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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Pendidikan Informatika dan Sains Indonesian Journal of Data and Science Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Pengabdian Kepada Masyarakat Abdi Putra Jurnal Algoritma The Indonesian Journal of Computer Science
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Universitas Nusa Putra
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Physics Public Health Other

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Perancangan Arsitektur Sistem Informasi Purchase Order Online Sayuran Korea Menggunakan Framework Zachman Dinata, Fajar Sukarsa; Sudin Saepudin; Mupaat
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4359

Abstract

Penelitian ini bertujuan untuk merancang sebuah Sistem Informasi yang sesuai untuk Ferdy's Farm, yang bergerak dalam penjualan sayuran Korea. Masalah utama yang dihadapi saat ini adalah pengolahan data pesanan sayuran yang masih dilakukan secara konvensional dan belum tersistemasi, mengakibatkan ketidakmaksimalan dalam manajemen stok dan pemesanan. Metode yang digunakan dalam penelitian ini menggunakan Model Framework Zachman, yang memberikan panduan yang terstruktur untuk memahami dan merancang sistem informasi berdasarkan perspektif bisnis dan teknologi. Melalui pendekatan ini, akan mengidentifikasi kebutuhan bisnis Ferdy's Farm, memetakan proses operasional yang ada, dan merancang infrastruktur teknologi informasi yang sesuai. Hasil yang diharapkan dari penelitian ini adalah rancangan implementasi sebuah sistem informasi yang diharapkan dapat mengatasi masalah pengolahan data pesanan sayuran, meningkatkan efisiensi dalam manajemen stok, pemesanan, dan distribusi, serta memberikan analisis kinerja yang akurat. Dengan demikian, Ferdy's Farm diharapkan dapat mencapai pertumbuhan bisnis yang lebih baik dan mengoptimalkan layanannya kepada pelanggan.
Analisis Motivasi Kinerja Pegawai Kecamatan Cibitung Menggunakan Metode Analytical Hierarchy Process (AHP) Andrean, Okta Teza; Saepudin, Sudin; Irawan, Carti; Mupaat
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2429

Abstract

This study aims to analyze the factors that influence the performance motivation of Cibitung Subdistrict employees using the Analytical Hierarchy Process (AHP) method with a total of 22 subdistrict employee respondents. The three main criteria analyzed include work environment, rewards, and leadership. Data were obtained through a paired comparison questionnaire, which was then processed using the AHP method to determine the priority weight of each criterion. The results show that leadership is the dominant factor (0.666), followed by rewards (0.601) and work environment (0.534). The Consistency Ratio (CR) value of 0.00086 indicates that the respondents' assessments are consistent. These findings are expected to serve as a basis for policy-making to improve employee performance in the environment.
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.  
Evaluasi Kematangan Manajemen Perubahan TI Menggunakan COBIT 2019 Domain BAI07 Mia Hamzani Dianasari; Sudin Saepudin; Hendri Ekasatria; Mupaat
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3090

Abstract

The website sukabumiupdate.com, as a high-traffic online media platform, routinely performs system updates, server migrations, and feature enhancements to maintain service speed and stability. These activities may pose risks of service disruption if not managed through a structured IT change management mechanism, particularly in the digital media industry, which demands real-time system availability. This study aims to evaluate the maturity level of the information technology change management process at sukabumiupdate.com using the COBIT 2019 framework, focusing on the BAI07 domain (Managed IT Change Acceptance and Transitioning), which plays a crucial role in ensuring successful system change acceptance and transition. The research adopts a quantitative descriptive approach with a case study method, involving observation, interviews, and documentation with four respondents. The assessment was conducted using a questionnaire instrument based on BAI07 activities, measured through the COBIT 2019 capability level assessment. The results indicate that the change management process has reached Capability Level 5 (Optimizing) with an achievement score of 88.46%, demonstrating that the process is consistently implemented, well-documented, and oriented toward continuous improvement. These findings address the research gap regarding the application of the BAI07 domain in the online media industry and provide both theoretical contributions to IT governance studies and practical guidance for digital media organizations in enhancing the effectiveness and efficiency of IT change management.
Analisis Sentimen Masyarakat Indonesia terhadap Pemindahan Ibu Kota Negara Indonesia pada Twitter Sri Lestari; Mupaat Mupaat; Adhitia Erfina
JUSIFO : Jurnal Sistem Informasi Vol 8 No 1 (2022): June
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v8i1.12116

Abstract

The relocation state capital of Indonesia raises various responses, especially from the Indonesian people. The discussion related to these issues is very interesting to study, how are the positive and negative sentiments of the Indonesian towards the government's decision. This study aims to analyze the sentiments of the Indonesian people regarding the relocation state capital of Indonesia, including the chosen name of Nusantara on Twitter. In this study, a comparison of 3 algorithms is used, namely the Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbor (KNN) algorithms. From this study, the results obtained are 1,141 positive comments, while negative sentiments are 591 comments. This shows that the Indonesian people have a positive opinion towards the new capital city of Indonesia. In the classification and model testing phase, 10-fold cross validation is used. From these tests, the SVM algorithm obtained an accuracy value of 85.71%, the Naïve Bayes algorithm obtained an accuracy value of 76.70%, the KNN algorithm obtained an accuracy value of 52.74%. This study shows that the SVM algorithm can work better than the Naïve Bayes algorithm and KNN. The accuracy value for the KNN algorithm obtains a low value, this is because the KNN algorithm is sensitive to features that are less relevant.
Co-Authors Aan Setiawati adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Adhitia Erfina Alex Ana Fatahali Ramadhan Andini Wangsa Putri Andrean, Okta Teza Anita Rahayu Arif Rakhman Suharso Arny Lattu Carti Irawan Carti Irawan Deudeu Sri Rahayu Dinata, Fajar Sukarsa Edwinanto Fahmi Fauzi Feri Fahmi Al Gadri Gina Syabani Yuda Hendri Ekasatria Indri Febriani Irawan, Carti Jelita Asian Marina Artiyasa Mega Putri Utami Mia Hamzani Dianasari Muchlis Ali Muhamad Natsir Muhammad Ikrar Dharda Muharrik Muslih, Muhamad Mutia Safitri Neng Sri Intan Septiani Nunik Destria Arianti Rohmat Rohmat Saepudin, Sudin Sagita Widya Sri Lestari Taufik Rido Tegar Alif Putra Tubagus Dzikril