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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmiah Sistem Informasi MDP Student Conference Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi
Filikano, Thomas
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Other

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ANALISIS RISIKO SISTEM INFORMASI AKUNTANSI MENGGUNAKAN ISO 31000:2018 DI PT. XYZ Filikano, Thomas; Gunawan, Andronikus; Andayani, Sri
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 1 (2024): Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/sm.v4i1.71

Abstract

In the dynamic business environment, risk management plays a crucial role in the sustainability and success of a company. This research aims to assist PT. XYZ, a local beverage distribution company, in identifying, analyzing, and managing the risks it faces using the ISO 31000:2018 approach. A case study research method is applied, focusing on the company's accounting information system. Risk assessment steps include identifying assets, potential risks, and risk impacts, followed by analyzing the likelihood and impact of risks. The analysis results classify risks into three levels: Low, Medium, and High. Out of the 18 identified risks, risk treatment is conducted by providing appropriate solutions to mitigate the identified risks, as listed in the recommendation table. It is hoped that the findings of this analysis will help PT. XYZ minimize losses caused by risks and enhance its business sustainability
Implementasi COBIT 5 Pada Domain APO12 Untuk Manajemen Risiko TI CV XYZ Gunawan, Andronikus; Putra, Andreas Alessandro Fernando; Filikano, Thomas; Andayani, Sri
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11230

Abstract

In the digital era, the rapid advancement of information technology (IT) has become a critical element in supporting business operations. However, the use of IT also introduces risks such as operational disruptions, data security threats, and system failures. This study aims to evaluate the implementation of IT risk management at CV XYZ, a mineral water distribution company in Sumatra, using the COBIT 5 framework in the APO12 (Manage Risk) domain. The research employs a qualitative case study approach, including literature review, problem identification, data collection through interviews with company executives, and descriptive data analysis. The findings reveal that CV XYZ operates at Capability Level 2 (Managed Process) for the data collection and risk analysis subdomains, and Level 3 (Established Process) for the risk profile maintenance and mitigation action determination subdomains. However, the company requires further optimization to achieve Level 5 (Optimizing Process). Strategic recommendations include strengthening risk policies, enhancing human resource skills, maintaining IT infrastructure, and improving disaster recovery systems to support sustainable business operations
Deteksi Cyberbullying Dalam Grup Telegram Menggunakan Support Vector Machine (SVM) Filikano, Thomas; Gunawan, Andronikus; Wijaya, Andri
Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Vol. 2 No. 1 (2024): Januari
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/jsimtek.v2i1.532

Abstract

Cyberbullying di platform media sosial, khususnya di grup Telegram, merupakan permasalahan serius yang perlu ditangani. Penelitian ini bertujuan untuk mengembangkan pendekatan deteksi cyberbullying menggunakan metode Support Vector Machine (SVM). Sebanyak 1400 pesan dengan label cyberbullying dan non-cyberbullying digunakan sebagai data pelatihan untuk melatih model SVM. Pengujian dilakukan pada data uji sebanyak 600 pesan guna mengevaluasi kinerja model. Hasil penelitian menunjukkan bahwa penggunaan kernel linear pada SVM menghasilkan akurasi tertinggi sebesar 89%. Performa sistem klasifikasi pesan cyberbullying juga mencapai nilai precision, recall, dan F1-score masing-masing sebesar 89%, 87%, dan 88%. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam menganalisis perilaku masyarakat di dunia maya terkait pesan-pesan cyberbullying di grup Telegram.
Deteksi Risiko Scam Airdrop Kripto Pada Platform Telegram Menggunakan Metode Lexicon-Based Filikano, Thomas; Wijaya, Andri
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 2 (2025): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i2.268

Abstract

Perkembangan pesat teknologi blockchain dan kripto telah menghadirkan berbagai inovasi, salah satunya airdrop kripto sebagai strategi pemasaran untuk menarik pengguna. Telegram menjadi platform utama penyelenggaraan airdrop melalui Telegram Mini Apps, namun juga rentan terhadap praktik scam yang merugikan pengguna. Penelitian ini mengimplementasikan metode lexicon-based untuk mendeteksi risiko scam airdrop kripto di Telegram. Data dikumpulkan dari grup Telegram Mini Apps menggunakan pustaka Telethon, lalu dianalisis dengan pendekatan lexicon-based yang menggabungkan VADER dan custom lexicon istilah scam. Analisis dilakukan terhadap unigram dan bigram guna menangkap konteks kata yang relevan. Hasil menunjukkan mayoritas pesan netral (252.164), diikuti positif (101.455) dan negatif (66.791). Frasa seperti “list date” dan “scam project” menjadi indikator risiko utama. Metode ini efektif mendeteksi risiko scam meski kebanyakan pesan bersifat informatif. Penelitian ini diharapkan dapat meningkatkan kewaspadaan pengguna dan berkontribusi pada ekosistem blockchain yang lebih aman. Untuk penelitian selanjutnya, disarankan pengembangan dashboard real-time dengan pembaruan lexicon berkala mengikuti dinamika komunitas kripto dan integrasi dengan machine learning.
Co-Authors Andri Wijaya Gunawan, Andronikus Putra, Andreas Alessandro Fernando Sri Andayani