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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Mantik Penusa Jurnal Informatika Universitas Pamulang JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jatilima : Jurnal Multimedia Dan Teknologi Informasi INFOKUM MEANS (Media Informasi Analisa dan Sistem) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara International Journal of Basic and Applied Science Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Ulead : Jurnal E-pengabdian Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Bosker Sinaga
STMIK Pelita Nusantara
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Physics Social Sciences Transportation Other

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PKM Pembuatan Dan Pelatihan Aplikasi Pemilihan Bibit Lele Terbaik Tarigan, Nera Mayana Br; Barus, Eviyanti Br; Sinaga, Bosker; Sembiring, Abdi Agustianta; Siregar, Nurika Sari
ULEAD : Jurnal E-Pengabdian Volume 4 Nomor 2 Januari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Katolik Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/ulead.v4i2.4616

Abstract

Program Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk mendukung para pembudidaya ikan lele dalam memilih bibit unggul dengan memanfaatkan aplikasi berbasis teknologi. Kendala utama yang sering dihadapi adalah minimnya pemahaman dalam menentukan kualitas bibit yang baik, sehingga berpengaruh terhadap rendahnya tingkat keberhasilan dalam budidaya. Kegiatan ini mencakup pengembangan serta pelatihan penggunaan aplikasi yang dirancang untuk membantu proses seleksi bibit lele berdasarkan sejumlah kriteria, seperti ukuran, kesehatan, dan tingkat kelincahan ikan. Pelaksanaan program dilakukan dalam beberapa tahapan, mulai dari perancangan aplikasi, pengujian, hingga sosialisasi dan pelatihan kepada pembudidaya. Berdasarkan hasil pelaksanaan, aplikasi yang dikembangkan terbukti mampu meningkatkan efisiensi dalam pemilihan bibit serta memperluas wawasan peserta mengenai karakteristik bibit lele berkualitas. Dengan demikian, program ini diharapkan dapat memberikan kontribusi positif terhadap peningkatan produktivitas dan keberlanjutan usaha budidaya lele di kalangan masyarakat.
Multivariate Data Analysis for Customer Segmentation Using Principal Component Analysis and K-Means Clustering Sinaga, Bosker
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

This study discusses multivariate data analysis for customer segmentation using Principal Component Analysis (PCA) combined with the K-Means clustering method. The problem faced is the high dimension of customer data which makes it difficult to segment and make targeted marketing decisions. The solution offered is the implementation of PCA to reduce the data dimension without losing important information, then followed by K-Means to segment customers based on demographic attributes and shopping behavior. Using a dataset of 200 customers, three customer clusters with different characteristics in terms of age, annual revenue, and shopping score were found. The results of the PCA show that the first two main components are able to explain more than 78% of the data variation, making it easier to visualize and interpret the cluster. These findings provide the basis for a more targeted marketing strategy according to customer segments. In conclusion, the combination of PCA and K-Means is effective in simplifying complex data and resulting in meaningful customer segmentation.
Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination Hasugian, Paska Marto; Sinaga, Bosker; Manurung, Jonson; Al Hashim, Safa Ayoub
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (71.292 KB) | DOI: 10.29099/ijair.v6i1.232

Abstract

Indonesia is the third-largest country in the world with rice production reaching 83,037,000 and became the highest production in southeast Asia spread in several provinces in Indonesia The problem found that such product has not been able to cover the needs of Indonesian people with a very high population so that in the research conducted information excavation to generate potential to the pile of data that has been described and analyzed by BPS with clustering topics. Clustering will help related parties, especially the ministry of agriculture, in determining land development priorities and can minimize the shortage of rice production nationally. Grouping process by involving the K-means algorithm to group rice production with a combination of the elbow method as part of determining the number of clusters that will be recommended with attributes supporting the area of harvest, productivity, and production. Method of researching with data cleaning activities, data integration, data transformation, and application of K-means with a combination of elbow and pattern evaluation. The results achieved based on the work description with a combination of K-Means and elbow provide cluster recommendations that are the best choice or the most optimal is iteration 2 which is the lowest rice production group with a total of 22 provinces, rice production with a medium category of 9 and production with the highest category with 3 regions
Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method Sinaga, Bosker; Tarigan, Nera Mayana Br; Marpaung, Rahmadina; Zamili, Kristof Rian
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5396

Abstract

Inventory stock management is an important aspect in the laundry business to ensure smooth operations and minimize costs. Laundry Detergent shortages or overstocks can cause service disruptions and unnecessary additional costs. Therefore, a method is needed that can help predict stock needs accurately, one of which is the linear regression method. The data used includes historical data on detergent use and other factors that influence demand over several time periods. Through linear regression analysis, a predictive model can be built to estimate detergent needs in the future, so that stocks can be managed more efficiently. Research Method, namely the survey research method, is a research method carried out using surveys or direct data collection from Laundry Luch. The method/algorithm used to analyze the data is the linear regression method. The aim of this research is to apply the linear regression method in detergent inventory stock and to carry out analysis using the linear regression method in detergent inventory stock. The research results from the data that have been collected show that the predicted stock of detergent supplies for Laundry Luch in January 2025, with an estimated total usage of 111 boxes of detergent and a target usage of 95 boxes of detergent, is 129 boxes of detergent. The research conclusion is that the linear regression method provides real benefits in supporting data-based decision making.
Ekplorasi Timeline : Waktu Respon Pesan Terbaik WhatSapp Group “Gurauan kita STMIK Amik” Susandri susandri; Sarjon Defit; Fristi Riandari; Bosker Sinaga
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1149

Abstract

WhatsApp merupakan salah satu aplikasi pesan instan yang banyak di gunakan saat ini. WhatsApp memungkinkan pengguna membuat grup. Sering pesan pada grup tidak terbaca dan terabaikan oleh anggota grup. Perlu dilakukan analisa waktu yang tepat sebuah pesan direspon anggota grup dengan cepat sehingga informasi dapat disampaikan dengan baik pada semua anggota. Penelitian ini melakukan explorasi WhatSapp Group “Gurauan kita STMIK Amik” untuk menentukan waktu terbaik menyampaikan pesan dengan metode timeline serta menganalisis anggota yg berjumlah 32 orang, emoji dan sentimen. Pada Analisis sentimen dari 1095 total pesan, sentimen positif 35.53% dan sentimen negatif 64.47%. Respon emoji dari anggota sebanyak 46% menggunakan pesan emoji diatas 50% dan 34% anggota menggunakan emoji dibawah 50% sedangkan 18 % anggota tidak pernah menggunakan emoji. Dalam penelitian ini dari proses timeline dapat disimpulkan waktu terbaik untuk mengirimkan pesan pada hari selasa dan jum’at pada jam 10, 13 sampai 15 siang dan jam 20 pada malam hari.
Co-Authors Adhar, Tengku Afan Agustina Purba Al Hashim, Safa Ayoub Anastasya Aritonang Rajagukguk Angelia M Manurung Arjon Samuel Sitio Barus, Eviyanti Br Barus, Nadela Bedizatulo Laia Br Barus, Maya Theresia Br Sitepu, Siska Feronika Br Tarigan, Nera Mayana Cindy Shintia Afriani Harahap Daniel Peris Halomoan Hutajulu Desimeri Laoli Dessy Sarah Simbolon Dina Fanita Ester Simanjuntak Fanita, Dina Fretty Wandani Ginting Fristi Riandari Harry Sutanto Hasanah, Holis Hasren Meliani Zebua Hasugian , Paska Marto Hasugian, Penda Sudarto Hengki Tamando Sihotang Humala Simangunsong Hutahaean, Harvei Desmon Ida Royani Simanungkalit Irwanda Prayogi Ivan NUSANTARA siagian Iwan Setiawan Jakaria Sembiring Jeprianto Sinaga Jijon Raphita Sagala Jimmi Herdianda Gurusinga Jimmi Herdianda Gurusinga Julius Sinaga Julius Sinaga, Julius Junius Sembiring Krisswanti, Yuri Laia, Erlina Logaraj Logaraj Lorena Ade Yolanda Sembiring Manurung, Jonson Marpaung, Meman Marpaung, Rahmadina Meman Marpaung Muhammad Ibnu Hawari Murni Marbun Nansia, Oktavio Nera Mayana Br Tarigan Nera Mayana Br Tarigan Br Tarigan Nera Mayana Br.Tarigan Nina Karina Lolo Bintang Nopriansya Nopriansya Oktavio Nansia Parastia, Devina Prayogi, Irwanda Puspa Sari Puspita Sari R. Mahdalena Simanjorang Ramen, Sethu Rehliasna Br Barus Riska Amelia Riski Hari Hadi Salomo Sijabat Santhia Sarjon Defit Sembiring, Abdi Agustianta Sethu Ramen Silalahi, Monalisa Hotmauli Simamora, Erli Susanti Simanjuntak, Ester Sinaga, Anita Sindar R M Sinaga, Jeprianto Siregar, Nurika Sari Sucitra Sidabutar Sulindawaty Sulindawaty, Sulindawaty Susandri, Susandri Tania, Keke Tarigan, Eviyanti Br Tarigan, Ita Roseni Br Tarigan, Nera Mayana Br Tengku Afan Adhar Uzitha Ram Zamili, Kristof Rian