, Leacock & Chodorow, Rada, and Jaccard Similarity. Cluster quality measurement uses the silhouette coefficient method. Based on the experimental results, the method of measuring semantic similarity data is capable to produce better quality clustering results than without semantic similarity. The best accuracy is 91.78% for the three semantic similarity methods, whereas without semantic similarity the best accuracy is 84.93%

Integrated AHP, Profile Matching, and TOPSIS for selecting type of goats based on environmental and financial criteria

Goat farm businessman should considered environmental and financial criteria in breeding their commodities. The environmental factors are temperature, humidity, rain intensity, and altitude. For financial criteria, used several sub criteria i.e NPV (Net Present Value), ROI (Return On Investment), BCR (Benefit Cost Ratio), PBP (Payback Period), and BEP (Break Event Point) to determine financial feasibility. This research aims to develop a decision support system for selecting type of goat to breed by combining AHP, Profile Matching, and TOPSIS. AHP method was used for calculating the weight, Profile Matching for environment suitability evaluation, and TOPSIS for producing a valid decision that represents the goat expert's decision. The result showed that three methods can be integrated, and an experimental results which was validated by expert show that Bligon goat had the highest preference value (0.8835847). This can be concluded that DSS decision was valid and it successfully represented expert’s consideration

Error Action Recognition on Playing The Erhu Musical Instrument Using Hybrid Classification Method with 3D-CNN and LSTM

Erhu is a stringed instrument originating from China. In playing this instrument, there are rules on how to position the player's body and hold the instrument correctly. Therefore, a system is needed that can detect every movement of the Erhu player. This study will discuss action recognition on video using the 3DCNN and LSTM methods. The 3D Convolutional Neural Network method is a method that has a CNN base. To improve the ability to capture every information stored in every movement, combining an LSTM layer in the 3D-CNN model is necessary. LSTM is capable of handling the vanishing gradient problem faced by RNN. This research uses RGB video as a dataset, and there are three main parts in preprocessing and feature extraction. The three main parts are the body, erhu pole, and bow. To perform preprocessing and feature extraction, this study uses a body landmark to perform preprocessing and feature extraction on the body segment. In contrast, the erhu and bow segments use the Hough Lines algorithm. Furthermore, for the classification process, we propose two algorithms, namely, traditional algorithm and deep learning algorithm. These two-classification algorithms will produce an error message output from every movement of the erhu player

A Web Based Expert System for Identifying Bloomed Plants

This research discusses the development of a web based expert system for identifying bloomed plants. The identification is based on the seven visible features of a bloomed plant, i.e. the root type, the type of the plant, the shape of the flowers, the shape of the leaves, the height of the plant, and the length and the width of the leaves. For inference process, the forward chaining method is used. The system is developed using PHP as the programming language and MySQL as the database management system. Based on some testing conducted to the system, it can be concluded that the system can identify bloomed plants with the accuracy of 100%. The system can accommodate the update on its knowledge as long as the update is only on the available features in the system. This drawback can be used as starting point for the future development of the system