Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Jurnal Informatika Upgris Jambura Journal of Electrical and Electronics Engineering Jurnal Tecnoscienza
Idris, Irma Surya Kumala
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mathematics Other

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

Found 3 Documents
Search

 1 
Quick Response Code Absensi Guru Menggunakan Secure Hashing Algorithm (SHA) Asiking, Agriyanto; N, Asmaul Husnah; Idris, Irma Surya Kumala
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v6i2.705

Abstract

Sistem Absensi guru yang diterapkan di sekolah masih dilakukan secara manual, yaitu guru menandatangani buku absen yang telah disediakan. Hal ini dikhawatirkan dapat meningkatkan potensi penyebaran COVID 19 dikarenakan menggunakan peralatan absensi yang sama. Berdasarkan permasalahan tersebut penelitian absensi akan dibuat menggunakan teknologi antara Quick Response Code yang menggunakan Secure Hash Algorithm (SHA) dan Smartphone android sehingga mengurangi kontak fisik atau penggunaan benda yang disentuh oleh banyak orang secara bergantian. Penelitian ini mengimplementasikan algoritma kriptografi SHA-256 untuk pembuatan Quick Response Code absensi. Hasil enkripsi dari SHA-256 akan dikombinasi dengan algoritma BCRYPT untuk menghindari serangan decode hash SHA-256. Pengamanan Quick Response Code dengan menggunakan enkripsi SHA-256 lebih optimal dengan mengkombinasikan fungsi BCRYPT pada Message yang telah dienkripsi SHA-256, sehingga menghindari serangan decode hash SHA-256
Analisis Sentimen Terhadap Data Kuisioner Evaluasi Dosen Menggunakan Algoritma Naïve Bayes Puspita Sari Jan, Sitti Rachmah; Mustofa, Yasin Aril; Idris, Irma Surya Kumala
Jurnal Informatika UPGRIS Vol 9, No 2: Desember 2023
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v9i2.17001

Abstract

Students' satisfaction with the quality of lecturers' way of teaching is oneof theimportant things in higher education institutions. Universitas Ichsan Gorontalo hasimplemented an online questionnaire as student feedback to determine and evaluatethe performance of lecturers. The Faculty of Computer Science is one of the facultiesthathasimplementedthequestionnairefillingsystem.Thequestionnaireismandatoryfor all students as a requirement to join a course contract at the beginning of thesemester. The evaluation of the performance of lecturers during lectures has a veryimportantrole.Itimprovesthequalityoflearningandacademicstandardization.Thisstudy aims to determine the level of student satisfaction with the services of lecturerswhen teaching. This study applies sentiment analysis using the Naïve Bayes Classifierclassificationmethod.ItalsoemploystheweightingmethodusingtheTermFrequency-Inverse Document Frequency (TF-IDF). The results of this study have determined theclassification of the lecturer service questionnaire data. The results are easy to read.Theresultsofthesurveyonthelevelofstudentsatisfactionwithlecturerservicesfrom1,989dataindicatethat1,946datahavepositivesentimentsand43datahavenegativesentiments.TheresultsgainedfromtheNaïve Bayesaccuracy is 97%accuracy.
Improving Naïve Bayes Accuracy with Particle Swarm Optimization in Sentiment Analysis of Ibu Kota Nusantara (IKN) Idris, Irma Surya Kumala; Mustofa, Mustofa
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.31589

Abstract

The development of Indonesia's new capital city, Ibu Kota Nusantara (IKN), has sparked extensive public discourse on social media, positioning sentiment analysis as a strategic approach to understanding public opinion. This study assesses the performance of the Naïve Bayes algorithm enhanced through Particle Swarm Optimization (PSO) in classifying public sentiment related to the IKN project, using Indonesian-language comments extracted from the social media platform X. The initial Naïve Bayes model achieved an accuracy of 78.3%, while the PSO-optimized model demonstrated an improved accuracy of 79.7% under optimal parameter settings. These findings indicate the potential of PSO to enhance feature selection effectiveness and reduce classification errors, particularly for positive sentiments. Despite the observed improvements, limitations such as reliance on automated sentiment labeling and challenges posed by linguistic context remain. This study contributes an early exploration of optimization-based methods for public opinion classification and highlights the need for further research involving advanced approaches such as deep learning tailored to the Indonesian language.Pembangunan Ibu Kota Nusantara (IKN) menimbulkan diskursus publik yang luas di media sosial, menjadikan analisis sentimen sebagai pendekatan strategis untuk memahami opini masyarakat. Studi ini mengevaluasi kinerja algoritma Naïve Bayes yang ditingkatkan menggunakan pendekatan Particle Swarm Optimization (PSO) dalam tugas pengelompokan sentimen publik terhadap proyek IKN, dengan menggunakan data komentar berbahasa Indonesia dari platform media sosial X. Hasil awal dari model Naïve Bayes standar mencatat akurasi sebesar 78,3%, sedangkan setelah proses optimasi dengan PSO, akurasi meningkat menjadi 79,7% pada pengaturan parameter terbaik. Hasil ini memperlihatkan potensi PSO dalam meningkatkan efektivitas seleksi fitur dan mengurangi kesalahan klasifikasi, terutama pada sentimen positif. Meski pendekatan ini menunjukkan perbaikan, keterbatasan seperti ketergantungan pada pelabelan otomatis dan konteks linguistik masih menjadi tantangan. Studi ini memberikan kontribusi awal dalam pengembangan metode klasifikasi opini publik berbasis optimasi, serta mendorong eksplorasi pendekatan lanjutan seperti deep learning untuk konteks bahasa Indonesia.
Co-Authors Asiking, Agriyanto Mustofa Mustofa Mustofa, Yasin Aril N, Asmaul Husnah Puspita Sari Jan, Sitti Rachmah