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All Journal Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Journal of Computer System and Informatics (JoSYC) Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Informatics
Elsya Sabrina Asmita Simorangkir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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The implementation of machine learning for classifying eligible students for a scholarship at Budidarma University Elsya Sabrina Asmita Simorangkir; Nirwan Yakub; Amran Manalu; Tarmizi; Rian Farta Wijaya
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aimed to assist Universitas Budidarma in deciding the recipients of the Kartu Indonesia Pintar (KIP Kuliah) scholarship program. KIP Kuliah is managed by the Ministry of Education, Culture, Research, and Technology and aims to support academically talented students in furthering their education in higher education institutions. The study utilized a Decision Tree data mining classification method with the C4.5 and Cart algorithms. The results showed that if a potential student has the KIP Kuliah scholarship and a high test score, they will pass the verification and validation process. The accuracy of the C4.5 and Cart algorithms was 100% due to the use of matching data in the research. This study aims to make the selection process for KIP Kuliah recipients more efficient and targeted.
Deteksi Outlier Hasil Clustering Algoritma K-Medoids Menggunakan Metode Boxplot Pada Data KIP Kuliah Simorangkir, Elsya Sabrina Asmita; Siahaan, Andysah Putera Utama; Marlina, Leni; Nasution, Darmeli; Sitorus, Zulham
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5479

Abstract

In the process of forming clusters with the K-Medoids algorithm, cluster result anomalies often occur, such as outliers. This value appears as a revelation in existing data patterns. Outliers occur due to measurement errors, rare events, or due to other unexpected factors. In this research, the dataset used is data on prospective KIP recipient students at Budi Darma University, where there is a high level of interest in KIP Kuliah while the quota is limited, which means that KIP Kuliah administrators sometimes have difficulty determining which students are eligible to receive KIP Kuliah. For this reason, the K-Medoids clustering technique was used to cluster data on 54 prospective students who were eligible to receive KIP Kuliah Merdeka and those who were not eligible. From the cluster results, outlier detection was carried out using the box plot method with the aim of finding out whether each cluster member was actually in the appropriate cluster or not. The result is that the data cluster is divided into 2 (K-2). In the max min centroid selection, cluster I consists of 52 members and cluster II consists of 2 members, where the outlier data consists of 3 data, while in random centroid selection (python), cluster I consists of 36 members and cluster II 18 members with data The outlier consists of 4 members. The accuracy of the clustering results between max min and random centroid selection has an accuracy of 64.81%, and the outlier accuracy is 75%.
Outlier detection in the clustired data Bu'ulolo, Efori; Syahputra, Rian; Simorangkir, Elsya Sabrina Asmita
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2025.1005.pp394-404

Abstract

The purpose of this study is to detect outliers in data clusters. Outliers in data cluster datasets often occur in the data clustering process, especially in the K-Means algorithm. Outliers in cluster data are members/cluster items that are far from the centroid value and are not found in the dominant cluster. Outliers in cluster data are caused by various factors such as inaccurate K values, inaccurate centroid point values, poor data quality and others. To detect outliers in cluster data using the blox plot method, Z-Score and relative size factor (RSF). The input value is the sum of squared error (SSE), calculated by summing the squares of the distance of each data point from the cluster centroid. The dataset used consists of 3 (three) variances, namely high data variance, medium data variance and low data variance. The method used for outlier detection in this study can detect outliers in all data variances used, only not all outlier detection methods are optimal for all data variances. The plox plot method is optimal for high data variance and medium data variance, the RSF method is optimal for medium data variance and the Z-Score method is not optimal for high data variance.
Evaluasi Performa Jaringan pada Lingkungan Virtualisasi dengan Pendekatan SNMP Simanullang, Maradona Jonas; Sihotang, Ameliana; Simorangkir, Elsya Sabrina Asmita; Aritonang, Mhd Adi Setiawan; Gurusinga, Tria Adelia Putri Br; Halawa, Yudisa
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 6 No 2 (2026): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v6i2.1845

Abstract

Penelitian ini bertujuan untuk mengevaluasi kinerja jaringan pada lingkungan virtualisasi dengan memanfaatkan Simple Network Management Protocol (SNMP) sebagai sistem monitoring. Metode yang digunakan adalah pendekatan kuantitatif melalui eksperimen dengan membandingkan kondisi jaringan sebelum dan sesudah implementasi monitoring berbasis SNMP. Pengumpulan data dilakukan selama lima hari dengan total 720 sampel menggunakan tools ping, iPerf, serta monitoring berbasis SNMP. Parameter yang dianalisis meliputi latency, packet loss, throughput, jitter, dan availability. Hasil penelitian menunjukkan adanya peningkatan pada indikator kinerja jaringan, di mana latency menurun sebesar 37,9%, packet loss menurun sebesar 68,4%, dan jitter menurun sebesar 44,3%, sementara throughput meningkat sebesar 23,7% serta availability meningkat dari 96,2% menjadi 99,1%. Peningkatan tersebut tidak secara langsung disebabkan oleh SNMP, melainkan berkaitan dengan peningkatan visibilitas jaringan dan efektivitas monitoring, sehingga memungkinkan deteksi dan penanganan gangguan jaringan secara lebih cepat. Dengan demikian, SNMP berkontribusi secara tidak langsung dalam meningkatkan kinerja dan keandalan jaringan pada lingkungan virtualisasi.
Improving K-Means clustering performance on non-linear data using variance-weighted distance metrics Elsya Sabrina Asmita Simorangkir; Efori Bu'ulolo
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 2 (2026): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i2.366

Abstract

K-Means is one of the most widely used clustering algorithms because of its simplicity and computational efficiency. However, its performance often decreases when handling non-linear data due to the assumption that all attributes contribute equally to the distance calculation process. This study proposes a Variance-Weighted Distance Metrics K-Means (VWDM-KMeans) method that assigns attribute weights based on variance values to improve clustering quality. The proposed approach consists of Min-Max Normalization, variance calculation, weight generation, and integration of variance-based weights into the distance metric used by K-Means. Experiments were conducted on a non-linear dataset containing 103 records and 3 attributes (x, y, and z) with K = 3 clusters. The generated attribute weights were 0.3207, 0.3342, and 0.3451 for attributes x, y, and z, respectively. The performance of VWDM-KMeans was compared with conventional K-Means and K-Medoids using the number of iterations, Sum of Squared Errors (SSE), and Silhouette Score (SS). The results showed that VWDM-KMeans converged in 5 iterations, compared to 6 iterations for K-Means and 3 iterations for K-Medoids. In terms of cluster compactness, VWDM-KMeans achieved the lowest SSE value of 2.7932, outperforming K-Means (8.2429) and K-Medoids (8.9602). Furthermore, VWDM-KMeans obtained a Silhouette Score of 0.4854, equal to K-Means and higher than K-Medoids (0.4696). These findings demonstrate that incorporating variance-based attribute weighting into the distance calculation process improves cluster compactness while maintaining cluster separation quality and stability. Therefore, VWDM-KMeans can serve as an effective and computationally efficient alternative for clustering non-linear data.
Implementasi Sequitur Dalam Kompresi Pola Teks Berulang Elsya Sabrina Asmita Simorangkir
Informatics Vol. 2 No. 01 (2026): January
Publisher : Armari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63215/Informatics.v2i1.40

Abstract

Kemajuan teknologi telah berperan penting dalam mengubah cara manusia bertukar data dan informasi. Dari penggunaan media cetak, kini beralih ke media online, yang menuntut pengguna untuk memiliki akses cepat terhadap informasi. Namun, pergeseran ini juga menimbulkan tantangan baru terkait keterbatasan ruang penyimpanan. Pertumbuhan data teks yang sangat cepat menuntut adanya metode penyimpanan yang efisien. Salah satu pendekatan yang dapat diterapkan untuk mengatasi masalah ini adalah dengan menggunakan teknik kompresi data. Dengan kompresi data, informasi dapat disimpan lebih efisien, memungkinkan pengguna untuk menghemat ruang penyimpanan. Algoritma Sequitur membangun tata bahasa dengan mengganti frase berulang dalam urutan yang diberikan dengan aturan baru pada data sekuensial, khusunya data teks. Penyimpanan data dan kompresi saling berkaitan karena kompresi membantu memaksimalkan kapasitas penyimpanan serta meningkatkan efisiensi. Penelitian ini bertujuan untuk menghasilkan strategi dan mengkaji kinerja Algoritma Sequitur dalam kompresi pola teks berulang
Pelatihan Konfigurasi Perangkat Mikrotik untuk Meningkatkan Kompetensi Siswa SMKS YWKA Medan Simanullang, Maradona Jonas; Santoso, Ahmad Imam; Simorangkir, Elsya Sabrina Asmita; Ringo, Rimmar Siringo; Kurniawan, Brama; Tembusai, Zoelkarnain Rinanda; Aritonang, Mhd Adi Setiawan
Jurnal Pustaka Mitra (Pusat Akses Kajian Mengabdi Terhadap Masyarakat) Vol 6 No 4 (2026): Jurnal Pustaka Mitra (Pusat Akses Kajian Mengabdi Terhadap Masyarakat)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakamitra.v6i4.2050

Abstract

Perkembangan teknologi informasi telah meningkatkan kebutuhan tenaga kerja yang memiliki kompetensi di bidang jaringan komputer. Mikrotik merupakan salah satu sistem operasi jaringan yang banyak digunakan pada institusi pendidikan maupun industri. Namun demikian, siswa sekolah menengah kejuruan masih memiliki keterbatasan pengalaman praktik dalam melakukan konfigurasi perangkat jaringan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi siswa SMKS YWKA Medan dalam melakukan konfigurasi perangkat Mikrotik. Kegiatan diikuti oleh 23 siswa dan dilaksanakan melalui metode ceramah, demonstrasi, diskusi, dan praktik langsung. Materi pelatihan meliputi pengenalan RouterOS, konfigurasi IP Address, DHCP Server, Network Address Translation (NAT), Firewall, dan pengujian jaringan. Evaluasi dilakukan menggunakan pre-test dan post-test. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan keterampilan siswa dalam melakukan konfigurasi jaringan menggunakan Mikrotik. Peserta mampu melakukan konfigurasi dasar jaringan secara mandiri dan memahami implementasi jaringan komputer pada lingkungan nyata. Kegiatan ini berhasil meningkatkan kompetensi siswa sebesar 85,6% serta mendukung kesiapan mereka dalam menghadapi kebutuhan dunia kerja.
Co-Authors Amran Manalu Andysah Putera Utama Siahaan Darmeli Nasution Efori Buulolo Gurusinga, Tria Adelia Putri Br Halawa, Yudisa Kurniawan, Brama Mhd Adi Setiawan Aritonang Nirwan Yakub Rian Farta Wijaya Rian Syahputra Ringo, Rimmar Siringo Santoso, Ahmad Imam Sihotang, Ameliana Simanullang, Maradona Jonas Sitorus, Zulham Syamsiar, Syamsiar Tarmizi Tembusai, Zoelkarnain Rinanda