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All Journal Jurnal Abdi Masyarakat Saburai (JAMS) Jurnal Dedikasi untuk Negeri Journal of Artificial Intelligence and Digital Business Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Abdi Akommedia : Jurnal Pengabdian Masyarakat Journal of Public Relations and Digital Communication (JPRDC) Global Science: Journal of Information Technology and Computer Science Journal Of Electrical Engineering And Technology
Julianto, Ribut
Universitas Indonesia Mandiri
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Penerapan Agile Development dalam Pengembangan Sistem Informasi Akademik Berbasis Web Julianto, Ribut; Arifa Hulmi, Zeliya
JURNAL ILMU KOMPUTER, SISTEM INFORMASI, TEKNIK INFORMATIKA Vol 3 No 1 (2024)
Publisher : PT Akom Media Informatika

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Abstract

Sistem informasi akademik merupakan salah satu komponen kritis dalam pengelolaan perguruan tinggi modern. Pengembangan sistem tersebut membutuhkan pendekatan metodologi yang mampu mengakomodasi perubahan kebutuhan secara cepat dan menghasilkan produk yang berkualitas. Penelitian ini mengkaji penerapan metode Agile Development, khususnya kerangka kerja Scrum, dalam pengembangan Sistem Informasi Akademik (SIA) berbasis web pada institusi pendidikan tinggi. Metode penelitian menggunakan pendekatan kualitatif deskriptif dengan studi kasus pada proses pengembangan SIA. Hasil penelitian menunjukkan bahwa penerapan Agile melalui mekanisme sprint, daily standup, sprint review, dan sprint retrospective mampu meningkatkan kualitas sistem secara iteratif, mempercepat siklus pengiriman fitur, serta meningkatkan keterlibatan pengguna dalam proses pengembangan. Sistem yang dihasilkan mencakup modul manajemen mahasiswa, manajemen nilai, jadwal perkuliahan, dan pelaporan akademik. Pengujian menggunakan metode blackbox menunjukkan bahwa seluruh fitur utama berjalan sesuai kebutuhan. Penelitian ini menyimpulkan bahwa Agile Development merupakan pendekatan yang efektif dan adaptif untuk pengembangan sistem informasi akademik di lingkungan perguruan tinggi.
Implementasi Kriptografi Hibrida AES-256 dan ECC dengan Deteksi Anomali Berbasis Autoencoder untuk Keamanan Data Bisnis pada Infrastruktur Cloud Computing Setiayadi, Didik; Julianto, Ribut; Ade Ningrum, Cahya
JURNAL ILMU KOMPUTER, SISTEM INFORMASI, TEKNIK INFORMATIKA Vol 2 No 2 (2023)
Publisher : PT Akom Media Informatika

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Abstract

Adopsi infrastruktur cloud computing oleh sektor bisnis di Indonesia terus meningkat pesat, namun diiringi oleh eskalasi ancaman kebocoran data, akses tidak sah, dan serangan siber yang semakin canggih. Skema enkripsi tunggal berbasis AES atau RSA saja dinilai tidak lagi mencukupi untuk menghadapi lanskap ancaman modern yang memanfaatkan kelemahan pada lapisan kunci (key management) maupun pola akses anomali. Penelitian ini mengusulkan arsitektur keamanan data berlapis yang mengintegrasikan dua komponen utama: (1) skema kriptografi hibrida yang menggabungkan AES-256 untuk enkripsi data massal berkecepatan tinggi dengan Elliptic Curve Cryptography (ECC) kurva P-384 untuk manajemen kunci yang efisien dan aman, serta (2) model deteksi anomali akses berbasis Autoencoder deep learning yang mampu mengidentifikasi pola akses mencurigakan secara real-time tanpa memerlukan data berlabel. Sistem diimplementasikan pada lingkungan cloud AWS (Amazon Web Services) menggunakan infrastruktur multi-region dan diuji menggunakan dataset akses log dari tiga perusahaan sektor finansial dan manufaktur di Indonesia selama periode 12 bulan, mencakup 4,7 juta event akses. Hasil evaluasi menunjukkan: overhead enkripsi-dekripsi AES-256/ECC hanya sebesar 3,2% dibandingkan sistem tanpa enkripsi, model Autoencoder mencapai AUC-ROC 0,9712 dalam deteksi anomali akses dengan false positive rate 1,8%, dan sistem secara keseluruhan mampu memenuhi standar keamanan ISO/IEC 27001:2013 serta regulasi POJK No.11/2022 tentang Penyelenggaraan Teknologi Informasi oleh Lembaga Jasa Keuangan. Arsitektur yang diusulkan memberikan kerangka keamanan cloud yang komprehensif, efisien, dan dapat diadaptasi oleh pelaku industri di Indonesia.
The Application of the K-Medoids Method for Clustering Meta Ads Audiences Based on Promotional Content Effectiveness Julianto, Ribut; Gunawan , Tedi; Apriadi, Eko Aziz
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6515

Abstract

The rapid growth of digital advertising platforms has encouraged businesses to adopt data-driven strategies in order to enhance the effectiveness of their promotional activities. One of the most widely used digital advertising services today is Meta Ads, which provides various performance metrics related to audience interactions. This study aims to segment Meta Ads audiences based on the effectiveness of promotional content using the K-Medoids clustering algorithm, which is known for its robustness in handling outliers compared to other clustering methods. The dataset used in this research consists of advertising access data obtained from ARTECH – PT. Arij Teknologi Inovasi. The data were processed and analyzed using RapidMiner as a data mining tool. After undergoing data preprocessing stages, including data cleaning and normalization, a total of 495 Meta Ads records were deemed suitable for clustering analysis. The results of the study show that the K-Medoids algorithm successfully grouped the data into two distinct clusters. Cluster 1 consists of 465 items and represents the dominant audience segment with relatively homogeneous interaction behavior, indicating consistent engagement patterns with promotional content. Meanwhile, Cluster 0 contains 30 items, representing a smaller but more specific audience segment with different access and interaction patterns. These findings demonstrate that the K-Medoids algorithm is effective in identifying meaningful audience segments from digital advertising data. The resulting clusters can be utilized to support more targeted digital marketing strategies, improve promotional content design, and optimize advertising budget allocation to achieve better campaign performance.
AI Recommendation Systems and Digital Promotion Effectiveness on TikTok Social Commerce in Indonesia Efi Ero Sofia; Eko Aziz Apriadi; Ribut Julianto; Muawan Bisri
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7105

Abstract

The development of artificial intelligence has brought fundamental changes to the social commerce ecosystem, particularly on the TikTok platform, which has now become one of the largest online shopping channels in Indonesia. This study aims to analyze the effect of the artificial intelligence recommendation system on the effectiveness of digital promotion on the TikTok platform in Indonesia, while considering the moderating roles of user trust and privacy concerns. The method used is quantitative, with a cross-sectional survey design involving 320 active TikTok users aged 17–45 years. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.0 software. The results show that the AI recommendation system has a positive and significant effect on both user engagement (β = 0.612) and purchase conversion (β = 0.548). User trust was found to strengthen this relationship, while privacy concerns significantly weakened it. These findings confirm that the effectiveness of AI-based promotion does not depend solely on the sophistication of the algorithm, but also on the ecosystem of trust built among the platform, marketers, and consumers. This study provides practical contributions for digital business actors in Indonesia in designing promotional strategies that are more human-centered and responsible.
Co-Authors Abdillah, Nazril Ade Ningrum, Cahya Arifa Hulmi, Zeliya Azzahra SR, Rizka Annisa bisri, muawan Chairunisa, Chairunisa Dandi Yohananda Saputra Utama Didik Setiadi Dukhi, Rizki Efi Ero Sofia Eko Aziz Apriadi Fajar Sasora Febrianto Indrajaya, I Kadek Gunawan , Tedi Komarudin, Agus Krisna Widi Nugraha Kurniawan, Yogiek Indra Muawan Bisri Nayla Rihhadatul Ayni Prasetio, Yugo Dwi Ranto Siswanto Rian Ardianto Rosyid Ridlo Al-Hakim Setiadi, Didik Setiayadi, Didik Shifa Sukmatul Hikmah Siswanto, Ranto Sopian, Ence Winny Purbaratri Yohananda Saputra Utama, Dendi