Journal of Information Technology and Informatics Engineering
Journal of Information Technology and Informatics Engineering (JITIE) is a peer-reviewed scientific journal focusing on theoretical foundations, engineering methodologies, and applied research in the fields of Information Technology and Informatics Engineering. The journal serves as a scholarly forum for the dissemination of original research, experimental studies, and technological innovations that contribute to the advancement of computing and intelligent systems. The journal is intended for academics, researchers, engineers, and professionals, and aims to support the development of science, engineering practices, and technological solutions relevant to industry, government, and society. Journal of Information Technology and Informatics Engineering (JITIE) is published four times a year (Februari, June and October and follows an open access publishing model to ensure global accessibility. The scope of JITIE includes, but is not limited to, the following areas: Computer Science and Informatics Engineering Software Engineering and Information Systems Development Artificial Intelligence and Intelligent Computing Machine Learning, Deep Learning, and Neural Networks Data Science, Big Data Analytics, and Data Engineering Computer Networks, Distributed Systems, and Network Security Cybersecurity, Cryptography, and Information Assurance Cloud Computing, Edge Computing, and High-Performance Computing Internet of Things (IoT), Embedded Systems, and Smart Devices Human–Computer Interaction (HCI) and User Experience Engineering Computer Vision, Image Processing, and Pattern Recognition Natural Language Processing and Text Analytics Database Systems, Information Retrieval, and Knowledge Management Decision Support Systems and Intelligent Information Systems Digital Signal Processing and Computational Modeling Computational Intelligence and Optimization Techniques Information Technology Infrastructure and Architecture Applied Informatics in Industry, Health, Education, and Government Smart Systems, Digital Innovation, and Industry 4.0 Other emerging topics in Information Technology and Informatics Engineering Note: Submitted manuscripts must present original research, be properly cited, and must not have been previously published or simultaneously submitted to other journals.
Articles
19 Documents
<![CDATA[Implementasi Algoritma K-means Clustering Data Penjualan Pada Warung Sembako Isan Menggunakan Rapidminer ]]>
<![CDATA[Muhammad Azriel]]>;
<![CDATA[Daviqia Fadel]]>;
<![CDATA[Fajri Maulana Azzam Harahap]]>;
<![CDATA[Irsad Fauzan]]>;
<![CDATA[Muhammad Fadlan Jabbar]]>;
<![CDATA[Maulana Fansyuri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[This study aims to apply the K-Means Clustering algorithm with the help of RapidMiner software on sales data at Warung Sembako Isan. In managing small businesses such as grocery stores, processing sales data manually often faces various challenges, such as errors in recording and difficulties in identifying sales trends. Therefore, data mining techniques, especially clustering methods, are used to categorize products based on their sales capabilities. This process is carried out using RapidMiner, which allows analysis without the need for programming through a visual interface. The data were analyzed using the K-Means algorithm with parameter k = 3, which produces three categories: products with high potential, medium potential, and low potential. The results of this clustering make it easier for shop owners to understand product performance, develop storage strategies, and plan more efficient promotions. This study shows that the use of simple technology can improve operational efficiency and assist MSMEs in data-based decision making.]]>
<![CDATA[Penerapan Algoritma K-Nearest Neighbor Menggunakan Rapidminer Pada Kepuasan Hidup Pekerja Commuter di Indonesia ]]>
<![CDATA[Muhammad Fadli Juliana Putra]]>;
<![CDATA[Bayu Pangestu]]>;
<![CDATA[Sopyan Hidayat]]>;
<![CDATA[Bintang Ardian Nugroho]]>;
<![CDATA[Dastin Ramadhani]]>;
<![CDATA[Maulana Fansyuri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[The level of life satisfaction of commuter workers in Indonesia is classified using the K-Nearest Neighbor (K-NN) algorithm using the RapidMiner application. This study aims to provide a better understanding of the social and economic conditions of workers who have to travel long distances every day. To collect data, a questionnaire covering various information such as income, number of dependents, location of residence, travel time, and level of life satisfaction was sent. Before being entered into the model, the data is then processed through a cleaning stage, normalizing numeric values, and dividing into test data and training data. One of the reasons for RapidMiner is its visual interface, which allows users to create classification models without writing programming code. The test results show that the K-NN algorithm can accurately classify the level of life satisfaction of commuter workers. Model performance is greatly influenced by the selected variables, namely the K value, and data quality. This study is expected to help related parties, this approach is considered effective in helping data-based decision making. ]]>
<![CDATA[Prediksi dan Klasifikasi Transaksi Penjualan Terbaik Dalam Toko Bangunan Dengan Metode K-Nearest Neighbors (K-NN) ]]>
<![CDATA[Adrian Chandra Kusumah]]>;
<![CDATA[Nandi Adi Nugroho]]>;
<![CDATA[Genta Aldora Leopriandis]]>;
<![CDATA[Achmad Khautsar Rizaldi]]>;
<![CDATA[Firmansyah]]>;
<![CDATA[Maulana Fansyuri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[This study uses the K-Nearest Neighbors (K-NN) method to predict and classify the best-selling products in a hardware store. With the current development of information technology, sales trend analysis and prediction have become an important part of the business decision-making process. The popular K-NN classification algorithm is used to analyze sales data from a public dataset to determine which products are most in demand by consumers. The process of data collection, selection, preprocessing, transformation, data mining, and evaluation of results are all part of the Knowledge Discovery in Database (KDD) stages. The analysis results show that products in the “active” category sell more than products in the “passive” category. Out of the total data, 56 were successfully categorized as active data, and the remaining 29 were categorized as passive data. This study is expected to provide deeper insights into consumer behavior and assist building material store management in making better decisions using the data they possess. This is anticipated to enhance the company's competitiveness and improve operational efficiency.]]>
<![CDATA[Penerapan Metode Clustering K-Means Menggunakan RapidMiner untuk Klasifikasi Prestasi Siswa di Sekolah Swasta ]]>
<![CDATA[Eko Andri Wibowo]]>;
<![CDATA[Ririn Aryanti]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[This study discusses the application of the K-Means Clustering algorithm in grouping the level of academic achievement of students in private schools with the help of RapidMiner software. The data analyzed include assignment scores, midterm exams, final exams, and attendance. The K-Means algorithm was chosen because of its ability to group unlabeled numeric data and recognize hidden patterns in the dataset. The analysis was carried out on data from 5,000 students obtained through the Kaggle platform. The clustering results produced two main groups, namely students with high academic achievement and students with lower achievements. This process allows schools to understand the characteristics of each group of students and develop more effective coaching strategies and educational policies. The use of RapidMiner has been proven to help the data analysis process efficiently and intuitively, without the need for advanced programming skills.]]>
<![CDATA[Pengembangan Sistem Informasi Manajemen Aset Kantor Berbasis Cloud Computing di Universitas Pamulang untuk Efisiensi Operasional ]]>
<![CDATA[Femilda Aprilia]]>;
<![CDATA[Veivi Novi Lestari]]>;
<![CDATA[Junjung Adi Prasetyo Lenggogeni]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[This study aims to develop a cloud computing-based canteen asset management information system at Pamulang University to improve operational efficiency and optimize asset management. The rapid development of information technology drives digital transformation in various sectors, including campus facilities such as canteens, which demand high efficiency and accuracy in asset management. Using the System Development Life Cycle (SDLC) approach through needs analysis, system design, conceptual implementation, and theoretical evaluation based on literature studies, this study identifies problems such as manual recording, difficulty in real-time monitoring, and lack of automated reporting. The proposed system utilizes a Software as a Service (SaaS) architecture for high scalability and multi-platform access, providing digital asset catalog features, inventory management, real-time asset tracking, and analytical dashboards to monitor canteen operational performance. The implementation of cloud computing is expected to improve data security, reduce IT infrastructure costs, and facilitate access to asset information from anywhere. The concept of this system is designed to support various user roles, such as canteen staff and university management, and provides a framework that can be applied to other similar facilities.]]>
<![CDATA[Implementasi Algoritma K-nearest Neighbor (KNN) Menggunakan Rapid Miner Untuk Prediksi Penyakit Diabetes Berdasarkan Dataset Pima Indian ]]>
<![CDATA[Tetta Thirza Herdyawan]]>;
<![CDATA[Dimas Cahyo Saputra]]>;
<![CDATA[Gabriel Carol Aldosion]]>;
<![CDATA[Salsha Sabilla Nurhidayat]]>;
<![CDATA[Sukrinah]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[The objective of this research is to use the publicly accessible Pima Indian dataset to use the K-Nearest Neighbor (KNN) algorithm for diabetes prediction. A straightforward yet powerful classification technique, the KNN method is particularly useful for processing medical data. RapidMiner software was utilized for this study's analysis method, which included data pre-processing, training and test data separation, and classification model validation. Numerous health indicators, including age, blood pressure, body mass index, and glucose levels, are included in the Pima Indian dataset and are utilized as predictive features. The test results demonstrate that the KNN algorithm can categorize patients with or without diabetes with a reasonably high degree of accuracy. Accuracy, precision, recall, and confusion matrix metrics were used to assess the model's performance. As a result, using KNN to this dataset may be a way to help the decision support system for diabetes early diagnosis.]]>
<![CDATA[Prediksi Diabetes Berdasarkan Faktor Medis Pasien Menggunakan Algoritma Decision Tree ]]>
<![CDATA[Ahmad Reza]]>;
<![CDATA[Amar Naufal Al-kharits]]>;
<![CDATA[Muhamad Bustomi]]>;
<![CDATA[Nazar Maulana]]>;
<![CDATA[Taupik Abdul Rahman]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[Early detection of diabetes risk is crucial to prevent severe complications. This study develops a predictive model for diabetes using the Decision Tree algorithm based on patient medical data. The dataset consists of 768 records with eight health-related attributes, of which 99 labeled instances are used to train the model. The process includes data cleaning, target attribute assignment, and model construction using RapidMiner. Results indicate that variables such as age and glucose levels significantly influence diabetes classification. Although the initial findings show promising potential, further validation with larger and more balanced datasets is needed to improve the model's accuracy.]]>
<![CDATA[Prediksi Tingkat Kepuasan Pasien Fisioterapi Menggunakan Algoritma Naive Bayes ]]>
<![CDATA[Kaila Nazuwa]]>;
<![CDATA[Indra Bagoes Mu’afa]]>;
<![CDATA[Muhamad Firly]]>;
<![CDATA[Ahmad Taher]]>;
<![CDATA[Refo Altalario Bintang Anugrah]]>;
<![CDATA[Maulana Fansyuri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[This study aims to predict patient satisfaction levels in physiotherapy services using the Naive Bayes algorithm. Patient satisfaction is a key indicator of healthcare service quality, and this prediction is based on attributes such as age, gender, session duration, and therapist expertise. The dataset, consisting of 31 entries, was analyzed using RapidMiner software. The classification process applied the Naive Bayes model, known for its simplicity, computational efficiency, and strong performance even with limited data. Evaluation results showed an accuracy rate of 90%, with balanced precision and recall between the "satisfied" and "dissatisfied" categories. These find-ings demonstrate that data mining techniques can serve as valuable tools to support continuous improvement in physiotherapy service quality.]]>
<![CDATA[Analisis Faktor Cuaca Terhadap Keputusan Bermain Badminton Menggunakan Algoritma Naive Bayes ]]>
<![CDATA[Gusti Alfian]]>;
<![CDATA[Ageng Samudro Ndiko Laksono]]>;
<![CDATA[Ardiansyah]]>;
<![CDATA[Mahis Duhan]]>;
<![CDATA[Raffa Nurprasetyo Araya]]>;
<![CDATA[Maulana Fansyuri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[By utilizing the Naive Bayes algorithm as a classification method, this study investigates how weather factors influence a person's decision to play badminton. The Badminton dataset, including attributes such as weather conditions, air temperature, humidity levels, and wind conditions, was collected and processed using RapidMiner software. The preprocessing stage involved data cleaning and transforming the attributes to be suitable for analysis. To predict the decision to play badminton based on weather conditions, the Naive Bayes algorithm was chosen due to its capability to compute class probabilities easily and effectively.This study found that weather factors significantly influence a player's decision to play badminton, and the Naive Bayes model demonstrated the ability to make reasonably accurate predictions. In conclusion, the Naive Bayes algorithm can be effectively used to predict players' decisions in playing badminton]]>
<![CDATA[Klasterisasi Mahasiswa Berdasarkan Performa Akademik Menggunakan Algoritma K-Means pada RapidMiner: Studi Kasus dengan Dataset Student Academic Performance ]]>
<![CDATA[Siti Khodijah]]>;
<![CDATA[Athaya Rima Hariyanto]]>;
<![CDATA[Berliani Salsabiilah]]>;
<![CDATA[Winona Septi Aulia]]>;
<![CDATA[Maulana Fanyusri]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE) ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[One of the primary indicators used to convey the effectiveness of the learning process and to create more efficient teaching methods is student academic performance. This study uses the RapidMiner application to use the K-Means Clustering method in order to group students according to their academic performance. The synthetic data, which includes details about student involvement, attendance rates, and academic grades, is taken from the Kaggle platform. This study was carried out in a number of steps, including cluster quality assessment, attribute selection, algorithm application, and data pre-processing.Based on the results, three student groups with characteristics of high, medium, and low academic performance were examined. The Davies-Bouldin Index examination indicated that the clustering results were optimal. These findings are expected to serve as a guide for educational institutions to develop more appropriate and successful teaching strategies.]]>
