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[Data Mining Menggunakan Algoritma K-means Untuk Menentukan Game Terpopuler Pada Platform Steam Dengan Rapidminer ]]>
<![CDATA[Deryl Iman Condro Baskoro]]>;
<![CDATA[I Putu Ganesa Weda Pratama]]>;
<![CDATA[Aryo Chandra Ray Hash]]>;
<![CDATA[Muhammad Fakih]]>;
<![CDATA[Muhammad Fauzan]]>;
<![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[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.]]>
<![CDATA[Analisis Sentimen Terhadap Komentar Negatif (Hate Speech) Di Twitter Dengan Algoritma K-means Clustering Menggunakan RapidMiner ]]>
<![CDATA[Nazwa Alfira]]>;
<![CDATA[Muhammad Refa Tsalits Ramdhani]]>;
<![CDATA[Muhammad Ridho Putra Budika]]>;
<![CDATA[Muhammad Virgi Santoso]]>;
<![CDATA[Nyla Zahry]]>
<![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 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.]]>
<![CDATA[Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data Kategorikal Prediksi Keputusan Bermain Tenis Berdasarkan Cuaca ]]>
<![CDATA[Feriandri Lesmana]]>;
<![CDATA[Athila Defian Rizkimu]]>;
<![CDATA[Muhamad Ridwan Nurrulloh]]>;
<![CDATA[Maulana Farras Fathurrahman]]>;
<![CDATA[Abdul Habib Hasibuan]]>;
<![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[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.]]>
<![CDATA[Pemanfaatan Data Mining untuk Segmentasi Nasabah Kartu Kredit Menggunakan Metode K-Means ]]>
<![CDATA[Intan Pramesta Nurhayati]]>;
<![CDATA[Helmayana]]>;
<![CDATA[Adis Tiani]]>;
<![CDATA[Kezia Maruenci]]>;
<![CDATA[Yuriana Sari Harahap]]>;
<![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 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.]]>
<![CDATA[Analisis Sentimen Risiko Serangan Jantung Menggunakan K-means Clustering Dengan Rapidminer ]]>
<![CDATA[Bima Aditiya]]>;
<![CDATA[Ade Kurniaty]]>;
<![CDATA[Adi Muslim]]>;
<![CDATA[Aryazeyla Rachayudiza]]>;
<![CDATA[Diana Manullang]]>;
<![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 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]]>
<![CDATA[Data Mining Menggunakan Algoritma Decision Tree untuk Menentukan Kelulusan Mahasiswa dengan RapidMiner ]]>
<![CDATA[Kevin April Akhmallahudin]]>;
<![CDATA[Divia Cahyani]]>;
<![CDATA[Dwiky Rachmatullah]]>;
<![CDATA[Dzikrully Akbar]]>;
<![CDATA[Hilmi Malik]]>;
<![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[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.]]>
<![CDATA[Segmentasi Karakteristik Kebakaran Hutan Di Taman Nasional Montesinho Menggunakan Algoritma K-means Clustering Dalam Rapidminer ]]>
<![CDATA[Vira Yuniarti]]>;
<![CDATA[Syaepul Rahmat Dani]]>;
<![CDATA[Tegar Winata]]>;
<![CDATA[Yogi Wardana Saputra]]>;
<![CDATA[Zaky Ramadhan]]>;
<![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[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]]>
<![CDATA[Pengambilan Keputusan Medis Berbasis Algoritma K-nearest Neighbor (Knn) Dalam Klasifikasi Pasien Stroke ]]>
<![CDATA[Risma Ananta Maulida]]>;
<![CDATA[Suci Anisa Aulia]]>;
<![CDATA[Ridho]]>;
<![CDATA[Satrio Dzulfahmi Yulianto]]>;
<![CDATA[Shania Clara Efendi]]>;
<![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[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.]]>
<![CDATA[Rancang Bangun Aplikasi Perangkat Lunak Absensi Digital dengan Keamanan Data untuk Politeknik Jakarta Internasional ]]>
<![CDATA[Lanny Catrin Dale]]>
<![CDATA[ Journal of Information Technology and Informatics Engineering Vol 1 No 2 (2025): Journal of Information Technology and Informatics Engineering ]]>
Publisher : <![CDATA[PT Jurnal Cendekia Indonesi ]]>
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<![CDATA[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]]>
