Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) East Asian Journal of Multidisciplinary Research (EAJMR) Malcom: Indonesian Journal of Machine Learning and Computer Science
Ahmad Fahrudi Setiawan
Program Magister Fakultas Teknik Universitas Brawijaya
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Penerapan Algoritma Apriori dalam Menentukan Rekomendasi Paket Produk: Implementation of Apriori Algorithm for Product Bundle Recommendations Hibnastiar, Nur Aria; Setiawan, Ahmad Fahrudi; Susanto, Eko Heri
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1782

Abstract

Toko Surya Elektrik Turen di Kabupaten Malang mengalami kesulitan untuk menentukan paket produk yang biasa dibeli oleh konsumen. Untuk menyelesaikan masalah ini, penelitian ini menggunakan Apriori untuk mengidentifikasi pola hubungan antara produk dan data transaksi. Algoritma ini terbukti efektif dalam menemukan produk yang banyak dibeli bersama, tetapi tidak dapat diaplikasikan secara langsung untuk merekomendasikan paket produk karena terdapat produk yang direkomendasikan berulang kali. Oleh karena itu, untuk menghasilkan rekomendasi yang lebih relevan, penelitian ini menambahkan parameter kategori untuk membatasi pola hubungan yang sama atau berulang. Hasil penelitian menunjukkan Peningkatan nilai parameter minimum support dan minimum confidence berpengaruh signifikan terhadap aturan asosiasi yang terbentuk. Terdapat penurunan jumlah rekomendasi sebesar 80% dari pengujian minimum support 0.06 dan minimum confidence 0.6 dengan minimum support 0.1 dan minimum confidence 0.8. tetapi mengalami peningkatan nilai rata rata support sebesar 35% dan peningkatan rata rata nilai confidence sebesar 32%. Jumlah rekomendasi yang dibuat berkurang, tetapi kualitas asosiasi yang terbentuk meningkat. Selain itu penambahan parameter kategori pada algoritma Apriori mempengaruhi jumlah rekomendasi yang dihasilkan. Dimana kombinasi 4-itemset dengan kategori Lampu, Kabel, Fitting dan Stop Kontak menghasilkan 1 rekomendasi yang cocok dengan rata rata support sebesar 0.056 dan rata rata confidence 0.529.
Aspects of Legal Change and Social Change Nugroho, Ageng; Setiawan, Ahmad Fahrudi; Adriyanto, Adriyanto; Mutaqin, A. Imam Al
East Asian Journal of Multidisciplinary Research Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/eajmr.v4i1.13254

Abstract

Social life requires law as a tool to create harmony, resolve conflicts, and control social behavior. Law also acts as a social engineering tool that can direct societal change towards a better, fairer, and more prosperous life. However, law often faces challenges in responding to dynamic social changes due to developments in technology, economy, and social structure. This study aims to analyze the relationship between law and social change and the aspects that influence legal change. By using normative legal research methods, the results of the study show that legal change and social change are closely related. Law must be responsive to demands for change in society in order to remain relevant in carrying out its function as a social controller and a tool for change.
Peramalan Curah Hujan Menggunakan Metode Holt-Winters Exponential Smoothing Putra, Dzulfidho Wijianto; Setiawan, Ahmad Fahrudi; Vendyansyah, Nurlaily
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8820

Abstract

Rainfall is a crucial climatological parameter for agriculture, tourism, and water resource management. Its seasonal and fluctuating nature requires accurate forecasting methods to capture historical patterns. This study forecasts monthly rainfall using data from Ngaglik, Temas, and Tinjumoyo stations between January 2021 and December 2024, totaling 48 observations. The Holt–Winters Exponential Smoothing Additive method was chosen due to stable annual seasonal patterns. Model accuracy was assessed with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show varying optimal parameters across stations. Ngaglik achieved the best performance with α = 0, β = 0, γ = 0.81, yielding MAE 64.39 mm and RMSE 90.84 mm. Temas recorded MAE 69.25 mm and RMSE 102.19 mm with γ = 0.78, while Tinjumoyo produced MAE 73.95 mm and RMSE 109.42 mm with γ = 0.56. This study highlights the effectiveness of Holt–Winters Additive forecasting and provides accuracy evaluations to support data-driven decisions in rainfall-dependent sectors.
Co-Authors Adriyanto Adriyanto, Adriyanto Hibnastiar, Nur Aria Mutaqin, A. Imam Al Nugroho, Ageng Nurlaily Vendyansyah Putra, Dzulfidho Wijianto susanto, eko heri