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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
Afri Yudha
Darma Persada University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences Other

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Journal : Journal Technology Information and Data Analytic

 1 
Implementasi Data Mining Untuk Mendukung Program Reduksi Sampah di Daerah Khusus Jakarta Dengan Menggunkan Algoritma Time Series dan K-Means Clustering Muhammmad Krisna Adiputro; Afri Yudha
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.74

Abstract

This study aims to analyze the trend of waste growth in Jakarta using the ARIMA method and to group areas based on waste volume using the K-Means Clustering algorithm. The waste accumulation problem at the Bantargebang TPST continues to worsen each year, with increasing volumes from various sub-districts. Data used in this study were obtained from the DKI Jakarta Environmental Agency, covering the period from January 2022 to April 2024, focusing on organic waste, plastic, and household hazardous waste (B3). The research applies the CRISP-DM methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data processing includes cleaning, normalization, and splitting into training and testing sets. The analysis results show that the ARIMA model achieves good forecasting accuracy, with MAPE, MAE, and RMSE values around 3652. The K-Means algorithm successfully classifies Jakarta areas into three main clusters dominated by organic, plastic, and mixed waste types. A web-based system was developed using Streamlit and MongoDB Atlas to facilitate data analysis and visualization for policymakers, especially the Environmental Agency. The study concludes that ARIMA is effective in forecasting waste growth, while K-Means supports more targeted waste management strategies. It is recommended to enhance the system by incorporating external variables such as policy changes and socio-economic factors, and to improve model accuracy using more advanced machine learning techniques. Additionally, the system should be continuously updated and expanded to support more optimal and sustainable waste management across Jakarta.
Implementation of Support Vector Machine and Multilayer Perceptron Algorithms for Patient Diagnosis Based on Patient Profile and Complaints at Jatibening Public Health Center Romanda Ilham; Afri Yudha
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.104

Abstract

Community health centers (Puskesmas) are primary healthcare institutions that play a crucial role in providing services to the community, especially in areas with limited access. However, the disease identification process at the Jatibening Community Health Center still uses traditional methods that are time-consuming and potentially biased. This study aims to create a disease prediction system for patients using the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) machine learning algorithms that utilize data from patient profiles and complaints. The methods used in this study include collecting information from patient medical records, data processing, training SVM and MLP models, and assessing the model's accuracy level. Test results show that the MLP algorithm achieves 100% accuracy, while the SVM also demonstrates 100% accuracy in predicting the likelihood of a patient's disease based on factors such as age, gender, and chief complaint. Thus, the use of machine learning algorithms on patient data at the Jatibening Community Health Center can accelerate the initial diagnosis process and support more efficient medical decision-making
Co-Authors Adam Arif Budiman Ade Supriatna Aji Setiawan Ario Kurnianto Asyari Dariyus Bagus Tri Mahardika Linda Nur Afifa Muhammmad Krisna Adiputro Romanda Ilham Suzuki Syofian Yan Sofyan Yan Sofyan Andhana Saputra