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All Journal Jurnal Ilmiah Universitas Batanghari Jambi JSiI (Jurnal Sistem Informasi) Jurnal Tekno Insentif Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Sisfotek Global Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Sisfotek Global Academic Journal of Computer Science Research (AJCSR) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Tren Bisnis Global JPEG Jurnal Topik Global
M. Bucci Ryando
Institut Teknologi dan Bisnis Bina Sarana Global
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Analisis Keamanan Website Global Academic Information System menggunakan OWASP ZAP dan Model AI Lokal Asep Rio Saputra; Bayu Irfan Aditya; Nova Teguh Sunggono; M. Bucci Ryando
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.759

Abstract

Academic websites serve as central platforms for managing higher education services, including academic records, financial data, and institutional communication. However, such systems are increasingly vulnerable to cyberattacks due to their internet exposure and insufficient protection against security flaws. This study proposes an integrated solution that combines automated scanning with OWASP ZAP and a local artificial intelligence model (Mistral) executed via the Ollama platform. The entire process is automated using Python scripting, covering stages such as spidering, active scanning, JSON result extraction, and AI-based mitigation recommendation generation. The research was conducted on the Global Academic Information System website. The scan results revealed a total of 193 vulnerabilities, including 4 high, 8 medium, 111 low, and 70 informational risks. Each vulnerability was analyzed using the local AI model to produce specific technical recommendations, such as adding security headers, implementing CSRF tokens, and configuring secure cookies. All outputs were automatically compiled into a structured Excel report suitable for developers. This approach proves effective in streamlining the security audit process, reducing manual workload, and preserving data privacy, as all operations are conducted locally without reliance on cloud services. The study demonstrates that integrating OWASP methods with local AI provides a practical, adaptive, and standalone solution for web application security testing.
Analisis Text Mining terhadap Penggunaan Paylater Menggunakan Naïve Bayes Classifier Ryando, M. Bucci; Iqbal, Muchamad; Syahidah, Kurnia
Jurnal Tekno Insentif Vol 19 No 2 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i2.2065

Abstract

Abstrak Popularitas Paylater sebagai metode pembayaran e-commerce pasca-pandemi di Indonesia meningkat pesat, namun disertai risiko utang konsumtif, khususnya di kalangan Generasi Z. Penelitian ini bertujuan menganalisis sentimen dan opini publik guna memahami faktor yang memengaruhi keputusan adopsi Paylater. Dengan menggunakan metode analisis sentimen berbasis Naïve Bayes Classifier terhadap data Twitter (kini X), penelitian ini mengklasifikasikan tanggapan masyarakat terhadap penggunaan Paylater. Model yang dibangun divalidasi dengan nilai F1-score 0.432 dan Precision 0.508. Hasil analisis menunjukkan mayoritas sentimen (50,75%) bersifat netral atau ambigu, mencerminkan adanya keraguan publik terhadap penggunaan layanan ini. Selain itu, ditemukan dominasi sentimen negatif yang menyoroti isu peningkatan utang, kesulitan mengelola keuangan, serta ketergantungan terhadap fasilitas kredit konsumtif. Penelitian ini berkontribusi dalam pemanfaatan text mining untuk memetakan persepsi Generasi Z terhadap adopsi Paylater, sehingga hasilnya dapat menjadi dasar bagi perusahaan fintech dalam merumuskan strategi pemasaran yang lebih bijak dan bertanggung jawab. Kata kunci: paylater, analisis sentimen, text mining, generasi z, naïve bayes classifier. Abstract The popularity of Paylater as an e-commerce payment method in post-pandemic Indonesia has grown rapidly but is accompanied by the risk of consumptive debt, particularly among Generation Z. This study aims to analyze public sentiment and opinions to understand the factors influencing Paylater adoption decisions. Using a Naïve Bayes Classifier-based sentiment analysis method on Twitter (now X) data, this research classifies public responses toward Paylater usage. The developed model was validated with an F1-score of 0.432 and Precision of 0.508. The results indicate that the majority of sentiments (50.75%) are neutral or ambiguous, reflecting public uncertainty toward the service. In addition, dominant negative sentiments were identified, highlighting issues such as increasing debt, financial management difficulties, and dependency on credit facilities. This study contributes to the use of text mining in mapping Generation Z’s perceptions of Paylater adoption, providing insights that can help fintech companies develop more responsible and ethical marketing strategies. Keywords: paylater, sentiment analysis, text mining, z generation, naïve bayes classifier.
Co-Authors Achmad Sidik Akbar, Muhammad Ikmal Arni Retno Mariana Asep Rio Saputra Ash shidiq, Ammar Yusuf Batu Bara, Muhamad Aldi Mudin Bayu Irfan Aditya Bijaksana, Rhafael Dedi Dedi Dedi Dedi Eka Sumiati Ferawati Ferawati Ferry Maulana Ibrahim Fredy Susanto Hadinata, Wira Haerunnisa, Siti Halim Agung Haromaen, Muhammad Hidayatullah, Ilham Iqbal, Muchamad Khoirul Anwar Makmur Raya Maruf, Rohim Muhamad Naufal Robih Nabila Ana Mantika Nova Teguh Sunggono Novara Aulist Zakia Nur Asiyah Nurholipah Nurholipah Nurmaesah, Nunung Piskana, Marthin Pudoli, Ahmad Rahmat Tullah Ramadhanti, Nur Fairus Raya, Makmur Ridwan Abdul Hakim Saepul Nazar Septihadi, Ali Nugroho Setiawan, Prayoga Siddiq, Hafizh Sidik , Achmad Sidik, Achmad Siti Maesaroh Sri Rahayu Sutarman Sutarman Syahidah, Kurnia Syifa Aulia Tasya Amelia Tina Amalia Triono Triono Utomo, Prawido Wahyu Nurifai Widiawati Widiawati Zainul Hakim