The usefulness of forecasting method in predicting the number of disease incidence is important. It motivates development of a system that can predict the future number of disease occurrences. Fluctuation analysis of forecasting result can be used to support the making of policy from the stake holder. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of diasease incidence in human. The dataset for model development was collected from time series data of Malaria occurrences in United States obtained from a study published by Centers for Disease Control and Prevention (CDC). It resulted SARIMA (0,1,1)(1,1,1) ₁₂ as the selected model. The model achieved 21,6% for Mean Absolute Percentage Error (MAPE). It indicated the capability of final model to closely represent and made prediction based on the Malaria historical dataset.

Conventional classroom is still the main learning method applied in undergraduate program of Electrical Engineering and Information Technology Department, Gadjah Mada University. There are several problems in this method, such as large amount of students and limited number of meetings making difficult to understand each student. Student classification is a way to solve the problem by mapping the condition of each student based on certain parameters. Many methods have been applied to classify students that are based on IF-THEN rules and pattern recognition. However, many studies were done on intelligent tutoring systems and e-learning systems, not in a conventional classroom. Moreover, there are no researches that measure basic values by considering intelligence and non-intelligence performances. In this work, a student classification model was developed by applying neuro fuzzy concept; a combination of fuzzy's IF-THEN rules and neural network's ability to learn, so this method has the ability to learn from the generated rules to produce the best classification model. The model can be used to predict students' academic performance. Data were processed using ANFIS Editor-Matlab Fuzzy Logic. The results showed that combination of three parameter values -interest, talent, and motivation- is the best model for students classification, which has training RMSE value 0.12301 and testing average RMSE value 0.25611.

One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.

Adaptive learning (AL) is a learning process focusing on students’ personality through of recommendation-based learning and inquiry-based learning. This researc

In this paper we would like to discuss about stemming effect by using Nazief and Adriani algorithm against similarity detection result of Indonesian written The contents of the publication abstract similarity detection can be used as an early indication of whether or not the act of plagiarism in a writing. Mostly in processing the text adding a pre-process, one of it which is called a stemming by changing the word into the root word in order to maximize the searching process. The result of stemming process will be changed as a certain word n-gram set then applied an analysis of similarity using Fingerprint Matching to perform similarity matching between text. Based on the F₁-score which used to balance the precision and recall number, the detection that implements stemming and stopword removal has a better result in detecting similarity between the text with an average is 42%. It is higher comparing to the similarity detection by using only stemming process (31%) or the one that was done without involving the text pre-process (34%) while applying the bigram.

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.