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All Journal Journal of Research and Publication Innovation JRIIN :Jurnal Riset Informatika dan Inovasi
Rifky Firmansyah
Universitas Pamulang
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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SISTEM PERANCANGAN JUAL BELI KENDARAAN DENGAN MENGGUNAKAN METODE AGILE SOFTWARE DEVELOPMENT BERBASIS WEB (STUDI KASUS SHOWROOM ALDI MOTOR) Dian Sopyandi; Deri Andragi; Rifky Firmansyah; Roeslan Djutalov
Journal of Research and Publication Innovation Vol 1 No 3 (2023): JULY
Publisher : Journal of Research and Publication Innovation

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Abstract Aldi Motor Showroom is a company that sells and buys used motorcycles from various brands and types of motorbikes, this showroom requires media to carry out product promotion and marketing. As technology develops, it is necessary to create website-based media promotion and sales. This promotional and marketing media contains product information and specifications as well as profiles that will be used to assist in the promotion and delivery of information. Marketing that still relies on word of mouth and only by distributing brochures and limited partners with additional costs, promotions tend to be less effective and their reach is still limited to certain locations. The purpose of this research is to design a web-based used motorcycle sales system. Planning for this sales information system uses the Agile Software Development development method.
Deteksi Dini Website Phising Berbasis Karakteristik URL Menggunakan Algoritma Random Forest sebagai Upaya Penegakan Keamanan Siber Muhammad Faqih Alharits; Hanif Maulana Ar Rasyid; Rifky Firmansyah; Abdullah Rendra Zuriansyah; Ardiansyah Maulana; Firza Aditiya Ardiansah; Rahmawati
Jurnal Riset Informatika dan Inovasi Vol 4 No 4 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Phising merupakan salah satu ancaman keamanan siber yang paling merugikan, dengan modus menyamar sebagai website terpercaya untuk mencuri data sensitif pengguna. Penleitian ini mengembangkan sistem deteksi dini phising website berbasis karakteristik URL menggunakan algoritma Random Forest. Dataset yang digunakan terdiri dari 10.000 sampel URL (50% legitimate, 50% phising) dengan 48 fitur berbasis karakteristik URL. Model baseline Random Forest mencapai akurasi 98,55%, presisi 98,60%, recall 98,50%, F1-score 98,55%, dan AUC-ROC 99,09%. Hyperparameter tuning menggunakan RandomizedSearchCV menghasilkan model dengan performa serupa namun sedikit lebih rendah pada recall (98,20%). Analisis feature importance menunjukkan bahwa PctExtHyperlinks (20,69%), PctExtNullSelfRedirectHyperlinksRT (16,77%), dan FrequentDomainNameMismatch (7,78%) merupakan fitur paling informatif. Kajian etika profesi dilakukan terhadap implikasi false positive dan false negative, tanggung jawab profesional pengembang, serta kepatuhan terhadap regulasi Indonesia (UU ITE, UU PDP No. 27 Tahun 2022). Penelitian ini menunjukkan bahwa pendekatan machine learning berbasis Random Forest efektif untuk deteksi phishing dan dapat berkontribusi pada penegakan keamanan siber di Indonesia.
Co-Authors Abdullah Rendra Zuriansyah Ardiansyah Maulana Deri Andragi Dian Sopyandi Firza Aditiya Ardiansah Hanif Maulana Ar Rasyid Muhammad Faqih Alharits Rahmawati Roeslan Djutalov