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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Teknologi Informasi Komunikasi dan Industri Indonesian Journal of Informatic Research and Software Engineering
Siti Syahidatul Helma
Universitas Islam Negeri Sultan Syarif Kasim Riau
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Indonesian Journal of Informatic Research and Software Engineering

 1 
Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokan Suhu di Provinsi Riau : Comparison of K-Means and K-Medoids Algorithms for Temperature Grouping in Riau Province Faisal, Fahikra; Giopani , Luna Aliska Giopani; Fitriah, Ma’idatul Fitriah; Dwynne, Zaira Cindya Dwynne; Helma, Siti Syahidatul Helma; Mustakim, Mustakim
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 2 No. 2 (2022): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (782.874 KB) | DOI: 10.57152/ijirse.v2i2.434

Abstract

Indonesia merupakan negara yang beriklim tropis dimana setiap daerahnya memiliki suhu yang berbeda-beda. Suhu di Indonesia memiliki tingkatan mulai dari panas, sedang, dingin dengan suhu yang selalu berubah-ubah setiap bulan bahkan setiap harinya, peneliti melakukan penelitian dalam pengelompokkan data suhu di Provinsi Riau yang bersumber dari Badan Pusat Statistik (BPS) Riau mulai dari tahun 2019, 2020, 2021. Penelitian ini dilakukan untuk membandingan algoritma yang digunakan serta mengetahui algoritma yang tepat untuk pengelompokkan suhu berdasarkan hasil uji validitas DBI dimana semakin kecil nilai DBI maka semakin baik cluster yang dihasilkan. Berdasarkan hasil percobaan, algoritma K-Means memperoleh nilai DBI terbaik sebesar 0,2 pada K=6 dengan 10 iterasi sedangkan Algoritma K-Medoids memperoleh nilai terbaik sebesar 0,279 pada K=8 dengan 100 iterasi. Hal ini mengartikan bahwa algoritma K-Means lebih baik jika dibandingkan dengan K-Medoids dalam pengelompokkan suhu di Provinsi Riau. Penelitian ini diharapkan dapat memberikan informasi mengenai segmentasi suhu di Provinsi Riau kepada stakeholder sebagai bahan pertimbangan atau analisis suhu di waktu yang akan datang.
Information Gain Feature Selection for Temporal Sentiment Analysis of Pedulilindungi Application Review using Naïve Bayes Classifier Algorithm: Information Gain Feature Selection for Temporal Sentiment Analysis of Pedulilindungi Application Review using Naïve Bayes Classifier Algorithm Helma, Siti Syahidatul; Qudsi, Dini Hidayatul; Chatisa, Ivan
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 5 No. 2 (2025): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v5i2.2217

Abstract

Through the Instruction of the Minister of Home Affairs of the Republic of Indonesia Number 38 of 2021 concerning the Implementation of Restrictions on Community Activities (PPKM), all communities are required to use the Pedulilindungi application from August 31, 2021, to September 6, 2021, and updated regularly. Users can download and access the Pedulilindungi application through the Google Play Store application market. There, users can directly assess an application by providing reviews that can describe user responses and satisfaction with the application. The Naïve Bayes Classifier (NBC) algorithm is applied to perform modeling in classifying temporal sentiment analysis data. Prior to classification, a feature selection process with information gain is performed. Based on the experimental results, the best evaluation was produced on temporal data dated September 03, 2021, with an accuracy of 91.9% and precision and recall values of 99.9% and 91.9%, respectively.
Co-Authors Chatisa, Ivan Diah Handayani Dini Hidayatul Qudsi Dwynne, Zaira Cindya Dwynne Eva Normala Faisal, Fahikra Fitriah, Ma’idatul Fitriah Giopani , Luna Aliska Giopani Insanul Kamila Insanul Kamila Mustakim Mustakim Mustakim Mustakim Mustakim Mustakim Putri Anglenia Risma Rustiyan R Ulya Ramadhani Z Wisnu Ananta Kusuma