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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

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

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

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

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.

Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda Melinda; Yunidar Yunidar; Rizka Miftahujjannah; Siti Rusdiana; Amalia Amalia; Lailatul Qadri Zakaria
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1206

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.

The role of folklore in shaping the Leukonese characters: An anthropolinguistic study Tasnim Lubis; T. Thyrhaya Zein; Amalia Amalia
Studies in English Language and Education Vol 11, No 2 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/siele.v11i2.33338

This study aims to describe the relationship between folklore and the character of the Leukonese community on Simeulue Island, Aceh Province. Employing an ethnographic and anthropolinguistic approach, the study utilized audio-video recordings of oral Leukonese folklore, including nandong, nanga-nanga, and buai. The performances of nandong (songs), nanga-nanga (sad stories), and buai (poetry) were analyzed using the anthropolinguistic approach to delineate their components (text, co-text, and context) as an integrated whole. The meaning of the folklore was dissected through domain analysis, taxonomy analysis, and semantic componential analysis. Domain analysis explored the semantic relationship between folklore and the Leukonese character, identifying attributes. Taxonomy analysis categorized these attributes, influencing the Leukonese character in performance. Semantic componential analysis served to confirm and annotate these attributes in the Leukonese peoples character. The studys findings demonstrate that the presence and perpetuation of Leukonese folklore significantly impact the character of its people. Nandong teaches (1) the transmission of values and norms, (2) cultural identity and continuity, and (3) social cohesion. Meanwhile, buai fosters (1) cultural preservation and celebration, (2) community cohesion and participation, and (3) integration of Malay tradition. Finally, nanga-nanga instills (1) emotional expression and coping, (2) the reflection of social realities, and (3) empowerment and voice. Consequently, language serves as a means to express ideas, thoughts, and experiences, manifested in various oral traditions passed down through generations. These traditions often mirror the characteristic patterns of a community.

Sound changes in the Haloban language of the Banyak Islands, Aceh, Indonesia Tasnim Lubis; Dwi Widayati; Amalia Amalia; Liesna Andriany; Mukramah Mukramah
Studies in English Language and Education Vol 12, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/siele.v12i2.37945

Sound change is a universal phenomenon and is strongly influenced by contextual factors. Variant sounds that appear as cognates across languages can be identified and explained through linguistic analysis within a cultural framework. This study aims to identify sound changes in the Haloban language by comparing them with a related language spoken on Simeulue Island, the Defayan language. A qualitative method was employed in this research. Data were collected through recordings of Halobanese wordlist utterances and interviews with native Halobanese speakers. Six informants participated in the study, comprising three male and three female native speakers. Data collection involved documentation techniques, including recording, transcription, and translation into both Indonesian and English. The data were analyzed for regular and semi-regular (sporadic) sound changes between Proto-Austronesian (PAN) and Haloban. The Haloban language is spoken in only two villages in the Pulau Banyak District, Aceh Singkil Regency, Aceh Province. The results showed a regular sound change in which the PAN sound /b/ becomes /w/ in Haloban. Semi-regular changes included sound loss, addition, fusion, and unpacking. In comparison with the Defayan language, two regular sound changes were identified in Haloban: /b/ and /f/ becoming /w/. These sound changes contribute to the linguistic identity of the Halobanese people.

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