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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
I Nyoman Rai Widartha Kesuma
Politeknik Negeri Bali
Computer Science & IT Economics, Econometrics & Finance

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Deteksi Kecurangan pada Kompetisi Capture the Flag Menggunakan Two-Stage Similarity Analysis dan Tiered Weighted Risk Scoring Dimas Maulana; I Wayan Candra Winetra; I Nyoman Rai Widartha Kesuma
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp366-372

Abstract

Flag sharing, inter-team cooperation (teaming), and prohibited tools such as AI undermine how validly a Capture the Flag (CTF) event measures skills, corrupting the integrity of its scoring information system. GZCTF's only cheating signal is the dynamic flag that catches flag theft. Nothing else feeds a per-team risk profile. This study adds a detection module to the GZCTF backend built on two components. The first is Two-Stage Similarity Analysis: pairwise Longest Common Subsequence and Jaccard scores are blended into a Relative Sequence Index (RSI), after which Confidence Screening Detector (CSD) screening confirms suspicious groups. The second is a tiered Weighted Risk Scoring model that assigns 38 indicators to four evidence tiers (Hard, Strong, Behavioral, Context), caps every non-Hard tier, and gives network or identity correlations no direct score. Evaluation used a controlled simulation of ten team participations on a live instance, with ground-truth labels fixed at design time. Precision reached 1.000 with zero false positives, accuracy 0.900 and F1 0.909, and recall 0.833. The single miss came from collusion evidence attributed to only one member of a pair. An RSI threshold of 0.85 split every colluding pair from benign ones, and teams with purely network or identity correlation scored zero.
Pengembangan Aplikasi Kakao Assistant Berbasis Flutter dan Large Language Model sebagai Asisten Virtual Petani Kakao di Bali I Nyoman Rai Widartha Kesuma; Luh Gede Putri Suardani; Agus Adi Putrawan; I Putu Astya Prayudha
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp991-1001

Abstract

Cocoa (Theobroma cacao L.) represents a strategic agricultural commodity supporting rural smallholder livelihoods across Bali Province, notably in Tabanan, Jembrana, Gianyar, and Buleleng regencies. Nevertheless, farm productivity and bean quality remain severely threatened by Cocoa Pod Borer (Conopomorpha cramerella), Helopeltis sp., and black pod rot (Phytophthora palmivora), exacerbated by the limited availability of agricultural extension officers in remote rural areas. This study develops Kakao Assistant, a cross-platform mobile virtual assistant built on Flutter integrating Google Gemini 3.1 Flash Large Language Model (LLM) under a single-role architecture dedicated to Cocoa Farmers. System development follows the Modified Waterfall lifecycle comprising requirements analysis, system architecture design, implementation, and empirical testing. The application provides multimodal agronomic consultations (text inquiries and pod disease image diagnostics), regional agroclimatic contextualization for Bali plantations, Text-to-Speech (TTS) voice synthesis, message bookmarking, and an on-device model evaluation module ensuring transparency of artificial intelligence inferences. Black-box testing validated that 100% of the 11 functional scenarios functioned in strict compliance with system specifications. Prototype testing verified the module's capability to track response latency, network fault resilience, and local FIFO history logs prior to extensive community deployment across Bali cocoa farms.
Co-Authors Agus Adi Putrawan Dimas Maulana I Putu Astya Prayudha I Wayan Candra Winetra Luh Gede Putri Suardani