Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Universitas Batanghari Jambi International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) J-CEKI
Debi Irawan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Sentimen Distribusi Vaksin COVID-19 di Indonesia Menggunakan Algoritma Naïve Bayes Classifier Kevin Manurip; Debi Irawan
Jurnal Ilmiah Universitas Batanghari Jambi Vol 22, No 2 (2022): Juli
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v22i2.2397

Abstract

Since the Indonesian government officially announced the first case of COVID-19, traditional media and social media content related to COVID-19 has increased dramatically. On the one hand, the media talks about prevention, symptom recognition and about prevention, symptom recognition and treatment are massive. Sentiment Analysis or commonly called opinion mining, is a field of study that analyzes opinions, sentiments, evaluations, judgments, attitudes, and emotions towards entities and is implemented on social media content. This becomes interesting and important for certain parties who want to know the good and bad sentiments or opinions given by the Indonesian people towards the distribution of vaccines for the handling of COVID-19. From this research, the level of capability of the system that has been built to find the accuracy between the information requested by the user on the Sinovac vaccine results from a total of 1524 tweets, there are 819 positive tweets, 452 neutral tweets, and 253 negative tweets. The results of the AstraZeneca vaccine classification resulted in 211 tweets with a total of 100 positive sentiments, 80 tweets of neutral sentiment, and 31 tweets of negative sentiment. Sentiment classification results based on scraping data with the keyword Astrazeneca vaccine, resulted in 1266 tweets with a positive sentiment value of 712 tweets, neutral sentiment as many as 344 tweets, and negative sentiment as many as 210 tweets.
DEVELOPING AN INTERACTIVE LANDING PAGE TO INCREASE CONSUMER BUYING INTEREST IN VARIOUS FLAVORED RICE BALL PRODUCTS Martin Hari Purwadi; Agus Aulia Primanda; Asep Wasid; Debi Irawan; Febriansyah Ramadhan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 6 (2025): MAY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i6.770

Abstract

The development of digital technology has changed people's consumption patterns, including in making decisions to purchase fast food. This study aims to analyze how the development of interactive landing pages can increase consumer interest in buying rice balls with various flavors. Using quantitative research methods and experimental design approaches, two versions of the landing page were tested: static and interactive. The results show that interactive elements such as product animation, real-time testimonials, and quick ordering features can increase user engagement and influence purchase intentions. This study provides an important contribution to the digital marketing strategy of culinary MSMEs based on websites.
Analisis Efektivitas Regression Testing Menggunakan Metrik Kuantitatif pada Aplikasi Digital Banking: Studi Kasus BLU by BCA Digital Thomy Kurniawan; Dany Yudha Krisna; Debi Irawan; Gita Cahyani Lestari4; Imam Maliki
J-CEKI : Jurnal Cendekia Ilmiah Vol. 5 No. 1: Desember 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v5i1.12860

Abstract

Penelitian ini menganalisis efektivitas regression testing menggunakan metrik kuantitatif pada aplikasi digital banking BLU by BCA Digital. Tujuannya adalah untuk mengevaluasi efektivitas strategi pengujian regresi dan mengidentifikasi area perbaikan dalam konteks aplikasi finansial yang kompleks. Metode yang digunakan adalah pendekatan kuantitatif eksplanatori dengan desain studi kasus tunggal. Data primer dikumpulkan secara prospektif dari artefak pengujian dan pipeline CI/CD selama tiga siklus rilis, mencakup 3.301 skenario pengujian. Analisis data menggunakan teknik statistik deskriptif dan inferensial, dengan fokus pada metrik kuantitatif seperti Defect Detection Percentage (DDP), Test Case Effectiveness (TCE), Requirements Coverage, dan Test Execution Efficiency. Hasil penelitian menunjukkan bahwa regression testing berjalan sangat efektif dengan penyelesaian 100% dan tingkat kegagalan fungsional hanya 0,06%. Strategi hybrid yang memadukan automation (32,87% cakupan) dan manual testing terbukti optimal; automation memberikan efisiensi dan stabilitas tinggi, sementara manual testing berperan krusial dalam mendeteksi defect kompleks. Namun, ditemukan disparitas kinerja antar modul dan tingginya skip rate automation (18,06%) sebagai area kritis. Kesimpulannya, pendekatan hybrid efektif menjamin kualitas rilis, namun diperlukan ekspansi cakupan otomasi dan perbaikan maintainability script. Penelitian ini berkontribusi pada pengembangan framework evaluasi kuantitatif untuk quality assurance di industri digital banking Indonesia.
Co-Authors Agus Aulia Primanda Asep Wasid Dany Yudha Krisna Febriansyah Ramadhan Gita Cahyani Lestari4 Imam Maliki Kevin Manurip Martin Hari Purwadi Thomy Kurniawan