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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pengabdian Masyarakat IPTEK Journal of Computer Science and Research
Ardya, Dwika
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

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Analysis of Social Assistance Donor Classification at the Muhammadiyah Medan Orphanage Using SVM Helmy, Ahmad; Sitorus, Zulham; Ardya, Dwika; Hrp, Abdul Chaidir; T, Siti Isna Syahri; Sukrianto, Sukrianto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14299

Abstract

The Putra Muhammadiyah Orphanage in Medan City is a social institution that relies on donor assistance to support various social programs. The problem that occurs at the Putra Muhammadiyah Orphanage in Medan is the difficulty in identifying potential and non-potential donors who have the potential to provide sustainable social assistance contributions. This study aims to conduct a comprehensive analysis and classification of donors using the Support Vector Machine method, an effective method in machine learning to handle classification problems with SVM with high accuracy. The research data consists of donor data with several main characteristics such as the amount of donation, the frequency of donations given, and the type of assistance. The data is processed through a preprocessing stage including data normalization and data division into training and testing data. Then, the SVM model is trained to classify donors into two categories, namely Potential Donors and Non-potential Donors. Based on the data obtained from the donation bookkeeping records of the Putra Muhammadiyah Orphanage in Medan City, it can be concluded that around 55 potential donors out of 90 donors and 35 non-potential donors out of 90 donor data. From the results of the analysis and testing of the model conducted, it can be concluded that the SVM method can classify "Potential Donors" and "Non-Potential Donors" with a fairly high level of accuracy. The level of accuracy obtained reached up to 89% with a precision value of 93%, a recall value of 89% and an f1-score of 90%. With these results, this study can provide significant benefits in the management of social assistance, especially helping orphanages to determine who are potential and non-potential donors. Therefore, this study is expected to have an impact on improving the sustainability of social programs at the Putra Muhammadiyah Orphanage in Medan City.
Penerapan dan Sosialisasi Sistem Informasi Panti Asuhan Putera Muhammadiyah Kota Medan Khairul; Sukrianto; Ardya, Dwika; Syahri T., Siti Isna; Hrp, Abdul Khaidir; Helmy, Ahmad
Jurnal Pengabdian Masyarakat IPTEK Vol. 5 No. 1 (2025): Edisi Januari 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v5i1.10594

Abstract

Panti Asuhan Putera Muhammadiyah Kota Medan merupakan lembaga sosial yang bergantung pada bantuan donatur untuk mendukung berbagai program sosial. Permasalahan yang terjadi di Panti Asuhan Putera Muhammadiyah Medan adalah belum adanya Sistem Informasi yang mengelola dan memanajemen bantuan sosial dari para donatur dengan efektif dan efisien karena masih menggunakan pembukuan dengan cara manual. Sistem Informasi Panti Asuhan Putera Muhammadiyah Kota Medan bertujuan memudahkan panti asuhan dalam memanajemen bantuan sosial dari para donatur agar menjadi lebih efektif dan efisien dalam pengelolaan bantuan sosial. Dengan penerapan dan sosialisai sistem informasi, sistem yang dibangun mengintegrasikan beberapa aspek penting dalam Panti Asuhan yaitu pengelolaan data bantuan sosial, pengelolaan anak panti, pengelolaan donatur dan proses donasi yang biasanya dilakukan secara manual. Kedepannya dengan adanya sistem, pengelolaan data tersebut dan proses donasi akan menjadi lebih efektif dan efisien. Sistem yang dibangun dengan User Interfaceberbasis web mulai dari pembuatan website panti asuhan dan sistem berbasis web untuk proses donasi dengan proses transfer langsung ke rekening Panti Asuhan dan proses payment gateway melalui payment Virtual Account seperti Mobile Banking, Internet Banking dan E-Wallet seperti transfer melalui OVO, Gopay dan lain sebagainya. Selain itu juga, sistem yang dibangun akan menjaga privasi dan kerahasiaan data kas keuangan panti asuhan. Manfaat dari Penerapan Sistem Informasi Panti Asuhan Putera Muhammadiyah Kota Medan diharapkan dapat membantu berbagai program sosial di panti asuhan dan mendukung dalam pengelolaan bantuan sosial dari para donatur agar menjadi lebih efektif dan efisien dan pembukuan tersistem dengan baik.
Analysis and Classification of IT Professions in the Marketplace Using the Support Vector Machine Method: Analysis and Classification of IT Professions in the Marketplace Using the Support Vector Machine Method Ardya, Dwika; Iqbal, Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v2i2.64

Abstract

The development of the digital industry in Indonesia has driven an increasing demand for professional workers in the information technology (IT) sector. Along with this, the need arises to understand and map salary levels based on job profiles to create transparency and efficiency in the recruitment process. This study aims to predict the salary categories of IT professionals using the Support Vector Machine (SVM) method in well-known marketplace companies such as Gojek, Shopee, Tokopedia, Traveloka, Tiket.Com and Bukalapak. The dataset used contains 611 data entry records with attributes of company, work location, experience and skills as well as salary. The preprocessing process consists of label encoding, numeric normalization, and multi-hot encoding for skill features. The salary categories are divided into three: low, medium, and high. The SVM model is trained with the Radial Basis Function (RBF) kernel and evaluated with accuracy, precision, recall, and f1-score metrics. The evaluation results show that the SVM model is able to classify salary categories with an accuracy of 82%. This model shows the best performance in the Medium salary category with an f1-score of 0.93. This study proves that SVM can be used as an alternative to build an effective IT Salary Category prediction system.
Co-Authors Helmy, Ahmad Hrp, Abdul Chaidir Hrp, Abdul Khaidir Khairul Muhammad Iqbal Sitorus, Zulham Sukrianto Sukrianto, Sukrianto Syahri T., Siti Isna T, Siti Isna Syahri