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All Journal SMARTICS Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Shofia Hilabi, Shofa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Penerapan Data Mining Metode K-Nearest Neighbor Untuk Memprediksi Kelulusan Siswa Sekolah Menengah Pertama Puspita Sari, Desti; Shofia Hilabi, Shofa; Agustia Hananto
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8088

Abstract

Technology has become an important need for society that supports all activities to streamline student graduation with an average credit card, reduces problems related to predicting student graduation, the application of data mining to predict school graduation. The need for data mining stems from the amount of data that can be retrieved useful information and insights. Based on the results of calculations carried out using the orange modeling, the average grades of Class IX students vary from 80 to 90, and all students are declared PASS because their grades meet the graduation requirements set. is in SMPN 3 West Karawang. The K-Nearest Neighbor algorithm is useful for predicting a large number of graduates due to the consistent data processing of these predictions. For further research, use all student data to predict class growth towards student graduation.
Arsitektur Enterprise Aplikasi SIP Menggunakan Kerangka Kerja Zachman Sugiyanto, Yanto; Shofia Hilabi, Shofa; Huda, Baenil
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v5i1.9665

Abstract

The Pension Information System (SIP) is a digital platform specifically designed for comprehensive and transparent management of pension information. In modern administration, administrative efficiency and transparency are very important to improve the quality of public services. Therefore, the development of an appropriate pension information system (SIP) has a strategic role in increasing the productivity and work efficiency of civil servants. Enterprise application architecture has become an important element in the development of complex information systems. In this research, we investigate the application of the Zachman framework to the architectural design of enterprise pension information system (LIS) applications. Research methods include documentary analysis and interviews with experts in the field of software architecture. Our research results show that the application of the Zachman framework provides an organized and clear structure to the architectural design of enterprise pension information system (LIS) applications. By considering the various aspects provided by the Zachman framework, the architectural design process can be carried out systematically and efficiently. It is hoped that this research can contribute to the development of a company application architecture design methodology, especially those related to the implementation of pension information systems (SIP). The findings of this research also encourage further research regarding the application of new technology to support information system integration at the agency level.
Analisis Sentimen Terhadap Opini Proyek Kereta Cepat Menggunakan Metode Naïve Bayes Classifier Jabar Sanjaya; Tukino; Priyatna, Bayu; Shofia Hilabi, Shofa
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6598

Abstract

Twitter merupakan satu dari banyaknya media sosial yang digunakan oleh hampir Sebagian besar masyarakat. Penggunaaan twitter umumnya digunakan untuk memposting sesuatu, seperti beropini tentang infrastruktur negara misalnya. Adapun salah satu infrastruktur negara yang sedang ramai diperbincangkan adalah proyek kereta cepat, masyarakat memiliki banyak pandangan tentang proyek ini. Untuk mengetahui opini masyarakat, tentang hal itu maka akan dilakukan Sentimen Analisis. Penelitian ini bertujuan untuk mengetahui penerapan Sentimen Analisis menggunakan metode Naïve Nayes Classifier pada opini masyarakat tentang proyek kereta cepat. Penelitian dimulai dari proses pengambilan data menggunakan teknik crawling dan mendapatkan kurang lebih 2007 tweet yang membahas proyek kereta cepat. Yang kemudian data dari hasil sentimen di preprocessing¸ lalu setiap data di labeling. Dari hasil labeling diterapkan metode naïve bayes classifier. Dari hasil penelitian ini menunjukan 57,23% masyarakat lebih dominan berkomentar atau beropini positif tentang proyek kereta cepat. Dan sisanya 42,77% beropini negatif. Adapun hasil evaluasi model Naïve Bayes yaitu, Presisi 81%, Recall 81%, F1-score 81%. dan kemudian Akurasi sebesar 81%.
Co-Authors Hananto, Agustia Huda, Baenil Jabar Sanjaya Priyatna, Bayu Puspita Sari, Desti Sugiyanto, Yanto tukino, tukino