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All Journal INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal Innovations Computer Science Edusight International Journal of Multidisciplinary Studies (EIJOMS)
Nabilah, Laila
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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Implementasi Sistem Informasi Presensi Berbassis Web di TK Nailus Sa’adah Poerwandono, Edhy; Nabilah, Laila; Salfa Dhiyaa Azzizah, Putri; Maharani, Delia
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 17 No 1 (2025): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v17i1.355

Abstract

TK Nailus Sa’adah menghadapi kendala dalam pencatatan presensi yang masih dilakukan secara manual, menyebabkan inefisiensi, kesalahan pencatatan, serta sulitnya pengelolaan data kehadiran siswa dan guru. Penelitian ini bertujuan untuk mengem- bangkan sistem informasi presensi berbasis web guna meningkatkan efisiensi admin- istrasi dan akurasi data. Metode penelitian yang digunakan adalah kualitatif deskriptif, dengan teknik pengumpulan data melalui observasi dan wawancara. Sistem dikem- bangkan menggunakan metode Waterfall, dengan tahapan analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Teknologi yang digunakan mencakup framework Laravel dan database MySQL, serta fitur pencatatan otomatis, manajemen pengguna, dan pembuatan laporan real-time. Pengujian sistem dilakukan menggunakan black-box testing dan user acceptance testing (UAT), dengan hasil menunjukkan sistem berjalan sesuai kebutuhan pengguna. Implementasi sistem ini di- harapkan dapat mengatasi permasalahan administrasi presensi, mendukung digitalisasi di lingkungan pendidikan anak usia dini, serta meningkatkan kualitas layanan di TK Nailus Sa’adah.
Implementation of Naïve Bayes for Public Sentiment Analysis on QRIS and GPN Digital Dominance through Instagram Nabilah, Laila; Setiawan, Kiki
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.337

Abstract

This study examines public sentiment toward the dominance of QRIS and GPN compared to Mastercard and Visa, using data collected from Instagram comments. Employing the Knowledge Discovery in Databases (KDD) methodology and the Naïve Bayes Classifier, the research analyzed 820 comments retrieved through automated scraping and processed using text mining techniques such as case folding, tokenization, stopword removal, stemming, and TF-IDF transformation. The model achieved an accuracy of 84.27%, a precision of 86.09%, a recall of 94.7%, and an F1-score of 90.21%, indicating strong reliability in identifying sentiment polarity. The analysis revealed that 76.5% of the comments expressed positive sentiment, highlighting users’ appreciation of QRIS and GPN for their convenience, speed, and accessibility across both micro and macro-scale transactions. Negative comments, representing 23.5%, centered on concerns about connectivity, data security, and trust in financial governance. These findings suggest that while QRIS and GPN have been widely embraced as efficient digital payment solutions, there remains a need for improved infrastructure, user education, and data protection. The study demonstrates the effectiveness of the Naïve Bayes algorithm for large-scale sentiment analysis in multilingual online environments and provides empirical insights for policymakers to strengthen Indonesia’s digital payment ecosystem.
Implementation of Naïve Bayes for Public Sentiment Analysis on QRIS and GPN Digital Dominance through Instagram Nabilah, Laila; Setiawan, Kiki
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.337

Abstract

This study examines public sentiment toward the dominance of QRIS and GPN compared to Mastercard and Visa, using data collected from Instagram comments. Employing the Knowledge Discovery in Databases (KDD) methodology and the Naïve Bayes Classifier, the research analyzed 820 comments retrieved through automated scraping and processed using text mining techniques such as case folding, tokenization, stopword removal, stemming, and TF-IDF transformation. The model achieved an accuracy of 84.27%, a precision of 86.09%, a recall of 94.7%, and an F1-score of 90.21%, indicating strong reliability in identifying sentiment polarity. The analysis revealed that 76.5% of the comments expressed positive sentiment, highlighting users’ appreciation of QRIS and GPN for their convenience, speed, and accessibility across both micro and macro-scale transactions. Negative comments, representing 23.5%, centered on concerns about connectivity, data security, and trust in financial governance. These findings suggest that while QRIS and GPN have been widely embraced as efficient digital payment solutions, there remains a need for improved infrastructure, user education, and data protection. The study demonstrates the effectiveness of the Naïve Bayes algorithm for large-scale sentiment analysis in multilingual online environments and provides empirical insights for policymakers to strengthen Indonesia’s digital payment ecosystem.
Application of the Apriori Algorithm in Transaction Data in Rumah Makan Murah Marthy, Nicola; Tundo, Tundo; Nabilah, Laila; Maharani, Delia
Edusight International Journal of Multidisciplinary Studies Vol. 1 No. 2 (2024): Edusight International Journal of Multidisciplinary Studies
Publisher : Yayasan Meira Visi Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69726/eijoms.v1i2.36

Abstract

This research aims to apply the Apriori algorithm in transaction data analysis at a budget-friendly restaurant to identify purchasing patterns and relationships between frequently bought items. By leveraging historical transaction data, the Apriori algorithm can discover significant associations among various menu items, which can then be used to develop more effective marketing strategies, optimize product placement, and boost sales. The research process includes the collection and preprocessing of transaction data, application of the Apriori algorithm for association rule extraction, and analysis and interpretation of the results. The findings from this study are expected to provide valuable insights for budget-friendly restaurant managers to develop more efficient, data-driven business strategies.
Co-Authors Maharani, Delia Marthy, Nicola Poerwandono, Edhy Salfa Dhiyaa Azzizah, Putri Setiawan, Kiki Tundo, Tundo