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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Literasi Digital Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) Community: Jurnal Pengabdian Pada Masyarakat Prosiding Seminar Riset Mahasiswa Jurnal Informatika: Jurnal Pengembangan IT Green Engineering: Journal of Engineering and Applied Science
Fahmi Amiq
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : journal of applied informatics and computing

 1 
Proboboost: A Hybrid Model for Sentiment Analysis of Kitabisa Reviews Prasetya, Rakan Shafy; Fahmi, Amiq; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11138

Abstract

The rapid advancement of digital technology has significantly transformed public behavior in social activities, particularly in online donations and zakat payments. The Kitabisa application was selected in this study not only for its popularity but also due to its high user engagement and large volume of reviews on the Google Play Store, making it an ideal representation of public trust in Indonesia’s digital philanthropy ecosystem. This research aims to analyze user sentiment toward the Kitabisa application using a hybrid Proboboost model, which combines Multinomial Naive Bayes (MNB) and Gradient Boosting Classifier through a soft voting mechanism. The model is designed to address class imbalance and improve accuracy in short-text sentiment analysis for the Indonesian language. The study employed preprocessing techniques including case folding, text cleaning, stopword removal, and stemming using the Sastrawi algorithm. Feature extraction was performed using TF-IDF, with an 80:20 train-test split and 5-fold cross-validation to ensure model reliability. Experimental results indicate that the Proboboost model achieved an accuracy of 89.51% and an F1-score of 87.4%, outperforming the Naive Bayes baseline with 87.98% accuracy. The sentiment distribution demonstrates a dominance of positive sentiment (87.24%), followed by negative (8.53%) and neutral (4.23%) reviews. These findings suggest that users generally express satisfaction and trust toward the Kitabisa platform. The results also confirm that the hybrid Proboboost model effectively balances classification performance between majority and minority sentiment classes, offering deeper insights into user perceptions of digital philanthropic services.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.
Mapping the Persistent Danger Zones of Dengue Hemorrhagic Fever in Semarang City: A Spatio-Temporal Analysis Based on INLA Natanael Anggit Wicaksono; Amiq Fahmi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12928

Abstract

This study maps the spatio-temporal risk dynamics of Dengue Hemorrhagic Fever (DHF) across 16 districts in Semarang City (2016–2025). Traditional epidemiological approaches using raw incidence rates often ignore spatial autocorrelation and struggle with overdispersion anomalies. To address this, we implemented a Hierarchical Bayesian framework using Integrated Nested Laplace Approximations (INLA) with a Negative Binomial distribution and a Besag-York-Mollié (BYM2) spatial architecture. We specified the spatial topology through a manually validated binary adjacency matrix to minimize subjectivity in defining regional boundaries. Our structured model improved computational performance significantly, reducing the Deviance Information Criterion (DIC) by 34.31% and the Root Mean Square Error (RMSE) by 15.30% compared to a baseline Poisson regression model. Using Geopandas and NetworkX for visualization, we identified Tembalang and Banyumanik districts as absolute Epicenter Nodes with an Exceedance Probability of 1.000. Spatial spillover network analysis demonstrated the propagation of epidemiological pressure from these epicenters to surrounding buffer zones, synchronized with the seasonal peak in the first quarter. This framework provides a precise computational foundation for vector control strategies, shifting from localized reactive approaches to preventive cluster mitigation.
Sentiment Classification of Health Education YouTube Comments Using IndoBERT Embeddings with Logistic Regression and Naïve Bayes Andre Septa Wijaya; Amiq Fahmi; Yuventius Tyas Catur Pramudi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13016

Abstract

Class imbalance is a common issue in sentiment classification of social media data, particularly in mental health–related discussions where certain sentiment classes are underrepresented. This study focuses on sentiment classification of mental health–related YouTube comments by utilizing IndoBERT as a pre-trained language model to generate contextual text embeddings. Sentiment classification is subsequently performed using conventional machine learning algorithms, namely Logistic Regression and Naïve Bayes. The research framework includes data collection through the YouTube Data API, text preprocessing, semi-manual sentiment labeling into positive, neutral, and negative classes, and dataset partitioning using an 80:20 train–test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data to prevent data leakage. Feature representation is obtained from IndoBERT embeddings with a dimensionality of 768. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Logistic Regression outperforms Naïve Bayes, achieving an accuracy of 78%, compared to 56% for Naïve Bayes. This indicates that Logistic Regression is more effective in handling dense contextual embeddings generated by transformer-based models. Overall, the findings demonstrate that combining contextual embeddings with data balancing techniques can improve sentiment classification performance in mental health–related social media analysis, particularly in low-resource language settings.
Co-Authors -, Suhariyanto Abdul Rohim, Abdul Abu Salam Agus Winarno Agus Winarno Agus Winarno, Agus Al zami, Farrikh Alif, Moh. Fachri Alzami, Farrikh Andre Septa Wijaya Anggit Wicaksono, Natanael Apriyanti Apriyanti Ardianda Aryo Prakoso Ariel Bagus Nugroho Asih Rohmani Asih Rohmani, Asih Astuti, Yani Parti Budi Harjo Budiono Budiono Candra Irawan Catur Supriyanto Cinantya Paramita Ciputra, Indramawan Diana Purwitasari Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edy Mulyanto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Fhaldian, Wahyu Fikri Budiman Fikri Budiman Hadi, Heru Pramono Harun Al Azies Husna, Farida Amila Indra Gamayanto ISWAHYUDI ISWAHYUDI Karis Widyatmoko Kurnia Desita, Raafi Lalang Erawan Laurensius Tokan, Geraldinho Lintang Mekar Tanjung Mauridhi Hery Purnomo Megantara, Rama Aria Moch. Eko Rustiyono Muhammad Fais Ramadhani Muhammad Hilmy Munsarif Muhammad Naufal Muljono, - Mulyanto, Edy Muslih Muslih MY Teguh Sulistyono MY. Teguh Sulistyono Nasrudin Affandi Prasetyo Natanael Anggit Wicaksono Noorsidi Aizuddin Bin Mat Noor Nova Rijati Novi Hendriyanto, Novi Prasetya, Rakan Shafy Pujiono Pujiono Pujiono Pujiono Pujiono Putra, Wahyu Bagus Wicaksono Raden Arief Nugroho Ramadhan Rakhmat Sani Respati Wulandari Ridha Rahmawati Ridho Pambudi Rizky Adrianto Salsabila, Rizka Mars Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Suryo Adi Nugroho Syifa Sofia Wibowo Tacharri, Chusnuut Tsani, Maulida Aristia Utomo, Danang Wahyu Y. Tyas Catur Pramudi Yumna Huwaida, Imtiyaz Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana