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All Journal bit-Tech JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS)
Yanah, Septi
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Computer Science & IT

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ANALISIS SENTIMEN APLIKASI PEMILU MENGGUNAKAN ALGORITMA NAIVE BAYES Yanah, Septi; Purbaratri, Winny; Paylina, Shinta; Safitri, Agnes Novita Ida; Tachjar, Nani Krisnawaty
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 5, No 1 (2025): JEIS EDISI JANUARI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol5no1.684

Abstract

The Election Commission applications are vital for improving transparency, accessibility, and efficiency in electoral processes. Understanding public sentiment towards these programs is crucial for improving their performance and user experience. This study aimed to do sentiment analysis on user feedback regarding Election Commission applications using the Naive Bayes Algorithm. Sentiment analysis, a method in natural language processing (NLP), was employed to classify textual input into positive, negative, and neutral sentiments. The dataset was acquired from Google Play Store reviews and underwent preparation phases, including cleaning, tokenization, and vectorization. The Naive Bayes Algorithm, recognized for its effectiveness in text classification, was utilized to identify sentiment trends. The results revealed that most users expressed positive feelings, highlighting satisfaction with usability and transparency features. However, significant concerns regarding technological failures and data security were also acknowledged. These findings provide substantial insights for enhancing Election Commission applications and fostering public trust.Aplikasi Komisi Pemilihan Umum (KPU) adalah alat penting untuk meningkatkan transparansi, kemudahan, dan efisiensi prosedur pemilihan. Perspektif publik tentang aplikasi ini sangat penting untuk meningkatkan kinerja dan pengalaman pengguna mereka. Tujuan dari penelitian ini adalah untuk menganalisis perasaan pengguna tentang aplikasi Pemilu menggunakan algoritma Naive Bayes. Analisis sentimen merupakan sebuah teknik dalam pemrosesan bahasa alami (NLP) yang membagi masukan teks menjadi sikap positif, negatif, dan netral. Dataset diperoleh melalui ulasan di Google Play Store, dan kemudian menjalani langkah-langkah pra-pemrosesan seperti pembersihan, tokenisasi, dan vektorisasi. Untuk mengidentifikasi pola perasaan digunakan algoritma Naive Bayes yang terkenal karena kemanjurannya dalam kategorisasi teks. Hasil penelitian menunjukkan bahwa mayoritas pengguna mengungkapkan perasaan yang positif, dengan fokus pada kepuasan dengan fitur usability dan transparansi. Namun, ada juga banyak kekhawatiran tentang kegagalan teknologi dan keamanan data. Hasil ini memberikan wawasan penting untuk meningkatkan aplikasi pemilu dan menumbuhkan kepercayaan publik.
Decision Support System for Selecting Volleyball Starting Players Using the AHP and SAW Methods Yanah, Septi; Purbaratri, Winny; Purwaningsih, Mardiana; Tachyar, Nani Krisnawaty; Akmaliyah, Yasmin
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3783

Abstract

This study develops a decision support system to enhance the objectivity and reliability of selecting starting volleyball players, particularly for the spiker position, where traditional selection processes are often subjective and inconsistent. The research addresses the limitation of single-method decision models by integrating the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) into a unified multi-criteria decision-making framework. AHP is employed to derive consistent and structured criterion weights, while SAW is used to generate a transparent ranking of player alternatives. Seven evaluation criteria were used, including passing, service, smash, block, teamwork, body mass index (BMI), and speed. Data were collected through structured observations and expert evaluations involving the team coach and founder. The results indicate that the smash criterion has the highest weight (0.4138), confirming its dominant role in spiker performance. The final ranking shows that Neng Irma Sukmayani achieved the highest score (0.9678), followed by Siti Karlina (0.9602) and Nova Amelia Putri (0.9040). Compared to subjective selection approaches, the proposed system provides a measurable and reproducible evaluation process, improving decision transparency and consistency. The integration of AHP and SAW contributes by reducing weighting bias while maintaining computational simplicity in ranking. The system was implemented using PHP and MySQL and validated through black-box testing, demonstrating stable functionality across all features. This study contributes both theoretically, by strengthening hybrid MCDM applications in sports analytics, and practically, by providing a scalable decision support model for athlete selection.
Co-Authors Akmaliyah, Yasmin Mardiana Purwaningsih Paylina, Shinta Purbaratri, Winny Safitri, Agnes Novita Ida Tachjar, Nani Krisnawaty Tachyar, Nani Krisnawaty