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All Journal Jurnal Informatika Upgris Jambura Journal of Electrical and Electronics Engineering Jurnal Tecnoscienza Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
Idris, Irma Surya Kumala
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mathematics Other

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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.
Ensemble Approach to Sentiment Analysis of Google Play Store App Reviews Mustofa, Yasin Aril; Idris, Irma Surya Kumala
Jambura Journal of Electrical and Electronics Engineering Vol 6, No 2 (2024): Juli - Desember 2024
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.v6i2.25184

Abstract

In the current digital era, sentiment analysis of Google Play Store application reviews has become a critical key to understanding public opinion on technology products. This study aims to evaluate the effectiveness of ensemble approaches in sentiment analysis compared to individual classification algorithms. The methods employed include ensemble techniques such as Random Forest and Boosting, along with individual algorithms like Naive Bayes and Support Vector Machine (SVM). This research incorporates extensive preprocessing steps, including cleaning, case folding, tokenization, stopword removal, and normalization, to prepare the data before classification. The results demonstrate that ensemble models, particularly Random Forest, achieve superior performance in sentiment classification of app reviews, with accuracy reaching 94.15% for Zoom app reviews and 80.69% for Shopee app reviews. This performance confirms that ensemble approaches are more effective in handling the complexity and variability of review data compared to individually operated algorithms. The study provides valuable insights for application developers to enhance their products based on user feedback. However, there is still room for improvement in terms of optimizing algorithms for highly unbalanced data and developing methods that can handle more complex language nuances. Recommendations for future research include the use of Deep Learning techniques and cross-domain testing to assess the effectiveness of these models in various sentiment analysis settings.
Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM) Idris, Irma Surya Kumala; Mustofa, Yasin Aril; Salihi, Irvan Abraham
Jambura Journal of Electrical and Electronics Engineering Vol 5, No 1 (2023): Januari - Juni 2023
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.v5i1.16830

Abstract

Analisis Sentimen merupakan cabang dari penelitian text mining yang melakukan proses pengklasifikasian dokumen teks. Analisis sentimen dapat melakukan ekstraksi pendapat, emosi, dan evaluasi tertulis seseorang tentang topik tertentu menggunakan teknik pemrosesan Bahasa alami. Pada penelitian ini melakukan analisis sentiment terhadap penggunaan aplikasi Shopee menggunakan algoritma Support Vector Machine (SVM). Tujuan dari penelitian ini adalah untuk mengklasifikasi data komentar dari pengguna aplikasi Shopee kedalam komentar positif dan negatif dengan mempelajari pendapat pengguna tentang aplikasi Shopee melalui ulasan yang diberikan, dan untuk mengetahui kinerja dari metode pengklasifikasi yang digunakan. Pada penelitian ini data diperoleh dengan cara mengangkat data dari ulasan penggunakan aplikasi Shopee menggunakan metode scraping dan berhasil mendapat 3000 data ulasan. Hasil penelitian menggunakan algoritma Support Vector Machine terbukti mampu menghasilkan kinerja yang cukup baik dengan hasil akurasi sebesar 98% dan f1-score sebesar 0.98 atau sebesar 98%.Sentiment analysis is a branch of text mining research that carries out the process of classifying text documents. Sentiment analysis can extract one's opinions, emotions, and evaluations about a certain topic using natural language techniques. In this study, sentiment analysis was carried out on the use of the Shopee application using the Support Vector Machine (SVM) algorithm. The purpose of this study is to classify comment data from Shopee application users, positive and negative comments by studying user opinions about the Shopee application through the reviews provided, and to determine the performance of the classifier method used. In this study, the data was obtained by collecting data from reviews on the use of the Shopee application using the scraping method and managed to get 3000 data reviews. The results of research using the Support Vector Machine algorithm are proven to be able to produce quite good performance with an accuracy of 98% and an f1-score of 0.98 or 98%. 
KLASIFIKASI MUTU GREENBEAN COFFEE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Muhamad, Azhar; Idris, Irma Surya Kumala; Andi Bode
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 4 No 2 (2025)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v4i2.620

Abstract

ABSTRACT AZHAR MUHAMAD. T3118209. QUALITY CLASSIFICATION OF GREEN BEAN COFFEE USING CONVOLUTIONAL NEURAL NETWORK METHOD Coffee is one of Indonesia's foreign exchange sources and plays an important role in the development of the plantation industry. In the commercial process, a product must have advantages, especially in terms of quality to survive in world market competition. The Convolutional Neural Network (CNN) method is a Deep Learning method that can identify and classify an object in a digital image. The training process is carried out by looking for a model structure that matches the training data and validation data so that overfitting does not occur in the CNN network. The experimental results in this study indicate that the Convolutional Neural Network method can classify the quality of green bean coffee with an accuracy rate of 90%, recall of 92%, precision of 86%, and F1-Score of 88% from 30 images by taking 15 sample images from each class using confusion matrix testing.
Co-Authors Andi Bode Asiking, Agriyanto Muhamad, Azhar Mustofa Mustofa Mustofa, Yasin Aril N, Asmaul Husnah Puspita Sari Jan, Sitti Rachmah Salihi, Irvan Abraham