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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Journal of Information Technology and Computer Science (JOINTECS) Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Kompak Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom Jurnal Infortech JAMI: Jurnal Ahli Muda Indonesia Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika DEVICE Jurnal Dialektika Informatika (Detika) Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Sosial dan Abdimas JURNAL ILMU SOSIAL DAN ILMU POLITIK, STISIPOL RAJA HAJI Kohesi: Jurnal Sains dan Teknologi Ulil Albab Jurnal Infortech
Yudi Ramdhani
Satu University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Statistical comparison of MLP and LSTM for mobile health sentiment analysis Ghanim Kanugrahan; Win Ce; Vito Hafizh Cahaya Putra; Yudi Ramdhani; Febriyanti Panjaitan
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp818-826

Abstract

This study investigates user sentiment towards the Mobile JKN public health application by applying text classification models based on deep learning. Two approaches were compared: a multi-layer perceptron (MLP) with TF IDF features and long short-term memory (LSTM) with Word2Vec embeddings. The dataset consists of 114,364 Indonesian-language user reviews collected from the Google Play Store. To address class imbalance, we applied random oversampling. Each model was evaluated using 5-fold stratified shuffle split cross-validation. The results showed that MLP models achieved higher accuracy (up to 83.90%), while LSTM models demonstrated better recall and precision on minority classes such as neutral sentiment. However, statistical validation using the Wilcoxon signed-rank test revealed that the performance differences between models were not statistically significant (p > 0.05). These findings suggest that both models are viable for sentiment analysis, with trade-offs depending on the evaluation metric of interest. Future work may explore hybrid architecture and larger datasets for improved performance and statistical confidence.
Comparative Analysis of Statistical Machine Learning and Deep Learning for Daily CSPI Forecasting Haerul; Yudi Ramdhani; Novia Andini
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.13007

Abstract

Forecasting stock market indices is challenging due to the complex, nonlinear, and dynamic behavior of financial markets. Although statistical, machine learning, and deep learning methods have been widely applied, their comparative performance for daily Composite Stock Price Index (CSPI) forecasting remains insufficiently explored. This study systematically compares nine forecasting models representing statistical (ARIMA, SARIMA, ETS), machine learning (Random Forest, Support Vector Regression, XGBoost), and deep learning (LSTM, BiLSTM, GRU) approaches using the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. Historical daily CSPI closing prices from 1995 to 2019 were preprocessed through missing-value handling, feature engineering, normalization, and chronological data partitioning. Statistical models were trained on the original price scale, whereas machine learning and deep learning models used normalized data. Hyperparameters were optimized using Bayesian Optimization with the Optuna framework. Because the models were evaluated on different scales, cross-paradigm comparisons primarily relied on the coefficient of determination (R²) and Directional Accuracy (DA). The results show that machine learning models generally outperformed statistical and deep learning approaches on the testing dataset. Random Forest achieved the highest predictive performance with an R² of 0.902, while Support Vector Regression produced the lowest MAE and MAPE among normalized models. Although LSTM achieved the lowest validation error, its performance did not generalize consistently to unseen data. Directional Accuracy remained relatively low (31–41%), indicating that predicting market direction is more difficult than forecasting price magnitude. Future studies should incorporate macroeconomic indicators and market sentiment to improve forecasting performance.
Co-Authors Achmad Nizar Hidayanto Ade Mubarok Adi Nurseptaji Adi Nurseptaji Adi Nurseptaji Ali Akbar Rismayadi Ali Akbar Rismayadi Alpiansah, Agung Bia Amin Fahri Andre Prayoga Arey Arey Asti Herliana, Asti B. Hariyanto, Oda I. Cakra Mahendra Putra Ce, Win Cucu Ika Agustyaningrum Dhia Fauziah Apra Djaya Siswaja, Hendy Doni Purnama Alamsyah Doni Purnama Alamsyah Dwiniati, Dwiniati Dwiza Riana Dwiza Riana Dwiza Riana Erfian Junianto Fadila Andini Febriyanti Panjaitan Febriyanti Panjaitan Fitri Khoirunnisa Fitriyani Fitriyani Ghanim Kanugrahan Haerul Hafizh Cahaya Putra, Vito Hariyanti, Ifani Hery Oktafiandi Hikmawati, Nina Kurnia Hiya Nalatissifa Hizaz Zakaria Yahya Iedam Fardian Anshori, Iedam Fardian Ina Najiyah Indriyati, Susana Irgi Mahendrata Saputra Kanugrahan, Ghanim Marko, Niki Mayya Nurbayanti Shobary Meirynda Lastika Rahimsyah Miftah Farid Adiwisastra Miftahul Rizal Moch Iqbal Tawakal Muckti, Masaldi Kharisma Muhamad Zakhy Syahaf Muhammad Amar Mustajab Mustajab, Muhammad Amar Nadia, Putri Nadiyah Hidayati Nanda Dwi Husna Sadikin Nandi Dwi Husni Sadikin Niki Marko Novia Andini Oktafiandy, Hery Oktaviani, Fani Rahma Permai, Antika Pratama Syahdan Nabil Pratiwi Pratiwi Rangga Sanjaya Rein Lantin Resdiana Pratama Riski Mardhianto Rizal Rosidin Rizki Tri Prasetio, Rizki Tri Sadikin, Nanda Dwi Husna Sadikin, Nandi Dwi Husni Salman Topiq Salsabila Ayuni Kaffah Sandini, Dwi Saputra, Irgi Mahendrata Sari Susanti Sari Susanti, Sari Satrio Rully Priyambodo Siti Rendani Anjaryanti Siti Utari, Diah Suherman, Himam Dwipratama Syarif Hidayatulloh Syarif Hidayatulloh Syarif Hidayatulloh Taufik Agung Pramana Tessa Putri Mallini Toni Arifin Wartika, Wartika Win Ce