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All Journal INFORMATIKA Jurnal Sains Informatika Terapan (JSIT) Jurnal Media Teknik Elektro dan Komputer
Anisa Ninda Cahyani
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Analisis Sentimen Pengguna Aplikasi MyASN di Twitter Menggunakan Algoritma Support Vector Machine Amirah Nafiah Zalfa; Anisa Ninda Cahyani
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.132

Abstract

This study aims to analyze user sentiment toward the MyASN application based on public discussions on X. A total of 1059 tweets were collected through a web scraping process using the Tweet-Harvest tool on Google Colab, with data obtained from tweets containing keywords and hashtags related to MyASN and BKN. The collected data were preprocessed through case folding, tokenizing, normalization, stopword removal, and stemming, then represented using the TF-IDF weighting scheme. Sentiment labels were assigned into three categories: positive, negative, and neutral. The classification process employed the Support Vector Machine (SVM) algorithm, with data divided into 80% training data and 20% testing data. The experimental results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 98.1% with a precision value of 0.983, a recall of 0.981, and an F1-score of 0.982. Evaluation based on sentiment class shows that in the negative class, SVM produced a precision of 1.000, a recall of 0.977, and an F1-score of 0.989. In the neutral class, it achieved a precision of 0.929, a recall of 1.000, and an F1-score of 0.963, while in the positive class, SVM achieved a precision of 0.885, a recall of 1.000, and an F1-score of 0.939. These results show that the indicates that the implemented SVM model demonstrates strong reliability in handling text-based sentiment classification, particularly in datasets with imbalanced sentiment distributions. Overall, the results demonstrate that SVM is effective in capturing user sentiment patterns and can provide meaningful insights for evaluating and improving the MyASN application service.
ANALISIS SENTIMEN ULASAN PRODUK SKINCARE PADA PLATFORM SEPHORA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) Aisyah Fadilla Suffi Pohan; Anisa Ninda Cahyani; Retno Anjani; Nazla Windriani; Lutfi Nur Yasin; Amirah Nafiah Zalfa; Hayati Hayati
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 2 (2026): Oktober : JURNAL TEKNIK INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i2.1958

Abstract

The rapid growth of the skincare industry has sparked a surge in consumer reviews across digital platforms, including Sephora. These digital opinions hold valuable insights into public preferences regarding skincare products; however, the sheer volume of data poses a challenge for manual analysis. This study aims to classify the sentiment of skincare product reviews on Sephora by implementing the Support Vector Machine (SVM) algorithm. A dataset of 49,918 cleaned reviews was utilized following data preprocessing. Sentiment labels were automatically assigned based on ratings: scores of 4–5 were categorized as positive sentiment, while scores of 1–3 were categorized as negative sentiment. Text feature representation was constructed using Term Frequency-Inverse Document Frequency (TF-IDF), limited to the top 5,000 features. The data was subsequently split into training (80%) and testing (20%) sets using a stratified sampling approach. The SVM model employed a linear kernel with a parameter value of C = 1. Testing results demonstrated an accuracy of 90.49% and a macro F1-score of 84.70%. Specifically, the positive class achieved a precision of 92.19%, recall of 96.12%, and F1-score of 94.12%, whereas the negative class recorded a precision of 82.53%, recall of 69.22%, and F1-score of 75.29%. Comparative analysis revealed that SVM outperformed Naive Bayes and Logistic Regression in terms of accuracy and macro F1-score. However, the recall achieved for the negative class indicates a need for further optimization in detecting negative responses. This study confirms the effectiveness of integrating TF-IDF and linear SVM for skincare review sentiment classification, while noting that rating-based annotation has limitations in fully capturing the emotions expressed in the text.
Implementasi Server Penyimpanan Virtual Berbasis Casaos Di Diskominfo Sumatera Utara Retno Anjani; Nazla Windriani; Anisa Ninda Cahyani; Aisyah Fadilla Suffi Pohan; Hayati; Amirah Nafiah Zalfa
Jurnal Sains Informatika Terapan Vol. 5 No. 3 (2026): Jurnal Sains Informatika Terapan (Oktober, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i3.1402

Abstract

Transformasi digital di lingkungan pemerintahan mendorong kebutuhan akan sistem penyimpanan data yang fleksibel, efisien, dan mudah dikelola. Salah satu alternatif yang dapat diterapkan adalah Network Attached Storage (NAS) berbasis open source menggunakan CasaOS pada lingkungan virtualisasi. Penelitian ini bertujuan untuk mengimplementasikan server penyimpanan virtual berbasis CasaOS pada lingkungan Proxmox Virtual Environment (VE) di Dinas Komunikasi dan Informatika Provinsi Sumatera Utara. Objek penelitian berupa sistem CasaOS yang dijalankan pada container Ubuntu 22.04 di dalam Proxmox VE dan diakses menggunakan client berbasis Linux Mint. Tahapan implementasi meliputi konfigurasi lingkungan virtual dengan skema jaringan NAT, instalasi dan konfigurasi CasaOS, pengujian akses layanan melalui web browser, pengujian pengelolaan berkas, serta pengujian konektivitas jaringan menggunakan iperf3 antara server CasaOS dan client. Kebaruan penelitian ini terletak pada penerapan CasaOS sebagai server penyimpanan virtual berbasis open source pada lingkungan Proxmox VE dalam konteks infrastruktur teknologi informasi instansi pemerintahan. Hasil implementasi menunjukkan bahwa layanan CasaOS dapat diakses melalui web browser dan digunakan untuk melakukan pengelolaan berkas. Pengujian menggunakan iperf3 menghasilkan kecepatan transfer data rata-rata sekitar 2,00 Gbit/s dengan total transfer sekitar 2,33 GBytes selama 10 detik. Hasil tersebut menunjukkan bahwa CasaOS yang diimplementasikan pada Proxmox VE dapat berfungsi sebagai alternatif server penyimpanan berbasis open source untuk mendukung penyimpanan dan pengelolaan berkas pada lingkungan virtual.
Co-Authors Aisyah Fadilla Suffi Pohan Amirah Nafiah Zalfa Hayati Hayati Hayati Lutfi Nur Yasin Nazla Windriani Retno Anjani