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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Computer Science and Research Indonesian Journal of Science, Technology, and Humanities
Dimas Prayogi
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Mathematics Social Sciences

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Data Mining Klasterisasi Aduan Masyarakat Kota Binjai Menggunakan Algoritma K-Means Dimas Prayogi; Yani Maulita; Rusmin Saragih
Indonesian Journal of Science, Technology and Humanities Vol. 1 No. 2 (2023): IJSTECH - October 2023
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/ijstech.v1i2.120

Abstract

Menerapkan metode K-Means dalam data mining untuk menganalisis aduan masyarakat Kota Binjai yang diterima oleh Satpol PP. Dengan metode ini, data aduan dapat dikelompokkan ke dalam klaster-klaster yang memiliki karakteristik serupa. Hal ini membantu dalam memahami pola aduan dan potensi masalah yang mungkin muncul di masyarakat. Membangun sistem data mining untuk aduan masyarakat di kantor Satpol PP Kota Binjai. Ini akan mencakup pengumpulan data, pemrosesan data, klasterisasi, dan penyajian hasil. Dengan adanya sistem ini, Satpol PP dapat terus memantau dan memahami dinamika aduan masyarakat secara lebih efisien. Dari 402 pengelompokan data Pengaduan berdasarkan Kelurahan, aduan dan Tindakan terdapat 4 group, dimana kelompok yang terbanyak pada pengujian pertama ini berada pada cluster 1 dengan jumlah 120 data pada group dengan pusat centroid  34.20, 6.97, 2.40 yaitu kelurahan tangsi aduan Pembangunan tanpa IMB dan Tindakan melakukan evaluasi. cluster 2 dengan jumlah 78 data pada group dengan pusat centroid  20.67, 12.69, 1.92 yaitu kelurahan pekan binjai aduan orang mabuk meresahkan dan Tindakan melakukan evaluasi. cluster 3 dengan jumlah 118 data pada group dengan pusat centroid  16.19, 3.73, 2.63 yaitu kelurahan mencirim aduan manusia silver dan Tindakan melakukan penertiban. . cluster 4 dengan jumlah 86 data pada group dengan pusat centroid  4.14, 7.58, 2.47 yaitu kelurahan binjai estate aduan Razia hotel dan Tindakan melakukan evaluasi
Analysis of Open Unemployment Rate Prediction Using Backpropagation Method P.A.M. Zidane R.W.P.P. Zer; Dimas Prayogi; M Arif Y Sinaga; Olivia Diwani Saragih; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5957

Abstract

The open unemployment rate (TPT) is one of the important indicators in assessing the economic health of a region. This study aims to develop an accurate prediction model for the open unemployment rate using the backpropagation algorithm, as well as to evaluate the factors that influence the prediction. Accurate TPT prediction can help the government and policy makers in designing strategies to alleviate unemployment based on the results of the analysis of the developed model. This study aims to analyze and predict the Open Unemployment Rate (TPT) in various provinces in Indonesia in 2024 to 2026 using an artificial neural network model with the Backpropagation algorithm. Based on the test results, the 3-6-1 architecture model showed a prediction ability with 100% accuracy, while other architectures also gave very good results, with 100% accuracy for the 3-3-1 model and 97.06% for the 3-12-1 model. The TPT prediction results show that the unemployment rate is predicted to continue to increase from year to year, indicating the potential for an increase in the number of unemployed in the future. On the other hand, the accuracy analysis shows that each architecture produces different results, with the 3-6-1 architecture producing a longer time for the testing process, but still providing optimal accuracy. This finding illustrates that choosing the right architecture greatly affects the accuracy and efficiency in predicting TPT, which can be an important basis in formulating policies to eradicate unemployment in Indonesia.
Heart Disease Prediction Using Logistic Regression and Random Forest with SHAP Explainability Dimas Prayogi
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents a web-based Heart Disease Prediction System developed using Logistic Regression and Random Forest algorithms, enhanced with SHAP explainability. The system predicts the likelihood of heart disease based on key clinical parameters such as age, sex, chest pain type, blood pressure, cholesterol, and heart rate. SHAP values are integrated to provide transparent and interpretable explanations of model predictions. The Random Forest model demonstrated superior performance in capturing nonlinear relationships compared to Logistic Regression. The web application offers an interactive and user-friendly interface that displays correlation heatmaps, feature importance plots, and SHAP visualizations to aid in clinical interpretation. The results indicate that chest pain type, ST depression, and exercise-induced angina are among the most influential predictors. The proposed system successfully achieves accurate and explainable heart disease prediction, contributing to early diagnosis and decision support in healthcare.
Co-Authors M Arif Y Sinaga Olivia Diwani Saragih P.A.M. Zidane R.W.P.P. Zer Rusmin Saragih, Rusmin Victor Asido Elyakim P Yani Maulita