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All Journal Indonesian Journal of Data and Science Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI)
Carti Irawan
Nusa Putra University
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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PERANCANGAN ENTERPRISEMENGGUNAKAN FRAMEWORK TOGAF PADA YAYASAN BAITUL HUDA Arny Lattu; Sudin Saepudin; Nunik Destria; Carti Irawan; Falentino Sembiring; Wisuda Jatmiko
Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Vol 4 No 2 (2022): JURNAL RISET SISTEM INFORMASI DAN TEKNOLOGI INFORMASI (JURSISTEKNI)
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/jursistekni.v4i2.133

Abstract

Yayasan Baitul Huda adalah salah satu pondok pesantren yang berada di daerah Sukabumi tepatnya di Kp.Dangdeur Kecamatan Surade, Kabupaten Sukabumi. Merupakan Yayasan yang belum sepenuhnya menerapkan dan memanfaatkan sistem dan teknologi informasi dalam bisnisnya. Baik itu aktivitas yang bersifat front maupun back. Sebagian besar masih berjalan secara manual dan yang terjadi adalah tidak adanya integrasi satu sama lain dari aktivitas bisnis tersebut. Ini sangat menyulitkan pertukaran data, pengontrol aktifitas- aktifitas tersebut karena semua dilakukan masing-masing tanpa adanya integrasi. Penelitian ini membuat sebuah perancangan enterprise architecture menggunakan framework TOGAF-ADM (Architecture Development method) untuk memberikan solusi dalam pengoptimalan pemanfaatan sistem dan teknologi informasi agar dapat diselaraskan dengan visi, misi, strategi, dan sumberdaya yang dimiliki oleh Yayasan Baitul Huda tersebut. TOGAF merupakan kerangka kerja dan metode yang bisa diterima secara luas untuk pengembangan arsitektur sebuah organisasi atau perusahaan, yang menjelaskan detail bagaimana membangun,mengelola dan mengimplementasikan EA dan sistem informasi dengan Architecture Development method(ADM).
Classification of Employee Attendance Categories Using the Gradient Boosted Trees Algorithm Mutia Safitri; Sudin Saepudin; Carti Irawan; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.301

Abstract

Employee attendance is a crucial factor in human resource management as it affects productivity and operational efficiency. However, the recording and analysis of employee attendance often encounter challenges, particularly in terms of the accuracy and effectiveness of the systems used. This study aims to develop an employee attendance classification model using the Gradient Boosted Trees algorithm to improve the accuracy of grouping attendance categories such as Present, Permission, Sick, Leave, and Absent into attendance level categories: High, Medium, and Low. The research method includes collecting employee attendance data throughout the year 2024. The model evaluation is carried out using metrics such as accuracy, precision, recall, and the confusion matrix. The results indicate that the developed model achieves an accuracy of 100.00%, with a mean precision of 100.00% and a mean recall of 100.00%.
Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services Ana Fatahali Ramadhan; Sudin Saepudin; Carti Irawan; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.316

Abstract

The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.  
Co-Authors Ana Fatahali Ramadhan Arny Lattu falentino sembiring Mupaat Mutia Safitri Nunik Destria Saepudin, Sudin Wisuda Jatmiko