Article Per Year (5 Year)
p-Index From 2021 - 2026
1.829
P-Index
This Author published in this journals
All Journal UG Journal Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JISICOM (Journal of Information System, Infomatics and Computing) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Icon : Jurnal Komputer dan Informatika
Susliansyah, Susliansyah
Universitas Bina Sarana Informatika
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Social Sciences

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Rekomendasi Pemilihan Mitra Kerja Proyek Dengan Menggunakan Metode Electre Pada Perusahaan Industri Susliansyah, S; Kusnadi, Yahdi; Irfiani, Eni; Indriyani, Fintri
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.340

Abstract

Industrial companies, which are one of the players in the economy, are currently experiencing increasingly fast and rapid competition in industrial development. Industrial companies in selecting partners are still using the conventional way of recording partner data and assessing each partner, but some of these records are missing, making it difficult for the company to choose which one has a good performance. In addition, the company still applies subjective methods, namely based on the experience of being partners and being close to people who have power, in the end the company is unable to recommend which partners to accept or which are not accepted. The method that will be used to solve problems is by using the ELECTRE method, which has seven stages, namely Normalization of the Decision Matrix, Weighting the Normalized Matrix, Determining Concordance and Discordance Sets on the Index, Calculating Concordance and Discordance Matrices, Calculating Dominant Concordance and Discordance Matrices, Determine, Aggregate Dominance Matrix and Elimination of Less Favorable Alternatives. The results show that A2, A8 and A4 are the best alternatives from the other 12 alternatives. While the lowest alternatives are A1, A3, A5, A6, A7, A9, A10, A11 and A12.
Pengelompokkan Data Pembelian Tinta Dengan Menggunakan Metode K-Means Susliansyah, Susliansyah; Sumarno, Heny; Priyono, Hendro; Hikmah, Noer
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v3i2.156

Abstract

PT. Mayer Indah Indonesia is engaged in the production of goods, where the most important part to prepare the needs for production needs is the purchasing department, but in the purchasing section it is difficult to determine which items must be bought a lot, are and few in meeting the demand requirements of each part because of the needs goods for production are very unpredictable, eventually causing some goods demand not to be fulfilled because the goods are out of stock. To solve the problems experienced by the purchasing part, datamining using clustering algorithm is k-means method, where the initial stages determine the centroid randomly and do the first iteration calculation and determine the new centroid from the first iteration, then the second iteration calculation is done, because the results of the first and second iterations in the smallest layout of the three groups, the calculation stops. The results obtained by using the ink purchase data seen from the three attributes of incoming goods, items purchased and stock of goods, making it easier and help the purchasing department in classifying items that must be purchased a lot, medium and little.
USE OF UI/X ON WEBSITE RECOMMENDATION OF LAPTOP SPECIFICATIONS WITH K-MEANS ALGORITHM Susliansyah, Susliansyah; Sumarno, Heny; Priyono, Hendro; Maulida, Linda; Indriyani, Fintri
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 1 (2025): March 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i1.20552

Abstract

The process of choosing a laptop that suits their needs is often a challenge for consumers because of the variety of specifications and features offered. Many consumers find it difficult to make the right choice, especially because the information available is often not well structured. In addition, each individual's needs vary, ranging from use for daily productivity to special needs such as gaming or graphic design. Therefore, this study aims to develop a prototype design of a laptop recommendation system using the K-Means clustering algorithm, which is able to group laptop specification data into certain clusters based on the similarity of features. A total of 25 laptop specification data were used in this analysis, with the main parameters being RAM capacity and SSD capacity. The data was processed using the data mining method, and the K-Means algorithm was applied to perform grouping. The optimal number of clusters is determined using the elbow method to ensure accurate and relevant results. The results of the grouping show that laptops can be classified into specific groups that represent consumer needs, such as use for daily productivity or high-load work. The prototype design of this system was created using Figma to visualize an intuitive and easy-to-use user interface (UI). With this prototype design, it is hoped that it can be a reference in the development of a system that makes it easier for consumers to choose a laptop that suits their preferences and needs.
IMPLEMENTASI METODE K-MEANS UNTUK PENGELOMPOKKAN KATA BERINDIKASI CYBERBULLYING PADA KOMENTAR TIKTOK Syawal, Muhamad Akmal; Rayhan, Cahya Muhammad; Pradhana, Bintang Arfiandi; Richardo, Yusuf Jordi; Rizal, Khairul; Susliansyah, Susliansyah
Journal of Information System, Informatics and Computing Vol 9 No 2 (2025): JISICOM (December 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i2.2163

Abstract

Perkembangan pesat media sosial seperti TikTok membawa peningkatan kasus cyberbullying yang berdampak negatif pada korban, termasuk tekanan mental. Penelitian ini bertujuan mengimplementasikan metode clustering sebagai pendekatan pembelajaran tanpa pengawasan untuk mengelompokkan kata-kata indikasi cyberbullying dalam komentar TikTok. Dataset komentar TikTok diolah melalui pra-pemrosesan teks seperti tokenisasi dan normalisasi. Metode clustering digunakan untuk mengelompokkan komentar berdasarkan kemiripan pola kata tanpa memerlukan data berlabel. Hasil pengelompokan mengidentifikasi pola kata konsisten yang menjadi indikasi cyberbullying, mendukung deteksi otomatis tindakan bullying di media sosial. Penelitian ini menyimpulkan bahwa pendekatan clustering efektif dalam mengenali ciri cyberbullying dan direkomendasikan untuk pengembangan sistem moderasi konten di TikTok guna mengurangi dampak negatif cyberbullying.
Co-Authors Aditia Dwinanto Annisa Dwi Wijayanti Asep Saepudin Deleaniara. M, Chyntia Marianna Eni Irfiani Fintri Indriyani Frisma Handayanna Hendro Priyono Heny Sumarno, Heny Hikmah, Noer Indra Riyana Rahadjeng Linda Maulida Maulida , Linda Maulida, Linda Nisan Nisan Pangestu, Rizqyana Pradhana, Bintang Arfiandi Priyono, Hendro Rayhan, Cahya Muhammad Richardo, Yusuf Jordi Ririn Restu Aria Rizal, Khairul Sarita, Nur Syawal, Muhamad Akmal Wijayanti, Annisa Dwi Yahdi Kusnadi Yahdi Kusnadi, Yahdi