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

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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.
Analisis Pengaruh Pertumbuhan Ekonomi, Upah Minimum dan Indeks Pembangunan Manusia terhadap Ketimpangan Pendapatan di Provinsi Sulawesi Selatan Helmy, Ahmad; Abbas Selong, Syamsu Nujum,
SEIKO : Journal of Management & Business Vol 7, No 1.1 (2024)
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v7i1.6514

Abstract

Pengaruh pertumbuhan ekonomi, upah minimum dan IPM terhadap ketimpangan pendapatan di Provinsi Sulawesi Selatan menarik untuk kembali diteliti terutama dengan adanya pandemic Covid-19 pada periode 2020-2022. Penelitian ini menggunakan data sekunder dari BPS untuk mengestimasi pengaruh pertumbuhan ekonomi, upah minimum dan IPM terhadap ketimpangan pendapatan di Provinsi Sulawesi Selatan. Penelitian ini menerapkan metode analisis regresi linear berganda. Pada tahun 2007-2022, angka ketimpangan pendapatan, pertumbuhan ekonomi, upah minimum, dan IPM cenderung membaik namun berfluktuasi. Secara simultan berdasarkan analisis regresi linear berganda, pertumbuhan ekonomi, upah minimum, dan IPM berpengaruh signifikan terhadap ketimpangan pendapatan, sedangkan secara parsial pertumbuhan ekonomi tidak berpengaruh signifikan terhadap ketimpangan pendapatan, upah minimum berpengaruh positif, dan IPM berpengaruh negatif. Hasil ini menegaskan bahwa dalam jangka panjang, peningkatan kualitas sumber daya manusia dan peningkatan upah minimum lebih dibutuhkan dibandingkan pertumbuhan ekonomi dalam peningkatan pendapatan masyarakat di Provinsi Sulawesi Selatan. Kata Kunci: Ketimpangan pendapatan, pertumbuhan ekonomi, upah minimum, indeks pembangunan manusia
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.
Trend Analysis and Job Classification in the Field of Artificial Intelligence Using the Support Vector Machine (SVM) Method Helmy, Ahmad; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed the global job landscape, creating new opportunities while redefining existing roles. This study aims to analyze emerging trends and classify job roles in the AI domain using the Support Vector Machine (SVM) method. A dataset was collected from various online job marketplaces and professional platforms to identify key skills, qualifications, and job categories associated with AI-related professions. The data preprocessing involved text normalization, feature extraction using TF-IDF, and classification modeling through SVM. The experimental results demonstrate that the SVM model achieved high accuracy in categorizing AI-related occupations into predefined job clusters, such as Data Scientist, Machine Learning Engineer, AI Researcher, and AI Product Manager. Furthermore, the trend analysis revealed a growing demand for AI professionals with strong interdisciplinary skills combining data analytics, programming, and domain expertise. These findings provide insights for educational institutions, job seekers, and policymakers to align skill development strategies with the evolving needs of the AI workforce.
Co-Authors Abbas Selong, Syamsu Nujum, Ardya, Dwika Hrp, Abdul Chaidir Hrp, Abdul Khaidir Khairul Muhammad Iqbal Sitorus, Zulham Sukrianto Sukrianto, Sukrianto Syahri T., Siti Isna T, Siti Isna Syahri