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All Journal AMMA : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Hari Setyawan
Universitas Pamulang
Humanities Chemistry Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Other

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Pembekalan Ilmu Teknologi Informasi Dan AI (Artificial Intelligence) Sebagai Pendukung Persaingan Dunia Kerja Pada PKBM Bahrul Ulum Rajin Nahampun; Norita Sinaga; Hari Setyawan
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 4 : Mei (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

Artificial Intelligence (AI) is increasingly transformative in various fields, including education and the world of work. For school students, AI can help personalize learning, provide recommendations for materials according to needs, and facilitate access to information through virtual assistants such as chatbots. In the world of work, AI increases efficiency by automating routine tasks, advanced data analysis, and predictive decision-making. However, challenges such as the digital divide and the need for skill enhancement need to be addressed so that the benefits of AI can be felt evenly. With the right understanding and utilization, AI has the potential to drive innovation and productivity in the digital era.
Penerapan K-Means Klustering dengan Algoritma Kluster Dinamik untuk Meningkatkan Kualitas Kluster pada Segmentasi Dokter Potensial: Studi Kasus PT. XYZ: Penelitian Hari Setyawan; Jiyan Suhada; Kelvin Andrean
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4781

Abstract

The traditional K-Means algorithm has a significant weakness in its reliance on random cluster center initialization, which often results in unstable and suboptimal segmentation. This study aims to improve the quality of potential physician segmentation by proposing the integration of a dynamic clustering algorithm into the K-Means framework. The applied method is K-Means augmented with a dynamic clustering algorithm, using RFM (Recency, Frequency, Monetary) attributes derived from physician profile data. Cluster quality is evaluated using the Davies- Bouldin Index (DBI) and Purity. The proposed approach successfully improved segmentation accuracy by 25.15% compared to traditional K-Means. Quantitative analysis shows a significant improvement in cluster quality, indicated by a decrease in the DBI value from 0.846 to 0.411. Furthermore, the Purity value increased from 0.5294 to 0.7647, indicating improved cluster homogeneity. These results demonstrate that the dynamic clustering algorithm effectively addresses initialization sensitivity by iteratively adjusting cluster configurations based on inter- cluster and intra-cluster similarity. The final segmentation yielded four clusters of potential physicians with distinct RFM characteristics, enabling more targeted marketing strategies. The implementation of this model provides strategic benefits for pharmaceutical companies, including the ability to allocate promotional and sponsorship resources more efficiently based on a more accurate and reliable mapping of potential physicians.
Analisis Perbandingan Kinerja Metode Load Balancing Per Connection Classifier dan Equal Cost Multi Path pada Jaringan Internet: Penelitian Jiyan Suhada; Hari Setyawan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5322

Abstract

Kebutuhan akan jaringan internet yang stabil dan andal semakin meningkat seiring dengan pertumbuhan jumlah pengguna dan layanan berbasis digital. Load balancing merupakan salah satu solusi yang umum diterapkan untuk mendistribusikan trafik jaringan agar beban koneksi dapat terbagi secara merata. Dua metode load balancing yang sering digunakan adalah Per Connection Classifier (PCC) dan Equal Cost Multi Path (ECMP). Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja kedua metode tersebut pada jaringan internet. Metode penelitian yang digunakan adalah metode eksperimental dengan menerapkan konfigurasi PCC dan ECMP pada perangkat router dalam topologi jaringan yang sama. Pengujian dilakukan dengan skenario trafik yang identik, meliputi aktivitas browsing, streaming, dan pengunduhan data dengan melibatkan beberapa klien secara bersamaan. Parameter kinerja jaringan yang dianalisis meliputi throughput, delay, jitter, dan packet loss, serta stabilitas koneksi. Hasil penelitian menunjukkan bahwa metode PCC memiliki kinerja yang lebih baik dalam menjaga stabilitas koneksi dan konsistensi jalur trafik, terutama pada aplikasi berbasis sesi. Sementara itu, metode ECMP menunjukkan nilai throughput yang relatif lebih tinggi, namun cenderung mengalami fluktuasi dan ketidakstabilan pada koneksi tertentu. Berdasarkan hasil analisis tersebut, dapat disimpulkan bahwa metode PCC lebih sesuai digunakan pada jaringan yang membutuhkan stabilitas koneksi tinggi, sedangkan ECMP dapat diterapkan pada jaringan dengan kebutuhan distribusi trafik yang sederhana.
Co-Authors Jiyan Suhada Kelvin Andrean Norita Sinaga Rajin Nahampun