Article Per Year (5 Year)
p-Index From 2021 - 2026
3.207
P-Index
This Author published in this journals
All Journal TEKNIK Jurnal Transformatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Building of Informatics, Technology and Science JUTIS : Jurnal Teknik Informatika Jurnal ABDINUS : Jurnal Pengabdian Nusantara Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat DECODE: Jurnal Pendidikan Teknologi Informasi The Indonesian Journal of Computer Science
Rochadiani, Theresia Herlina
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia Laurenso, Justin; Jiustian, Danny; Fernando, Felix; Suhandi, Vartin; Rochadiani, Theresia Herlina
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.4871

Abstract

In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to find out which regions in Indonesia have the potential to increase marketing based on profits (margins) to support the target market analysis process so that companies do not suffer losses and increase business success. In this study, the analysis will be carried out using vape quantity, margin, and purchasing power data in each region, which is processed using 3 algorithms: K-Means, Hierarchical, and BIRCH. The results of the clustering of the three algorithms produce two clusters. The K-means, Hierarchical, and BIRCH algorithms produce the same clusters: a potential cluster consisting of 18 cities and a non-potential cluster consisting of 45 cities. To see the performance of the model results, an evaluation was carried out using the Silhouette score, Davies Bouldin, Calinski Harabasz, and Dunn index, which obtained results of 0.765201, 0.376322, 315.949434, and 0.013554. From these results, it can be concluded that the clustering results are not too good and not too bad because the greater the Silhouette Score, Calinski Harabasz, and Dunn Index value, the better the clustering results while for Davies Bouldin the smaller the value means the better the clustering results.
Prediction of Air Quality Index Using Ensemble Models Rochadiani, Theresia Herlina
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8532

Abstract

The impact of air pollution on health is measured by the Air Quality Index (AQI). Accurate AQI prediction is essential for pollution reduction and public health recommendations. Traditional methods of monitoring air quality are inaccurate and time-consuming. This study uses IoT-based air quality data from Kampung Kalipaten, Tangerang to build an AQI prediction model with machine learning, specifically an ensemble model. Ensemble techniques such as bagging and boosting, which increase the reliability of predictions by reducing model bias and inconsistency, improve AQI prediction. Four ensemble models used in this study, they are Random Forest Regressor, Gradient Boosting Regressor, Adaboosting Regressor, and Bagging Regressor. As the evaluation, RMSE and R2 metrics used. Random Forest Regressor perform the best with RMSE value of 0.6054 and R2 value of 0.6271, although no significant differences of RMSE and R2 value of the rest models.
Co-Authors Anggraini, Astria Febrian Aprilia, Novia Pramesti Cahyadi, Marcelinus Fajar Canady, Richwen Chitiawan, Tommy Danendra, Danica Recca Djuardi, Rich Deshan Fernando, Felix Gabrielle Sheila Sylvagno Hadibrata, Lymanto Indrawan, Vincenzo Matalino Ito Wasito Jaklin, Vanessa Jericho, Vito Jiustian, Danny Karyadi, Phance Laurenso, Justin Mandasari, Rhea Mayatopani, Hendra Muhammad Muhammad Novia Pramesti Aprilia Panna, Ariya Purnomo, Bryan Elmer Santoso, Handri Sofian, Thamrin steven willian Sucipto, Nadya Rudie Suhandi, Vartin Tanaka, Steven Tanuwijaya, Yefta Thamrin Sofian Thendiwijaya, Alfred Gerald Tua, Jonathan Erik Maruli Wulandari , Islamiati