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Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Published by Universitas Samudra
ISSN : 27752089     EISSN : 27747115     DOI : https://doi.org/10.33059/j-icom.v2i1.3417
Core Subject :
Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Perancangan Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Enterprise Computing Cloud Computing Technology Management Topik kajian lainnya yang relevan Dengan artikel yang memiliki sitasi primer dan tidak pernah dipublikasikan secara online atau versi cetak sebelumnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 133 Documents
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Analisis Pemanfaatan Tools Forensik dalam Investigasi Credential Phishing pada Discord Web Menggunakan Metode OSCAR Zulki Yanto Rivai; Anton Yudhana; Imam Riadi
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.15306

Abstract

Credential phishing on Discord Web can generate digital traces across browser storage and network traffic, requiring appropriate forensic tools according to the characteristics of each evidence source. This study analyzes the utilization of forensic tools in investigating credential phishing on Discord Web using the OSCAR method, consisting of Obtain, Strategize, Collect, Analyze, and Report. A controlled experimental scenario was conducted using a phishing domain represented in browser and network artifacts as xn--dscord-waa.online. FTK Imager was used to acquire the browser profile, while DB Browser for SQLite, Autopsy, and Wireshark were used for artifact examination. The results show that DB Browser for SQLite identified the phishing URL in browser History, Discord-related cookies, email, and username, while no phishing-domain cookie or saved login data was found. Autopsy identified the phishing domain, login.html, email, and username in browser storage artifacts. Wireshark identified DNS resolution, the phishing server IP address, and TLS SNI associated with the phishing domain and official Discord services. The findings demonstrate that each tool contributes different evidence according to the source and characteristics of the artifacts. The OSCAR method provides a structured workflow for positioning and utilizing forensic tools throughout the investigation.
Analisis Kinerja Algoritma Apriori dalam Mengidentifikasi Pola Pembelian Produk pada Data Transaksi Sri Hartati Hartati; Fany Andhika Zulindra; Rina Arifiani
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.15379

Abstract

Republik Computer Slawi frequently faces inventory imbalances, characterized by stockouts for some products and overstocking for others. This study aims to analyze the performance of the Apriori algorithm in identifying product purchasing patterns to serve as a basis for data-driven decision-making. The analysis was conducted on 1,399 sales transaction records using RapidMiner, with a minimum support of 5% and a minimum confidence of 60%. The results demonstrate that the Apriori algorithm successfully generated frequent itemsets—up to 3-itemsets—and produced 10 association rules with confidence values ​​ranging from 0.855 to 0.981. The "Mouse" product exhibited the highest support value at 0.773, while the combination of "Mouse" and "Laptop Bag" showed the highest support among 2-itemsets at 0.662. Performance metrics yielded an RMSE of 0.060 and a Squared Error of 0.004, indicating a low level of prediction error for the model. Consequently, the Apriori algorithm proves effective in identifying purchasing patterns and can be utilized to support more optimal inventory management at Republik Computer Slawi.
Sistem Pakar Deteksi Gangguan Konsentrasi Belajar Siswa Menggunakan Metode Certainty Factor Aan Risdiana; Efri Gresinta; Rahmat Hidayat
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.15414

Abstract

This study aims to develop a web-based expert system to detect learning concentration disorders in students using the Certainty Factor method. The research was conducted at SDN Baru 06 Pagi Pasar Rebo, focusing on 62 third-grade students. The absence of a counseling teacher or child psychologist at the school makes the identification process rely heavily on the teachers subjective intuition. The system uses 15 behavioral symptoms categorized into three dimensions: inattention, hyperactivity, and impulsivity. The expert knowledge base (CFpakar) was formulated through a Focus Group Discussion (FGD) with third-grade teachers. The Certainty Factor algorithm combines expert confidence and user input to calculate the CF value per dimension and the final combined CF, classifying the severity into five categories: Not Indicated, Mild, Moderate, Severe, and Very Severe. The results show that the system successfully calculates the confidence level of the disorder, provides dimension-specific diagnostic profiles, and generates printable reports via DomPDF. This system assists teachers in identifying students concentration issues systematically and designing appropriate learning interventions.

Page 14 of 14 | Total Record : 133

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