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Era revolusi industri 4.0 mengalihkan peralatan yang bersifat tradisional menuju alat – alat yang serba digital. Penyuluhan mengenai model –model pembelajaran dan teknologi dalam pembelajaran dilakukan di SD Muhammadiyah Mertosanan yang  bertujuan untuk memberi wawasan kepada guru-guru SD Muhammadiyah Mertosanan mengenai berbagai macam model pembelajaran dan berbagai teknologi yang berkaitan dengan pembelajaran. Metode yang digunakan untuk mencapai tujuan tersebut adalah penyuluhan yang dilakukan secara on line. Hasil yang diperoleh dari pelaksanaan kegiatan ini adalah mendapat respon yang positip dari para peserta pengabdian dengan banyaknya pertanyaan-pertanyaan yang diajukan dan adanya masukan untuk diadakan kegiatan pengabdian lanjutan.

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.

Electronic government (e-gov) is applied to support performance and create more efficient and effective public services. Grouping data in soft-set theory can be considered as a decision-making technique for determining the maturity level of e-government use. So far, the uncertainty of the data obtained through the questionnaire has not been maximally used as an appropriate reference for the government in determining the direction of future e-gov development policy. This study presents the maximum attribute relative (MAR) based on soft set theory to classify attribute options. The results show that facilitation conditions (FC) are the highest variable in influencing people to use e-government, followed by performance expectancy (PE) and system quality (SQ). The results provide useful information for decision makers to make policies about their citizens and potentially provide recommendations on how to design and develop e-government systems in improving public services.

University is an educational institution that has objectives to increase student retention and also to make sure students graduate on time. Student learning performance can be predicted using data mining techniques e.g. the application of finding essential association rules on student learning base on demographic data by the university in order to achieve these objectives. However, the complete data i.e. the dataset without missing values to generate interesting rules for the detection system, is the key requirement for any mining technique. Furthermore, it is problematic to capture complete information from the nature of student data, due to high computational time to scan the datasets. To overcome these problems, this paper introduces a relative tolerance relation of rough set (RTRS). The novelty of RTRS is that, unlike previous rough set approaches that use tolerance relation, non-symmetric similarity relation, and limited tolerance relation, it is based on limited tolerance relation by taking account into consideration the relatively precision between two objects and therefore this is the first work that uses relatively precision. Moreover, this paper presents the mathematical properties of the RTRS approach and compares the performance and the existing approaches by using real-world student dataset for classifying university’s student performance. The results show that the proposed approach outperformed the existing approaches in terms of computational time and accuracy.

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the comparison results of the proposed method with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.

One part of improving and developing lecturer pedagogical competence and evaluating the substance of the courses taught in the lecture process is to use Lecturer Evaluation Effectiveness by Students (EDOM). The EDOM is a feedback questionnaire that can be used by educators to assess and evaluate pedagogical competencies that they have and aims to make continuous improvements to various matters related to the teaching process in the classroom. In addition to questions in the form of a Likert scale, EDOM also accommodates open-ended questions. However, this type of question tends not to be considered in the feedback assessment and is not explored more deeply to gain insight. Therefore, in this study text mining was carried out to find out the sentiments given by students, whether positive or negative sentiments using Convolutional Neural Network (CNN). The experimental results showed that CNN was able to achieve accuracy, precision, recall, and F1-Score of 87.95%, 87%, 78%, and 81%, respectively.

Students are one of the substances that need to be considered in relation to the world of education, because students are translators of the dynamics of science, and carry out the task of exploring that knowledge. As a subject with potential and, at the same time, objects in their activities and creativity, students are expected to be able to develop their qualities. The quality can be seen from the academic achievements achieved, which are evidence of the effort earned by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria to be declared eligible to continue their studies, the student is declared to be not graduating on time or even dropout (DO). The high number of students not graduating on time or dropouts at higher institutions can be minimized by the policies of higher institutions by directing and detecting at-risk students in the early stages of education. Therefore, in this paper, we present the use of Fuzzy Soft Set Classification (FSSC), which is based on the Fuzzy Soft set theory to predict student graduation. The 2068 dataset was taken from the Directorate of Information Systems, Ahmad Dahlan University. The results showed that the FSSC reached up to 0.893292 in terms of accuracy. So, it is expected to be able to detect students at risk in the early stages of education so that higher education can minimize students not graduating on time or dropout by providing appropriate treatment and designing strategic programs.

Saedudin, R.R.; Kasim, S.; Mahdin, H.; Sutoyo, E.; Riyadi Yanto, I.-T.; Hassan, R., and Ismail, M.A., 2018. A Relative Tolerance Relation of Rough Set (RTRS) for potential fish yields in Indonesia. In: Ashraf, M.A., and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Responses to Human and Climatic Changes throughout Asia.The sea is essential to life on earth, including regulating the climate, producing oxygen, providing medicines, providing habitats for marine animals, and feeding millions of people. It must be ensured that the sea continues to meet the needs of life without sacrificing the people of future generations. The sea regulates the planet's climate and is a significant source of nutrients. The sea becomes an essential part of global commerce, while the contents of the ocean become the solution of human energy needs today and the future. The wealth and potential of the sea as a source of energy for humans today and the future needs to be mapped and described to provide a picture of marine potential to all concerned. As part of the government, the Ministry of Marine Affairs and Fisheries is responsible for the process of formulating, determining, and implementing policies in the field of marine and fisheries based on the results of mapping and extracting information from existing conditions. The results of this information can be used to predict the marine potential in a marine area. This prediction process can be developed using data-mining techniques such as applying the association rule by looking at the relationship between the quantity of fish based on the plankton abundance index. However, this association rules data-mining techniques that require complete data, which are data sets with no missing values to generate interesting rules for detection systems. The problem is often that required marine data are not available or that marine data are available, but they contain incomplete data. To address this problem, this paper introduces a Relative Tolerance Relation of Rough Set (RTRS). Novelty RTRS differs from previous rough approaches that use tolerance relationships, nonsymmetric equation relationships, and limited tolerance relationships. The RTRS approach is based on a limited tolerance relationship considering the relative precision between two objects; therefore, this is the first job to use relative precision. In addition, this paper presents the mathematical approach of the RTRS and compares it with other existing approaches using the marine real dataset to classify the marine potential level of the region. The results show that the proposed approach is better than the existing approach in terms of accuracy.

In this paper, a violence video classification model based on deep neural network (DNN) with chicken swarm optimization (CSO) is proposed. Violence is a self-sufficient attribute, the contents that one would not be favorable to see in movies or web videos. This is a challenging problem due to strong content variations among the positive instances of violence. Currently, deep neural network has shown its efficiency in various field that relevant to its implementation and attracts plenty of researchers in awe. However, conservative deep neural network has limitations and a tendency to easily fall into local minima. Regardless of the conventional methods applied to overcome this issue, but these techniques seem insufficiently accurate and does not adopt well to certain webs or user needs. Therefore, the purpose of this study is to assess the classification performances on violence video using Deep Neural Network with Chicken Swarm Optimization (DNNCSO). Hence, in this paper different architectures of hidden layers in DNN have been implemented using the try-error method and the importance of the parameters to examine the effect of the number of hidden layers to the classification performance. The algorithm is evaluated based on error convergence and accuracy. The results have proved the effectiveness of the proposed method up to 77–79% as compared to the conventional DNN which holds 63%. Based on the promising outcome of proposed method, in future the study intended to work on improvised bio-inspired algorithm possibly with different domains and relevant features.

The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.

Clustering is the process of breaking down a huge dataset into smaller groups. It has been used in some field studies including pattern recognition, segmentation, and statistics with remarkable success. Clustering is a technique for dividing multivariate datasets into groups. No inherent distance measure on data category makes clustering data more challenging than numerical data. Data category can be assumed following the data from a multinomial distribution. Thus, the standard model parametric model can be used in latent class clustering based on the independent product of multinomial distributions. Meanwhile, multi-valued attributes on the categorical data can be decomposed into the standard set on a multi soft set. In this paper, a clustering technique based on soft set theory is proposed for categorical data through a multinomial distribution. The data will be represented as a multi soft set which is every soft set has its probability of being a member of the cluster. The data with the highest probability will be assigned as the member of the cluster. The experiment of the proposed technique is evaluated based on the Dunn index with regard to the number of clusters and response time. The experiment results show that the proposed technique has the lowest response time with high stability compared to baseline techniques. This study recommends a maximum number of clusters in implementation on the real data.

Every development activity is always related to human or community aspects. This can also lead to changes in the characteristics of the community. The community's increasing awareness and critical attitude need to be accommodated to avoid the emergence of social conflicts in the future. This research is to find out how the public perception about the impact of development on the environment. Two methods are used, i.e., MDA (Maximum Dependency Attribute) and MSMD (the Multi soft set multivariate distribution function). The MDA is to determine the most influential attribute and the Multi soft set multivariate distribution function (MSMD) is to group the selected data into classes with similar characteristics. This will help the police producer plan the right mediation and take quick activity to make strides in the quality of the social environment. The experiment conducted level of impact based on the clustering results with the greatest number of member clusters is cluster 1 (very low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The experiment obtains the level of impact based on the clustering results. The greatest number of member clusters is cluster 1 (extremely low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The scatter area impact is spread at districts 6, 7, 10, 11, the most of very high impact and districts 1,2,3,4,5,8 the lowest impact.