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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding SNATIF ANDHARUPA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Community: Jurnal Pengabdian Pada Masyarakat Scientific Journal of Informatics
Nova Rijati
Teknik Informatika Universitas Dian Nuswantoro
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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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
Pelatihan Diklat Pemanfaatan Aplikasi Online Menggunakan Cek Plagiarisme Dengan Turnitin Untuk Guru Dan Dosen Pada Perkumpulan Profesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Rakasiwi, Sindhu; Marjuni, Aris; Rijati, Nova; Himawan, Heribertus; Jatmoko , Cahaya; Sinaga, Daurat
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.534

Abstract

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Data Studio is an application launched by Google that can be used to convert raw data into interesting and strategic information for users. That way, the information becomes more appropriate to understand quickly and accurately.
Development of a Mental Health Classifier Using LSTM and Text Preprocessing Techniques Haryoko, Priyo; Syukur, Abdul; Rijati, Nova
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21216

Abstract

Purpose: This study aims to address undiagnosed mental health conditions using social media for early detection. By applying advanced preprocessing techniques and LSTM models, the research improves classification accuracy for depression and PTSD. It highlights deep learning’s potential to process unstructured data and provides a scalable solution for real-world mental health monitoring. Methods: Data was collected from Twitter using keywords like "depression" and "anxiety." Preprocessing included normalization, tokenization, stemming, and stopword removal. An LSTM-based model with GloVe embeddings, LSTM layers, and dropout was developed. The model’s performance was evaluated using metrics like accuracy, precision, recall, and F1-score to ensure robust and applicable results. Result: The LSTM model achieved 90% accuracy, outperforming Random Forest (89%) and SVM (89%). Preprocessing steps like tokenization and stemming boosted performance by 15%. The model effectively captured temporal dependencies in text, showcasing its ability to analyze unstructured social media content for mental health detection. Novelty: This study integrates advanced text preprocessing with LSTM to enhance mental health detection. Unlike traditional methods, it captures temporal nuances using GloVe embeddings. The scalable framework provides a reliable solution for real-world applications, paving the way for multilingual and cross-platform research in mental health analytics.
Pelatihan Desain Foto dan Katalog Produk Bank Sampah Pentul Asri Semarang Rahmasari, Erisa Adyati; Sulistiyawati, Puri; Haryadi, Toto; Rijati, Nova
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 3 (2023): September 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v6i3.1563

Abstract

Bank sampah pentul asri berdiri sejak tahun 2018. Stereotype yang muncul yaitu bank sampah hanya mengumpulkan sampah – sampah dari masyarakat dan belum diolah dengan baik menjadi produk yang bermanfaat. Hal ini tentunya menjadi penghambat kemajuan program dari bank sampah tersebut yang mana cukup disayangkan bahwa program ini mendapat dukungan penuh dari wilayah tingkat RW maupun kelurahan setempat sehingga layak untuk dipublikasikan lebih jauh. Masalah lain yang timbul yaitu pengelola bank sampah hanya fokus pada bagaimana cara mengubah sampah menjadi produk, sedangkan potensi – potensi dari produk yang telah dihasilkan pun juga belum dipromosikan kepada masyarakat secara luas. Promosi menjadi kegiatan wajib di era serba digital dan online ini, untuk mempublikasikan produk – produk hasi olahan bank sampah Pentul Asri. Diperlukannya upaya pelatihan desain foto dan katalog produk bank sampah Pentul Asri Semarang sekaligus menjadi tema dan judul kegiatan pengabdian kepada masyarakat yang diajukan untuk memberikan pengetahuan, motivasi, serta ketrampilan dalam menciptakan konsep desain foto dan katalog produk untuk memperkenalkan sekaligus mempromosikan hasil olahan bank sampah. Adapun hasil luaran dari kegiatan ini yaitu konsep fotografi dan pemanfaat media katalog sebagai upaya pengenalan dan promosi produk – produk bank sampah Pentul Asri yang secara tidak langsung juga turut memajukan potensi wilayah kelurahan Tinjomoyo.
An image encryption based on Fibonacci sequence and fusion of advanced encryption standard-least significant bit method Purwanto Purwanto; Aris Marjuni; Erna Zuni Astuti; Christy Atika Sari; Nova Rijati; Pulung Nurtantio Andono; Md Kamruzzaman Sarker
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26078

Abstract

Image encryption is a vital field ensuring the secure transmission of digital images. In this study, encryption is the core process, employing complex mathematical algorithms and cryptographic keys to transform the original image into a secure format, shielding visual data from unauthorized access during transmission. To enhance security, the research integrates Fibonacci and advanced encryption standard (AES)–least significant bit (LSB) methodologies for a complex key generation system. This mechanism introduces intricate transformations within the image data, creating patterns challenging for potential attackers to decipher. Evaluation of the algorithm’s performance reveals efficiency in terms of mean squared error (MSE) and peak signal-to-noise ratio (PSNR). The RGB cover image achieves the lowest MSE of 0.0001 and the highest PSNR values ranging from 44.31 to 49.27. Integration of the Fibonacci sequence notably improves visual quality, enhancing both MSE and PSNR metrics. Unified average changing intensity (UACI) and normalized pixel change rate (NPCR) assessments consistently show the effectiveness of the algorithm, with the RGB cover image presenting the highest UACI and NPCR values. Future research directions involve exploring advanced encryption algorithms, optimizing techniques for high-dimensional datasets, and addressing ethical implications in image encryption, contributing to the development of adaptable and secure solutions.
Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM) Urfiyati, Novia; Rijati, Nova; Pujiono, Pujiono; Soeleman, Arief; Firdaus, Iqbal; Nurhuda, Yeni Agus
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.4966

Abstract

Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.
Co-Authors Abdul Syukur Ajib Susanto Aris Marjuni Astuti, Yani Parti Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Cahaya Jatmoko Christy Atika Sari Desi Purwanti Kusumaningrum Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Edi Sugiarto Erna Zuni Astuti Etika Kartikadarma Firdaus, Iqbal Galih Sabilah Rosat Heribertus Himawan Ifan Rizqa Marjuni, Aris Md Kamruzzaman Sarker Muhammad Aulia Anhar Nurhuda, Yeni Agus Pratama, Ahmad Sabil Deva Pratama, Yudha Hady Pratiwi, Vila Rusantia Priyo Haryoko Pujiono Pujiono Pulung Nurtantio Andono Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Rahmasari, Erisa Adyati Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Soeleman, Arief Sugiyanto - Syamsiar, Syamsiar Toto Haryadi Urfiyati, Novia Wicaksana, Endra Maulia Yaacob, Noorayisahbe Mohd. Yuliman Purwanto Yuliman Purwanto Zainal Arifin Hasibuan