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A VIRTUAL-REALITY EDU-GAME: SAVE THE ENVIRONMENT FROM THE DANGERS OF POLLUTION Mawsally, Dita Aluf; Sudarmilah, Endah
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8194

The virtual reality educational game, with theme ?saving the environment from the danger of pollution? ? aims to help children aged 10-13 years in understanding pollution and how to reduce it. The game uses smartphone and VR Box. This edu game is designed under SDLC (Software Development Life Cycle) of the waterfall model. Contained assets are obtained from the Unity Asset Store which will simulate learning techniques with several educational games. This can be seen as an advantage from the traditional learning method. Based on a black box test, this game runs well on target devices. Usability test is conducted using the System Usability Scale (SUS), and this game gets a score of 71.58 which is in the "good" criteria.

KNOWLEDGE EXTRACTION ON REDUCING THE NUMBER OF STUDENTS USING EXPLORE, ELABORATE AND EXECUTE TECHNIQUES Guterres, Juvinal Ximenes; Iriani, Ade; Purnomo, Hindriyanto Dwi
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8685

The decline in the number of students at East Timor's private universities can create new problems. This will be a burden for universities and other private institutes to develop. The financial and financing components are the factors that determine the implementation of teaching and learning activities that are used, among others, for the cost of facilities and teaching equipment. This study aims to extract knowledge of the decline in the number of students that occur every year at UNITAL. The method used is knowledge capture with Explore, Elaborate, and Execute techniques. Data collection techniques carried out by observation, interviews, documentation and questionnaires. Explore techniques to investigate results data, observations, interviews, documentation and questionnaires related to the reduction in the number of students arrested in the form of causes and effects of problems, then elaborate techniques describe interview, observation, brainstorming, and documentation data. The execute process is the stage of executing data from knowledge capture techniques based on explore and elaborate techniques and then produces a tacit and explicit knowledge from the actors or actors. The results obtained from this research are able to identify that the decline in students is caused by the main factors, namely, the quality of human resources consisting of lecturers, staff and technicians, service quality, facilities and infrastructure, buildings, classrooms, laboratory facilities, libraries and UNITAL academic information systems. Externally, there is a lack of cooperation with universities, both domestic and foreign, promotion to the public both directly and through social media. The results of knowledge capture is wealth that can be stored in a repository to be shared to help the processing of knowledge with the help of Information Technology.

THE DESIGN OF EXPLORATORY APPLICATION AND PREPROCESSING OF EVENT LOG DATA IN LMS MOODLE-BASED ONLINE LEARNING ACTIVITIES FOR PROCESS MINING Aulia, Demaspira; Waspada, Indra
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8023

Process Mining is one of the sub-studies of Data Mining that focuses on the events of a system. An area that benefits from process mining is education, especially online learning. This study used Moodle as a platform to provide online event activity log data in online learning. Moodle-based process mining requires several stages that are not easily understood directly by teachers. As a solution, some efforts are needed to integrate Moodle with process mining. This study built an application that could contribute to the Preprocessing and Exploratory Data Analysis (EDA) stages of Moodle event log data ? as an important part of the process mining stage. Preprocessing was implemented by using the simple heuristic filtering method, while EDA was employed through visualization using flow control and dotted charts. Eventually, the application built in this study successfully performed preprocessing in Moodle event log data and could display the results visually, as a tool of control flow analysis and dotted chart analysis.

SILHOUETTE DENSITY CANOPY K-MEANS FOR MAPPING THE QUALITY OF EDUCATION BASED ON THE RESULTS OF THE 2019 NATIONAL EXAM IN BANYUMAS REGENCY Ananda, Ridho
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8375

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.

EFFECTIVENESS OF SVM METHOD BY NAÃ¯VE BAYES WEIGHTING IN MOVIE REVIEW CLASSIFICATION Zain, Fadli Fauzi; Sibaroni, Yuliant
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7770

Classification of movie review belongs to the realm of text classification, especially in the field of sentiment analysis. One familiar text classification method used is support vector maching (SVM) and Naïve Bayes. Both of these methods are known to have good performance in handling text classification separately. Combining these two methods is expected to improve the performance of classifier compared to working separately. This paper reports the effort to classify movie reviews using the combined method of Naïve Bayes and SVM with Naïve Bayes as weights. This combined method is commonly called NBSVM. The results showed the best accuracy is obtained if the classification is done by the NBSVM method, which is equal to 88.8% with the combined features of unigram and bigram and using pre-processing in the form of data cleansing only.

ARCHITECTURE OF BACK PROPAGATION NEURAL NETWORK MODEL FOR EARLY DETECTION OF TENDENCY TO TYPE B PERSONALITY DISORDERS Hayat, Cynthia; Limong, Samuel; Sagala, Noviyanti
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7923

Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, i.e training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis category of type B personal disorder, I.e. anti social, borderline, histrionic, and narcissistics. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%.

ANALYSIS OF SLOW MOVING GOODS CLASSIFICATION TECHNIQUE: RANDOM FOREST AND NAÃ¯VE BAYES Jollyta, Deny; Gusrianty, Gusrianty; Sukrianto, Darmanta
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8263

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.

CASE BASE REASONING (CBR) AND DENSITY BASED SPATIAL CLUSTERING APPLICATION WITH NOISE (DBSCAN)-BASED INDEXING IN MEDICAL EXPERT SYSTEMS Santoso, Herdiesel; Musdholifah, Aina
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8323

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ? 90.

A Virtual-Reality Edu-Game: Save The Environment from the Dangers of Pollution Dita Aluf Mawsally; Endah Sudarmilah
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8194

The virtual reality educational game, with theme “saving the environment from the danger of pollution” – aims to help children aged 10-13 years in understanding pollution and how to reduce it. The game uses smartphone and VR Box. This edu game is designed under SDLC (Software Development Life Cycle) of the waterfall model. Contained assets are obtained from the Unity Asset Store which will simulate learning techniques with several educational games. This can be seen as an advantage from the traditional learning method. Based on a black box test, this game runs well on target devices. Usability test is conducted using the System Usability Scale (SUS), and this game gets a score of 71.58 which is in the "good" criteria.

Knowledge Extraction on Reducing the Number of Students Using Explore, Elaborate and Execute Techniques Juvinal Ximenes Guterres; Ade Iriani; Hindriyanto Dwi Purnomo
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8685

The decline in the number of students at East Timor's private universities can create new problems. This will be a burden for universities and other private institutes to develop. The financial and financing components are the factors that determine the implementation of teaching and learning activities that are used, among others, for the cost of facilities and teaching equipment. This study aims to extract knowledge of the decline in the number of students that occur every year at UNITAL. The method used is knowledge capture with Explore, Elaborate, and Execute techniques. Data collection techniques carried out by observation, interviews, documentation and questionnaires. Explore techniques to investigate results data, observations, interviews, documentation and questionnaires related to the reduction in the number of students arrested in the form of causes and effects of problems, then elaborate techniques describe interview, observation, brainstorming, and documentation data. The execute process is the stage of executing data from knowledge capture techniques based on explore and elaborate techniques and then produces a tacit and explicit knowledge from the actors or actors. The results obtained from this research are able to identify that the decline in students is caused by the main factors, namely, the quality of human resources consisting of lecturers, staff and technicians, service quality, facilities and infrastructure, buildings, classrooms, laboratory facilities, libraries and UNITAL academic information systems. Externally, there is a lack of cooperation with universities, both domestic and foreign, promotion to the public both directly and through social media. The results of knowledge capture is wealth that can be stored in a repository to be shared to help the processing of knowledge with the help of Information Technology.

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Journal Mail Official
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Kota surakarta,

Jawa tengah

INDONESIA