Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) International Journal of Electrical Engineering, Mathematics and Computer Science Journal of Engineering, Electrical and Informatics
Kiki Setiawan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Deteksi Pola Pasien Kanker Serviks dengan Algoritma Extra Trees dan K-Nearest Neighbor Abdi Dharma; Porman Manalu; Gidion Stepen Sinaga; Riael Siringoringo; Imam S. Palangai; Kiki Setiawan; Andrian
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 3 No. 1 (2020): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/jikomsi.v3i1.80

Abstract

Proses pencarian kerja dan proses rekrutmen secara konvensional dinilai kurang efektif dan efisien dari segi biaya dan waktu. Untuk membantu para pencari kerja dalam mendapatkan pekerjaan yang diinginkan dan membantu penyedia kerja dalam mencari kandidat potensial, diperlukan sistem rekomendasi. Sistem rekomendasi pekerjaan yang ideal harus dapat memenuhi beberapa tujuan seperti merekomendasikan pekerjaan yang paling relevan kepada pengguna, memastikan bahwa setiap posting yang diposting akan menerima sejumlah lamaran dari kandidat yang memenuhi syarat dan memastikan bahwa setiap pekerjaan yang diposting tidak menerima terlalu banyak. aplikasi. Untuk mengatasi masalah ini, metode sistem rekomendasi dapat diterapkan. Salah satu metode yang dapat diterapkan adalah metode Fuzzy C-Means (FCM). FCM menggunakan model pengelompokan fuzzy sehingga data dapat menjadi anggota dari semua cluster yang dibentuk dengan derajat atau tingkat keanggotaan yang berbeda, yaitu antara 0 dan tingkat data dalam satu cluster ditentukan oleh derajat keanggotaan. Hasil dari penelitian ini adalah sebuah website rekomendasi pekerjaan yang menerapkan metode Fuzzy C-Means yang mampu memberikan rekomendasi lowongan kerja kepada pengguna berdasarkan kualifikasi dan jurusan yang dimiliki. Website juga menyediakan fasilitas untuk menguji metode Fuzzy C-Means.
Classification of Sales of Best-Selling Products in Ira Store Using Naive Bayes Algorithm and K-Nearest Neighbor Algorithm Yuma Akbar; Kiki Setiawan; Muhammad Joko Umbaran Kharis Bahrudin; Intan Purwasih
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.13

Abstract

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.
Implementation of Naive Bayes Algorithm and Support Vector Machine for Public Sentiment Analysis towards Imported Clothing Ban Veri Arinal; Frencis Matheos Sarimole; Kiki Setiawan; Ahmad Ramdani
Journal of Engineering, Electrical and Informatics Vol. 2 No. 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics:
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i3.313

Abstract

This research was conducted to find out the public's opinion on the Issue of Imported Clothing on Twitter social media. One of the algorithms that can be used to carry out sentiment analysis is Naïve Bayes and Support VectorMachine. In this research the author aims to use the Naïve Bayes Algorithm and Support Vector Machine in analyzing positive and negative sentiment labels. The final result of the comparison with these two test methods, namely the prediction of public sentiment on the issue of imported clothing based on data obtained from Twitter and implemented using the SVM (Support Vector Machine) method, shows an accuracy value of 87.89%. Of the 603 test data, it is predicted that 194 data are Positive Sentiment and 409 data are Negative Sentiment. For prediction results from Negative Sentiment, there are 603 data predicted Negative and 2 data predicted Positive. and the Naive Bayes method shows an accuracy value of 97.01%. Of the 603 test data, it is predicted that 409 data are Negative Sentiment and 194 data are Positive Sentiment.
Co-Authors Abdi Dharma Ahmad Ramdani Akbar, Yuma Andrian Arinal, Veri Gidion Stepen Sinaga Imam S. Palangai Intan Purwasih Muhammad Joko Umbaran Kharis Bahrudin Porman Manalu Riael Siringoringo Sarimole, Frencis Matheos