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All Journal TEKNIK INFORMATIKA Linguistika JSI: Jurnal Sistem Informasi (E-Journal) Science and Technology Indonesia Kuttab: Jurnal Ilmu Pendidikan Islam Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis Jurnal Ilmiah Spectral Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Manajemen Bisnis dan Kewirausahaan Golden Ratio of Mapping Idea and Literature Format Kajian Moral dan Kewarganegaraan SWARNA Ihsanika : Jurnal Pendidikan Agama Islam Indonesian Journal of Science, Technology, and Humanities Sciencestatistics: Journal of Statistics, Probability, and Its Application JMAEKA: Jurnal Manajemen Ekonomi Akuntansi Integra: Journal of Integrated Mathematics and Computer Science
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A Hybrid ARIMA–GRU Model for Forecasting Palm Oil Prices at PT Sawit Sumbermas Sarana in Central Kalimantan Kurniasari, Dian; Shella, Tiara Pramay; Usman, Mustofa; Warsono
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 1 (2025): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252112

Abstract

The palm oil industry plays a strategic role in Indonesia's economic landscape. As one of the world’s largest producers, Indonesia holds substantial potential in marketing both crude palm oil (CPO) and palm kernel oil on domestic and international fronts. Palm oil prices consistently correlate with CPO prices, given that the pricing of palm oil is benchmarked against CPO, resulting in market fluctuations. Forecasting future palm oil prices becomes an essential measure in response to this volatility. The ARIMA (AutoRegressive Integrated Moving Average) model has been widely recognized as a reliable method for time series forecasting. Despite its strengths, ARIMA faces challenges in identifying the non-linear components that are often present in real-world data. The Gated Recurrent Unit (GRU) model, which incorporates an update gate and a reset gate, offers an alternative that effectively captures complex non-linear patterns. A hybrid model integrating ARIMA and GRU has therefore been developed with the aim of improving predictive accuracy. This hybrid approach includes two stages: the ARIMA model for initial predictions and a GRU model that processes the residuals from the ARIMA output. In this study, the ARIMA-GRU hybrid model demonstrated strong performance, yielding a Mean Squared Error (MSE) of 868.4690, a Root Mean Squared Error (RMSE) of 29.4698, a Mean Absolute Percentage Error (MAPE) of 0.0117, and an overall accuracy of 99.9824%.
Integrating VAR and CNN Models for Accurate Forecasting of Money Supply in Indonesia Warsono; Sulandra, Ardelia Maharani; Kurniasari, Dian; Usman, Mustofa; Susetyo, Budi
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252230

Abstract

Economic forecasting serves as a fundamental element in supporting decision-making processes across multiple sectors. One of the main areas of interest in this field is the estimation of the money supply within an economy. The Vector Autoregressive (VAR) model is a commonly applied method for forecasting; however, it often encounters limitations when processing data with nonlinear patterns. Convolutional Neural Networks (CNNs) offer an alternative approach, particularly effective in identifying nonlinear structures that are not adequately captured by VAR models. A hybrid VAR-CNN model is therefore proposed, combining the respective strengths of both techniques to improve the accuracy of predictions. This research applies to the hybrid VAR-CNN model to forecast economic variables for the period from July 2022 to June 2023. The model consists of two main components: the first utilizes forecasted values generated by the VAR model, while the second processes the residuals from the VAR output using a CNN. With 80% of the data allocated for training and 20% for testing, the hybrid VAR-CNN model demonstrates improved performance over alternative forecasting methods. Evaluation based on Mean Absolute Percentage Error (MAPE), supremum (D) values, and p-values confirms the effectiveness of this hybrid approach.
The Kernel Function of Reproducing Kernel Hilbert Space and Its Application on Support Vector Machine Utami, Bernadhita Herindri Samodera; Warsono; Usman, Mustofa; Fitriani
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1096-1108

Abstract

Reproducing Kernel Hilbert Space (RKHS) is a Hilbert space consisting of functions that can be represented or reproduced by a kernel function. The development of data science has made RKHS a method that refers to an approach or technique using the concept of reproducing kernels in certain applications, especially machine learning. Support Vector Machine (SVM) is one of the machine learning methods included in the supervised learning category for classification and regression tasks. This research aims to determine the form of linear kernel functions, polynomial kernel functions, and Gaussian kernel functions in Support Vector Machine analysis and analyze their performance in Support Vector Machine classification and regression. Application of the RKHS method in SVM classification analysis using World Disaster Risk Dataset data published by Institute for International Law of Peace and Armed Conflict (IFHV) from Ruhr-University Bochum in 2022 obtained results that are based on the results by comparing the predictions of training data and testing data using linear kernel functions, polynomial kernels and Gaussian kernels, it is recommended that classification using linear kernels provides the best prediction performance.
Evaluating User Satisfaction in The Halodoc Application Using a Hybrid CNN-BiLTSM Model for Sentiment Analysis Kurniasari, Dian; Su'admaji, Arif; Lumbanraja, Favorisen Rosyking; Warsono
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.42762

Abstract

The growing demand for digital healthcare services in Indonesia has driven the adoption of Online Healthcare Applications (OHApps) such as Halodoc. Despite over 65 million users, maintaining user satisfaction remains a challenge. This study employs sentiment analysis using a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to classify user review ratings. A dataset of 10,000 Google Play Store reviews was divided into COVID-19 and post-pandemic segments. The methodology includes data collection, pre-processing, and dataset segmentation for training, validation, and testing. Results indicate that the CNN-BiLSTM model surpasses traditional machine learning by combining CNN’s feature extraction with BiLSTM’s long-term dependency capture, achieving 98.71% accuracy on COVID-19 data and 98.16% post-pandemic. Additionally, the model demonstrates strong performance across other key evaluation metrics, with precision, recall, and F1-score. Misclassification analysis highlights minor errors, particularly in ratings 4 and 5. These findings help healthcare providers enhance digital services by identifying user concerns, improving platform features, and optimizing customer engagement. Beyond healthcare, this approach has real-world applications in e-commerce and financial services, where sentiment analysis informs user experience improvements.
Co-Authors ACHMAD HERIYANTO ACHMAD ZULFIKAR WIDIYANTO Aisyah, Synthia Nur Angestika Wilandari Anggi Yusniar Arif Rahman Arista, Roynaldi Asmiati Budi Santosa Budi Susetyo Chalimatuz Sa'diyah Dani Chandra Utama Davied Wahyu Wismanindra Dede Muhammad Hidayat Desyafitri, Naia DEWI PUTRIANA Dewi, Ria Rahmawati Diah Ayu Wulandari Dian Kurniasari DIANTINA, RIA Djoko Sutopo Erna Retna Rahadjeng Fajri Ariandi FARID NURDIANSYAH Favorisen R. Lumbanraja Fitriani Hendro Kuncoro, Hendro Humairosi, Lutfia Iman Pasu Marganda Hardianto Purba Iwan Setiawan Jihadi, Muhammad Krisnanda, Mikhael Ekklesia Kusmawan, Novit Ari Lestari, Novi Puji Lukman Rosyidi Maharani, Maria Marsella, Dhea MAYA MUSTIKA KARTIKA SARI, MAYA MIFTAHQUL JAYA ROSITA Muhammad Syaifullah Mursidi Mustofa Usman Nauval Adi C Notiragayu Oksiana Jatiningsih Okzarima, Wenty Putri, Amelia Fallizia R, Lukman Rahmanu Wijaya Rizqi Anisa Sarmini Shella, Tiara Pramay Shiroth, Salman Fathy Siti Khusnul Sriati Su'admaji, Arif Suciati, Indah Sulandra, Ardelia Maharani Syabrinildi Syafiq Mughni syafira, qorina aulya Tri Utami Utami, Bernadhita Herindri Samodera Wamiliana Wardhani Utami Dewi Widiarti Zahro, Fatimataz ZAKI BASTOMI