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Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 27743993     DOI : 10.26740/jeisbi
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science
Journal of Emerging Information Systems and Business Intelligence (JEISBI) aims to provide scholarly literature focused on studies and research in the fields of Information Systems (IS) and Business Intelligence (BI). This journal also includes public reviews on the development of theories, methods, and applications relevant to these topics. All published works are presented exclusively in English to reach a global audience of readers and researchers. The journal’s scope includes but is not limited to the following fields: Data Mining Generative Artificial Intelligence Big Data Analytics Business Intelligence Enterprise Architecture UI/UX Business Process Management Enterprise System System Development Decision Support System IS/IT Strategy and Planning IT Investment and Productivity IT Project Governance IS Business Value Audit SI/TI Cybersecurity and Risk Management IS/IT Operations and Service Management IT Ethics Organizational and Human Behavior Technology Digital Sociology
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 288 Documents
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Analysis of User Satisfaction MELISA using End User Computing Satisfaction (EUCS) and Importance Performance Analysis (IPA) Methods: Analisis Kepuasan Pengguna MELISA menggunakan Metode End User Computing Satisfaction (EUCS) dan Importance Performance Analysis (IPA) Ningrum, Puspita Westi Erlitiya; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.63646

Abstract

The Ministry of Education and Culture made an innovation by launching the Merdeka Belajar Kampus Merdeka or MBKM program to help students preparing the transformation. To succeed the program, UNESA built a digital service called UNESA MBKM Information System or "MELISA". MELISA has been used since 2022 and only conducted an evaluation in 2023 after which there were improvements by adding several features and changing some of its appearance. In this study, researchers measured the level of user satisfaction of MELISA using the End User Computing Satisfaction (EUCS) and Importance Performance Analysis (IPA) methods. The purpose of this study is to determine the level of user satisfaction of MELISA and to determine the aspects that need to be improved and maintained by MELISA. Data collection was carried out by distributing questionnaires to UNESA students class of 2021. The number of samples used in this study were 153 respondents. The results of this study indicate that the level of user satisfaction based on the gap value obtained negative results on all indicators. Based on the results of the suitability level analysis, the result is 80.0% which includes <100%. Based on these two results, it indicates that MELISA's performance is still unable to meet the expectations of its users or still does not satisfy its users. In addition, based on the interpretation of the IPA diagram, indicators that are included in quadrant I, which means that they really need priority to make improvements, including user friendly (E1), transparency (C4), and suitability (F2).
ANALISIS PERBANDINGAN METODE NAÏVE BAYES DENGAN C45 UNTUK MENGUKUR TINGKAT KEPUASAN MAHASISWA Saputra, Kresna Yudha Bayu; Indriyanti, Aries Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.63707

Abstract

Penelitian ini bertujuan untuk membandingkan klasifikasi Data di Kelompok Informatika Universitas Negeri Surabaya memakai dua teknik berbeda, yaitu Naïve Bayes dan C4.5, untuk mengukur tingkat kepuasan mahasiswa terhadap pembelajaran tatap muka. Kepuasan mahasiswa menjadi indikator penting dalam menilai mutu kesempatan belajar serta efektivitas strategi pengajaran. Data dari survei kepuasan mahasiswa dapat dianalisis lebih mendalam menggunakan algoritma C4.5 dan Naïve Bayes. Dalam desain penelitian ini, 150 mahasiswa akan memberikan jawaban mereka pada kuesioner yang mencakup berbagai aspek terkait pendidikan mereka, seperti kehadiran, partisipasi dalam diskusi, fasilitas, kualitas pembelajaran, dan layanan sistem informasi. Kedua metode tersebut digunakan untuk menganalisis data yang terkumpul guna menentukan nilai f1-score, akurasi, presisi, dan recall dari masing-masing metode tersebut. The research results show that the C4.5 algorithm has an accuracy rate of 97.33%, which is superior in handling student satisfaction data classification compared to Naïve Bayes which has an accuracy of 91.33%, especially on datasets with a data sharing ratio of 20:80. These findings provide valuable insights for educational institutions in choosing appropriate analytical methods for evaluating student satisfaction. Thus, the results of this research can be used as a basis for decision making in an effort to improve the quality of offline learning in the university environment.
ANALISIS TINGKAT KEPUASAN DAN PENERIMAAN MAHASISWA TERHADAP SIDIA DENGAN MENGGUNAKAN METODE EUCS DAN TAM Sa'diyah, Lailatul Mukharromatus; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.63736

Abstract

In the current era of globalization, technological developments in the world of education have created various new features that can help in the teaching and learning process. One implementation is E-learning, which enables the online learning process. Surabaya State University (UNESA) implemented E-Learning specifically for students in 2015 to facilitate teaching and learning activities to be more flexible because the material in UNESA E-Learning can be accessed anytime and anywhere, is easy to understand, saves energy, costs and time. Since 2023, the UNESA Single Sign On (SSO UNESA) Dashboard has changed its appearance and tools. One of the SSO facilities that has undergone changes is UNESA E-learning. Therefore, this research aims to conduct a comprehensive analysis of the level of student acceptance and satisfaction with the use of SIDIA. This analysis will be carried out using the End User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) methods. The data collection process was carried out by distributing questionnaires to 100 respondents. Based on the results of the analysis carried out using the EUCS method, the results obtained were that all hypotheses were accepted, namely Content had an effect on user satisfaction, Accuracy had an effect on user satisfication, Timeliness had an effect on user satisfication, Format had an effect on user satisfication, and Ease of Use had an effect on user satisfication . This shows that users are satisfied with SIDIA. Meanwhile, for the TAM method, the results also showed that all hypotheses were accepted, namely Perceived Ease of Use (PEOU) had an effect on Acceptance of IT, and Perceived Ease of Use (PEOU) had an effect on Acceptance of IT. This shows that the user accepts the use of the SIDIA system. Keywords: E-Learning, SIDIA, Satisfaction, Acceptance, Users, EUCS (End user Computing Satisfaction), TAM (Technology Acceptance Model).
Implementasi Algoritma K-Nearest Neighbors dalam Mengetahui Kepuasan Pengguna Aplikasi CapCut: Implementation of the K-Nearest Neighbors Algorithm in Assessing User Satisfaction with the CapCut Application Iranti, Alda Maretina; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.63772

Abstract

USABILITY AND USER EXPERIENCE TESTING OF THE THREADS APPLICATION USING THE SYSTEM USABILITY SCALE (SUS) AND USER EXPERIENCE QUESTIONNAIRE (UEQ): PENGUJIAN USABILITY DAN USER EXPERIENCE APLIKASI THREADS MENGUNAKAN SYSTEM USABILITY SCALE (SUS) DAN USER EXPERIENCE QUESTIONNAIRE (UEQ) Anggraini, Ayu; Suyatno, Dwi Fatrianto
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.63909

Abstract

Technology now plays an important role in life. So the progress of information technology is also accelerating rapidly. The latest data from the central statistics agency shows a fairly high increase in internet usage in Indonesia which can reflect the openness of the community to new technologies. From people's openness to new technology, mobile apps have become popular, one of which is the Threads application. Meta claims that Threads attracted 100 million users within a week of its launch. However, reports from online research researchers such as Sensor Tower and SimiliarWeb show that the number of Threads users has decreased significantly. On the other hand, usability and user experience are important elements in application development. The use of SUS and UEQ measurement tools allows users to test the usability and user experience of an application from the end user's perspective based on the experience after using the application. Based on the results of data processing using the System Usability Scale (SUS), the results of the calculation in the form of the number of average score values of 54.35 enter the Grade Scale with category D, Adjective Range with category OK, Acceptable with category Marginal, and NPS (Net Promoter Score) with category Ditractor. Based on the results of data processing using the User Experience Questionnaire (UEQ), the value of attractiveness (mean 0.4), perspicuity (mean 0.605), efficiency (mean 0.15), dependability (mean 0.065), stimulation (mean -0.095), and novelty (mean -0.155) has an average value between -0.8 and 0.8, which indicates a neutral evaluation level. Based on the benchmark results, the value obtained on each UEQ variable using the UEQ Data Analysis Tool gets a Bad value on all variables, namely attractiveness, efficiency, perspicuity, dependability, stimulation, and novelty. Overall, based on the user experience after using the Threads application, it is rated less by respondents so it needs to be improved to meet user expectations.
ANALISIS KEPUASAN PENGGUNA TERHADAP E-LEARNING UNIVERSITAS NEGERI SURABAYA: ANALYSIS OF USER SATISFACTION WITH E-LEARNING AT UNIVERSITAS NEGERI SURABAYA Herviyandasari, Jasica Ardana; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.64045

Abstract

Pembelajaran daring atau online telah menjadi bagian integral dari pendidikan di Indonesia, termasuk pendidikan tinggi, terutama di masa pandemi COVID-19. Salah satu metode pembelajaran daring yang banyak digunakan adalah E-Learning. Universitas Negeri Surabaya (Unesa) telah mengimplementasikan E-Learning untuk memfasilitasi proses belajar mengajar jarak jauh. Saat ini, mahasiswa dapat mengakses pendidikan e-learning melalui SIDIA(Sinau Digital UNESA). Kepuasan Penggunaan Sistem E-Learning UNESA diukur dengan menggunakan model End User Computing Satisfaction (EUCS) yang mencakup lima dimensi yaitu akurasi, konten, format, kemudahan penggunaan, dan ketepatan waktu. Pengumpulan data dilakukan dengan menyebarkan kuesioner kepada Mahasiswa Sistem Informasi, angkatan 2022 yang mengikuti mata kuliah Literasi Digital yang berjumlah 150 mahasiswa. Teknik analisa yang digunakan adalah analisis deskriptif. Pada penelitian ini dilakukan 3 uji instrumen yaitu Uji Valilditas, Uji Reabilitas, dan Convergen Validity. Hasil penelitian menunjukkan bahwa secara keseluruhan, mahasiswa merasa puas dengan sistem yang digunakan. Nilai yang memiliki interval tertinggi ada pada indikator Timeliness T1(Kecepatan) yaitu 4,08. Sedangkan yang terendah ada pada indikator Content C2 (Manfaat) yaitu 3,90. Mengindikasikan perlunya peningkatan dalam kualitas dan relevansi konten yang disajikan. Meskipun demikian, beberapa area seperti peningkatan interaktivitas konten dan keandalan server masih memerlukan perhatian lebih untuk memastikan pengalaman pengguna yang lebih optimal di masa yang akan datang. Penelitian ini memberikan wawasan yang berharga bagi pengembangan lebih lanjut dari sistem ELearning di Unesa dan institusi pendidikan lainnya.
PENERAPAN METODE K-NEAREST NEIGHBOR (K-NN) UNTUK PREDIKSI PENJUALAN PAKAIAN (STUDI KASUS: UMKM KRESNA) Rahmawati, Lutvia; Indriyanti, Aries Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.64064

Abstract

Analisis Sentimen Masyarakat terhadap Kebijakan Iuran Tabungan Perumahan Rakyat (Tapera) pada Platform X Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine Rizqiyah, Anis Maulidatur; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.64074

Abstract

Pemerintah Indonesia menetapkan perubahan terhadap PP Nomor 25 Tahun 2020 tentang Penyelenggaraan Tabungan Perumahan Rakyat (Tapera) melalui PP Nomor 21 Tahun 2024. Dalam perubahan tersebut gaji pekerja Indonesia akan dipotong 3% untuk Tapera. Hal tersebut menimbulkan perdebatan dikalangan masyarakat, terutama pengguna platform X. Pada platform tersebut, masyarakat berbagi opini dan pandangan mereka terhadap kebijakan Tapera. Penelitian ini bertujuan untuk mengklasifikasikan tweet terkait kebijakan Tabungan Perumahan Rakyat (Tapera) menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM), sehingga didapatkan informasi mengenai sentimen masyarakat terhadap kebijakan tersebut. Data sejumlah 1280 tweet didapatkan dari hasil crawling web X. Data tersebut diproses menggunakan library sklearn dan diberikan label menggunakan InSet Lexicon. Data juga diproses menggunakan SMOTE. Klasifikasi dilakukan dengan membagi data ke dalam rasio 80:20, 70:30 dan 60:40. Hasil klasifikasi menggunakan algoritma Naïve Bayes dan SVM kemudian dievaluasi menggunakan confusion matrix dan k-fold cross validation. Dari hasil klasifikasi didapatkan bahwa sentimen masyarakat cenderung kearah negatif terhadap kebijakan Tapera. Didapatkan juga bahwa algoritma SVM memiliki akurasi yang lebih baik dibandingkan dengan algoritma Naïve Bayes. Sebelum SMOTE, SVM memiliki akurasi 84% pada rasio 80:20 dengan kernel linear dan C=2, sedangkan Naïve Bayes memiliki akurasi 81% pada rasio 80:20 dengan model Complement dan alpha 0.01. Setelah SMOTE, SVM memiliki akurasi 93% pada rasio 80:20 dengan kernel rbf dan C=3, sedangkan Naïve Bayes memiliki akurasi 89% pada rasio 60:40 dengan model Complement dan alpha 0.1.
Web-Based Decision Support System for Best Laptop Selection Using MABAC Method Mochammad Rafi Diaz Ardhana; Aries Dwi Indriyanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64267

Abstract

The advancement of technology in the modern era has made devices such as laptops essential in daily life. According to a report from Ministry of Communication and Information Technology of Indonesia that published in 2017, from a survey of 2,121 respondents showed that more than half percent respondent use laptop for work and study, while 34.94% use laptop for entertainment. However, selecting the right laptop often poses a challenge, especially for students in the Informatics Engineering Department at Universitas Negeri Surabaya, who frequently use outdated laptops. To address this issue, a Decision Support System (DSS) is needed, utilizing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. In this study, the MABAC method was used to select laptops based on criteria such as price, CPU, RAM, and storage. By applying the MABAC method, the DSS is believed to effectively address the issue of selecting the most suitable laptop, thereby enhancing productivity and performance. This research successfully developed a web-based Decision Support System (DSS) for selecting the best laptops using the Multi-Attributive Border Approximation Area Comparison (MABAC) method, which simplifies the evaluation process for users. The DSS incorporates 10 criteria: price, processor, RAM, storage, storage type, screen size, graphics card, laptop weight, battery, operating system, and warranty. The MABAC calculations ranked the Asus Vivobook 14 A1400EA as the best laptop with a score of 0.15, followed by the HP 14s EP0022TU and Lenovo Ideapad Slim 3 14ITL6 with scores of 0.05, while the Dell Latitude 3420 ranked last with a score of -0.05.
Rendering Performance Analysis of Astro JS, Next JS, Nuxt JS, and SvelteKit Frameworks Using Google Lighthouse, PageSpeed Insight, and JMeter: Rendering Performance Analysis of Astro JS, Next JS, Nuxt JS, and SvelteKit Frameworks Using Google Lighthouse, PageSpeed Insight, and JMeter Ahmad Jourji Zaidan; Dwi Fatrianto Suyatno
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64283

Abstract

The development and popularity of modern technology encourage innovation in developing web applications to increase loading speed and retain users. This research analyzes the performance of websites that use Astro JS, Next JS, Nuxt JS, and SvelteKit frameworks using the SSR (Server Side Rendering) technique. The advantage of the SSR technique is that it can improve website performance, SEO (Search Engine Optimization) optimization, and user experience. The tools used in this research are Google Lighthouse, PageSpeed Insight, and JMeter. The metrics measured in Google Lighthouse and PageSpeed Insight testing are FCP (First Contentful Paint), TBT (Total Blocking Time), SI (Speed Index), LCP (Large Contentful Paint), and CLS (Cumulative Layout Shift). While JMeter testing, the metrics measured are Response Time (Min, Max, Average), Error Rate, and Throughput. The development method used is the XP (Extreme Programming) method. The results of this study show Astro JS has superior performance in most Web Vitals metrics, followed by Next JS which shows superiority in several metrics. Nuxt JS and SvelteKit each only excelled in one Web Vitals metric. In stability and reliability of the system testing using JMeter, Nuxt JS showed the best performance by excelling in the response time, error rate, and throughput metrics. SvelteKit also performed well with dominance in several stability metrics, while Astro JS and Next JS only excelled in a small number of them. This research was conducted to provide insight for developers in choosing the right framework based on their needs.

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