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All Journal JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika (JUTIF)
Renaldy, Ramadhan
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Computer Science & IT

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Optimizing Customer Data Security in Water Meter Data Management Based on RESTful API and Data Encryption Using AES-256 Algorithm Adrianto, Syahrul; Agus Herlambang, Bambang; Renaldy, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9358

Abstract

Good, accurate and secure data management is certainly one of the main needs for companies that provide public services. This research aims to develop a web application-based information system to manage customer water meter data at a regional water company in Semarang. This system was built using the RESTful API architecture using the PHP programming language framework, namely Laravel and the development of web page displays using the Javascripts framework. The data used is the original database managed by the company every month which is managed using a database management system by meter reader officers. To increase the security of customer data, a cryptographic algorithm is used, namely the Advanced Encryption Standard (AES) algorithm with a 256-bit key length to secure data that is considered sensitive and contains high privacy. This system is intended for meter readers to update customer water meter data per month in an efficient and structured manner. This research uses a Research and Development (R&D) based software development method with system testing using black-box testing method to ensure application functionality and data exposure testing method to ensure data security in the database. The test results show that the system successfully manages customer water meter data in realtime per data sent and secures customer data.
Optimizing Support Vector Machine (SVM) for Sentiment Analysis of Blu by BCA Reviews with Chi-Square Widodo, Aldi; Herlambang, Bambang Agus; Renaldy, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10541

Abstract

One of the products resulting from the development of financial technology is the blu by BCA application. This app can be downloaded by BCA bank users via the Google Play Store and has received various user responses in the form of reviews. Analyzing these user reviews can serve as a valuable reference for further development and decision-making by BCA regarding the blu app. Sentiment analysis is conducted using the Support Vector Machine (SVM) algorithm, with SMOTE and TF-IDF techniques, and feature selection via Chi-Square. Sentiment classification using the SVM algorithm and feature selection has produced various outcomes in previous studies. Therefore, further research is necessary to analyze reviews of the blu application. This study aims to optimize the SVM method in analyzing user sentiment on the blu by BCA application by applying Chi-Square feature selection to improve sentiment classification performance. The research method includes the following stages: scraping, preprocessing, labeling, TF-IDF transformation, Chi-Square feature selection, SMOTE, data splitting, data mining, and evaluation. The testing results show that the RBF kernel achieved the highest performance with an accuracy of 0.8623, precision of 0.8623, recall of 0.8623, and F1-score of 0.8623. After applying Chi-Square feature selection, the accuracy improved to 0.8726, with precision of 0.8747, recall of 0.8725, and F1-score of 0.8723. This optimization successfully increased the accuracy by 0.0103 or 1.03%, while also improving precision, recall, and F1-score, indicating that feature selection contributes significantly to sentiment classification performance.
Peningkatan Performa Prediksi Survival Pasien Gagal Jantung Menggunakan Stacking Ensemble Learning Salwa, Faiza Rulla; Novita, Mega; Renaldy, Ramadhan
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2126

Abstract

Prediksi kelangsungan hidup pasien gagal jantung merupakan aspek penting dalam mendukung pengambilan keputusan medis secara dini dan tepat. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi kelangsungan hidup pasien gagal jantung dengan menerapkan metode Stacking Ensemble Learning yang menggabungkan tiga base learners, yaitu Decision Tree, Naive Bayes, dan K-Nearest Neighbor, serta menggunakan Support Vector Machine sebagai meta-learner. Dataset yang digunakan adalah Heart Failure Clinical Records dari UCI Machine Learning Repository yang telah melalui proses pra-pemrosesan berupa standardisasi numerik dan pembagian data menggunakan stratified sampling dengan rasio 80:20. Eksperimen dilakukan menggunakan validasi silang (5-fold cross-validation) dan tuning hyperparameter pada meta-learner menggunakan GridSearchCV untuk menemukan kombinasi terbaik dari parameter C dan gamma. Hasil evaluasi menunjukkan bahwa model stacking mampu mencapai akurasi sebesar 98,7% dan F1-score 0,9791, mengungguli semua model tunggal. Keberhasilan ini menunjukkan bahwa strategi penggabungan beberapa model ringan mampu meningkatkan kinerja sistem prediktif secara signifikan, tanpa menambah kompleksitas yang berlebihan. Oleh karena itu, pendekatan ini sangat potensial untuk diterapkan pada sistem pendukung keputusan klinis berbasis data, khususnya dalam konteks prediksi penyakit kronis.
Banking Stock Price Prediction Dashboard Using Long Short-Term Memory Navila, Ilma; Nada, Noora Qotrun; Renaldy, Ramadhan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5802

Abstract

The high volatility of banking sector stocks (BBCA, BBRI, BMRI) and the limitations of conventional forecasting methods in handling non-linear data necessitate robust and adaptive predictive models. This study aims to develop an integrated stock price prediction system utilizing a Stacked Long Short-Term Memory (LSTM) architecture embedded within a Flask-based interactive web dashboard. Adopting the CRISP-DM framework, the model was trained using daily and hourly historical data from Yahoo Finance to accommodate both short-term and medium-term forecasting. Backtesting evaluation demonstrated that the LSTM model achieved Mean Absolute Percentage Error (MAPE) values below 2% for daily single-step predictions and below 0.5% for hourly intraday predictions. Furthermore, in a 7-period recursive projection, the proposed LSTM proved highly robust in mitigating error accumulation compared to Linear Regression and Support Vector Regression (SVR), successfully maintaining MAPE values below 5% for all issuers. The implementation of this dashboard system provides a significant impact on financial informatics by bridging advanced deep learning predictive algorithms into a practical, real-time decision support system for investment analysis.
Co-Authors Adrianto, Syahrul Bambang Agus Herlambang, Bambang Mega Novita Navila, Ilma Noora Qotrun Nada, Noora Qotrun Salwa, Faiza Rulla Widodo, Aldi