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All Journal JOIN (Jurnal Online Informatika) Jurnal Informatika Jurnal Ilmiah Universitas Batanghari Jambi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Responsif : Riset Sains dan Informatika Jurnal Sosial dan Abdimas Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Teknik Indonesia Intellektika: Jurnal Ilmiah Mahasiswa Ulil Albab SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Ina Najiyah
Universitas Adhirajasa Reswara Sanjaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation

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Analisis Sentimen Media Sosial X Terhadap Kebijakan Presiden Republik Indonesia Prabowo Subianto Najiyah, Ina; Rizal, Miftahul
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10385

Abstract

This study aims to identify and measure the tendency of public sentiment towards the implementation of the policies of the President of the Republic of Indonesia, Prabowo Subianto. The methodology used is text mining-based sentiment analysis, utilizing a data corpus taken from the social media platform X. This study adopts the SEMMA (Sample, Explore, Modify, Model, Assess) workflow as a procedural framework. Data retrieval is carried out automatically using crawling techniques. Next, the data goes through a comprehensive text pre-processing stage, including cleaning, case folding, normalization, convert negation, tokenizing, stopword removal, stemming. Sentiment polarity is determined automatically through a lexicon-based approach, implemented with the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm. The modeling phase uses two machine learning classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM). Performance testing is carried out on three different training and testing data distribution schemes (90:10, 80:20, and 70:30). The evaluation findings show that the Naïve Bayes algorithm achieved the highest accuracy rate of 81.25% at a ratio of 80:20. Meanwhile, SVM consistently recorded superior accuracy, reaching a maximum value of 92.60% at a ratio of 90:10. Based on a comprehensive assessment of performance metrics (accuracy, precision, recall, and f1-score), the Support Vector Machine (SVM) algorithm was proven to provide significantly superior performance compared to Naïve Bayes in this sentiment classification task
Analisis Efektivitas Penerapan A/B Testing dalam Meningkatkan Performa Website pada PT Pamor Putra Mandiri Hari Noer Fazri; Ina Najiyah
Intellektika : Jurnal Ilmiah Mahasiswa Vol. 4 No. 3 (2026): Mei : Intellektika : Jurnal Ilmiah Mahasiswa
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/intellektika.v4i3.3669

Abstract

. A website is a crucial digital promotion medium for companies to convey information and attract potential customers. However, many websites experience low user interaction despite having high visitor traffic. This study aims to analyze the effectiveness of A/B Testing implementation in improving the performance of the PT Pamor Putra Mandiri website. A quantitative experimental approach was used, applying A/B Testing to two page variations: Variation 1 (Rafting & Outdoor Activity theme) and Variation 2 (Camping & Accommodation theme). The evaluation focused on three main performance metrics: Click, Page Visit, and Engagement. The results show that Variation 1 consistently outperformed Variation 2 across all metrics. Statistical testing using a two-proportion z-test confirmed that these differences were statistically significant (p-value < 0.05). Therefore, A/B Testing proves to be an effective method in identifying superior content strategies and page structures, as well as a relevant tool for data-driven decision-making in corporate website development.
Co-Authors Abillutfi, Ramadhan Muhammad Ade Mubarok Agus Hermawan Ali Akbar Rismayadi Asti Herliana, Asti Handayani, Rissa Hari Noer Fazri Hariyanti, Ifani Hikmah Abdillah, Hilman Imam Saepul Azmi Ivan Sofyan Muckti, Masaldi Kharisma Musaropah, Resa Novia Sri Mulyani Oktaviani, Fani Rahma Pramanik, Niskala Dian Ramadhan, Muhammad Azis Rangga Habdulatif Riyand Fibrianto Rizal, Miftahul Salman Topiq Sandini, Dwi Sari Susanti, Sari Suharyanto Suharyanto Wisnuwardana, Dirga Yudi Ramdhani