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All Journal Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Informatics Management and Information Technology Journal of Artificial Intelligence and Digital Business
Simanullang, Rahma Yuni
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Sistem Pendukung Keputusan Penerima Bantuan Covid-19 Menggunakan Metode Simple Additive Weighting (SAW) Simanullang, Rahma Yuni; Melisa, Melisa; Mesran, Mesran
TIN: Terapan Informatika Nusantara Vol 1 No 9 (2021): Februari 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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

Abstract

This study aims to determine the acceptance of covid19 assistance. The COVID 19 pandemic is devastating, resulting in a lot of unemployment. The poverty rate is increasing, and there are also cases of rampant mortality, the mobility of the community itself and security insecurity. With that, the government plans to provide special assistance for people who cannot afford to face the COVID 19 pandemic. The distribution of social assistance as a realization of the social safety net program during the COVID 19 pandemic has worried many homeworkers, starting from reduced income, setting targets to distribution. However, the mechanism for distributing aid often becomes very complicated, or the nominal amount decreases and there is often an inaccurate point of view because the criteria for beneficiaries do not match the data that is inaccurate / does not match the reality in the field, resulting in misunderstandings between the community. In essence, the Simple Additive Weighting (SAW) method is often known as the weighted method. The basic concept of the Simple Additive Weighthing (SAW) method is to find the weighted sum of the performance branches for each alternative on all criteria. The Simple Additive Weighting (SAW) method requires a decision matrix normalization process. So the author uses the Simple Additive Weighting method or it is often said with the term SAW. To solve this problem, namely by using one of the methods to obtain a multiple and complete assessment of criteria with a conferential thinking framework to carry out hierarchical considerations, then calculate the weight of each criterion to determine the priority recommendations for receiving COVID 19 assistance according to the data. On the results of this study.
Implementation of Apriori Algorithms to Analyze and Determine Consumer Purchase Patterns in Gadget Stores as Sales Increase Strategy Simanullang, Rahma Yuni; ', Khairunnisa; Wanny, Puspita; Utari, Utari; Novelan, Muhammad Syahputra
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7355

Abstract

This study aims to identify the pattern of product purchases that often occur simultaneously at a gadget store in order to develop a more effective sales strategy. The research problem focuses on how to find associations between products based on sales transaction data. The proposed solution is to apply data mining techniques, specifically a priori algorithms, to analyze transaction data and find significant association rules. The A priori algorithm is used through several stages, including the calculation of support for each item, the elimination of items with support below the minimum threshold, the formation of itemset combinations, and the calculation of confidence to generate association rules. The results showed two association rules that met the minimum confidence threshold (60%), namely: (1) If customers buy USB-C, they tend to buy Powerbank (confidence: 67%), and (2) If customers buy Smartwatches, they tend to buy Screen Protectors (confidence: 67%), and (3) If customers buy Screen Protectors, they tend to buy Smartwatches (confidence: 100%). These patterns can be used by the store for strategic product placement and bundling promotions.
Pengelompokan Pola Interaksi Pengguna Media Sosial Menggunakan Algoritma K-Means untuk Pemetaan Aktivitas Online Simanullang, Rahma Yuni; Iqbal, Muhammad
Journal of Informatics Management and Information Technology Vol. 6 No. 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v6i1.954

Abstract

This study aims to cluster social media user interaction patterns in order to obtain a more structured and informative mapping of online activities. User activity on social media platforms is increasing and diverse, ranging from the frequency of uploads, the intensity of comments, the pattern of information dissemination, to responses to certain content. This complexity creates problems in understanding the characteristics of user behavior as a whole, especially when the resulting data is very large and unstructured. To address this challenge, this study applies the K-Means Clustering algorithm, one of the data mining methods that is effective in clustering data based on similar characteristics. The dataset used comes from user activities that include the number of posts, the number of likes, the number of comments, and the level of daily interactions. K-Means is used to divide the data into several clusters that represent the types of user activities, such as active, semi-active, and passive users. The results show that the K-Means algorithm is able to produce a clear and measurable mapping of online activities, with evaluation values ??using SSE and silhouette scores indicating optimal cluster formation. From the grouping process, Cluster 1 was obtained as passive users consisting of U001, U002, U005, U007, and U009 with a final centroid value of C1 of (0.13; 0.13; 0.10; 0.18), Cluster 2 as active users consisting of U003, U004, U006, and U010 with a final centroid value of C2 of (0.53; 0.55; 0.59; 0.49; 0.62), and Cluster 3 as very active users consisting only of U008 with a final centroid value of C3 of (1.00; 1.00; 1.00; 1.00).
Penerapan Teorema Bayes pada Sistem Pakar untuk Diagnosis Infeksi Human Metapneumovirus (HMPV) Khairunnisa, Khairunnisa; Tambunan, Maha Valne Datin Mahfujah; Rambe, Siska Mayasari; Simanullang, Rahma Yuni; Syahri, Rahma; Utari, Utari; Wanny, Puspita; Amin, Muhammad
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5519

Abstract

Human Metapneumovirus (HMPV) adalah virus pernapasan yang dapat menimbulkan infeksi pada saluran pernapasan atas maupun bawah, khususnya pada kelompok yang memiliki risiko tinggi seperti anak-anak, lansia, serta individu dengan sistem kekebalan tubuh yang lemah. Secara klinis, infeksi HMPV menunjukkan gejala yang serupa dengan penyakit pernapasan lain, seperti influenza dan Respiratory Syncytial Virus (RSV), sehingga sering menimbulkan kesulitan dalam proses diagnosis pada tahap awal. Permasalahan ini semakin diperburuk oleh keterbatasan fasilitas pemeriksaan laboratorium khusus, misalnya RT-PCR, yang belum tersedia secara merata, terutama di daerah dengan keterbatasan sumber daya kesehatan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pakar berbasis Teorema Bayes sebagai alat bantu dalam melakukan diagnosis dini infeksi HMPV berdasarkan gejala klinis yang dialami pasien. Pendekatan probabilistik melalui Teorema Bayes diterapkan untuk mengelola ketidakpastian data dengan menghitung tingkat kemungkinan terjadinya infeksi berdasarkan pembobotan gejala yang ditetapkan oleh pakar serta input yang diberikan oleh pengguna. Metode penelitian yang digunakan meliputi analisis permasalahan, pengumpulan data melalui studi literatur dan konsultasi pakar, serta pengembangan sistem pakar dengan mengintegrasikan Teorema Bayes. Sistem yang dikembangkan menganalisis dua belas gejala utama yang berkaitan dengan infeksi HMPV. Hasil pengujian menunjukkan bahwa sistem menghasilkan nilai probabilitas infeksi HMPV sebesar 80% yang termasuk dalam kategori hampir pasti. Temuan ini membuktikan bahwa penerapan Teorema Bayes dalam sistem pakar dapat berperan secara efektif dalam mendukung diagnosis dini infeksi HMPV, meningkatkan akurasi pengambilan keputusan medis, serta membantu tenaga kesehatan dalam memberikan penanganan awal yang lebih cepat dan tepat.
Co-Authors ', Khairunnisa Khairunnisa Khairunnisa Mesran, Mesran Muhammad Amin Muhammad Iqbal Muhammad Syahputra Novelan Nasution, Annio Indah Lestari Rambe, Siska Mayasari Syahri, Rahma Tambunan, Maha Valne Datin Mahfujah Utari Utari Wanny, Puspita