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All Journal Infotech Journal UNEJ e-Proceeding NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Intelligent Decision Support System (IDSS) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Multicience INTELEKTIVA Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis
Sri Sumarlinda
Universitas Duta Bangsa Surakarta
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Multicience

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IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR CLUSTTERING OF STUDENTS INTERESTING IN COMPUTING FIELDS WIJI LESTARI; SRI SUMARLINDA
INTERNATIONAL JOURNAL OF MULTI SCIENCE Vol. 1 No. 05 (2020): INTERNATIONAL JOURNAL OF MULTISCIENCE - AUGUST EDITION
Publisher : CV KULTURA DIGITAL MEDIA

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Abstract

The computing field interested is important for students of the Faculty of Computer Science. Student interest also relates to research for the thesis required for graduation. At the Faculty of Computer Science, University of Duta Bangsa Surakarta, there are several research topics that are of interest to students including: Information Systems, Decision Support Systems, Multimedia, Expert Systems and Intelligent Information Systems. Clustering is a method for grouping a number of data into several clusters or groups. Data that have closeness will be grouped into one cluster. Competitive Network is part of Artificial Neural Networks with unsupervised learning. This algorithm can be used for clustering. This study aims to produce a clustering system for mapping students interests on research topics at the Faculty of Computer Science, Duta Bangsa University Surakarta with a competitive network artificial neural network. The input data used were the results of the student's focus questionnaire on research topics which would be grouped into 5 clusters. The clustering process uses the Competitive Network algorithm with clustering parameters of the number of clusters 5, 1000 and Kohonen parameter of 0.01
CLUSTERING MODEL OF LECTURERS PERFORMA IN PUBLICATION USING K-MEANS FOR DECISION SUPPORT DATA WIJI LESTARI; SRI SUMARLINDA
INTERNATIONAL JOURNAL OF MULTI SCIENCE Vol. 1 No. 10 (2021): INTERNATIONAL JOURNAL OF MULTISCIENCE - JANUARY EDITION
Publisher : CV KULTURA DIGITAL MEDIA

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This study aims to produce a clustering model using the K-Means algorithm built to map the performance of lecturers' publications. The method used was research and development which includes the stages of data collecting, data preprocessing, clustering process and cluster analysis. The input data consists of 87 with 8 attributes, namely number of articles in Sinta indexed journal, number of articles in Scopus indexed journal, number of citation in Scopus, H-index in Scopus, number articles in Google Scholar indexed journal, number of citation in Googe Scholar, H-index in Google Scholar and H-index10 in Google Scholar. The K-Means algorithm was used with 3 clusters and 100 epoches. The results of clustering were distributed in 3 clusters, namely cluster 1 with 17 members, cluster 2 with 32 members and cluster 3 with 38 members. The results of the cluster analysis with the identification of cluster members and the value of the cluster centroid indicated that cluster 1 is lecturers with relatively high publication performance performance and cluster 3 shows relatively low publication performance performance. The data from clustering results can be used for decision support model input data for broader lecturer performance performance.
IMPLEMENTATION OF K-NEAREST NEIGHBOR (KNN) AND SUPORT VECTOR MACHINE (SVM) FOR CLASIFICATION CARDIOVASCULAR DISEASE WIJI LESTARI; SRI SUMARLINDA
INTERNATIONAL JOURNAL OF MULTI SCIENCE Vol. 2 No. 10 (2022): INTERNATIONAL JOURNAL OF MULTISCIENCE - JANUARY, 2022 EDITION
Publisher : CV KULTURA DIGITAL MEDIA

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Abstract

Data mining and machine learning are two tools that play an important role in the study of data analysis and decision systems. Classification is a function of data mining. In the classification function, sorting or mapping occurs based on the proximity or similarity of data attributes to the specified label. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The KNN algorithm is a non-parametric method used for classification and regression. Cardiovascular disease prediction models with KNN and SVM algorithms are used to identify and predict cardiovascular disease. The KNN algorithm uses Euclidian distance for the prediction process of training data. The SVM algorithm uses a hyperplane for the data prediction training process. The dataset used is 400 with 7 attributes, namely age, gender, systolic blood pressure, cholesterol, talach, oldpeak and slope. The results of the implementation of the KNN and SVM algorithms produce performance with an accuracy of 75.75% on KNN and 76.00% on SVM. The precision value is 76.78% for KNN and 83.93% for SVM. Meanwhile, the recall resulted in 77.14% for KNN and 67.14% for SVM.
Co-Authors Aditya Prasetyo Wibowo Afu Ichsan Pradana AzizahBinti Rahmat Desi Mandasari Dewangga Ranggi Wiku Faiz Mu’ammar Hadi Faulinda Ely Nastiti Hanifah Permatasari Indra Hastuti INDRA HASTUTI Intan Oktaviani Iqbal Hanan Junaidi Joko Endriyanto Moh Muhtarom Muhammad Mujib Al Khafid Nafaria Rohmandani Nurmalitasari Nurmalitasari Puput Yulianto Rifal Bayu Ardi Rina Arum Prastyanti Rina Arum Prastyantia Sopingi Wiji Lestari Wiji Lestari Wijiyanto Zalizah Awang Long