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All Journal bit-Tech JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS)
Safitri, Agnes Novita Ida
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Computer Science & IT

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Simple Additive Weighting Method for Internet Service Provider Vendor Selection Decision Support System Rafli, Muhammad; Purbaratri, Winny; Safitri, Agnes Novita Ida; Indiarto, Budi; Wicaksono, Fandan Dwi Nugroho
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2423

Abstract

The increasing demand for high-speed, stable, and reliable internet services at IKPIA Perbanas—driven by educational, research, and administrative needs—has posed challenges in selecting the most suitable Internet Service Provider (ISP). With numerous vendors offering diverse bandwidth packages and pricing, a structured and objective decision-making method is essential. This study proposes the development of a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist in the selection of the most appropriate ISP. The research adopts a quantitative approach, utilizing both primary and secondary data. Primary data were collected through a questionnaire distributed via Google Forms to five experts comprising the tender selection team. Secondary data were obtained through observation and interviews. Seven key criteria were identified: bandwidth, benefit, experience, service level agreement (SLA), support, hardware, and security. Each criterion was weighted and evaluated using the SAW method. The resulting system calculated normalized performance ratings and preference values for each vendor. The analysis showed that PT. B achieved the highest preference value (0.97), followed by PT. E (0.93), indicating PT. B as the most suitable vendor. The developed system successfully supports transparent, criteria-based ISP selection, enhancing the efficiency and objectivity of the procurement process at IKPIA Perbanas.
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.
Co-Authors Budi Indiarto Paylina, Shinta Purbaratri, Winny Rafli, Muhammad Tachjar, Nani Krisnawaty Wicaksono, Fandan Dwi Nugroho Yanah, Septi