1,720,972 research outputs found
IT and education, the case study of e-learning in Indonesia
Full text linkE-learning in Indonesia began in mid-nineties with the advent of internet preceded by information technology introduction to Indonesia in late 70s and early 80s. However, those e-learning initiators hit hard by the economic and political crises which hit Indonesia in 1997s until early 21st century. Beginning the year 2000, many organizations took the initiatives to conduct e-learning in their environments, in spite of the economic crises. Based on a survey towards about 60 e-learning sites, the author found some constraints. First, the infrastructure which does not support the learning. Out of 223 million people., only 20 million own fixed telephone facilities, a must-prerequisite to access remote e-learning facilities. Using the cellular phones for internet connection is out of question as it is very expensive; on the other hand the Internet subscriber in Indonesia is limited. In 2004 there are 1,3 million internet subscribers with 14 million users. Second, on content management. Although the majority of e-learning operators are higher education institutions, there is no standard in the contents. While the contents are aimed to university students, the contents are not always reaching the academic intellectual standards. Third, there is no coordination in conducting the e-learning. The pre- and post Soeharto presidency (1998) marked the paradigm from centralized to decentralized university administration. The Directorate General of Higher Education (DGHE) which was once the regulator now is the facilitator on higher education affairs. The results is not a chaos, but an uncoordinated efforts toward e-learning. In certain universities, each department established its own e-learning facilities without bothering other departments’ efforts, let alone at the at the national level. Fourth, the cultural factors. Indonesian in general prefer talking over writing, the result of oral tradition legacy, prefer attending the lecture over self-study. Hence Indonesian students prefer to communicate or interact directly with other students and or lecturer than to communicate in a virtual way as commonly found in e-learning. What needed by Indonesia are the better coordination among e-learning operators, the grand strategy of e-learning as dictated by the higher level of decision makers and making e-learning not as e-learning itself, but as a tool to equip students to stay up to date, information technology literate and to be competitive, in a flexible way
Metode Berbasis Model (Model-based) dalam Analisis Cluster
Full text linkMetode cluster berbasis model adalah metode cluster yang didasarkan pada aspek statistik, yaitu kriteria kemungkinan maksimum. Metode cluster berbasis model ini mempunyai beberapa model dengan berbagai macam sifat geometris yang diperoleh melalui komponen Gauss. Penyekatan data dilakukan dengan menggunakan kemungkinan maksimum melalui algoritma Ekspektasi-Maksimum (EM), kemudian dengan pendekatan model Bayes berdasarkan Bayesian Information Criterion (BIC) diperoleh model terbaik. Penelitian ini bertujuan untuk mengkaji hasil pengelompokan metode gerombol berbasis model. Basil pengelompokan dari 2 contoh data penerapan (data Iris dan data Diabetes) menunjukkan bahwa metode gerombol berbasis model cukup efektifmemisahkan kelompok-kelompok yang saling tumpang tindih
KAJIAN METODE BERBASIS MODEL PADA ANALISIS KELOMPOK DENGAN PERANGKAT LUNAK MCLUST
Full text linkWard method and K-mean method are clustering method in which grouping only base on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has tenmodels with a variety of geometrical characteristics. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to assess the effectiveness of ten models from the model-based clustereng and then tocompare result of grouping methods between model-based clustering with Ward clustering and K-mean clustering. This study used simulated data and applied data. Simulated data are generated with the R programs versions 2.14.1. Proses analysis was performed by using the Mclust programs vesions 4.0 with an interface the R programs versions 2.14.1. The results showed that model-based clustering was more effective in separating the condition of one separate group and two overlap groups than ward clustering and K-mean clustering.
Metode Ward dan metode K-rataan adalah metode kelompok yang teknik-teknik pengelompokannya hanya memperhatikan ukuran jarak antar objek-objek pengamatan tanpa mempertimbangkan aspek statistiknya. Metode kelompok berbasis model adalah metode kelompok yang didasarkan pada aspek statistik, yaitu kriteria kemungkinan maksimum. Metode kelompok berbasis model mempunyai sepuluh model dengan berbagai macam sifat geometris. Penyekatan data dilakukan dengan menggunakan algoritma Ekspektasi-Maksimum (EM), kemudian dengan pendekatan Bayesian Information Criterion (BIC) diperoleh model terbaik. Penelitian ini bertujuan untuk mengkaji efektivitas dari sepuluh metode berbasis model dan kemudian membandingkan hasil pengelompokannya dengan metode Ward dan metode K-rataan. Penelitian ini menggunakan data simulasi yang dibangkitkan melali program R versi 2.14.1 dan dianalisis dengan menggunakan program Mclust versi 4.0 dengan interface program R. Hasil penelitian menunjukkan bahwa metode kelompok berbasis model lebih efektif memisahkan kelompok-kelompok yang saling tumpang tindih dibandingkan dengan metode gerombol Ward dan K-rataan
Penggunaan Uji F dalam Menguji Kesamaan Ratan Populasi pada Data yang Berdistribusi Lognormal
Full text linkPERBANDINGAN METODE MODEL-BASED DENGAN METODE K-MEAN DALAM ANALISIS CLUSTER
Full text linkK-mean method is a clustering method in which grouping techniques are based only on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has several variations with a variety of geometrical characteristics obtained by mean Gauss component. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to comparing result of grouping methods between model-based clustering and K-mean clustering. The results showed that model-based clustering was more effective in separating overlap groups than K-mean
Analisis Kelas Laten (Class Laten) Untuk Pengelompokan Data Kategorik
Full text linkDalam analisis kelompok pada bidang sosial, seringkali para peneliti mengunakan instrumen sebagai sumber informasi data yang akan dianalisis. Pada banyak kasus, kuesioner digunakan untuk mengukur suatu variabel yang tidak dapat diukur secara langsung karena objek yang diamati tidak memiliki nilai kuantitatif. Untuk dapat mengukur variabel yang tidak dapat diukur secara langsung, digunakan variabel indikator (manifes) yang memiliki tipe data kategorik. Dengan informasi yang diperoleh dari variabel indikator dibentuk sebuah variabel laten. Alat statistik yang dapat digunakan untuk mengelompokkan variabel laten adalah analisis Kelas laten (Latent Class Analysis). Pendugaan parameter dalam analisis analisi Kelas laten digunakan metode Maximum Likelihood Estimation (MLE) dengan iterasi algoritma
Expectation-Maximum (EM). Dengan pendekatan Bayesian Information Criterion (BIC) dan Akaike Information Criterion (AIC) diperoleh model terbaik. Untuk melihat kecocokan model digunakan Statistik Pearson Chi kuadrat ( ) 2 χ dan Statistik ratio likelihood ( ) 2 G. Penelitian ini bertujuan untuk mengelompokkan data kategorik dengan menggunakan analisis kelas laten. Penelitian ini menggunakan dua data sekunder, Diagnoses of carcinoma (karsinoma), data sampel yang terdiri dari 7 dikotomis tentang ada atau tidaknya karsinoma pada servik uterus dan General Social Survey 1982, data sampel tentang sikap warga Amerika Serikat terhadap survei sosial. Dengan menggunakan paket program poLCA versi 1.4 pada program R versi 3..0.2, hasil penelitian menunjukkan bahwa untuk data karsinoma, model terbaik terdapat pada tiga model kelas laten, yaitu kelompok yang secara konsisten dinilai positif ada penyakit karsinoma diwakili 44,47%, secara konsisten dinilai negatif terkena penyakit karsinoma diwakili 37,36% dari populasi, dan 18,17 % dari populasi dinilai meragukan. Untuk data survei sosial, model terbaik terdapat pada tiga model kelas laten, yakni tipe responden ideal (62,1%), optimis (20,7%), ragu-ragu (17,2%)
Pemanfaatan E-Learning Sebagai Media Pembelajaran Pada Pendidikan Tinggi Jarak Jauh
Full text linkSeiring dengan perkembangan teknologi dan informasi yang sangat pesat, pemanfaatan internet dalam bidang pendidikan terus berkembang khususnya dalam pendidikan tinggi jarak jauh. E-learning merupakan suatu model pembelajaran yang memanfaatkan teknologi komputer dan jaringan internet. Melalui e-learning proses belajar mengajar dapat dilakukan tanpa adanya tatap muka antara pengajar dan peserta didik dan tidak lagi dibatasi oleh waktu dan tempat. E-learning menjadi salah satu solusi bagi permasalahan dunia pendidikan yang semakin sibuk dengan berbagai layanan yang menawarkan fleksibilitas dan mobilitas yang tinggi. Universitas Terbuka (UT) sebagai perguruan tinggi jarak jauh sudah memanfaatkan e-learning sebagai media pembelajaran, seperti tutorial online, suplemen berbasis web, latihan mandiri, kit tutorial, dan sebagainya. Makalah ini merupakan telaah pemanfaatan teknologi dan informasi berbasis e-learning pada pendidikan tinggi jarak jauh
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
SISTEM UJIAN ONLINE SEBAGAI UPAYA PENINGKATAN PELAKSANAAN UJIAN DALAM PENDIDIKAN TERBUKA JARAK JAUH *)
Full text linkUniversitas Terbuka (UT) as a higheropen anddistanceeducationinstitution has beencarrying outstudents assessmentin the form ofpaper and pencil and online examinations. This studywas conductedto determinethe effectiveness ofthe implementation of theOnline ExaminationSystem(Sistem Ujian Online=SUO), in terms of the reliability(robustness) of SUO application, the readiness ofHuman Resources inimplementingan online exam, the infrastructure that supporting SUO, and the studentresponses. The results showed that SUO applicationand support application had beenwell developedbyUT, proven that the SUO havebeenin operation in 30 out of 37UTregional centersin 2010. Studentsdid notface manyproblems in registering in the online exam.Infrastructureandhuman resourceswere considered satisfactory. Respondentsalso said that SUO was veryflexiblein terms of choosing theexamschedule andgetting the immediate feedback. Based on the research results, a SUO modelhad been developed and to be implementedatUT. The SUOmodel isdescribed inseveralbusinessprocesses, which includethe preparation, execution, processingexam results, as well assupervisionandevaluation. In the future, UTstillneeds toimprovefacilities, infrastructure andquality of human resources thatcansupport theonline exam. The implementation of online examrelies heavily oninformationtechnology (IT) and itis expectedthatSUOis adaptablewith this rapidly changed technology
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