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All Journal Data Science: Journal of Computing and Applied Informatics Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika
Amalia Amalia
Universitas Sumatera Utara
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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Improving Data Collection on Article Clustering by Using Distributed Focused Crawler Dani Gunawan; Amalia Amalia; Atras Najwan
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.726 KB) | DOI: 10.32734/jocai.v1.i1-82

Abstract

Collecting or harvesting data from the Internet is often done by using web crawler. General web crawler is developed to be more focus on certain topic. The type of this web crawler called focused crawler. To improve the datacollection performance, creating focused crawler is not enough as the focused crawler makes efficient usage of network bandwidth and storage capacity. This research proposes a distributed focused crawler in order to improve the web crawler performance which also efficient in network bandwidth and storage capacity. This distributed focused crawler implements crawling scheduling, site ordering to determine URL queue, and focused crawler by using Naïve Bayes. This research also tests the web crawling performance by conducting multithreaded, then observe the CPU and memory utilization. The conclusion is the web crawling performance will be decrease when too many threads are used. As the consequences, the CPU and memory utilization will be very high, meanwhile performance of the distributed focused crawler will be low.
PEMBENTUKAN KOMITE SEKOLAH DI KB TANAH MERAH KECAMATAN GALANG MELALUI PENDEKATAN ANTROPOLINGUISTIK Tasnim Lubis; Amalia Amalia; Fahmi Fahmi; Nurul Adilla Alatas Abus; Raisya Aulia Lubis; Muhammad Dafitra; Abiyulail Alatas Abus
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 (2022): Volume 3 Nomor 3 Tahun 2022
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v3i3.7197

Abstract

Komite sekolah merupakan sebuah lembaga yang bertujuan untuk membangun mutu sekolah agar sesuai dengan visi dan misi sekolah serta memiliki kekhasan yang dapat menjadi kekuatan dan daya tarik sekolah tersebut. Adapun tujuan dari kegiatan pengabdian ini adalah untuk membentuk komite sekolah di Kelompok Bermain (KB) Tanah Merah Kecamatan Galang. Pembentukan komite sekolah ini dilakukan untuk membantu menyelesaikan permasalahan yang dihadapi oleh para pengajar di Kelompok Bermain tersebut. Permasalahan utamanya adalah permasalahan sosial, yaitu permasalahan komunikasi antara pihak sekolah dan wali murid pihak sekolah kesulitan dalam menyampaikan kepada para wali murid bahwa visi misi dan tujuan Kelompok Bermain menitikberatkan pada peletakan dasar kearah pertumbuhan dan perkembangan fisik (koordinasi motorik halus dan kasar), kecerdasan daya pikir, daya cipta kecerdasan emosi, kecerdasan spiritual, sosial emosional (sikap dan perilaku serta agama), bahasa, dan komunikasi, sesuai dengan keunikan dan tahap-tahap perkembangan yang dilalui anak usia dini. Pendekatan antropolinguistik diaplikasikan dalam membentuk dan memilih anggota komite agar dapat bekerjasama dengan pihak sekolah dan medukung visi dan misi Kelompok Bermain. Dalam prosedur pembentukan komite dan pemilihan anggota dilakukan secara pendekatan komunikasi dengan pihak sekolah dalam mendapatkan informasi mendalam mengenai performansi anggota yang mewakili wali murid dan tokoh masyarakat. Selanjutnya dilakukan persamaan persepsi agar para anggota komite dapat melakukan tugasnya dengan baik.
Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.705 KB) | DOI: 10.17529/jre.v14i1.9027

Abstract

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.
Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.9027

Abstract

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.
Co-Authors Abiyulail Alatas Abus Atras Najwan Dani Gunawan Fahmi Fahmi Lubis, Tasnim Maya Silvi Lydia Miftahul Huda Muhammad Dafitra Nurul Adilla Alatas Abus Raisya Aulia Lubis Siti Dara Fadilla