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All Journal Jurnal Teknologi Industri Pertanian Jurnal Masyarakat Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ekonomi, Manajemen Akuntansi dan Perpajakan (Jemap) J I M P - Jurnal Informatika Merdeka Pasuruan Applied Information System and Management Jurnal Teknoinfo JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi Energi & Kelistrikan Indonesian Journal of Applied Informatics Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Literasiologi CSRID (Computer Science Research and Its Development Journal) Antivirus : Jurnal Ilmiah Teknik Informatika Industri Inovatif : Jurnal Teknik Industri Jurnal Ilmu Komputer dan Bisnis Aisyah Journal of Informatics and Electrical Engineering Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal of Innovation and Future Technology (IFTECH) TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL AKTUAL AKUNTANSI KEUANGAN BISNIS TERAPAN (AKUNBISNIS) Journal of Intelligent Computing and Health Informatics (JICHI) Teknika Jurnal Sistem Informasi Journal of Industrial and Engineering System Jurnal Sains Indonesia Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Journal Software, Hardware and Information Technology Jurnal Media Informatika JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Mandiri IT J-Intech (Journal of Information and Technology) Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains dan Teknologi Jurnal Sains Informatika Terapan (JSIT) Paradigma Indonesian Journal Computer Science (ijcs) Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Jurnal Komputer dan Teknologi (JUKOMTEK) CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Information Technology, Software Engineering and Computer Science Jurnal Ilmiah Sistem Informasi Bulletin of Artificial Intelligence Riau Jurnal Teknik Informatika International Journal of Education, Vocational and Social Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Journal of Information Technology Jurnal Teknoinfo Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Teknik Informatika dan Teknologi Informasi Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Sumanto Sumanto
Universitas Bina Sarana Informatika
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Penerapan Algoritma K-Means untuk Pengelompokan Kerentanan Wilayah terhadap Kasus DBD di Kota Bandung Zahwa Asfa Rabbani; Alya Avisa; Paulus Paulus; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6239

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through bites of the Aedes aegypti mosquito. This illness remains a major public health concern in Indonesia, particularly in urban regions like Bandung City, where population density and environmental variations contribute to disease transmission. The purpose of this study is to apply the K-Means Clustering algorithm to group areas based on their level of vulnerability to DHF spread in Bandung City. The dataset, obtained from the Bandung Open Data portal covering the 2016–2024 period, was processed using the Orange Data Mining application. The analysis began with data preprocessing, which included cleaning, attribute selection, and normalization to ensure optimal clustering performance. The data were then grouped into three primary clusters representing high, medium, and low risk zones. The findings indicate that the K-Means algorithm effectively detects the spatial and temporal distribution of DHF cases and presents it through scatter plot visualizations that illustrate yearly patterns. High-risk regions are typically characterized by dense population, poor sanitation, and limited environmental management. These findings provide essential insight for local health authorities to design more targeted prevention and control strategies. Furthermore, this research can serve as a foundation for developing a decision support system that aids in monitoring, evaluating prevention efforts, and optimizing health resource allocation to reduce the incidence of DHF in the future.
Penerapan dan Perbandingan Algoritma SVM, Naive Bayes, dan Gradient Boosting dalam Prediksi Stroke Joseph Melchior Nababan; Iqro Mukti Arto; Putra Satria; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6254

Abstract

Stroke is a major cardiovascular disease that significantly contributes to global mortality and disability rates. Early detection through stroke risk prediction is essential in reducing its impact. This study focuses on evaluating and comparing the performance of three machine learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Boosting (GB)—in predicting stroke occurrence. The research utilizes the Healthcare Stroke Dataset, which contains 5,109 records and 11 predictor variables. Modeling was performed using Orange Data Mining software, with 70% of the data allocated for training and 30% for testing. The results show that the SVM algorithm achieved the highest performance, obtaining an AUC score of 0.919 and an accuracy of 96.0%, followed by Gradient Boosting with an AUC of 0.885 and accuracy of 95.2%, and Naive Bayes with an AUC of 0.803 and accuracy of 88.2%. Therefore, SVM is identified as the most effective algorithm for predicting stroke risk within this dataset.
Klastering Penyakit Diabetes Melitus dengan Algoritma K-Means berdasarkan Karakteristik Klinis Audy Aulia Azzahra; Fajar Yoga Adiansyah; Erlangga Rizki Ekaptra; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6281

Abstract

Diabetes Mellitus is a complex and progressive chronic metabolic disorder that requires a personalized management strategy tailored to each individual’s clinical, physiological, and lifestyle characteristics. Addressing this challenge, the present study aims to apply the K-Means algorithm to identify clustering patterns among diabetic patients using the Knowledge Discovery in Databases (KDD) framework. The dataset was obtained from the Kaggle repository, consisting of 769 patient medical records with key variables such as glucose levels, body mass index (BMI), blood pressure, age, and other metabolic parameters relevant to the diagnosis of Diabetes Mellitus. The research methodology includes several stages: data selection, preprocessing to handle missing values, duplication, and normalization to ensure the dataset is properly structured for analysis. The implementation of the K-Means algorithm was carried out using Orange Data Mining software to produce optimal clustering patterns. The analysis identified three primary clusters (C1, C2, C3) that demonstrated significant differences, particularly based on glucose levels as the dominant variable in cluster formation. The scatter plot visualization revealed clear separations among clusters, with high intra-cluster homogeneity and strong inter-cluster heterogeneity. These findings confirm the effectiveness of the K-Means algorithm as an unsupervised learning method capable of uncovering hidden patterns within clinical diabetes data. The results are expected to serve as a foundation for developing more adaptive and precise clinical decision support systems, assisting healthcare professionals in designing targeted management and intervention strategies aligned with each patient’s risk profile.       
Penerapan Metode Logistic Regression untuk Memprediksi Potensi Penyakit Liver pada Pasien Tarmidzi Ibrahim; Imam Wahyudi; Vemi Januar Pratama; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6284

Abstract

Liver disease is a major global health concern that often goes undiagnosed in its early stages due to the absence of specific symptoms. Implementing data-driven approaches for early detection can significantly enhance diagnostic accuracy and improve clinical outcomes. This study aims to develop a predictive model using the Logistic Regression algorithm to identify individuals at high risk of liver disease. The data analysis process was conducted visually through data mining software, encompassing several stages such as data loading, feature selection, exploratory data analysis, and model evaluation. The dataset includes various clinical and laboratory attributes of patients, such as blood test results, liver function indicators, and demographic factors. The model’s performance was assessed using multiple evaluation metrics, with a focus on Classification Accuracy (CA) and the Area Under the ROC Curve (AUC) to measure predictive precision and classification ability. The results show that the Logistic Regression model achieved an accuracy of 71.8% and an AUC score of 0.746. These findings indicate that the model demonstrates good predictive performance and effectively identifies early-stage liver disease cases. However, further optimization is necessary to improve overall model efficiency and ensure more robust predictive capabilities in clinical applications.
Analisis Komparatif Sentimen Negatif Pengguna Platform E-Commerce Shopee dan Tokopedia selama Periode Diskon Faris Syahrendra; Cahyani Ayu Sulistyawati; Ginting Wibi Prasetyo; Sumanto Sumanto; Roida Pakpahan; Imam Budiawan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110824

Abstract

Abstrak : Fenomena potongan harga besar pada platform jual beli online sering kali menimbulkan kekecewaan bagi pengguna karena masalah dalam layanan, harga, dan pengiriman. Studi ini bertujuan untuk menganalisis dan membandingkan perasaan pengguna terhadap Shopee dan Tokopedia selama masa promosi dengan cara menggunakan pendekatan machine learning. Data ulasan diambil dari Google Play Store, yang terdiri dari 929 ulasan untuk Shopee dan 1.111 ulasan untuk Tokopedia. Dua algoritma untuk klasifikasi sentimen, yaitu Naive Bayes dan Neural Network, diimplementasikan dan dievaluasi dengan metode validasi silang 10-fold. Temuan yang berasal dari penilaian analitis menunjukkan bahwa model Naive Bayes menunjukkan tingkat akurasi dan presisi tertinggi yaitu 91,0%, sementara Neural Network memperoleh 83,9%. Selain itu, ulasan positif mendominasi sentimen terhadap Shopee (70%), sedangkan Tokopedia lebih banyak diwarnai oleh sentimen negatif (60%). Penemuan ini menandakan bahwa pengguna lebih puas dengan pengalaman diskon di Shopee dan memberikan masukan strategis untuk peningkatan layanan e-commerce.===============================================Abstract :Large-scale discount events on e-commerce platforms often lead to user disappointment due to issues with service, pricing, and delivery. This study aims to analyze and compare user sentiment towards Shopee and Tokopedia during promotional periods using a machine learning approach. Review data were sourced from the Google Play Store, consisting of 929 reviews for Shopee and 1,111 for Tokopedia. Two algorithms for sentiment classification, namely Naive Bayes and Neural Network, were implemented and evaluated using the 10-fold cross-validation method. Findings from analytical assessments indicate that the Naive Bayes model demonstrates the highest level of accuracy and precision at 91.0%, while the Neural Network obtained 83.9%. Furthermore, positive reviews dominated the sentiment towards Shopee (70%), whereas Tokopedia was largely characterized by negative sentiment (60%). These findings indicate that users are more satisfied with the discount experience on Shopee and provide strategic input for the improvement of e-commerce services.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Risiko Penyakit Paru Berdasarkan Data Diagnostik Pasien Alwan Kapi Muntaha; Kevin Dwi Satria; Desiana Nuranudin Putri; Sumanto Sumanto; Roida Pakpahan; Imam Budiawan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110867

Abstract

Abstrak : Penyakit paru-paru termasuk salah satu faktor utama penyebab tingginya angka kematian di seluruh dunia. Kondisi ini terjadi karena penyakit paru-paru sering kali sulit terdeteksi pada tahap awal akibat gejalanya yang tidak spesifik. Perkembangan teknologi machine learning memberikan peluang untuk membantu proses diagnosis secara otomatis dengan memanfaatkan data diagnostik pasien. Penelitian ini bertujuan untuk mengklasifikasikan risiko penyakit paru menggunakan berbagai algoritma machine learning pada aplikasi Orange3, serta menentukan model dengan akurasi terbaik. Dataset yang digunakan terdiri dari 5.000 data pasien dengan 18 atribut yang mencakup faktor demografis, gaya hidup, riwayat medis, dan kondisi klinis seperti kadar oksigen, tingkat stres, dan kebiasaan merokok. Lima algoritma diuji, yaitu iDecision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), dan Neural Network. Hasil pengujian menunjukkan bahwa Neural Network menghasilkan nilai akurasi tertinggi sebesar 89,15%, diikuti oleh Decision Tree (85,12%) dan Naïve Bayes (83,63%). Temuan ini membuktikan bahwa Neural Network lebih unggul dalam mengenali pola kompleks antarvariabel dan mampu memberikan prediksi yang lebih akurat. Dengan demikian, penelitian ini menegaskan potensi penerapan machine learning berbasis data diagnostik non-citra sebagai sistem pendukung keputusan untuk diagnosis dini penyakit paru.=================================================Abstract : Lung disease is a major contributing factor to high mortality rates worldwide. This is because lung disease is often difficult to detect in its early stages due to its nonspecific symptoms. The development of machine learning technology provides an opportunity to assist the automated diagnosis process by utilizing patient diagnostic data. This study aims to classify the risk of lung disease using various machine learning algorithms in the Orange3 application, and determine the model with the best accuracy. The dataset used consists of 5,000 patient data with 18 attributes covering demographic factors, lifestyle, medical history, and clinical conditions such as oxygen levels, stress levels, and smoking habits. Five algorithms were tested: Decision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Neural Network. The test results showed that Neural Network produced the highest accuracy value of 89.15%, followed by Decision Tree (85.12%) and Naïve Bayes (83.63%). These findings prove that Neural Network is superior in recognizing complex patterns between variables and is able to provide more accurate predictions. Thus, this study confirms the potential of applying machine learning based on non-image diagnostic data as a decision support system for early diagnosis of lung disease.
Perbandingan Kinerja Algoritma Machine Learning dalam Klasifikasi Penyakit Fundus Menggunakan Citra Fundus Digital Kurniawan, Deny; Triyanto, Dedi; Sari Marita, Lita; Christian, Ade; Sumanto, Sumanto
Jurnal Ilmiah Sistem Informasi Akuntansi Vol. 5 No. 2 (2025): Volume 5, Nomor 2, December 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jimasia.v5i2.1476

Abstract

Penyakit fundus mata seperti diabetic retinopathy, cataract, dan glaucoma merupakan penyebab utama gangguan penglihatan hingga kebutaan apabila tidak terdeteksi sejak dini. Diagnosis penyakit fundus secara konvensional masih sangat bergantung pada penilaian visual tenaga medis, yang berpotensi menimbulkan subjektivitas dan keterlambatan penanganan. Oleh karena itu, penelitian ini bertujuan untuk menerapkan dan membandingkan metode machine learning dalam mendeteksi penyakit fundus berdasarkan citra fundus digital serta menentukan algoritma dengan kinerja terbaik. Penelitian ini menggunakan dataset publik yang terdiri dari 600 citra fundus yang terbagi secara seimbang ke dalam empat kelas, yaitu Normal, Background Diabetic Retinopathy, Cataract, dan Glaucoma, dengan masing-masing kelas berjumlah 150 citra. Dataset dibagi menjadi data training dan data testing dengan rasio 80:20. Tiga algoritma machine learning yang digunakan adalah Support Vector Machine (SVM), K-Nearest Neighbors (KNN), dan Random Forest. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa seluruh model mampu mencapai tingkat akurasi di atas 80%, dengan SVM menghasilkan akurasi tertinggi sebesar 88,0%, diikuti oleh KNN sebesar 87,7% dan Random Forest sebesar 82,5%. Hasil ini menunjukkan bahwa metode machine learning, khususnya SVM, efektif digunakan dalam mendeteksi penyakit fundus dan berpotensi dikembangkan sebagai sistem pendukung diagnosis dini. Meskipun demikian, penelitian lanjutan masih diperlukan dengan dataset yang lebih besar dan beragam untuk meningkatkan kemampuan generalisasi model.
Evaluasi Kinerja Algoritma Machine Learning SVM dan KNN pada Klasifikasi Penyakit Ginjal Triyanto, Dedi; Kurniawan, Deny; Sari Marita, Lita; Christian, Ade; Sumanto, Sumanto
Jurnal Ilmiah Sistem Informasi Akuntansi Vol. 5 No. 2 (2025): Volume 5, Nomor 2, December 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jimasia.v5i2.1477

Abstract

Penyakit ginjal, mulai dari penyakit ginjal kronis hingga kondisi yang lebih serius seperti kista, batu ginjal, dan tumor, merupakan masalah kesehatan global yang memerlukan deteksi dini untuk mencegah komplikasi lebih lanjut. Metode diagnosis konvensional masih bergantung pada interpretasi subjektif tenaga medis, sehingga berpotensi menimbulkan ketidakkonsistenan dan keterlambatan penanganan. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja algoritma machine learning Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) dalam mendeteksi penyakit ginjal secara otomatis. Penelitian ini menggunakan dataset penyakit ginjal yang terdiri dari 4.000 data pasien yang terbagi secara seimbang ke dalam empat kelas, yaitu normal, kista, batu ginjal, dan tumor, dengan masing-masing kelas berjumlah 1.000 data. Dataset dibagi menjadi data training dan data testing dengan rasio 80:20. Proses pelatihan dan pengujian model dilakukan menggunakan algoritma SVM dan KNN, dengan evaluasi kinerja berdasarkan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan performa yang sangat tinggi, dengan SVM mencapai akurasi sebesar 99,6% dan KNN mencapai akurasi sebesar 99,8%. Hasil ini menunjukkan bahwa metode machine learning efektif digunakan dalam mendukung deteksi penyakit ginjal. Namun demikian, penelitian lanjutan dengan dataset yang lebih beragam dan data klinis nyata masih diperlukan untuk meningkatkan robustnes dan kemampuan generalisasi model.
ANALISIS MACHINE LEARNING UNTUK PREDIKSI PENYAKIT PARU-PARU MENGGUNAKAN RANDOM FOREST Ade Christian; Hariyanto Hariyanto; Ahmad Yani; Sumanto Sumanto
Journal of Innovation And Future Technology Vol. 7 No. 1 (2025): Vol 7 No 1 (Februari 2025): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v7i1.3906

Abstract

Lung diseases, including COPD, lung cancer, and asthma, are serious global health issues, causing over seven million deaths annually. Advanced technologies, such as deep learning and the Random Forest algorithm, have been effectively utilized to detect and classify lung diseases from imaging data with high accuracy. This study aims to demonstrate the effectiveness of Random Forest in predicting lung diseases. The dataset used consists of 30,000 records with 11 attributes, collected from Kaggle and processed using Orange software version 3.36.2. The implementation of the Random Forest algorithm was conducted with 10 decision trees and six attributes considered at each split. The model was tested using Cross Validation with 10 folds. The testing results showed an AUC value of 0.993, indicating a very high level of accuracy. A confusion matrix was used to measure the model's performance through various metrics, including accuracy, precision, recall, F1-score, and AUC. This model achieved high accuracy, with ROC AUC values of 0.453 for predicting the presence of lung disease and 0.547 for predicting its absence. These results confirm that the Random Forest algorithm is an effective predictive tool for identifying lung diseases. This study makes a significant contribution to the development of more accurate and efficient diagnostic techniques, assisting medical professionals in identifying lung diseases in patients. With a deeper understanding of how this algorithm operates in the healthcare domain, it is expected to significantly enhance the quality of patient diagnosis and care.
K-Means dan Data Mining Tools: Strategi Efektif untuk Menganalisis Siswa Putus Sekolah Ade Christian; Hariyanto Hariyanto; Ahmad Yani; Sumanto Sumanto
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 3 No. 1 (2025): Volume 3 Number 1 January 2025
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v3i1.167

Abstract

Pendidikan memiliki peran penting dalam membangun sumber daya manusia yang berkualitas, namun permasalahan putus sekolah masih menjadi tantangan serius, terutama di tingkat Sekolah Menengah Kejuruan (SMK). Untuk mengatasi masalah ini, penelitian ini membandingkan tiga aplikasi data mining, yaitu RapidMiner, Orange, dan Weka, dalam mengelompokkan siswa putus sekolah menggunakan algoritma K-Means. Data yang digunakan diperoleh dari berbagai sumber dan diproses melalui tahapan pengujian, penerapan algoritma K-Means, serta perbandingan hasil klasterisasi. Hasil penelitian menunjukkan bahwa RapidMiner memiliki akurasi tertinggi sebesar 86%, diikuti oleh Orange dengan 80%, dan Weka dengan 73%. Perbedaan akurasi ini menunjukkan bahwa setiap aplikasi memiliki keunggulan dan keterbatasan masing-masing dalam pemrosesan data dan pengelompokan siswa berdasarkan pola tertentu. Dari hasil perbandingan ini, RapidMiner terbukti lebih optimal dalam menghasilkan klaster yang lebih akurat dan stabil dibandingkan dengan dua aplikasi lainnya. Meskipun penelitian ini menunjukkan hasil yang signifikan, masih terdapat beberapa keterbatasan, seperti jumlah dataset yang terbatas dan penggunaan satu algoritma saja (K-Means). Oleh karena itu, penelitian selanjutnya dapat menggunakan dataset yang lebih besar dan beragam, serta mengeksplorasi algoritma lain, seperti DBSCAN atau Hierarchical Clustering, untuk meningkatkan kualitas analisis. Selain itu, integrasi teknik machine learning yang lebih kompleks juga direkomendasikan guna meningkatkan akurasi prediksi. Hasil penelitian ini diharapkan dapat membantu lembaga pendidikan dalam mengidentifikasi pola siswa berisiko putus sekolah, sehingga dapat digunakan sebagai dasar dalam pengambilan keputusan dan strategi intervensi pendidikan yang lebih efektif. Kata Kunci: Data Mining; K-Means, Klasterisasi; RapidMiner; Orange; Weka; Siswa Putus Sekolah.
Co-Authors Aberahamo Onoma Marundrury Achmad Rivai Syahputra Ade Budiman, Ade Ade Christian Ade Christian Ade Christian Ade Christian Adhiani, Budhi Adi Pangestu Adi Supriyatna Adinugroho, Wisnu Aditia Yudhistira Adryan Raihan Syakir Agung Wibowo Agus Buono Agus Santoso Ahmad Habibullah Ahmad Rais Ruli Ahmad Syukri Gozali Ahmad Yani Ahmad Yani ahmad yani Ahmad Yani , Ahmad Yani Alamsyah, Muhammad Arkan Alghifar Firgiawan Alghiffary, Muhammad Adya Ali Mahmudi Ali, Muhamad Hafis Ali, Satrio Nur Alwan Kapi Muntaha Alya Avisa Andi Diah Kuswanto Andi Setiawan Andika Amansyah Andri Amico Andriansyah Tri Laksono Anggreani, Namira Anita Adelia Syahfitri Apip Supiandi Ardiyansyah, Rizqi Ari Sulistiyawati Ariskawati, Mila Arnata Nur Rasyid Arshad, Muhammad Waqas Arya, Yudi Asmawati Asmawati Audy Aulia Azzahra Aulia Rachmat, Daffa Aziz Bayu Permata Azkia, Farah Diba Bib Paruhum Silalahi Bismo Raharjo, Yohanes Aryo Budhi Adhiani Budhi Adhiani Christina Budi Santoso Budiman, Ade Surya Cahya, Titus Dwi Cahyani Ayu Sulistyawati Damayanti Damayanti Darmawi . Dedi Darwis Dedi Triyanto Dedi Triyanto Deny Kurniawan DENY KURNIAWAN Desiana Nuranudin Putri Desyanti Desyanti Dewi, Revinta Arrova Diah, Andi Dinda Aprillia Dwiki Gilang Ramadhani Dyah Ayu Megawaty Dyani Kalyana Mitta Eka Dyah Setyaningsih Eka Putri Alvi Syahrina Elisabeth Sri Hendrastuti Erlangga Rizki Ekaptra Fadila Shely Amalia Fahrian Fahroni, Aldiwa Alfa Thira Nur Faiz Djarot, Raihan Jamal Faiz Najwan Zaky Fajar Akbar Fajar Yoga Adiansyah Fajrian, Ihsan Fardha Hasykir Farhan Fadhilah Faris Syahrendra Faruk Ulum Fathur Rismansyah Fauzan Nawwir Andriansyah Fauzan, Muhammad Indra Fransiscus Andre Suwarno Ganda Wijaya Ganda Wijaya, Ganda Ghofar Taufiq Gilang Virgiawan Ginting Wibi Prasetyo Hafis Nurdin Harianto Harianto Hariyanto Hariyanto HARIYANTO HARIYANTO Hartanti Hartanti Hartono Hartono Heni Nur Kusumawati Herdinan Tito Hetty Rohayani Hidayat, Manarul Hilmy Ibrahim, Farras Idha Rizqi Pratiwi Imam Budiawan Imam Budiawan Imam Budiawan Imam Budiawan Imam Budiawan Imam Wahyudi Iman Febriansya Putra Indah Oktavia Zalmi Indra Chaidir, Indra Indra, Ahmad Indriani , Karlena Indriyanti, Zahra Kiky Dwi Insani Abdi Bangsa Iqro Mukti Arto Jefina Tri Kumalasari Jefina Tri Kumalasari Jefina Tri Kumalasari Joko Tri Haryanto Joseph Melchior Nababan Juanny Cheristy Souisa Julkarnaen Karo-Karo Jumaryadi, Yuwan Junhai Wang Junhai Wang Junhai Wang Junhai Wang Junhai Wang Kadir, Fauwas Abdul Kaisar Ages Querio Karlena Indriani Karlisa Priandana Kevin Dwi Satria Kotjek, Rafie Kumalasari Kumalasari Kuswanto, Andi Diah Laura Gabriel da Silva Lia Mazia, Lia Lise Pujiastuti Lise Pujiastuti Lita Sari Marita Maharani Rona Makom Makom, Maharani Rona Mantriwira, Daniel Mardinawat Mardinawat Mardinawati Mardinawati Mardinawati, Mardinawati Megawaty, Dyah Ayu Meydina Aulia Savitri Micho Respati Putra Mochamad Fathur Milzam Mochamad Wahyudi Muhamad Fadli Fadhlullah Muhamad Rendi Gibran Muhammad Furqon Prasetyo Muhammad Haikal Abidin Muhammad Hendra Hernawan Muhammad Hussein Umar Muhammad Raviansyah Muhammad Rifqi Asy'ari Muksin Hi Abdullah Musfiroh Musfiroh, Musfiroh Nabilla, Adinda Naufal Hermawan, Rezan Nindya Dwi Lestari Ningtyas, Listina Ade Widya Nirwana Hendrastuty Noviyanto Noviyanto Nurfia Oktaviani Syamsiah Nurrahman, Alvin Paduloh Paduloh Pakpahan, Roida Pasaribu, A. Ferico Octaviansyah Paulus Paulus Permata, Permata Prasetyo Adi Suwignyo Prasetyo, Romadhan Edy Pribadi, Denny Pricillia Pujiastuti, Lise Purwandani, Indah Putra Satria Putra, Imam Hanif Qais Abdurrachman Rachmat Adi Purnama Raditya Rimbawan Oprasto Rafi Kurniawan Rafi Rasendriya Raihan Naufal Ramadhan Raihan Primadana Raihan Raihan Ramadani, Achmes Dade Ramadhan, Muhammad Gilang Ramadhani, Varla Octavia Rani, Maulidina Cahaya Rasya Abel Putra Gumulija Ratiyah* Ratiyah Ratnasari, Arum Retno Winarti Reynaldi , Reynaldi Rian Hidayat Riansyah Gustian Ridwan, Asrifia Rifda Ilahy Rosihan Rifki Nur Hidayat Putra Riska Aryanti Rivaldi, Muhammad Rizal Maulana Rizky Daud Antony Pangaribuan Rizqi Ramadhani, Muhammad Rofiqi, Ainur Roida Pakpahan Roida Pakpahan Roida Pakpahan Roida Pakpahan Roida Pakpahan Roni Saputra Pratama Ruhul Amin Rumidjan Rumidjan, Rumidjan Rusda Wajhillah Ryan Dwi Aprilyanto Ryan Randy Suryono Ryehan Alfiansyah Safinah Faatin Sanriomi Sintaro Santosa, Teguh Budi Saputra, Sabita Abigail Saputra, Yusup Saputri, Fifin Sefriani, Shintia Putriayu Sentanu, Quinn Abrar Athallah Sentot Achmadi Setiawan, Dandi Setiawansyah Setiawansyah Setiawansyah Siregar, Denny Solihin Solihin Sopyan Sri Hendrastuti, Elisabeth Sri Sugiharti Suci, Bintang Dyas SUKAMTI . Sulaiman Sulaiman Sulistyo Sulistyo Sumarna Sumarna Sumarna Sumarna Suparno Suparno Suwandi Suwandi Tabrani, Tabrani Tarmidzi Ibrahim Taufig, Ghofar Teguh Budhi Santosa Teguh Budi Santosa Temi Ardiansah Teuku Vaickal Rizki irdian Tri Widian Ratnasari Trisna Andhika Saputra Ulum, Faruk Umam, Hairul Ummu Radiyah, Ummu Vemi Januar Pratama Vera Agustina Yanti Wahyudi, Agung Deni Wang, Junhai Wardani, Maidy Tri Wattilah, Florentina Widya Viona Septi Tanjung Wijaya, Filzah Wina Ningsih Yakobus Linus Jumadi Yamani, Teuku Arrasy Yanuar Laik, Abraham Adrian Yunardus Yunardus Yundari, Yundari Yuri Rahmanto Zahwa Asfa Rabbani Zayyan Nauval Araf Zidan, Muhammad `Diah Kuswanto, Andi