Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Kursor Jurnal Sistem Informasi dan Informatika (SIMIKA)
Mohammad Idhom
University of Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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

Found 3 Documents
Search

 1 
MATURITY LEVEL OF INFORMATION TECHNOLOGY USING COBIT FRAMEWORK 4.1 (CASE STUDY: CLOUD COMPUTING SERVICE PROVIDER) Mohammad Idhom; Ronggo Alit; Akhmad Fauzi
Jurnal Ilmiah Kursor Vol 9 No 2 (2017)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i2.146

Abstract

This research is focused on the analysis of cloud computing service provision by both division IT Project and IT Support Division at PT. Eikon Technology in Surabaya to improve business process in this company, by examining the extent of cloud services provided by the company to customers through the two divisions by applying information technology. The total detailed control objective used was 38 detail controls performed by the IT Project division and 40 control details performed by the IT Support division. The result of this research is analysis of current and expected maturity level from sub domain PO4, PO7, DS1, DS5, and DS7, got the average total maturity level is 2.7 and the expected maturity level is 3.76. Recommendations and conclusions are expected to develop the process of information technology governance in the form of providing cloud computing services to better and efficient in supporting the business in PT Eikon Technology, Surabaya
IDENTIFIKASI POLA DAN KARAKTERISTIK IKM DI KOTA SURABAYA MENGGUNAKAN METODE BIRCH DENGAN EVALUASI SILHOUETTE SCORE Fitri Indah Sari; Mohammad Idhom; Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4001

Abstract

Small and Medium Industries (SMIs) play a crucial role in regional economic development, particularly in metropolitan areas such as Surabaya, Indonesia. Nevertheless, the high heterogeneity of SMI characteristics poses challenges for designing effective and targeted development policies. This study proposes a data-driven clustering framework to identify patterns and characteristics of SMIs in Surabaya by employing the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm. The novelty of this research lies in the application of BIRCH for large-scale SMI data clustering combined with systematic parameter evaluation using the Silhouette Score to ensure cluster quality and stability. BIRCH was chosen for its efficiency in handling large and heterogeneous datasets through hierarchical summarization, addressing limitations of methods such as K-Means that require predefined cluster numbers and are sensitive to initial centroids. The dataset was preprocessed through missing value handling, data type transformation, categorical label encoding, and numerical standardization. After preprocessing, 31,472 records with six variables were analyzed. Various combinations of threshold and branching factor parameters were evaluated using the Silhouette Score to determine the optimal configuration. The best result was obtained with a threshold of 0.7 and a branching factor of 50, achieving a Silhouette Score of 0.743 and forming five distinct clusters. The resulting clusters exhibit clear structural patterns in terms of land area, initial capital, labor force, business scale, company type, and risk level. The findings demonstrate that BIRCH effectively produces well-separated and interpretable clusters, providing a robust analytical basis for evidence-based policymaking in SMI development.
ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN TOKO BANGUNAN Muhimmatul Arofah; Mohammad Idhom; Kartika Maulida Hindrayani
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/v9bepx08

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economy but often face challenges in inventory control and understanding consumer behavior. This study aims to compare the performance of the Apriori and FP-Growth algorithms in identifying consumer purchasing patterns from 7,778 transaction records at UD. Kurnia, a building material store, between August 2023 and July 2024. Unlike previous research that relied only on support and confidence metrics, this study applies the lift metric, which measures the strength of item associations, to minimize misleading rules. The algorithms were tested under 15 combinations of minimum support and lift threshold values. Results show that both algorithms generate the same association rules, but Apriori is significantly faster. At a minimum support of 0.0005 and a lift threshold of 1.5, Apriori completes processing in 3.23 seconds, while FP-Growth takes 21.81 seconds. With these findings, store owners can make more precise inventory decisions and implement data-driven cross-selling strategies, such as offering semen gresik when colt pasir is purchased.
Co-Authors Akhmad Fauzi Fitri Indah Sari Kartika Maulida Hindrayani Muhimmatul Arofah Ronggo Alit Trimono