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Hendra Kurniawan
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hendra.kurniawan@darmajaya.ac.id
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jodmapps@darmajaya.ac.id
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Jl. Z.A. Pagar Alam No. 93 Gedong Meneng, Bandar Lampung Lampung
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Journal of Data Science Methods and Applications
Published by Institut Informatika dan Bisnis Darmajaya
ISSN : -     EISSN : 30905605     DOI : https://doi.org/10.30873/jodmapps
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Library & Information Science
Theoretical Foundations: Architecture, Management and Process for Data Science Artificial Intelligence Classification and Clustering Data Pre-Processing, Sampling and Reduction Deep Learning Educational Data Mining Forecasting High Performance Computing for Data Analytics Learning Classifiers Learning Theory Optimization Methods Probabilistic and Statistical Models and Theories Scientific Data and Big Data Analytics Statistical Learning Machine Learning and Knowledge Discovery: Big Data Visualization, Modeling and Analytics Data and Knowledge Visualization Database Technology Knowledge Based Neural Networks Knowledge Discovery (Heterogeneous, Unstructured and Multimedia Data) Knowledge Discovery in Network and Link Data Knowledge Discovery in Social Networks Learning for Streaming Data Machine Learning for High-Performance Computing Multimedia/Stream/Text/Visual Analytics Spatial/Temporal Data Computational Data Science: Big Data Computational for Big Data Analysis Computational Intelligence for Pattern Recognition and Medical Imaging Computer Application for Data Analytics Computer Architecture for Data Analytics Computer Graphics for Data Analytics Data Acquisition, Integration, Cleaning Data Visualizations Data Wrangling Databases Decision Making from İnsights, Hidden Patterns Intelligent Information Retrieval Optimization for Data Analytics Probabilistic And İnformation-Theoretic Methods Search and Mining Support Vector Machines Time Series Analysis Applications: Bioinformatics Applications Biomedical Informatics Applications Biometrics Applications Collaborative Filtering Applications Data and Information Semantics Applications Data Mining Algorithms Applications Data Mining Systems Applications Data Streams Mining Applications Database and Information System Performance Applications Database Systems & Applications Electronic Commerce and Web Technologies Applications Electronic Government & E-participation Applications Graph Mining Applications Healthcare Applications Image Analysis Applications Information Retrieval Applications Multimedia Data Mining Applications Natural Language Processing Applications Pre-Processing Techniques Applications Spatial Data Mining Applications Statistical and Scientific Databases Applications Web Search Applications
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 14 Documents
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Klasterisasi Data Penjualan Menggunakan Algoritma K-Mean Dengan RapidMiner Panjaitan, Tiodora Priska; Asmaul Dwi Akbar; Sabrina Nur Rahmah; Stefani Cinthia Ernadi; Mochammad Akmal Fatoni; Fatkhul Inayah; Uli Vicilia Sitorus
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

ABSTRACTThis research aims to identify the optimal number of clusters in the dataset using the K-Means algorithm and the Elbow method in Rapidminer software. The method used is K-Means to cluster data and the Elbow method to determine the optimal number of clusters. The results of research using the K-Means algorithm have obtained the optimal number of clusters. From the results of processing test data with the number of clusters (k= 2 – 5), it was found that cluster 2 had the highest number of domestic chicken egg sales compared to cluster 1, namely 41 purchases.
Analisis Data Penjualan Menggunakan Algoritma K-Means Clustering Pada Toko Superindo Kelvin Hidayat; Muhammad Rezky Adytama; Hapip Aditya Darmawan; Yanda Arnando; Abdul Mukarim
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Supermarket are increasingly popular among consumers for transactions, with superindo being one of the leading ones behaviour and optimize sales through sales data analysis. Data mining, especially the k-means clustering method, is used to group sales data based on certain characteristics, so taht it can halp in formulating more targeted marketing strategies. This research uses sales data from superindo for april 2024 and is analyzed using the clustering method with the k-means algorithm. The research results show that applying this method is effective in grouping sales data into several clusters, which provides valuable insight clustering results which can be used to improve superindo’s marketing strategy. This research provides a strong basis for the development of more effective marketing strategies.
Perbandingan Algoritma Naïve Bayes, Decision Tree, KNN, dan Random Forest Untuk Memprediksi Data Penduduk Penerima BPJS Di Lampung Timur Rachma Annisa W.P; Dede Aprizal; Riana Kristina Dewi; Devi Sari Ayuandita; Anisa Oktaviani; Marcella Azzahra
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

This study aims to predict population data in Lampung Timur using various classification algorithms. The algorithms used include Naive Bayes, k-Nearest Neighbors (k-NN), Decision Tree, and Random Forest. The dataset used was derived from population data processed with RapidMiner. The data was processed using steps such as reading from Excel files, data duplication, and model training with the aforementioned algorithms. Evaluation results show that the Naive Bayes algorithm has the highest accuracy of 86.89% with good precision and recall for both BPJS and UMUM classes. Additional analysis indicates that from the dataset used, there are 1924 residents who have BPJS and 1960 residents who do not have BPJS. These results suggest that the Naive Bayes algorithm performs best in predicting population data in Lampung Timur and that there is still a significant number of residents who do not utilize BPJS services. Implementing this classification algorithm can aid in better decision-making regarding the distribution of BPJS services in Lampung Timur.
Analisis Perbandingan Algoritma Klasifikasi Decision Tree, K-Nearest Neighbors, Naive Bayes, dan Random Forest pada Data Pemilihan Legislatif KPU Menggunakan Kurva ROC Naura Fayza I; Nicholas Svensons; Sri Asni Fatmawati; Pricillia Rotua S; Khanaya Erviona
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

In the context of the digital information era, analysis of general election data is crucial for understanding political dynamics. Legislative election data from the Indonesian General Election Commission (KPU) provides insight into voter behavior and election results. Selection of an appropriate classification algorithm is the main challenge in producing accurate predictions. This study compares four classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Random Forest, using Receiver Operating Characteristic (ROC) curves as the main evaluation. The results show Random Forest performs best in handling legislative election data, providing important insights for future policy and research.
Analisis Klastering dari Data Behavior Online Gaming Menggunakan Algoritma K-Means Salsabila Shahibah; Novita Triyasri; Adisty Anggi Inanti; Jovita Rachel; Niko Diki Pratama; Andriansyah
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Games that require an internet connection are called online games. Just like offline games, online games also have many genres. Some of them are Action, advanture, sports, RPG, and simulation. The emergence of various types of online games provides many choices to eliminate boredom in filling free time. In addition, there are also various levels such as easy, medium, and hard. The levels in this game also affect the habits of players in playing games. This study aims to find the optimal cluster in the dataset using clustering analysis using the K-Means algorithm on the RapidMiner application. The results of this study show that cluster 1 at k=3 from (k=2-7) is the best cluster compared to other clusters
Prediksi Diagnosa Penyakit Jantung (Cardiovascular Diseases) Menggunakan Algoritma Machine Learning Rini Nurlistiani; Mia Sabina; Asmaul Dwi Akbar
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Heart disease remains a global health concern, being the leading cause of mortality with substantial impacts on the population. This research addresses the challenges in early detection and prediction of heart diseases, considering the complex and diverse nature of Cardiovascular Diseases (CVD). With limitations in diagnostic tools and healthcare resources, the study explores the application of machine learning algorithms for accurate predictions. Building upon previous research, various machine learning algorithms, including Random Forest, Multilayer Perceptron, Gaussian Processes, and M5P, were employed to predict heart disease-related data. The research involved comprehensive data pre-processing, visualization, model fitting, and evaluation stages. The dataset, sourced from the Hungarian Institute of Cardiology, comprised 14 attributes. Results demonstrated the effectiveness of the selected machine learning models, with Random Forest exhibiting outstanding performance, closely followed by Multilayer Perceptron. Gaussian Processes performed relatively well, while M5P provided a complex model structure offering additional insights. The use of 10-fold cross-validation enhanced the stability of model evaluation. Statistical analysis and data visualization contributed to a thorough understanding of model performance and dataset characteristics. In conclusion, this research contributes to developing accurate predictive models for heart disease detection. The findings offer valuable insights into algorithm performance and dataset characteristics, guiding future health science and information technology efforts for improved preventive and diagnostic measures. The methodology employed, including machine learning algorithms and cross-validation, presents a robust approach for future research in cardiovascular health prediction
Prediksi Penyakit Paru-Paru Dengan Algoritma Naïve Bayes Selvida Widi Audria; Izzah Farikhah; Reza Maulana Saputra; Neni Purwati
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Paru-paru memiliki peran penting dalam tubuh manusia, yaitu sebagai organ utama dalam sistem pernapasan, berfungsi mengolah karbon dioksida yang dibawa oleh darah menjadi oksigen dari udara yang dihirup, yang kemudian disebarkan ke seluruh tubuh untuk memenuhi kebutuhan oksigen. Gangguan paru-paru juga berisiko terhadap Kesehatan hingga kematian. Diperlukan metode yang akurat untuk mendiagnosis penyakit paru-paru agar penanganan dapat dilakukan dengan tepat. Dataset yang digunakan sebanyak 10000 dengan 10 attribut yakni usia, jenis kelamin, kebiasaan merokok, status pekerjaan, kondisi rumah tangga, aktivitas begadang, aktivitas rumah tangga, kepemilikan asuransi, riwayat penyakit bawaan, dan label hasil. Tujuan penelitian ini adalah untuk memprediksi penyakit paru-paru menggunakan model naïve bayes dengan hasil validasi yang robust dan reliable dengan penerapan dual-validation framework yakni Split validation dan k-Fold Cross Validation. Metode yang digunakan adalah pengumpulan data, pengelolaan data (seleksi dan pembersihan data), penerapan metode, pengujian metode dan kesimpulan. Hasil dari penerapan model naïve bayes dari data testing sebanyak 2000 menunjukkan nilai accuracy tertinggi sebesar 86.90%, precision tertinggi sebesar 87.65% diperoleh dari iterasi sebanyak 10 Fold Cross Validation, sedangkan nilai recall tertinggi diperoleh dari penerapan Split Validation sebesar 87.75%, sehingga hasil tersebut termasuk klasifikasi yang sangat baik diterapkan untuk melakukan prediksi penyakit paru-paru yang di derita masyarakat.
Perbandingan Performa Model Naïve Bayes dan Regresi Logistik dalam Klasifikasi Kecanduan Media Sosial pada Siswa Nurjoko; Agung, Agung Dwi Praditya; Triyari, Novita; Rafly, M. Rafly Octa; Agus Rahardi
Journal of Data Science Methods and Applications Vol. 1 No. 2 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Kecanduan media sosial di kalangan pelajar menjadi isu yang semakin relevan seiring meningkatnya penggunaanplatform digital dalam kehidupan sehari-hari. Penelitian ini bertujuan untuk membangun model klasifikasi gunamemprediksi tingkat kecanduan media sosial berdasarkan data survei siswa. Dataset yang digunakan terdiri dari 705responden dengan 13 atribut yang mencakup aspek demografis, akademik, kebiasaan digital, dan kondisi psikososial.Dua model machine learning yang digunakan dalam penelitian ini adalah Gaussian Naïve Bayes dan Regresi Logistik.Setelah melalui proses data preparation, analisis dan evaluasi model menggunakan data uji sebesar 20%, diperolehhasil bahwa Regresi Logistik memiliki performa yang lebih unggul dengan akurasi mencapai 98%, jauh di atas NaïveBayes yang hanya mencapai 69%. Regresi Logistik juga menunjukkan keseimbangan metrik yang baik, termasukprecision, recall, dan nilai ROC AUC sebesar 0,98. Temuan ini mengindikasikan bahwa Regresi Logistik lebih efektifdan sesuai untuk mengidentifikasi siswa yang berisiko mengalami kecanduan media sosial.
Prediksi Kanker Payudara di Indonesia menggunakan Algoritma Support Vector Machine dan Regresi Logistik Zoe, Zeo Enitisya; ray, raynaldo syah pratama; Prita, Prita Yana Faliha; Keyla, Keyla Artaliani; Della, Rahma Della Mustika Ayu
Journal of Data Science Methods and Applications Vol. 1 No. 2 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Breast cancer is one of the leading causes of death among women worldwide, including in Indonesia. Early detection and risk factor analysis are crucial in the efforts toward more effective prevention and treatment. This study aims to identify risk patterns of breast cancer through the analysis of clinical patient data. The dataset includes several clinical variables such as age, menopausal status, tumor size, cancer grade, the number of affected lymph nodes, and hormone status (progesterone and estrogen). The analytical method used is descriptive and exploratory to uncover common patterns within the data. The results indicate that age, menopausal status, and tumor size show a significant correlation with the level of breast cancer risk. These findings are expected to contribute to the development of decision support systems in breast cancer diagnosis and promote greater public awareness of the importance of early detection and regular breast health monitoring.
Pemanfaatan Machine Learning untuk Prediksi Kepuasan Pelanggan pada UMKM Digital Agus Rahardi; Nursalim, Nursalim; Andini, Rekha Aprilia; Tri, Anugrah Tri Agil S; Gilang, Gilang Ramadhan; Dwi, Dwi Salim; Nurjoko
Journal of Data Science Methods and Applications Vol. 1 No. 2 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

UMKM digital memainkan peran penting dalam perekonomian Indonesia, namun mempertahankan kepuasan pelanggan tetap menjadi tantangan utama. Penelitian ini bertujuan untuk membangun model prediksi kepuasan pelanggan menggunakan algoritma Machine Learning seperti Logistic Regression, Decision Tree, dan Random Forest. Dataset simulasi sebanyak 10.000 entri pelanggan digunakan, mencakup fitur-fitur seperti frekuensi pembelian, nilai transaksi rata-rata, rating layanan, dan metode pembayaran. Model dievaluasi berdasarkan metrik B. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan akurasi dan kinerja terbaik dalam mengklasifikasikan kepuasan pelanggan. Temuan ini menunjukkan potensi besar penggunaan Machine Learning dalam membantu UMKM digital meningkatkan kualitas layanan dan loyalitas pelanggan.

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