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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Prosidia Widya Saintek JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Aviv Yuniar Rahman
Universitas Widya Gama Malang
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation

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Implementation of the LSTM Model for Speech-to-Text Systems in the Recognition of the Walikan Language of Malang: Author's Country: Indonesia Raynanda Raynanda; Aviv Yuniar Rahman; Istiadi Istiadi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/m0pcpk09

Abstract

This study developed a Speech-to-Text (STT) system based on the Long Short-Term Memory (LSTM) model to recognize and convert speech in the Malang Walikan language into text. The Malang Walikan language has a unique linguistic structure in the form of word reversal, which poses a challenge in speech recognition. The data used consisted of 1,000 sentences collected from social media and direct recordings. The data was processed using Mel Frequency Cepstral Coefficients (MFCC) and then used to train the LSTM model.The system's performance was evaluated using the Word Error Rate (WER), Character Error Rate (CER), and Average Test Loss metrics. The best results obtained showed a WER value of 1.0 on a 699:300 data split, a CER of 0.78 on a 799:200 split, and an Average Test Loss of 11.0147 on a 299:700 split.The high Average Test Loss value indicates the model's difficulty in minimizing prediction errors, which may be caused by the model's mismatch with the data patterns or overfitting. To improve the model's performance, it is recommended to improve the quality of the training data, optimize the parameters, and apply regularization techniques.
LSTM-Based Classification of Indonesian Regional Song Lyrics by Language: Author's Country: Indonesia Muhammad Rizky Anandita Priatama; Aviv Yuniar Rahman; Syahroni Wahyu Iriananda
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/jh41mx81

Abstract

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.
Implementation of a Bi-LSTM Model for Automatic Text Classification of Mathematics, Science, and Indonesian Language Questions: Author's Country: Indonesia Mochamad soffan muslim; Aviv Yuniar Rahman; Rangga Pahlevi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/k7tge561

Abstract

This study aims to implement the Bidirectional Long Short-Term Memory (Bi-LSTM) model for automatic text classification of Mathematics, Natural Sciences (IPA), and Indonesian language questions to support efficient question grouping in digital education systems. The dataset used consists of 2,718 questions, which are evenly distributed across three subject categories. The research stages include text preprocessing, tokenization and padding, splitting the dataset into training and testing sets, designing the Bi-LSTM model architecture, and conducting training and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Bi-LSTM model achieves an accuracy of 97% on the test data, with an average F1-score of 0.97. The confusion matrix analysis indicates that most predictions are correctly classified with a relatively low misclassification rate across categories. Based on these results, it can be concluded that the Bi-LSTM model is effective for automatic text classification of educational questions and has strong potential for further development in technology-based question grouping systems.
DistilBERT-Based E-Commerce Sentiment Analysis Zahri Aksa Dautd; Aviv Yuniar Rahman; Fitri Marisa
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

The rapid advancement of digital technology has driven significant growth in Indonesia’s e-commerce sector, with Shopee emerging as one of the largest platforms generating millions of product reviews daily. These reviews contain valuable consumer opinions that can be analyzed to assess customer satisfaction, yet their massive volume makes manual analysis inefficient and subjective. This study aims to develop an automated sentiment analysis model using DistilBERT to classify Shopee product reviews into positive and negative sentiments. The dataset comprises approximately 1 million Englishlanguage reviews covering various product categories, including electronics, fashion, beauty, and household items. The research methodology involves text preprocessing, tokenization using DistilBertTokenizerFast, and fine-tuning of the DistilBERT model under multiple data-split ratios (90:10, 80:20, 70:30, 60:40). Experimental results demonstrate that DistilBERT achieved the highest accuracy of 94.8%, outperforming baseline models such as Naïve Bayes (88.4%) and SVM (89.6%). These findings confirm that DistilBERT effectively maintains a balance between accuracy, precision, and recall while offering high computational efficiency. This research contributes both methodologically and practically by establishing DistilBERT as a scientifically robust and resource-efficient solution for large-scale sentiment analysis in Indonesia’s e-commerce environment.
KLASIFIKASI CITRA TIGA VARIETAS APEL MALANG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Djourdi Amrida Pratama; Aviv Yuniar Rahman; Rangga Pahlevi Putra
Prosidia Widya Saintek Vol. 5 No. 2 (2026)
Publisher : Universitas Widyagama Malang

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Abstract

Kemiripan warna kulit, bentuk, dan tekstur permukaan antarvarietas apel Malang dapat menyebabkan proses identifikasi visual menjadi kurang konsisten. Penelitian ini bertujuan merancang dan mengevaluasi model Convolutional Neural Network (CNN) untuk mengklasifikasikan citra varietas Wanglin, Manalagi, dan Anna. Dataset primer terdiri atas 1.939 citra yang dibagi secara terstratifikasi dengan rasio 70:15:15 menjadi 1.357 citra latih, 291 citra validasi, dan 291 citra uji. Seluruh citra diubah ukurannya menjadi 224 × 224 piksel dan dinormalisasi, sedangkan augmentasi dan class weight diterapkan selama pelatihan. Arsitektur CNN yang sama dilatih dari awal menggunakan Adam, SGD, dan RMSprop. RMSprop dipilih sebagai konfigurasi utama karena mencapai akurasi validasi terbaik pada epoch ke-16, lebih awal dibandingkan SGD pada epoch ke-29. Pada data pengujian, model RMSprop menghasilkan akurasi 94,50%, balanced accuracy 94,65%, dan macro F1-score 94,65%. F1-score setiap kelas mencapai 93,49% untuk Wanglin, 95,51% untuk Manalagi, dan 94,94% untuk Anna. Hasil tersebut menunjukkan bahwa model CNN mampu mengklasifikasikan ketiga varietas apel Malang dengan kinerja yang relatif seimbang dalam lingkup dataset penelitian.
Co-Authors Djourdi Amrida Pratama Fitri Marisa Istiadi Istiadi Mochamad soffan muslim Muhammad Rizky Anandita Priatama Rangga Pahlevi Rangga Pahlevi Putra Raynanda Raynanda Syahroni Wahyu Iriananda Zahri Aksa Dautd