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All Journal Humanika JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence JAMAIKA: Jurnal Abdi Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Journal of Research and Publication Innovation LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains JRIIN :Jurnal Riset Informatika dan Inovasi Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) APPA : Jurnal Pengabdian Kepada Masyarakat AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Journal of Information Technology and Informatics Engineering
Maulana Fansyuri
Universitas Pamulang
Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Transportation Other

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Prediksi dan Klasifikasi Transaksi Penjualan Terbaik Dalam Toko Bangunan Dengan Metode K-Nearest Neighbors (K-NN) Adrian Chandra Kusumah; Nandi Adi Nugroho; Genta Aldora Leopriandis; Achmad Khautsar Rizaldi; Firmansyah; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Prediksi Tingkat Kepuasan Pasien Fisioterapi Menggunakan Algoritma Naive Bayes Kaila Nazuwa; Indra Bagoes Mu’afa; Muhamad Firly; Ahmad Taher; Refo Altalario Bintang Anugrah; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Analisis Faktor Cuaca Terhadap Keputusan Bermain Badminton Menggunakan Algoritma Naive Bayes Gusti Alfian; Ageng Samudro Ndiko Laksono; Ardiansyah; Mahis Duhan; Raffa Nurprasetyo Araya; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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
Data Mining Menggunakan Algoritma K-means Untuk Menentukan Game Terpopuler Pada Platform Steam Dengan Rapidminer Deryl Iman Condro Baskoro; I Putu Ganesa Weda Pratama; Aryo Chandra Ray Hash; Muhammad Fakih; Muhammad Fauzan; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data Kategorikal Prediksi Keputusan Bermain Tenis Berdasarkan Cuaca Feriandri Lesmana; Athila Defian Rizkimu; Muhamad Ridwan Nurrulloh; Maulana Farras Fathurrahman; Abdul Habib Hasibuan; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Pemanfaatan Data Mining untuk Segmentasi Nasabah Kartu Kredit Menggunakan Metode K-Means Intan Pramesta Nurhayati; Helmayana; Adis Tiani; Kezia Maruenci; Yuriana Sari Harahap; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Analisis Sentimen Risiko Serangan Jantung Menggunakan K-means Clustering Dengan Rapidminer Bima Aditiya; Ade Kurniaty; Adi Muslim; Aryazeyla Rachayudiza; Diana Manullang; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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
Data Mining Menggunakan Algoritma Decision Tree untuk Menentukan Kelulusan Mahasiswa dengan RapidMiner Kevin April Akhmallahudin; Divia Cahyani; Dwiky Rachmatullah; Dzikrully Akbar; Hilmi Malik; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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.
Segmentasi Karakteristik Kebakaran Hutan Di Taman Nasional Montesinho Menggunakan Algoritma K-means Clustering Dalam Rapidminer Vira Yuniarti; Syaepul Rahmat Dani; Tegar Winata; Yogi Wardana Saputra; Zaky Ramadhan; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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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
Pengambilan Keputusan Medis Berbasis Algoritma K-nearest Neighbor (Knn) Dalam Klasifikasi Pasien Stroke Risma Ananta Maulida; Suci Anisa Aulia; Ridho; Satrio Dzulfahmi Yulianto; Shania Clara Efendi; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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Abstract

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.
Co-Authors Abdul habib Hasibuan Abdul Syukur Achmad Khautsar Rizaldi Ade Kurniaty Adi Muslim Adinda Fatmah Adis Tiani Adrian Chandra Kusumah Afra Anggita Salsabila Ageng Samudro Ndiko Laksono Agus Pangondian Silalahi Ahmad Farhan, Ahmad Ahmad Kahfi Djaelani Ahmad Taher Ajeng Trias M, Rizkyanti Akbar Prayudi, Lalu Alfatah, Alif Amalia Monitha Januari, Rossa Andika Arya Pratama Ardiansyah Arifin, Teguh Ariyadi Anatasia, Alfi Arjuno Wibowo, Rayhan Aryazeyla Rachayudiza Aryo Chandra Ray Hash Athila Defian Rizkimu Aulia Rahman, Verrel Aulia Ramadhan, Salsabila Azzahra, Amalia Bagus Firmansyah Bayu Pangestu Bima Aditiya Bintang Ardian Nugroho Budiman Nadapdap, Panri Dastin Ramadhani Daviqia Fadel Deanova, Ryanda Deko Triyadi DENI SETIAWAN Deryl Iman Condro Baskoro Devi Yunita Devi Yunita Devi Yunita Dian Nurul Iman Diana Manullang Diky Hernadi Dimas Aribi Dimas Setiawan Divia Cahyani Dwi Santoso, Rendi Dwiky Rachmatullah Dwitama Saputra, Farhan Dzikri Fauzi Ramdhani Dzikrully Akbar Eduard Elmansius Zebua Ekrinifda, Ardilla Eprilianto, Winky Erika Alfira Lia Fachri Ramdhani, Tyas Fajri Maulana Azzam Harahap Fatimah Az-zarro Fauzan Hazami, Ahmad Fazril Ramadhan Ferhat Aziz Feriandri Lesmana Firmansyah Fready Anggara Genta Aldora Leopriandis Gideon Triman Harefa Gusti Alfian Hanif, Abdul Helen Chandra Dewi Helmayana Hernadi, Diky Hibatullah Ferniko, Tegar hidayatullah Al Islami Hilmi Malik I Putu Ganesa Weda Pratama Ikhlas Syahidan Zai, Muhammad Ikhwanul Maghsauma Indra Bagoes Mu’afa Intan Pramesta Nurhayati Irsad Fauzan Jordi Ricaldo Kaila Nazuwa Kanisisus Heatubun, Petrus Kartika Putri, Dila Kevin April Akhmallahudin Kezia Maruenci Khoirun nisya Khoirunnisya Khoirunnisya Khoirunnisya Khoirunnisya, Khoirunnisya Kidunga, Lyra Laela S, Mutiara Lusiyanti Lu’ay Shafa Apta Hermawan Ma'mum, Sukron Mahis Duhan Marsiano, Joseph Marvella, Shera Maulana Farras Fathurrahman Meriansyah, Yuda Mikael Immanuel Christianto Moh Fiqhi Nur Hidayatulloh Mohamad Ryan Herdiyana Mohamad Ryan Syekhan Ramadan Muhamad Faisal Muhamad Firly Muhamad Ridwan Nurrulloh Muhammad Akhdan Irfan Muhammad Azriel Muhammad Azzam Pridana Muhammad Fadlan Jabbar Muhammad Fadli Juliana Putra Muhammad Fakih Muhammad Fauzan Muhammad Fiqih Muhammad Ikhwan Muhammad Rifaldi, Aldi Muhammad Rizki Rahmatullah Murni Nabila, Dhaifina Naia Natasya, Ris Nandi Adi Nugroho Nani Suningrat Nasywa Sakha Ningrum Nata Pratama, Fadhil Nice, Kristina NUR HASANAH Nur Naimah, Fatika Nurhasanah Nurhasanah Nurhasanah Nurhasanah Nurkholis Ajie Kurniawan, Muhammad Nursarah Sahirah Pramudya Wirananda, Muhammad Pratama, Arijal Pratama, Reza Putra Mulya, Ageng Raffa Nurprasetyo Araya Rafly Thabroni, Mochammad Refo Altalario Bintang Anugrah Rianto, Risky Ricky Tri Setiawan Putra Ridho Riki Baehaki Risma Ananta Maulida Rivan Saputra, Rivan Rizki Murtadho Rizki Prayogi Widartama Robby Azzukruf Routya Faizan, Alfreza Sagita Octaviani, Kezia Saputra, Saldy Satrio Dzulfahmi Yulianto sesilawati Shahrudin Shania Clara Efendi Shelvi Eka T Sheny Aprilia Ningsih Sherlvi Eka Tassia Silviana, Fijriani Siswirawan, Andhika Prasetyo Sopyan Hidayat Suci Anisa Aulia Sudarno Sudarno Susanna Dwi Yulianti Kusuma Syaepul Rahmat Dani Tassia, Shelvi Eka Tegar Winata Teguh Riyanto Tipalahi, Ramdan Tri Mustakim, Raka Ulfa Valentino Rattu, Samuel Vira Yuniarti Wafiqah Nur Azizah Wahyu Nur Pambuko Wulandari Ega M, Nadya Yaqumi, Zesi Yehezkiel Yogi Wardana Saputra Yulianti K, Susanna Dwi Yuriana Sari Harahap Zaky Ramadhan