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All Journal Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics The Asian Journal of Technology Management (AJTM) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Network Engineering Research Operation [NERO] Jurnal Abdimas Mahakam Indonesian Journal of Electrical Engineering and Computer Science Frontier Energy System and Power Engineering Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal ilmiah teknologi informasi Asia
Sulistyo, Danang Arbian
Institut Teknologi Dan Bisnis ASIA Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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High-accuracy classification of banana varieties using ResNet-50 and DenseNet-121 architectures Riska, Suastika Yulia; Sulistyo, Danang Arbian; Siti Maharani, Farah Shafiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp322-335

Abstract

Bananas are a popular fruit in Indonesia due to their affordability, availability, and rich nutritional content. Identifying different banana types is crucial for consumption and processing, yet some types are difficult to distinguish visually. This study aims to classify banana types using convolutional neural network (CNN) architectures, specifically ResNet-50 and DenseNet-121. The dataset consists of five banana classes, which were processed using preprocessing techniques to enhance image quality prior to model training. The results demonstrate that the proposed models can classify banana types with high accuracy. The research methodology includes data collection, preprocessing, CNN model implementation, and performance evaluation using a confusion matrix. The dataset was split into training and testing sets in an 80:20 ratio, with validation data extracted from the training set in a 90:10 ratio. The models were trained on the training data, validated with validation data, and tested on the testing data to assess final performance. The study concludes that the CNN architectures employed are effective in classifying banana types, with the DenseNet-121 model achieving 93.02% accuracy, outperforming the ResNet-50 model, which achieved 92.44%. These results indicate that the models can capture essential features from banana images and produce accurate predictions.
Peningkatan Akurasi Deteksi Intrusi Jaringan dengan Model Hybrid Convolutional Neural Network dan Long Short-Term Memory Pratama, Ficho Pranandasya Andrian; Sulistyo, Danang Arbian; Mukti, Fransiska Sisilia
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.895

Abstract

The evolving cyber threats demand more sophisticated and accurate intrusion detection systems (IDS). This research develops a hybrid CNN-LSTM model with comprehensive data preprocessing techniques to enhance network attack detection accuracy. The UNSW-NB15 dataset consisting of nine attack categories and 49 features was used as research data. The methodology begins with data preprocessing including data cleaning, categorical transformation using categorical codes, class balancing with upsampling, StandardScaler normalization, and 80:20 data splitting. The hybrid model architecture combines three CNN blocks for spatial feature extraction with two LSTM layers for modeling temporal dependencies. The model was compiled using Adam optimizer with 0.0005 learning rate and equipped with EarlyStopping, ReduceLROnPlateau, and ModelCheckpoint callbacks. Evaluation results show the CNN-LSTM model achieves 99% accuracy, precision, recall, and F1-score, significantly outperforming the standard CNN model which only reaches 96%. Learning curves demonstrate rapid convergence without overfitting indication. This research proves that the combination of CNN's spatial feature extraction capability and LSTM's temporal dependency modeling is highly effective for anomaly detection in complex sequential data such as network traffic.
Multilingual Parallel Corpus for Indonesian Low-Resource Languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almu’iini; Arya Astawa, I Nyoman Gede; Andika Dwiyanto, Felix
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3412

Abstract

Indonesia has an extraordinary number of languages, with more than 700 regional languages such as Javanese, Madurese, Balinese, Sundanese, and Bugis. Despite the wealth of languages, digital resources for these languages remain scarce, making the preservation and accessibility of digital languages a significant challenge. Research was conducted to address this gap by building a multilingual parallel corpus consisting of more than 150,000 phrase pairs extracted from Bible translations in five regional languages in Indonesia. Rigorous preprocessing, normalization, and Unicode tokenization were performed to improve data quality and consistency. The encoder-decoder architecture was a key focus in the development of the NMT model. Evaluation focused on forward and backward translation directions, which were measured using BLEU scores. The results show that forward translation consistently outperforms backward translation. The Indonesian Javanese model produced a score of 0.9939 for BLEU-1 and 0.9844 for BLEU-4, indicating a high level of translation quality. In contrast, reverse translation tasks, such as translating from Sundanese to Indonesian, presented significant challenges, with BLEU-4 scores as low as 0.3173. This illustrates the complexity of the translation system from Indonesian to local languages. If future research focuses on transformer-based models and incorporates additional linguistic parameters to enhance the accuracy of natural language processing (NLP) models for Indonesia's underrepresented regional languages, this work provides a dataset that can be utilized for that purpose.
Co-Authors A.N. Afandi Achmad Noercholis Achmad Noercholis Aji Prasetya Wibawa Andika Dwiyanto, Felix Andrew Nafalski Aripriharta - David Armanda Didik Dwi Prasetya Fadhli Almu’iini Ahda Farokhah, Lia Fitria, Vivi Aida Fransiska Sisilia Mukti Fransiska Sisilia Mukti Gunawan Gunawan Hanip Afandi I Nyoman Gede Arya Astawa Lia Farokhah Lilis Widayanti Machrus Tohir Machrus Tohir Moh Ashari Muhammad Rofiq Muhammad Rofiq Muhammad Rofiq Philip Faster Eka Adipraja Pratama, Ficho Pranandasya Andrian Siti Maharani, Farah Shafiyah Suastika Yulia Riska Sulthan Shidqi Sulthan Shidqi Vivi Aida Fitria Wahyuni, Ida Yogie Susdyastama Putra