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All Journal JURNAL TEKNOLOGI INFORMASI Jurnal Teknologi Informasi dan Komunikasi
Ade Lestari Hasibuan
Universitas Labuhanbatu
Computer Science & IT Control & Systems Engineering

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Analisis Forensik Digital Dalam Pengungkapan Kasus Penipuan Investasi Binary Option Rizki Habibah; Sahat Parulian Sitorus; Hikmah Aldinar Siregar; Yolanda Listia Listia; Ade Lestari Hasibuan; Aldi Rahmansyah; M. Idris Sagala
JURNAL TEKNOLOGI INFORMASI Vol 12, No 1 (2026): Jurnal Teknologi Informasi
Publisher : Universitas Respati Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52643/jti.v1i1.7541

Abstract

Kasus penipuan berkedok investasi melalui platform Binary Option telah menyebabkan kerugian finansial besar di Indonesia. Penelitian ini bertujuan menganalisis peran forensik digital dalam mengidentifikasi, mengamankan, dan memeriksa bukti elektronik yang terkait dengan aktivitas affiliator ilegal. Penelitian menerapkan model investigasi forensik digital standar mengacu pada pedoman ACPO dan NIST SP 800-86. Hasil analisis menunjukkan ditemukan jejak data terhapus, komunikasi tersembunyi, serta bukti transaksi yang disamarkan. Bukti-bukti ini memperkuat unsur tindak pidana penipuan, penyebaran informasi bohong, dan pencucian uang. Penelitian menyimpulkan bahwa forensik digital merupakan komponen utama dalam mengungkap kejahatan siber kompleks dan harus menjadi prosedur wajib dalam penegakan hukum modern.
PENERAPAN DATA MINING UNTUK SEGMENTASI MENU KOPI BERDASARKAN KARAKTERISTIK PEMINAT MENGGUNAKAN ALGORITMA K-MEANS Rizki Habibah; Hikmah Aldinar Siregar; Ade Lestari Hasibuan; Yolanda Listia; Aldi Rahmansyah; M. Idris Sagala
Jurnal Teknologi Informasi dan Komunikasi Vol 19 No 1 (2026): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v19i1.360

Abstract

The growth of coffee menu variations requires business owners to understand consumer interest characteristics in a structured manner, while menu data and consumer preference information are often not fully utilized in decision making. This study aims to segment coffee menus based on consumer-interest characteristics using a data-mining approach. The method applied is clustering using the K-Means algorithm implemented in the Orange Data Mining software, with two main attributes: price and interest category. The analysis process includes data preprocessing, determining the optimal number of clusters, and evaluating cluster quality using the silhouette coefficient. The results show that the K-Means algorithm successfully groups coffee menus into three clusters with distinct price ranges and consumer-interest characteristics. The evaluation yields a silhouette coefficient of 0.725, indicating a strong cluster structure with clear separation between groups. Visualization of the clustering results reveals three main segments: an economical cluster characterized by low prices and high consumer interest, a middle cluster with moderate prices and varying levels of interest, and a premium cluster with high prices and consistently strong consumer interest. These segmentation results provide a clear representation of consumer preference patterns and support decision making in product planning and pricing strategies for coffee businesses.
Co-Authors Aldi Rahmansyah Hikmah Aldinar Siregar M. Idris Sagala Rizki Habibah Sitorus, Sahat Parulian Yolanda Listia Yolanda Listia Listia