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All Journal Indonesian Journal of Data and Science Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Journal of Vocational, Informatics and Computer Education
Carti Irawan
Nusa Putra University
Computer Science & IT Decision Sciences, Operations Research & Management Education 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.  
Application of the Simple Additive Weighting (SAW) Method to Determine Work Placement Readiness of Vocational Training Institution Students (Case Study: LPK Global Partner Bridge) Arrasyid Alifyan Cahaya; Arny Lattu; Carti Irawan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.669

Abstract

Purpose – This study applies the Simple Additive Weighting (SAW) method within a Decision Support System (DSS) to assess work placement readiness of students at LPK Global Partner Bridge, a language and communication skills training institution preparing students for overseas employment, and generate structured recommendations. Methods – A quantitative descriptive approach was used with total sampling of 108 students from the 2025 cohort. Data were derived from attendance and final training scores using five criteria: Attendance, Script and Vocabulary, Conversation and Expression, Listening, and Reading. Weights were assigned through expert judgment and normalized proportionally. A threshold of 0.70, based on Indonesia’s Minimum Competency Standard (KKM), was applied. The SAW process included decision matrix construction, Max normalization, and weighted summation to obtain preference values (Vi), implemented in Microsoft Excel. Findings – Preference values ranged from 0.5061 to 0.9864, with a mean of 0.8036. A total of 92 students (85.19%) were classified as Ready, while 16 students (14.81%) were categorized as Not Ready. Research Implications – The model offers a structured and data-driven evaluation framework but is limited to quantitative criteria and excludes non-technical factors such as motivation and psychological readiness. Originality – Unlike prior SAW studies that primarily focus on ranking alternatives, this study extends the method to categorical classification of work placement readiness (Ready / Not Ready), using institutional training data. This approach enables more practical decision support for placement evaluation, providing a replicable framework for vocational training institutions.
Co-Authors Ana Fatahali Ramadhan Arny Lattu Arrasyid Alifyan Cahaya falentino sembiring Mupaat Mutia Safitri Nunik Destria Saepudin, Sudin Wisuda Jatmiko