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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Christina Juliane
STMIK LIKMI, Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka Firman Sukmayadi; Alamsyah Firdaus; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4848

Abstract

The process of determining the concentration of expertise carried out at the YPC Tasikmalaya Vocational School has shortcomings such as making decisions based on the wishes of students without paying attention to academic grades at the previous level of education. So that there are some students who feel it is not right in choosing the concentration of expertise, resulting in a lack of competence possessed by students with the concentration of expertise selected. The choice of concentration of expertise is the right of every student, but if it is wrong it can cause a decrease in learning motivation and low learning achievement. This problem can be solved by using clustering method with K-Means algorithm. This study aims to classify students' interests in choosing a concentration of expertise at YPC Tasikmalaya Vocational School based on the Merdeka Curriculum. The results showed that the grouping of students' interests in choosing the concentration of expertise was formed into 4 clusters. The cluster with the most members is cluster 0, namely students who have an average score of 79 Mathematics, then Indonesian and English 83. Furthermore, the cluster with the least number of members is cluster 2, namely students who have an average score of 78 Mathematics and English, then Indonesian 79.
Analisa dan Penerapan Metode Algoritma K-Means Clustering Untuk Mengidentifikasi Rekomendasi Kategori Baru Pada List Movie IMDb Abraham Situmorang; Arifin Arifin; Ilpan Rusilpan; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4729

Abstract

IMDb (Internet Movie Database) is a comprehensive website that offers information about movies from all over the world, as well as various information about director, actor, actress, and writer biographies and award nominations. Visitors to the IMDb website can browse ratings and reviews based on the movies they plan to watch. Top 250 Movies and Most Popular Movies are two categories on IMDb. Because the results of the highest rating and the largest votes are only displayed based on the highest order of votes or ratings, the two existing categories are judged less useful and irrelevant to the suggestions for visitors to choose and decide on a film. This is due to the results of the highest rating and the most numerous votes, as determined by the highest ruling on either the votes or the rating. As a result of this, data mining with the K-means clustering algorithm is used to geolocate data in order to view data and accuracy using Davies-Bouldin Index (DBI) to combine ratings and votes with average approach to determine the centroid. Based on the results of this study, it is concluded that the DBI population with the highest accuracy is Cluster K=2 with population 509, with a score of 0.456, based on the voting and rating information, it can be deduced that a new category of movies called Best Recommended Movie is being recommended to potential moviegoers on the imdb.com website.
Komparasi Algoritma Klasifikasi Data Mining Menggunakan Optimize Selection untuk Peminatan Program Studi Khaerul Anam; Bani Nurhakim; Christina Juliane
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2160

Abstract

The selection of a study program is a unique opportunity for a student. STMIK IKMI Cirebon is now a KIP Kuliah provider, offering three study program. The research problem is the unavailability of a model of student interest in the study program, so it is necessary to carry out an interest in the study program by applying an algorithm to the classification model. The algorithm used as a comparison is the Decision Tree algorithm (C4.5), Naive Bayes, k-Nearest Neighbor and Support Vector Machine. The classification model applies the Optimize Selection operator by looking for the dominant attribute in its influence on the specialization of the student study program. Finally, the comparison model will be tested by parametric t-test in order to test the significance of the algorithm. The results of the algorithm accuracy test obtained that the SVM algorithm has the best accuracy with a value of 80.76%. While the algorithm with the lowest accuracy is Naive Bayes with a value of 74.64%. While the other two algorithms have a sequential accuracy rate of 80.47% for Decision Tree and 76.09% for k-NN. The results of this study are used to classify study preference for students in STMIK IKMI Cirebon which is useful for predicting study interest based on the background of students
Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19 Ahmad Rifai; Fatihanursari Dikananda; Christina Juliane
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2167

Abstract

The COVID-19 pandemic has greatly affected the learning environment, with the excuse of stopping the spread of COVID-19 infection. Teaching and learning activities that have generally been completed on campus face-to-face now have to be transferred to distance learning. However, one of the drawbacks of implementing distance learning is that it makes students less active, so that KBM feels tiring. The purpose of this study is to classify student learning outcomes during the COVID-19 Pandemic. The method used is the Knowledge Discovery Database (KDD) using the K-Means Algorithm. The number of clusters selected is the number of clusters with the smallest Davies Bouldin Index (DBI). The results of this study obtained 3 clusters with a DBI value of 1.379 and a centroid distance of 0.342. Cluster_1 is the data group with the highest quality index, Cluster_2 is the data group with the second highest quality index, and Cluster_0 is the data group with the lowest quality index of all clusters. By knowing the clusters of PJJ learning outcomes, it will make it easier for universities to take improvement steps to improve the quality of learning in accordance with the characteristics of each existing cluster
Penerapan Algoritma C4.5 Untuk Klasifikasi Tren Pelanggaran Kendaraan Angkutan Barang dengan Metode CRISP-DM Novie Hari Purnomo; Bayu Pamungkas; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5247

Abstract

Road damage due to ODOL (Over Dimension Over Loading) increases the road maintenance budget significantly, namely an average of IDR 43.45 trillion per year. In addition, many accidents involving ODOL trucks or overloading and dimensions have occurred. The level of violations caused by ODOL vehicles is still high, so technology is needed that is able to manage data and serve as a reference to find out the hidden approaches in the data set, as well as analyze the grouping between data and attributes to facilitate decision making and policy making. This study applies the CRISP-DM methodology using a decision tree model with the C4.5 algorithm. The purpose of this research is to classify trends in freight transport violations based on violation data in the UPPKB. The research data is primary data obtained from the Directorate of Road Transportation Infrastructure of the Ministry of Transportation through the online weighbridge system (JTO). The expected result of this research is to be able to find out the pattern of classification trends for freight vehicle disturbances based on the results of the C.45 algorithm decision tree, so that the research results can be used as a reference in making decisions and making policies. The results of this study indicate that the accuracy performance in data mining tests for the classification of trends in freight vehicle disturbances with 10 fold cross validation linear sampling produces an accuracy of 86.31% +/- 1.23% (micro average: 86.31%), shuffled sampling produces an accuracy of 86.34% +/ - 0.67% (micro average: 86.34%) and stratified sampling produces an accuracy of 86.34% +/- 0.67% (micro average: 86.34%).
Implementasi Data Mining Tingkat Kepemimpinan Siswa dengan K-Nearest Neighbor, Decision Tree, dan Naïve Bayes Didin Sayhidin; Gendhi Haris; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5351

Abstract

The process of monitoring and evaluating high school student leadership is deemed necessary because the level of student leadership is one of the prerequisites for high school students to face real challenges in the future. Data mining can be used to classify the level of leadership among high school students. The purpose of the research conducted in this case is to apply data mining using the K-NN, Decision Trees, and Naive Bayes models. This research is located in two different public high schools, namely SMA A as training data and SMA B as test data. This data was obtained in the same year, namely 2022. The data obtained were analyzed with the help of the Rapidminer application using K-NN, Decision Tree, and Naive Bayes. Student data that is processed is Basic Education Data (DAPODIK) in excel format. Before being analyzed, the text is processed first, namely tokenization, case folding, stop words, and details. The main goal of the steps above is also the main goal of this study to get the most accurate algorithm for classifying student leadership levels and knowing the results for comparison. The conclusion of this study is when measuring the performance of the three algorithms, the test results use confusion matrix validation. The K-NN algorithm was found to have the highest accuracy score compared to the Decision Tree and Naive Bayes. The accuracy value of the K-NN method using a dataset of high school students is 95.86%, the accuracy value of the Decision Tree algorithm is 94.65%, and the accuracy value of the Naïve Bayes algorithm is 79.55%.
Co-Authors Abraham Situmorang Ahmad Rifai Alamsyah Firdaus Arifin Arifin Bani Nurhakim Bayu Pamungkas Didin Sayhidin Fatihanursari Dikananda Firman Sukmayadi Gendhi Haris Ilpan Rusilpan Khaerul Anam Novie Hari Purnomo