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All Journal Techno.Com: Jurnal Teknologi Informasi CogITo Smart Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Riset Informatika Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology Servitium Smart Journal
Wilsen Grivin Mokodaser
Universitas Budi Luhur
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Evaluasi Kinerja Algoritma Apriori dan FP-Growth untuk Association Rule Mining pada Data Transaksi Ritel Soewignyo, Fanny; Soewignyo, Tonny Irianto; Mokodaser, Wilsen Grivin; Silitonga, Argha Orion
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14952

Abstract

Ledakan data transaksi ritel yang terekam melalui sistem Point of Sale (POS) dan platform daring menuntut metode analisis yang efektif untuk menggali pola pembelian konsumen. Association Rule Mining merupakan pendekatan populer untuk menemukan keterkaitan antarproduk, dengan algoritma Apriori dan FP-Growth sebagai dua metode yang paling banyak digunakan. Penelitian ini bertujuan memberikan gambaran empiris mengenai efektivitas kedua algoritma tersebut pada data transaksi ritel yang nyata. Metode yang digunakan meliputi tahapan data understanding untuk mengenali struktur data, data cleaning untuk menghapus nilai kosong dan menyeragamkan format, serta data transformation menggunakan TransactionEncoder untuk mengubah data mentah menjadi format biner (one-hot encoded). Selanjutnya algoritma Apriori dan FP-Growth dijalankan dengan parameter yang sama untuk menghasilkan frequent itemsets dan aturan asosiasi. Evaluasi kinerja dilakukan dengan mengukur waktu pemrosesan, jumlah aturan yang dihasilkan, serta nilai support, confidence, dan lift tertinggi. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan jumlah aturan yang sama (63 aturan) dengan support tertinggi 0,06, confidence tertinggi 0,51, dan lift tertinggi 3,29, tetapi waktu pemrosesan berbeda signifikan (Apriori 0,39 detik, FP-Growth 6,95 detik). Kesimpulannya, association rule mining efektif mengungkap pola pembelian konsumen, dan algoritma Apriori lebih efisien untuk dataset kecil hingga menengah, sedangkan FP-Growth lebih sesuai untuk dataset yang jauh lebih besar. Keywords - Association Rules, Apriori, FP-Growth, Frequent Itemset, Transaksi Ritel.
GreenPoin: Mobile Application with Reward Point System at Klabat University Adam, Stenly Ibrahim; Mokodaser, Wilsen Grivin; Wagiu, Wayne Gilbert
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.22163

Abstract

At Klabat University, the conventional method of deducting Sabbath points poses challenges for students and supervisors due to its inefficiency. To address this, the GREENPOIN smartphone application was developed as an innovative solution for managing Sabbath points. The application enables administrators to approve point redemptions, add supervisors, manage tasks, and monitor students' total Sabbath points. Students earn points by completing environmental cleaning tasks assigned by supervisors. The system was designed using prototype models and use case diagrams to evaluate user requirements. Key features include Mission Management and Validation for supervisors; Supervisor Management, Point Management, and Point Redemption Approval for administrators; and Login, Registration, Point Viewing, Mission Viewing, Task Submission, Point Redemption, History, and Point Reset for students. GREENPOIN has proven to enhance the efficiency of Sabbath point management, accelerate task validation, and streamline mission oversight. The application increases process transparency and encourages student participation in campus cleanliness initiatives. Future enhancements will include push notifications, an iOS version, mission-specific comments, and API integrations, such as Google Maps, to further improve functionality.
Co-Authors Cherry Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo George Morris William Tangka Joe Yuan Mambu Koapaha, Hartiny Longkutoy, Desmond Monica Dwijayanti Samidi Samidi Silitonga, Argha Orion Stenly Ibrahim Adam Stenly Richard Pungus Tangka, George Toetik Wulyatiningsih Tonny Irianto Soewignyo Triyono, Gandung Wagiu, Wayne Gilbert