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All Journal Jurnal Buana Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Mega Kartika Sari, Mega Kartika
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Computer Science & IT

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Pembangunan Aplikasi Klasifikasi Mahasiswa Baru untuk Prediksi Hasil Studi Menggunakan Naïve Bayes Classifier Sari, Mega Kartika; Ernawati, Ernawati; Wisnubhadra, Irya
Jurnal Buana Informatika Vol 7, No 2 (2016): Jurnal Buana Informatika Volume 7 Nomor 2 April 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.207 KB) | DOI: 10.24002/jbi.v7i2.492

Abstract

Abstract. Every university hopes to create the best potential graduates. Some of the efforts can be achieved by utilizing university data to be processed into information. The resulting information can help the university to determine the decisions to take in improving the students’ academic performance. One of the methods to process the data is by using Naive Bayes Classifier. This method requires some information such as GPA, average credits and attendance percentage. The prediction results are taken based on the data of the students at the university. Because there are large number of data to process it will require an information system that can classify data using Naïve Bayes Classifier. This application is built by using C# programming language with an average of 81,67% accuracy results (the accuracy depends on the data used). This application is expected to help the university to increase the achievements of its graduates.Keywords: Classification, Naive Bayes, Machine learning, Data miningAbstrak. Setiap universitas tentu berharap dapat meluluskan mahasiswa dengan prestasi terbaik. Usaha meningkatkan prestasi dapat diwujudkan dengan memanfaatkan data universitas untuk diolah menjadi informasi. Informasi yang dihasilkan dapat berguna untuk menentukan keputusan yang harus diambil pihak universitas dalam meningkatkan prestasi. Salah satu metode yang digunakan untuk memproses data ialah metode klasifikasi Naive Bayes. Metode ini menggunakan beberapa informasi seperti IPK, rata-rata sks dan persentase kehadiran mahasiswa sebagai data pelatihan. Data pelatihan tersebut digunakan untuk memprediksi IPK, rata-rata sks dan presentase kehadiran mahasiswa baru sebagai data uji. Adanya data yang cukup besar untuk diolah, maka dibutuhkan aplikasi klasifikasi mahasiswa baru menggunakan metode Naive Bayes Classifier. Aplikasi ini dibangun menggunakan bahasa pemrograman C# dengan rata-rata hasil akurasi 81,67%. Aplikasi ini diharapkan dapat membantu universitas dalam meningkatkan hasil prestasi akademik mahasiswa.Kata Kunci: Naive Bayes, Pembelajaran mesin, Penambangan data
PUBLIC SECTOR INNOVATION IN SMART CITIES: A FRAMEWORK OF AMBIDEXTROUS AI GOVERNANCE Wijaya, Agustinus Fritz; Lestari, Merryana; Sari, Mega Kartika; Ferdiandinata, Rio
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7672

Abstract

The increasing adoption of artificial intelligence (AI) in smart city public services has intensified governance challenges related to accountability, data security, transparency, and risk management. Existing AI governance studies tend to emphasize either innovation enablement or control mechanisms, offering limited guidance on how public institutions can balance both simultaneously. This study addresses this gap by proposing an Ambidextrous AI Governance Framework grounded in COBIT 2019, which systematically integrates exploration-oriented innovation and exploitation-oriented control within public-sector governance. Using a Design Science Research (DSR) approach, the framework was developed and evaluated through a 2025 case study at the Jakarta Provincial Communication and Information Agency. Validation was conducted via expert interviews, document analysis, and simulation-based governance maturity assessments. The findings indicate that baseline AI governance maturity remains at Levels 2–3, while simulation results suggest potential advancement toward Levels 4–5, particularly in risk management (APO12) and security services (DSS05). The study contributes theoretically by operationalizing organizational ambidexterity within public-sector AI governance and practically by offering structured guidance to support secure, transparent, and sustainable AI adoption in smart city environments.
Co-Authors Agustinus Fritz Wijaya Ernawati Ernawati Ferdiandinata, Rio Irya Wisnubhadra, Irya Merryana Lestari