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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan IJISTECH (International Journal Of Information System & Technology) The IJICS (International Journal of Informatics and Computer Science) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research JTIK (Jurnal Teknik Informatika Kaputama) Community Development Journal: Jurnal Pengabdian Masyarakat Patria : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing IJISTECH DISTANCE: Journal of Data Science, Technology, and Computer Science Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Biner : Jurnal Ilmiah Informatika dan Komputer SOROT: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Teknologi Jaringan
Dedi Candro Parulian Sinaga
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Implementation of K-Nearest Neighbor Algorithm for Scientific Determination of Aid Recipients at STM Agape Dedi Candro Parulian Sinaga; R. Fanry Siahaan; Nera Mayana Br Tarigan; Rodiah Hannum Lubis; Dwi Novia Amallia
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9484

Abstract

Providing assistance to underprivileged families is an important social effort to enhance community welfare; however, the selection of aid recipients often encounters problems such as subjectivity, unstructured data, and time inefficiency when conducted manually. This study aims to develop and evaluate a decision support system for determining aid recipients at STM Agape using the K-Nearest Neighbor (KNN) algorithm to improve accuracy and objectivity in the selection process. The research methodology employed a quantitative classification approach, where data were collected from families based on predefined criteria, including family income, number of dependents, housing conditions, and the occupation of the head of the household. The dataset was divided into training and testing data, and all attributes were normalized prior to processing. The KNN algorithm was applied using Euclidean distance to measure similarity between data instances, classifying each family into “eligible” or “ineligible” categories. The results indicate that the proposed system achieved higher classification accuracy and more consistent decision outcomes compared to manual selection methods. Additionally, the implementation of KNN reduced processing time and minimized subjective bias in determining aid recipients. These findings demonstrate that the KNN-based system is effective as a decision support tool, enabling STM Agape to distribute social assistance in a more targeted, objective, transparent, and efficient manner.
Implementation of K-Means Clustering for Student Achievement Classification Using Academic Performance Indicators Dedi Candro Parulian Sinaga; Endra Ary Prasasty Marpaung; Nera Mayana Br Tarigan; Vinsensius Fereri Purba; Dwicky Aditya
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9795

Abstract

Student achievement assessment in educational institutions is often conducted manually by relying on average scores or class rankings, making the results less objective and unable to represent students’ overall academic conditions. This study aims to implement the K-Means Clustering algorithm to classify student achievement using academic performance indicators. The dataset consists of 10 student records with four variables: average score, examination score, attendance percentage, and assignment score. The research stages include data collection, data validation, Min-Max normalization, initial centroid selection, Euclidean Distance calculation, cluster formation, centroid updating, and interpretation of clustering results. The number of clusters was set to K=3, representing high, medium, and low achievement categories. The results show that Cluster C1 consists of 4 students with high achievement, Cluster C2 consists of 3 students with medium achievement, and Cluster C3 consists of 3 students with low achievement. The final centroid values indicate that Cluster C1 has the strongest academic performance, while Cluster C3 requires more intensive academic support. These findings demonstrate that K-Means Clustering can classify student achievement objectively and support data-driven educational decision-making, academic guidance, and targeted learning strategy development.
PEMANFAATAN KECERDASAN BUATAN DALAM PEMBUATAN KONTEN PROMOSI UNTUK MENINGKATKAN PENJUALAN PT. DWITUNGGAL JAYALESTARI Martua Sitorus; Dedi Candro Parulian Sinaga; Endra Ary Prasasty Marpaung; Hotmian Manik; Listyawati Simamora
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 7 No. 3 (2026): Vol. 7 No. 3 (2026)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v7i3.59383

Abstract

Perkembangan teknologi digital yang pesat menuntut perusahaan untuk terus berinovasi dalam strategi pemasaran, salah satunya melalui pemanfaatan kecerdasan buatan (Artificial Intelligence/AI). Penelitian ini bertujuan untuk menganalisis bagaimana penerapan AI dalam pembuatan konten promosi dapat meningkatkan penjualan pada PT. Dwitunggal Jayalestari. Metode penelitian yang digunakan adalah pendekatan deskriptif kualitatif dengan teknik pengumpulan data berupa observasi, wawancara, serta studi literatur yang relevan. Hasil penelitian menunjukkan bahwa pemanfaatan AI mampu meningkatkan kualitas konten promosi melalui pembuatan desain visual, penulisan copywriting, serta penyesuaian konten berdasarkan preferensi dan perilaku konsumen. Teknologi AI juga memungkinkan perusahaan untuk melakukan analisis data secara cepat dan akurat sehingga strategi pemasaran menjadi lebih tepat sasaran. Selain itu, penggunaan AI dapat mempercepat proses produksi konten, menghemat biaya operasional, serta meningkatkan efisiensi kerja tim pemasaran. Implementasi AI dalam kegiatan promosi terbukti memberikan dampak signifikan terhadap peningkatan engagement pelanggan, seperti jumlah interaksi di media sosial dan tingkat respons terhadap kampanye digital. Hal ini berkontribusi pada peningkatan konversi penjualan secara bertahap. Namun, terdapat beberapa kendala dalam penerapannya, seperti keterbatasan sumber daya manusia yang memiliki kompetensi di bidang teknologi serta kebutuhan akan integrasi sistem yang optimal. Oleh karena itu, perusahaan disarankan untuk melakukan pelatihan sumber daya manusia serta mengembangkan strategi implementasi AI yang terarah agar dapat memaksimalkan potensi teknologi ini. Kegiatan ini diharapkan dapat menjadi referensi bagi perusahaan dalam mengoptimalkan pemanfaatan AI untuk mendukung efektivitas promosi dan peningkatan penjualan secara berkelanjutan.
Co-Authors Bella Puspa Dewi Dwi Novi Amalia Dwi Novia Amallia Dwi Novia Amalliaendra Dwicky Aditya Endra A. P. Marpaung Endra Ary Prasasty Marpaung Gunung Juanda Tampubolon Hotmian Manik Ifanlius Ndruru Listyawati Simamora Marpaung, Preddy Martua Sitorus Muliati Laia Nera Mayana Br Tarigan Nurika Sari Siregar Rodiah Hannum Lubis Rodiah Hannum Lubisndra Septianita Simajuntak Shadiq Arif Musyaffa Siahaan, R. Fanry Sianipar, Baringin Stanisnicolaus Baene Vinsensius Fereri Purba Wiknes Kumar