Journal of Information Technology and Informatics Engineering
Journal title : Journal of Information Technology and Informatics Engineering Initials : JITIE Frequency : 4 issues per Year P-ISSN : XXXX-XXXX E-ISSN : 3123-7827 Editor in Chief : Ninuk Wiliani, S.Kom., M.Kom.., PhD. Publisher : PT Jurnal Cendekia Indonesia Akreditasi : - Journal of Information Technology and Informatics Engineering (JITIE) is published by PT Jurnal Cendekia Indonesia as a platform to support academics, researchers, and practitioners in disseminating their research findings in the fields of Information Technology and Informatics Engineering. JITIE is a peer-reviewed journal dedicated to publishing high-quality research articles relevant to current developments in information technology, software engineering, data science, system development, and informatics applications. All publications in JITIE are open access, allowing articles to be freely accessible online without subscription fees. JITIE is a national journal with e-ISSN: 3123-7827, and publication fees are applied during the submission and review process. The journal is published six times a year, in the months of February, June and October. To maintain the integrity and quality of manuscripts, JITIE uses Turnitin for plagiarism checks, Mendeley for reference management, and is supported by Crossref (DOI) for the identification and indexing of scholarly works.
Creative Commons Attribution 4.0 International (CC BY 4.0).
Design and Development of a Digital Attendance Software Application with Data Security for Jakarta International Polytechnic
This research was conducted by researchers to utilize design according to technological developments. Design is a process that defines something that will be done using various techniques and includes a description of the architecture and component details as well as the limitations that will be experienced in the process [1]. Design is a series of procedures to translate the results of analysis of a system into a programming language to describe in detail how the system components are implemented [2]. Data security is an effort to protect information from unauthorized access, use, disclosure, change, or destruction (M. Hadiwintata, 2020). According to Kristanto (2020/3) a Database Management System (DBMS) contains a collection of interrelated data and a set of programs to access that data. A database is a collection of interrelated files, the relationship is usually indicated by the key of each existing file (Kristanto, 2020). MySQL is a software or SQL database management system or DBMS that is multithreaded and multiuser (Solichin, 2005). Hypertext Preposessor is an abbreviation of PHP created by Rasmus Ledof in 1994, at the beginning of PHP development it was called an abbreviation of Personal Home Page (Antonius Nugraha, 1994). This research aims to design a fingerprint software for digital attendance with data security for the Jakarta International Polytechnic to make it easier for students, admins, lecturers and the director to do digital attendance. vehicle washing service business. application with Data Security for Jakarta International Polytechnic with Data Security for Jakarta International Polytechnic
Rancang Bangun, Audio Web Recording, Internet Market, TAM, Beyoursound
Copyright (c) 2025 Lanny Catrin Dale (Author) https://creativecommons.org/licenses/by/4.0
Medical Decision Making Based on K-nearest Neighbor (Knn) Algorithm in Stroke Patient Classification
Stroke is a non-communicable disease and one of the leading causes of death and disability worldwide. Early detection of potential stroke risk is crucial to support effective prevention and management efforts. This study aims to develop a stroke risk classification system using the K-Nearest Neighbor (KNN) algorithm implemented through the RapidMiner platform. The dataset analyzed consists of 932 patient records with various medical and demographic attributes. The research process includes data preprocessing, variable transformation, normalization, and splitting the data into training and testing sets. Model evaluation shows an accuracy rate of 82.35%; however, the model has not performed well in identifying stroke cases due to data imbalance. These findings highlight the importance of addressing class imbalance in medical data and the need to consider alternative algorithms to improve detection of minority classes.
Stroke, K-Nearest Neighbor, KNN, RapidMiner, Data Mining
Computer Science; Data Science; Medical Informatics; Information Systems
Ilmu Komputer; Sains Data; Informatika Medis; Sistem Informasi
Copyright (c) 2025 Risma Ananta Maulida, Suci Anisa Aulia, Ridho, Satrio Dzulfahmi Yulianto, Shania Clara Efendi, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Forest Fire Characteristics Segmentation In Montesinho National Park Using K-means Clustering Algorithm In Rapidminer
his study aims to analyze the characteristics of forest fires using the K-Means Clustering algorithm in RapidMiner software. Forest fires are disasters that significantly impact ecosystems and human life, making data-driven analysis of their causal patterns crucial. The dataset includes critical variables such as the Fire Weather Index (FWI) system components (FFMC, DMC, DC, ISI), weather conditions (temperature, humidity, wind speed, rainfall), and spatial coordinates from the Montesinho National Park in Portugal. The research methodology involved data preprocessing, feature normalization, and the implementation of the K-Means algorithm with three clusters to classify fires based on risk levels.The analysis revealed that Cluster 1 was dominated by high-temperature and low-humidity fires (high risk), Cluster 2 was characterized by higher rainfall (low risk), and Cluster 0 exhibited large-scale fires with significant wind influence. The clustering demonstrated the effectiveness of K-Means in identifying forest fire patterns based on environmental factors, supported by a Silhouette Score of 0.62, indicating reasonably well-separated clusters.These findings provide a foundation for developing more accurate early warning systems for forest fires and support data-driven prevention and mitigation strategies
Copyright (c) 2025 Vira Yuniarti, Syaepul Rahmat Dani, Tegar Winata, yogi wardana saputra, zaky Ramadhan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Data Mining Using Decision Tree Algorithm to Determine Student Graduation with RapidMiner
Student graduation prediction is an important aspect in higher education management which is intended to project students' chances of completing their studies on schedule. Accurate prediction results can support educational institutions in formulating strategic policies to improve the quality of academic services and provide more effective interventions and provide more effective support to students at risk of experiencing delayed graduation. This study applies the Decision Tree algorithm with the help of the RapidMiner application to build a student graduation prediction model, using data such as age, graduation status, and cumulative achievement index as the main variables. The results of the analysis show that the developed model is able to achieve a prediction accuracy level of 96.57%. This finding confirms that data mining techniques have great potential in helping educational institutions identify students who need special attention in order to complete their studies on time. Therefore, the results of this study not only play a role in the development of prediction models in the academic realm, but the results of this study can also be used as an initial basis for subsequent research that focuses on graduation prediction in the higher education environment.
Copyright (c) 2025 Kevin April Akhmallahudin, Divia Cahyani, Dwiky Rachmatullah, Dzikrully Akbar, Hilmi Malik, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Heart Attack Risk Sentiment Analysis Using K-means Clustering With Rapidminer
This study discusses the analysis of patient grouping based on heart attack risk by applying the K-Means Clustering algorithm using RapidMiner software. In this modern era, patient health data is very important for early identification and prevention of serious diseases such as heart attack. To understand the patterns of patient characteristics related to this risk, a clustering process was carried out on a heart attack risk dataset obtained from Kaggle, consisting of 8,763 patient data entries. The research stages began with data collection, data preprocessing, and the implementation of the K-Means algorithm with a certain number of clusters (e.g., three), which will group patients based on their risk profiles (e.g., low, moderate, and high risk). The research results are expected to show the distribution of patient data into these clusters, for example, how many patients fall into the high, moderate, and low-risk clusters. With these results, the K-Means algorithm proves effective in identifying groups of patients with similar characteristics, as well as providing useful insights for early detection and intervention of heart attack risk automatically. This research is expected to serve as a basis for the development of a more accurate and adaptive risk identification system for the dynamics of health data
Copyright (c) 2025 Bima Aditiya, Ade Kurniaty, Adi Muslim, Aryazeyla Rachayudiza, Diana Manullang, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Utilization of Data Mining for Credit Card Customer Segmentation Using the K-Means Method
This study aims to cluster credit card users based on demographic information and card usage behavior using K-Means clustering algorithms. The BankChurners.xlx dataset, which contains over 10,000 customer data, was analyzed using RapidMiner software. The analysis process includes data preprocessing steps, including normalization, attribute selection, and categorical data encoding. The K-Means algorithm is then used to group customers into two clusters. The results of this clustering show the existence of two main segments with different characteristics, where the majority of customers fall into one larger group. Cluster quality assessment using the Davies-Bouldin index shows satisfactory separation results. This result can serve as a basis for strategic decision-making, particularly in designing marketing plans and developing services that are more precise and suited to the characteristics of each customer segment.
Copyright (c) 2025 Intan Pramesta Nurhayati, Helmayana, Adis Tiani, Kezia Maruenci, Yuriana Sari Harahap, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Naive Bayes Algorithm in Categorical Data Classification Prediction of Tennis Playing Decisions Based on Weather
Decision-making based on weather factors is often subjective and inconsistent. This research applies data mining classification methods to build an objective predictive model regarding the decision to play tennis based on weather conditions. The objective of this study is to analyze the performance of the Naive Bayes algorithm in predicting this decision. The methodology involves applying the Naive Bayes algorithm to the classic "Play Tennis" dataset, which consists of 14 instances with four categorical predictor attributes: outlook, temperature, humidity, and wind. The modeling and evaluation process was conducted visually using the Altair AI Studio (RapidMiner) platform, employing the cross-validation technique to test model stability. The test results show an average model accuracy of 57.14%. A deeper analysis of the confusion matrix reveals that the model has a strong bias towards predicting the 'Yes' class, yet is very weak in identifying the 'No' class (20.00% recall). Specifically, the model exhibits a high number of False Positive errors, where 4 out of 5 'No' cases were misclassified. In conclusion, the Naive Bayes model in its current configuration is not yet fully reliable for practical application due to its biased performance. This study recommends further optimization, such as applying data balancing techniques or using more complex alternative algorithms, to significantly improve predictive performance.
Copyright (c) 2025 Feriandri Lesmana, Athila Defian Rizkimu, Muhamad Ridwan Nurrulloh, Maulana Farras Fathurrahman, Abdul Habib Hasibuan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Negative Comments (Hate Speech) on Twitter with K-means Clustering Algorithm Using RapidMiner
This study discusses sentiment analysis of negative comments (hate speech) on the social media platform Twitter by applying the K-Means Clustering algorithm using RapidMiner software. In today’s digital era, Twitter has become one of the main platforms for the open dissemination of public opinion, including negative comments that may lead to hate speech. To understand the sentiment patterns in these comments, clustering was carried out on a dataset consisting of 27,325 tweets obtained from Kaggle. The research stages included data collection, preprocessing, and the implementation of the K-Means algorithm with three clusters, categorizing the comments into negative, neutral, and positive groups. The results showed that most of the comments fell into the negative cluster, comprising 14,032 entries, followed by 9,924 neutral entries and 3,369 positive entries. These findings demonstrate that the K-Means algorithm is effective in identifying the distribution of hate speech on social media and provides valuable insights for mitigating and monitoring negative content automatically. This study is expected to serve as a foundation for developing more accurate and adaptive sentiment analysis systems in response to the dynamics of digital communication.
Copyright (c) 2025 Nazwa Alfira, Muhammad Refa Tsalits Ramdhani, Muhammad Ridho Putra Budika, Muhammad Virgi Santoso, Nyla Zahry (Author) https://creativecommons.org/licenses/by/4.0
Data Mining Using K-means Algorithm To Determine The Most Popular Games On The Steam Platform With Rapid Miner
With the number of games increasing every year, it is a challenge to determine which games are the most popular on the Steam platform. This study uses the K-Means clustering algorithm in RapidMiner to group games based on their popularity. Ratings and estimated number of game downloads are the variables used in this study. Data were collected from the top game sales dataset on the Steam platform. Clustering produces two clusters: less dan most populer, indicate the level of game popularity. This study can help game developers and publishers understand what features users are most interested in in a game.
Copyright (c) 2025 Deryl Iman Condro Baskoro, I Putu Ganesa Weda Pratama, Aryo Chandra Ray Hash, Muhammad Fakih, Muhammad Fauzan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Student Clustering Based on Academic Performance Using the K-Means Algorithm in RapidMiner: A Case Study with the Student Academic Performance Dataset
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.
Copyright (c) 2025 Siti Khodijah, Athaya Rima Hariyanto, Berliani Salsabiilah, Winona Septi Aulia, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Weather Factor Analysis on Badminton Playing Decisions Using Naive Bayes Algorithm
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
Copyright (c) 2025 Gusti Alfian, Ageng Samudro Ndiko Laksono, Ardiansyah, Mahis Duhan, Raffa Nurprasetyo Araya, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Prediction of Physiotherapy Patient Satisfaction Level Using Naive Bayes Algorithm
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.
Copyright (c) 2025 Kaila Nazuwa Kaila (Author) https://creativecommons.org/licenses/by/4.0
Diabetes Prediction Based on Patient Medical Factors Using Decision Tree Algorithm
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.
Copyright (c) 2025 Ahmad Reza, Amar Naufal Al-kharits, Muhamad Bustomi, Nazar Maulana, Taupik Abdul Rahman (Author) https://creativecommons.org/licenses/by/4.0
Implementation Of K-nearest Neighbor (KNN) Algorithm Using Rapid Miner For Diabetes Disease Prediction Based On Indian Pima Dataset
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.
Copyright (c) 2025 Tetta Thirza Herdyawan, Dimas Cahyo Saputra, Gabriel Carol Aldosion, Salsha Sabilla Nurhidayat, Sukrinah (Author) https://creativecommons.org/licenses/by/4.0
Development of Cloud Computing Based Office Asset Management Information System at Pamulang University for Operational Efficiency
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.
Copyright (c) 2025 Femilda Aprilia, Veivi Novi lestari, Junjung Adi Prasetyo Lenggogeni (Author) https://creativecommons.org/licenses/by/4.0
Application of K-Means Clustering Method Using RapidMiner for Student Achievement Classification in Private Schools
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.
Copyright (c) 2025 Eko Andri Wibowo, Ririn Aryanti (Author) https://creativecommons.org/licenses/by/4.0
Prediction and Classification of Best Sales Transactions in Building Supply Stores Using the K-Nearest Neighbors (K-NN) Method
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.
Copyright (c) 2025 Adrian Chandra Kusumah, Nandi Adi Nugroho, Genta Aldora Leopriandis, Achmad Khautsar Rizaldi, Firmansyah, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Algoritma K-means Clustering Data Penjualan Pada Warung Sembako Isan Menggunakan Rapidminer
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.
Copyright (c) 2025 Muhammad Azriel, Daviqia Fadel, Fajri Maulana Azzam Harahap, Irsad Fauzan, Muhammad Fadlan Jabbar, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Application of K-Nearest Neighbor Algorithm Using Rapidminer on Life Satisfaction of Commuter Workers in Indonesia
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.
Copyright (c) 2025 Muhammad Fadli Juliana Putra, Bayu Pangestu, Sopyan Hidayat, Bintang Ardian Nugroho, Dastin Ramadhani, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0