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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Prosiding SNATIF ANDHARUPA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat
Nova Rijati
Teknik Informatika Universitas Dian Nuswantoro
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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An optimation of advanced encryption standard key expansion using genetic algorithm and least significant bit integration Marjuni, Aris; Rijati, Nova; Susanto, Ajib; Sinaga, Daurat; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Yaacob, Noorayisahbe Mohd.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8367

Abstract

Ensuring data security in today’s digital landscape is of paramount importance, driving the exploration of advanced techniques for safeguarding confidential information. This study introduces a robust approach that combines advanced encryption standard (AES) encryption with key expansion, genetic algorithms (GA), and least significant bit (LSB) embedding to achieve secure data concealment within digital images. Motivated by the pressing need for enhanced data protection, our work addresses the critical challenge of securing sensitive information from unauthorized access. Specifically, we present a systematic methodology that integrates AES encryption for robust data security, GA for optimization, and LSB embedding for subtle information concealment. Through comprehensive experimentation, involving images such as ‘Lena.jpg,’ ‘Peppers.jpg,’ and ‘Baboon.jpg,’ we demonstrate the efficacy of our approach. The imperceptible modification rates mean squared error (MSE) of 0.199, 0.101, and 0.105, coupled with high peak signal-to-noise ratios (PSNR) of 10.04 dB, 9.95 dB, and 9.79 dB respectively, underscore the fidelity and subtlety of the embedded information. This study contributes to the ongoing discourse on data security by offering a comprehensive and innovative approach that addresses the evolving challenges in safeguarding digital information.
Pengenalan Emosi terhadap Ulasan Pelanggan E-Commerce Menggunakan Deep Learning Berbasis Transformer Pratama, Ahmad Sabil Deva; Rijati, Nova
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.11090

Abstract

Penelitian ini mengeksplorasi penerapan arsitektur deep learning berbasis transformer untuk mengidentifikasi emosi dari ulasan pelanggan e-commerce berbahasa Indonesia. Menggunakan dataset yang terdiri dari 5.400 ulasan pelanggan, model ini dirancang untuk mengklasifikasikan lima kategori emosi: Happy, Sadness, Anger, Love, dan Fear. Hasil analisis menunjukkan kecenderungan pelanggan untuk berbagi pengalaman positif dengan dominasi emosi Happy dalam ulasan. Model Transformer berhasil mencapai akurasi klasifikasi keseluruhan sebesar 77,2%, dengan efisiensi waktu pelatihan yang optimal sekitar 90 detik. Evaluasi performa menggunakan confusion matrix dan metrik presisi, recall, dan F1-score memberikan wawasan tentang keefektifan model dalam membedakan emosi kompleks. Temuan ini merefleksikan potensi pemanfaatan teknologi deep learning dalam meningkatkan pemahaman terhadap perilaku pelanggan dan mendukung pengembangan strategi bisnis yang responsif.   Kata kunci: Transformer, Ulasan Pelanggan, Klasifikasi Emosi
Aplikasi Game Edukasi Matematika dan Bahasa Inggris bagi Anak Sekolah Dasar Pratama, Yudha Hady; Rijati, Nova
ANDHARUPA: Jurnal Desain Komunikasi Visual & Multimedia Vol. 9 No. 03 (2023): September 2023
Publisher : Dian Nuswantoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/andharupa.v9i03.8882

Abstract

     Abstrak Perkembangan teknologi informasi telah mengubah pola dan sistem pembelajaran konvensional menjadi inovatif menggunakan game edukasi. Tujuan penelitian ini yaitu mengembangkan game edukasi mata pelajaran matematika dan bahasa Inggris bagi anak usia dini umur 6-8 tahun. Penelitian menggunakan model Multimedia Development Life Cycle (MDLC) dan metode Finite State Machine yang diterapkan pada game dengan tambahan multimedia serta efek-efek tertentu menjadikan game menjadi lebih menarik dan menyenangkan untuk dimainkan. Hasil penelitian menunjukkan bahwa game edukasi sebagai media pembelajaran memudahkan anak usia dini umur 6-8 tahun untuk memahami materi matematika dan bahasa Inggris sehingga meningkatkan motivasi dan semangat untuk selalu belajar. Kata Kunci: bahasa inggris, finite state machine, game edukasi, matematika AbstractDevelopment of information technology has changed conventional learning patterns and systems to become innovative by using educational games. The purpose of this study is to develop educational games in mathematics and English for early childhood ages 6-8 years. The research uses the Multimedia Development Life Cycle (MDLC) model and the Finite State Machine method which is applied to games with additional multimedia and certain effects that make games more interesting and fun to play. The results of the study show that educational games as learning media make it easier for young children aged 6-8 years to understand math and English material so as to increase motivation and enthusiasm to always learn. Keywords: english, finite state machine, game education, mathematics
Classification Vehicle Tire Quality using Convolutional Neural Networks Pratiwi, Vila Rusantia; Rijati, Nova
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.v8i1.8074

Abstract

Tires are a very important component in a vehicle because they are related to driving safety. Defective tires often cause accidents ranging from minor to fatal accidents. Convolutional Neural Network (CNN) is a type of neural network that is used to detect and recognize objects in an image. CNN can imitate the image recognition system in the human visual cortex, making it suitable for identification and classification of image data. This research aims to develop and evaluate a CNN model that is able to classify vehicle tires as 'defective' or 'good'. Model uses a total of 1856 tire images from kaggle.com and is labeled 'defective' or 'good'. Dataset is split using four different data split ratios (60:40, 70:30, 80:20, and 90:10) to determine the optimal distribution that improves the generalization ability of the model. Model evaluation uses accuracy, precision and recall matrices, which are calculated based on the confusion matrix results from testing on 300 data samples. Research results show that the model achieves the best performance at a split ratio of 80:20, with an accuracy of 76.67%, precision of 77.33%, and recall of 76.32%.
Analyzing Sentiment of SiCepat Express User Reviews Wicaksana, Endra Maulia; Rijati, Nova
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8056

Abstract

The development of e-commerce in Indonesia has led to an increase in the number of users of product delivery services to deliver their customers' orders to their destination. SiCepat Ekspres is the number one fastest delivery service in Indonesia, besides JNE and JNT Express. The study aims to evaluate the performance of sentiment analysis methods in identifying and classifying sentiments related to SiCepat Ekspres. Data from Twitter media as many as 10,000 dataset records. The experimental results show that Random Forest with SMOTE is the best method, as it has the highest accuracy (91.10%), followed by improvements in precision, recall, and F-measure. SVM with SMOTE is in second place, with 90.50% accuracy and stable performance in other metrics. Naive Bayes with SMOTE shows improvement, but its performance remains slightly below Random Forest and SVM, with an accuracy of 88.80%.
Pemanfaatan Artificial Intelligence (AI) pada Fitur Aplikasi Capcut untuk Video Pembelajaran Astuti, Yani Parti; Sugiyanto, Sugiyanto; Rizqa, Ifan; Himawan, Heribertus; Purwanto, Purwanto; Kartikadarma, Etika; Rijati, Nova
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.3001

Abstract

Perkembangan teknologi saat ini tidak bisa lepas dengan dunia pendidikan. Berbicara tentang dunia pendidikan tidak lepas dengan peran siswa, mahasiswa, guru dan dosen. Untuk siswa dan mahasiswa mulai banyak keluhan dengan kurang menariknya pembelajaran yang mereka dapatan. Keluhan ini tentunya mengusik pada guru dan dosen sebagai pemberi materi. Untuk itu perlu adanya pendampingan kepada guru dan dosen dalam penyampaian materi pada siswa dan mahasiswa. Pada kegiatan pengabdian ini akan diadakan pendampingan guru dan dosen dalam pembuatan media pembelajaran yang merupakan salah satu bentuk pemberian materi yang dilaksanakan saat ini. Pada pendampingan ini diberikan materi tentang pemanfaatan Artificial Intelligent (AI) pada aplikasi Capcut dalam pembuatan video pembelajaran. Dalam pendampingan ini guru dikenalkan penggunaan aplikasi Capcut dalam pembuatan video pembelajaran. Setelah itu guru dan dosen diberikan cara pemanfaatan AI pada fitur aplikasi Capcut yang mana bisa memberikan efisiensi waktu dalam pengeditan, keramahan dan kemudahan dalam penggunaannya, menghasilkan video yang berkualitas, mempunyai fleksibilitas pemanfaatan yang dibutuhkan oleh siswa dan mahasiswa. Dengan adanya pemanfaatan tersebut, maka guru dan dosen lebih mudah dan kretif dalam pemanfaatan AI pada fitur aplikasi Capcut untuk membuat video pembelajaran. Dengan pendampingan ini, diharapkan siswa dan mahasiswa akan terpenuhi mendapatkan pengajaran yang menarik dan tidak membosankan.Kata kunci: Video pembelajaran, fitur, capcut
Co-Authors Ajib Susanto Aris Marjuni Astuti, Yani Parti Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Desi Purwanti Kusumaningrum Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dimas Irawan Ulumuddin Edi Sugiarto Erisa Adyati Rahmasari Etika Kartikadarma Heribertus Himawan Ifan Rizqa Marjuni, Aris Pratama, Ahmad Sabil Deva Pratama, Yudha Hady Pratiwi, Vila Rusantia Pujiono Pujiono Puri Sulistiyawati Purwanto Purwanto Purwanto, Purwanto Sinaga, Daurat Sinaga, Daurat Sugiyanto - Toto Haryadi, Toto Wicaksana, Endra Maulia Yaacob, Noorayisahbe Mohd. Yuliman Purwanto Yuliman Purwanto Zainal Arifin Hasibuan