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All Journal JSI: Jurnal Sistem Informasi (E-Journal) INOVTEK Polbeng - Seri Informatika
Gabriel Ekoputra Hartono Cahyadi
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Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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Peningkatan Akurasi Prediksi Cnn-Lstm Dan Cnn-Gru Untuk Mendiagnosa Skizofrenia Melalui Sinyal Eeg Gabriel Ekoputra Hartono Cahyadi; Sukemi Sukemi; Dian Palupi Rini
Jurnal Sistem Informasi Vol 14, No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jsi.v14i2.19071

Abstract

AbstrakSkizofrenia adalah gangguan jiwa yang umumnya muncul dalam bentuk halusinasi pendengaran, paranoia, atau cara berbicara dan berpikir yang kacau. Diagnosa penderita Skizofrenia dapat dilakukan dengan menggunakan pemeriksaan sinyal EEG. Penelitian ini melakukan analisa perbandingan metode yang terbaik untuk melakukan klasifikasi EEG menggunakan metode Deep Learning (DL). Penulis menggunakan metode 1D Convolutional Neural Network (1D CNN) yang menggunakan layer berbeda. 1D-CNN pertama menggunakan layer Long short-term memory (LSTM) dan 1D-CNN kedua menggunakan layer Gated Recurrent Unit (GRU). Dataset yang digunakan adalah 28 jenis sinyal EEG yang terdiri dari 14 penderita Skizofrenia dan 14 subjek normal. Hasil pengujian akurasi F1 Score dari CNN yang menggunakan layer LSTM memiliki nilai sebesar 95% dan CNN yang menggunakan layer GRU memiliki nilai 96%. Pengujian kedua metode tersebut menunjukkan bahwa nilai dari CNN-GRU lebih besar dari CNN-LSTM.   
Implementation of the TOPSIS Method and Usability Method for Marketplace Application Based on Data Visualization Sanjaya, M. Rudi; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2w11eh43

Abstract

The rapid development of technology in online marketplaces has significantly influenced consumer shopping behavior, with applications such as Shopee, Tokopedia, Zalora, and Bukalapak leveraging advances in information and communication technology to provide faster and more efficient shopping experiences. However, frequent system disruptions often affect user satisfaction, emphasizing the need for improved information systems. This study, conducted in South Sumatra with 334 respondents, utilized questionnaire data that were processed and visualized using R, where decision-support metrics were analyzed through the TOPSIS method with equal weights and a normalized respondent data matrix calculate_topsis  function(data, weights = c(0.2, 0.2, 0.2, 0.2, 0.2)), normalized_matrix as.matrix(data responden), and the methodology integrated both the usability approach and the TOPSIS method within an R Shiny environment. The findings show that data visualization effectively applied the usability and TOPSIS methods, with usability evaluation results indicating average scores of Memorability (4.263), Satisfaction (4.186), Learnability (4.146), Efficiency (4.101), and Low Error Rate (3.749), where Memorability achieved the highest score, while the TOPSIS results highlighted Learnability as the most significant factor.
Optimization of Sentiment Analysis on Tokopedia User Reviews Using Gridsearchcv and Smote with Machine Learning Algorithms Imran, Athallah Yasyfi; Sanjaya, M. Rudi; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5ax8km80

Abstract

Understanding user sentiment from e-commerce reviews is essential for platform improvement and business strategy. This study compares three machine learning algorithms—Logistic Regression, Random Forest, and XGBoost—for sentiment classification of Indonesian-language Tokopedia reviews. A dataset of 6,822 user reviews was preprocessed through tokenization, stopword removal, and TF-IDF vectorization. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set. Models were evaluated using accuracy, precision, recall, and F1-score. Results demonstrate that Random Forest achieved the highest accuracy at 86.86%, followed by Logistic Regression at 84.86%, and XGBoost at 82.60%. The application of SMOTE significantly improved classification performance across all models, particularly for minority sentiment classes. These findings indicate that tree-based ensemble methods, especially Random Forest, are effective for sentiment analysis in imbalanced e-commerce datasets. This research provides practical insights for e-commerce platforms to implement automated sentiment monitoring systems, enabling faster response to customer feedback and targeted service improvements. However, the study is limited to Tokopedia reviews and may not generalize to other platforms or languages. Future work should explore deep learning approaches and cross-platform validation to enhance model robustness.
Co-Authors Bayu Wijaya Putra Dian Palupi Rini Imran, Athallah Yasyfi Sanjaya, M. Rudi Sukemi Sukemi