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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) SITUSTIKA FIKUNMA
Sukisno
Universitas Islam Syekh Yusuf
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Implementation of Artificial Intelligence for User Behavior Prediction in Digital Information Systems Asep Abdul Sofyan; Arif Rahman; Sukisno; Haryanto; Dede Irawan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1058

Abstract

The rapid advancement of digital information systems has increased the need for intelligent technologies capable of analyzing and predicting user behavior effectively. Artificial Intelligence (AI) has emerged as one of the most significant technologies for enhancing system intelligence, personalization, operational efficiency, and data-driven decision-making processes. This study aims to analyze the implementation of Artificial Intelligence for user behavior prediction in digital information systems. The research employs several AI algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM), to evaluate predictive performance in analyzing user interaction data. The datasets used in this study consist of browsing history, transaction records, click frequency, session duration, login activities, and user preferences collected from digital platforms. The research process includes data collection, preprocessing, algorithm implementation, predictive analysis, and performance evaluation. The results indicate that AI-based predictive systems successfully improve behavioral prediction accuracy, personalization capabilities, cybersecurity monitoring, and operational effectiveness. Among all implemented algorithms, the Long Short-Term Memory (LSTM) model achieved the highest predictive accuracy due to its capability in analyzing sequential behavioral patterns. Furthermore, the findings demonstrate that AI implementation significantly contributes to the development of adaptive and intelligent digital information systems. Despite challenges related to privacy, computational complexity, and model interpretability, Artificial Intelligence provides substantial advantages for modern digital ecosystems and supports the advancement of intelligent user-centered services in the era of digital transformation.
SISTEM MONITORING KETINGGIAN PERMUKAAN AIR SUNGAI DI BENDUNGAN CIKUPA PANDEGLANG BANTEN Taufik Hidayat; Asep Hardianto Nugroho; Sukisno; moh ridwan
SITUSTIKA FIKUNMA Vol. 9 No. 2 (2020): Jurnal Situstika
Publisher : Admin Situstika

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Abstract

Indonesia merupakan negara dengan curah hujan yang tinggi,musim penghujan dapat berlangsung selama empat bulan dalam kurun waktu satu tahun. Dengan meningkatnya pembangunan di wilayah perkotaan, menyebabkan semakin sedikitnya daerah penyerapan air. Serta kebiasaan masyarakat membuang sampah di aliran air, juga merupakan faktor pendukung penyebab terjadinya banjir. Selain dapat menimbulkan kerugian harta benda, banjir juga dapat menimbulkan korban jiwa. Dibutuhkan sebuah sistem monitoring dan peringatan, agar menghindari terjadinya korban jiwa, dan meminimalisir kerugian materil yang terjadi akibat banjir. Sistem monitoring ketinggian permukaan air dibuat agar dapat mudah diakses kapan saja dan dimana saja. Sistem peringatan juga dibuat agar dapat menyampaikan peringatan dengan cepat, dan memiliki wilayah cakupan yang luas. Dengan menggunakan sistem ini dapat dipantau ketinggian air secara real time melalui halaman web, Sehingga sistem ini dapat membantu pengguna, ataupun dapat menekan kerugian yang ditimbulkan dari banjir yang terjadi
Co-Authors Arif Rahman Asep Abdul Sofyan Asep Hardianto Nugroho Dede Irawan Haryanto moh ridwan Taufik Hidayat