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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Practical Computer Science (JPCS) Jurnal Informatika Ekonomi Bisnis VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian Cahaya Pengabdian
Surojudin, Nurhadi
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Peran Literasi Teknologi Informasi dan Komunikasi dalam Pembelajaran Jarak Jauh di Masa Pandemi COVID-19 Anshori, Fajri; Surojudin, Nurhadi; Suratman, Suratman
Jurnal Pelita Teknologi Vol 18 No 2 (2023): September 2023
Publisher : Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pelitatekno.v18i2.3538

Abstract

This article discusses the importance of technological literacy in distance learning during the Covid-19 pandemic. The literature analysis involves four steps, revealing significant adaptations in the education sector. Distance learning requires technology as a link between teachers and learners, but its success depends on adequate technological literacy. This literacy involves understanding from device recognition to information management skills. In the context of distance learning, Technology Literacy and ICT facilitate learning, increase the effectiveness of PES, support communication and collaboration, and encourage the use of technology with socially responsible ethics. The article emphasizes that technology literacy is the key to the success of technology implementation in supporting education in the pandemic era.
Pengembangan Sistem Aplikasi E-Kaizen Berbasis Website Menggunakan Metode Agile (Studi Kasus PT Cataler Indonesia) Maulana, Donny; Surojudin, Nurhadi; Juluw, Sephia Maharani Niki
Journal of Practical Computer Science Vol. 4 No. 2 (2024): November 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v4i2.5233

Abstract

Technological developments are increasingly developing with technological developments, the activities carried out benefit many parties, one of which is in the manufacturing industry. PT Cataler Indonesia is still experiencing difficulties in the keizen application process because it still uses manual, namely using Microsoft Excel, data collection is in the form of paper, this is prone to data loss or document damage. Therefore, it is necessary to carry out research to develop a website-based e-kaizen application system. The aim of this research is to make it easier for employees to submit kaizen applications and process kaizen data. The method used is the Agile Method, a software development that emphasizes flexibility and responsiveness to change. In implementing the e-kaizen system, it uses the Javascript programming language and Firebase as the database. Based on the research, it can be concluded that the development of a website-based e-kaizen application system that replaces manual processes really supports the fulfillment of needs quickly, accurately and with more updates.
Sistem Aplikasi Manajemen Sekolah Menggunakan Metode Kualitatif dengan Pengembangan Sistem Watterfal Rosyid, Dimas Abdul; Surojudin, Nurhadi; Ardiatma, Dodit
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4707

Abstract

School management is very necessary in educational institutions, because school management can help in managing work at school. However, quite a few schools do not have a school management system, due to a lack of facilities and budget for school operations. Mi Ma'arif NU Salafiyah is one that requires a school management system. Data management is not well organized, and there is no school bell as a sign of time, so that there are often delays in entering class, taking a break and returning home from school when teaching and learning activities (KBM) take place. In this research the author used quantitative methods and produced a school management application system. In school management, this includes management systems such as automatic bells, manual bells, teacher absences, recording late students, teacher data, student data, subject data, class data, and web-based teacher teaching scheduling in classes. In the design, modeling or design will be used using Unified Modeling Language (UML). This management application system will later use the Waterfall method as a system development method, using programming languages such as PHP, CSS, Javascript, supported by MySQL as a database system, using the Bootstrap framework as front-end development on the website, and DomPDF is used as one of the libraries PHP to create PDF. The results of this research refer to a web-based school management system which was then implemented, then became a school management application at the Mi Ma'arif NU Salafiyah school, so that later school management could be smarter, more effective and efficient, as well as better in data processing, and can reduce errors due to human error.
Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Muhidin, Asep; Muhtajuddin Danny; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.745

Abstract

Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.
Analisis Klaster Penyebaran Berat Produk Mesin Sachet Menggunakan Metode Algoritma K-Means Ermanto, Ermanto; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.766

Abstract

This study aims to analyze the distribution patterns of sachet machine product weights using the K-Means algorithm as a clustering technique. The dataset consists of 940 entries of primary production records, each containing ten weight measurement samples per production cycle. The data underwent a cleaning process to ensure the absence of missing values, duplicates, and outliers, followed by the selection of relevant attributes (product weight samples) and transformation using Min-Max normalization to scale all variables within the 0–1 range. The clustering process was performed iteratively by updating the centroids until convergence was achieved. The evaluation results indicate that the optimal number of clusters is three (k=3) with a Silhouette Coefficient of 0.55, reflecting a good balance between intra-cluster homogeneity and inter-cluster separation. Cluster 1 represents products with relatively low weights (8.00–8.18 grams), Cluster 2 includes medium-weight products (8.19–8.34 grams), and Cluster 3 consists of high-weight products (8.36–8.98 grams). Overall, the product weights tend to be stable with low variation, although some anomalies were observed in certain machines. These findings demonstrate that the K-Means algorithm can effectively classify product weight data, providing valuable insights for quality control, product variation identification, and minimizing risks of deviation from production standards.
Penerapan Data Mining dengan Algoritma C4.5 dan K-nearest Neighbor untuk Prediksi Penjualan Bahan Bangunan Terlaris Surojudin, Nurhadi; Danny, Muhtajuddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1241

Abstract

The main problem faced by PT. Surya Kapuas Perkasa is the difficulty in accurately determining the types of building materials with the highest sales levels. Currently, stock determination still relies on manual estimates based on previous sales trends, which are prone to errors and inaccuracies. As a result, the company often faces the risk of overstocking products that are less in demand, or understocking products that are actually in high demand. This condition can impact the sales process, increase storage costs, and reduce customer satisfaction. To overcome this problem, a method is needed that can predict the sales of the best-selling building materials more objectively and based on historical data. This prediction will utilize sales data from the past three years by applying data mining classification techniques using the C4.5 algorithm and K-Nearest Neighbor (K-NN) through the RapidMiner application. With this approach, the company can accurately identify the types of building materials that are most in demand in the market, allowing for more precise and efficient stock management. Based on the research results, four types of building materials were found to be the best-selling out of a total of 16 types analyzed: Light Steel, Brick, Iron, and Cement, with a prediction accuracy rate of 87.16%.
Co-Authors . Ermanto ., Edora Abdul Ghofir Afriantoro, Irfan Andriani Anshori, Fajri Apisca, Dhea Djoko Nugroho Dodit Ardiatma Effendi, M. Makmun Effendi, Muhammad Makmun Elkin Rilvani Ermanto Fachrial Banyu Asmoro Halim Anshor, Abdul Ikhsan Romli Juluw, Sephia Maharani Niki Maulana, Donny muhidin, asep Muhtajuddin Danny Naufal, Luthfi Eka Nugroho, Agung Pradini, Purnama Sakhrial Pratama, Suria Purnama Sakhrial Putra, Fibi Eko Rosyid, Dimas Abdul Saiful Muktiali Siswandi, Arif Suratman Suratman Suryadi Suryadi, Dikky Turmud Zy, Ahmad Turmudi Zy, Ahmad Yoga Pratama, Evan Zy, Ahmad Turmudi