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Ade Putra Prima Suhendri
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Research Center Journal of Informatics and Information Systems
Published by PT. ALRCA INOVASI DIGITAL
ISSN : -     EISSN : 31642764     DOI : -
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Research Center Journal of Informatics and Information Systems (RCJIIS) is dedicated to pioneering and propagating high-quality research in the domains of Computer Science Applications, Artificial Intelligence, and Computer Vision and Pattern Recognition. Our journal serves as a premier platform for academics, researchers, and professionals eager to explore and share groundbreaking innovations and developments within these dynamic fields.
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Articles 8 Documents
 1 
IMPLEMENTASI WEBSITE LAYANAN KESEHATAN MENTAL BERBASIS SELF-ASSESSMENT DENGAN CHATBOT AI MENGGUNAKAN NATURAL LANGUAGE PROCESSING Agung Kurniawan; Meidy Fajar Wahyu
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

This study implemented a web-based mental health service platform that integrates self-assessment, an NLP-based chatbot, and centralized data storage for a mental health clinic in Pasar Kemis, Tangerang, Banten. The research addressed manual recording, limited early-screening access, service delays, and the absence of an integrated mechanism for receiving complaints and scheduling counseling. The system was developed through observation, interviews, literature study, database design, interface design, implementation, and system testing. The website provides user registration, screening, chatbot consultation, educational articles, counseling booking, and administrative management of users, schedules, assessments, and chat logs. The implemented system stores self-assessment results, chatbot interaction history, and user profiles in a structured database to support more organized service workflows. Testing and user feedback reported in the study indicate that the system functions as expected and is positively received in terms of usability, accessibility, and chatbot usefulness. The study concludes that the proposed platform can improve the efficiency, structure, and accessibility of early mental health services.
IMPLEMENTASI WEB SKYSTORE UNTUK MANAJEMEN INVENTORI SUKU CADANG PESAWAT DENGAN FITUR DASHBOARD ANALYTICS DAN SHELF LIFE NOTIFICATIONS MENGGUNAKAN METODE RAD BERBASIS LARAVEL (STUDI KASUS: PT GMF AEROASIA TBK) Muhamad Iqbal; Meidy Fajar Wahyu
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

Aircraft maintenance operations require accurate and traceable spare-part information, yet satellite-warehouse monitoring may remain dependent on spreadsheets even when an enterprise system is available. This study developed SkyStore, a web-based operational inventory application for a satellite warehouse at PT GMF AeroAsia Tbk. Requirements were identified through non-participant observation, a semi-structured interview with a warehouse supervisor, document review, and analysis of receiving, issuing, return-to-warehouse, in-transit, bin-location, and shelf-life processes. The application was developed through Rapid Application Development using Laravel and MySQL. SkyStore consolidates warehouse transactions, reference data, user permissions, dashboard summaries, import-export functions, and shelf-life notifications without replacing the corporate SAP platform. Functional evaluation covered 59 black-box scenarios across 15 modules, all of which produced the expected results. White-box basis-path evaluation covered 13 independent paths: three in login, five in incoming, and five in shelf-life processing, with cyclomatic complexity values of 3, 5, and 5. All tested paths conformed to the expected outcomes. The findings demonstrate functional and logic-path conformity, but do not establish improvements in processing time, inventory accuracy, or user acceptance because those outcomes were not measured.
Adaptive Linear Regression with Dynamic Statistical Validation for Non-Stationary Time-Series Modeling Ade Putra Prima Suhendri; Lely Panca Andriyanto; Amin Hidayat; Yuda Samudra
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

This study proposes an Adaptive Linear Regression (ALR) framework for dynamic trend detection in non-stationary time-series data through adaptive window optimization and statistical validation. Unlike conventional linear regression models that rely on fixed lookback windows, the proposed framework dynamically determines the optimal window size based on a coefficient of determination (R²) threshold, allowing the model to adapt to changing data characteristics while filtering noisy observations. The validated regression model is further enhanced by constructing dynamic statistical boundaries using the Z-score distribution of regression residuals, enabling adaptive identification of significant deviations from local trends without requiring manually tuned parameters. The proposed framework was evaluated using high-frequency cryptocurrency time-series data collected from 2020 to 2025, including BTC/USDT, ETH/USDT, and SOL/USDT, as representative non-stationary datasets with high volatility. Experimental results demonstrate that the proposed approach achieves more robust trend detection and superior predictive consistency than conventional fixed-window regression and widely used baseline methods. In addition, the adaptive framework exhibits improved risk-adjusted performance and lower maximum drawdown when applied to an algorithmic trading scenario, indicating its practical applicability for dynamic decision-support systems operating on volatile time-series data. Overall, the proposed ALR framework provides a statistically grounded, interpretable, and adaptive approach for modeling non-stationary time-series and offers a promising alternative for intelligent data-driven applications.
Development of a Web-Based Reporting System for PJLP Tasks at Dinas Pertamanan dan Hutan Kota Using the Waterfall Method Rusdiyanto Rusdiyanto; Ade Putra Prima Suhendri
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

This study has successfully developed a task reporting system for PJLP Dinas Pertamanan dan Hutan Kota, a service company specializing in park cleaning and maintenance in DKI Jakarta. Previously, the company faced difficulties in recording and tracking task reports because the system used was prone to errors and inefficient. This study focuses on developing a more effective and efficient system for managing task reports. The Waterfall method was employed, allowing for a quick and flexible response to changing needs. The system was designed as a web-based platform to enable access to asset information from various locations and at any time, in line with current demands for mobility and flexibility. The system’s performance was tested using the Department of Parks and Urban Forests as a case study, and the results demonstrated a significant improvement in task reporting, as well as increased employee work efficiency. With the implementation of this system, the City Parks and Forests Department has successfully improved efficiency in terms of time savings and achieved more structured and flexible data accuracy. This research provides tangible benefits to the City Parks and Forests Department and contributes to the development of data technology, particularly in report data management using the Waterfall approach.
DEVELOPMENT OF AN INCLUSIVE E-COMMERCE PLATFORM FOR MSMES WITH SPEECH IMPAIRED: A CASE STUDY AT THE INTEN SUWENO INTEGRATED CENTER MOH ARDIANSYAH ARDIANSYAH; Ade Putra Prima Suhendri
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

Penelitian ini dilatarbelakangi oleh keterbatasan aksesibilitas platform e-commerce bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) penyandang rungu wicara. Hambatan utama yang dihadapi meliputi keterbatasan komunikasi verbal, kurangnya fitur berbasis visual, serta tidak tersedianya dukungan bahasa isyarat dalam platform digital konvensional. Kondisi ini menyebabkan rendahnya efektivitas dalam pemasaran produk secara online. Penelitian ini bertujuan untuk mengembangkan platform e-commerce inklusif berbasis web yang mampu mengakomodasi kebutuhan UMKM rungu wicara di Sentra Terpadu Inten Suweno. Metode pengembangan sistem yang digunakan adalah Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan Laravel sebagai backend, Next.js dan TypeScript sebagai frontend, serta MySQL sebagai basis data. Fitur utama yang dikembangkan meliputi komunikasi berbasis visual, integrasi video bahasa isyarat untuk deskripsi produk, notifikasi visual, serta antarmuka yang sederhana dan mudah digunakan. Pengujian sistem dilakukan menggunakan metode blackbox testing serta evaluasi pengguna secara langsung. Hasil penelitian menunjukkan bahwa platform yang dikembangkan mampu meningkatkan pemahaman pengguna dalam menyampaikan informasi produk, mempermudah komunikasi dengan pembeli, serta meningkatkan efektivitas pemasaran digital. Dengan demikian, platform ini diharapkan dapat mendukung peningkatan aksesibilitas dan pemberdayaan ekonomi bagi penyandang rungu wicara.
A Comparative Analysis of Machine Learning Approaches for Diabetes Prediction: Stability of Hold-Out Testing versus Cross-Validation Ade Putra Prima Suhendri; Meidy Fajar Wahyu; Amin Hidayat; Lely Panca Andriyanto
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 1 (2026): Juli 2026
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

Automated medical diagnostic systems often face operational challenges when dealing with clinical datasets that contain noise, biological zero-value anomalies, and class imbalance issues. This study offers an empirical benchmark of six supervised machine learning algorithms—Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB)—evaluated directly on the un-imputed Pima Indians Diabetes dataset. We systematically compare the performance differences between a single 80:20 hold-out test split and a 10-fold stratified cross-validation, assessing accuracy, stability, and discrimination across various thresholds. Results show that while the hold-out test favors Random Forest (92.86%) and Decision Tree (92.21%) in accuracy, stratified cross-validation reveals that ensemble models like Gradient Boosting (88.94% CV accuracy) and Random Forest (88.55% CV accuracy) provide better operational stability. Additionally, threshold-independent metrics indicate that Gradient Boosting and Random Forest share top ROC-AUC scores (0.98), with Gradient Boosting leading in Precision-Recall AUC (0.97). Lower stability is observed in linear and probabilistic models such as SVM (81.17% accuracy, 0.84 ROC-AUC) and Gaussian Naïve Bayes on raw features. These results emphasize that relying solely on single hold-out evaluations may overestimate how well classifiers generalize, with ensemble methods emerging as the most robust approach for unconstrained clinical risk assessment.
Development of a Flutter-Based Student Task Management Application Incorporating SQLite and Cubit State Management Ival Arip Hamzah; Dani Ekaputra; Muhamad Lutfi Asihab
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 2 (2026): Desember 2026 (On Progress)
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

The rapid advancement of mobile technology offers significant convenience in managing daily academic activities. Higher education students frequently encounter challenges when organizing coursework assignments originating from diverse modules with differing submission deadlines, often leading to submission delays and reduced academic productivity. To address these operational issues, a mobile task management application was developed using the Flutter framework, incorporating SQLite for offline persistent local database storage and Cubit for state management architecture. The application provides core functional capabilities consisting of Create, Read, Update, and Delete (CRUD) operations, accompanied by detailed task metadata including course title, deadline date, priority rating, and completion status. Because data is structured and stored locally on the client device, the software functions seamlessly without an active internet connection. System evaluation results demonstrate that the application operates stably on Android devices, all CRUD workflows execute accurately according to specification, and the Cubit state management pattern successfully preserves user interface consistency across dynamic state transitions
Development of FitSchedule: An Offline Workout Planner Application Using Flutter, Cubit, and SQLite Sri Sugiarti; Efa Mutiara Sari; Siti Rahmah Azzahra
Jurnal Publikasi Ilmu Komputer Terapan Vol 1 No 2 (2026): Desember 2026 (On Progress)
Publisher : PT. ALRCA INOVASI DIGITAL

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Abstract

The rapid advancement of mobile technology has significantly transformed the way people manage health and fitness activities. Although numerous fitness applications are available on mobile platforms, many rely on cloud services, require user authentication, or depend on a stable internet connection, limiting accessibility for users who prefer lightweight offline solutions. This study aims to develop FitSchedule, an Android-based workout planner application that enables users to organize workout programs, manage exercise lists, record workout history, and monitor training progress without requiring internet connectivity. The application was developed using the Waterfall software development methodology, which consists of requirement analysis, system design, implementation, testing, and maintenance. Flutter was employed as the cross-platform development framework with Dart as the programming language. The application adopted the Cubit architecture from the Flutter BLoC library to separate business logic from the presentation layer, thereby improving maintainability and scalability. SQLite was utilized as the local database to ensure persistent offline data storage. The primary features include workout program management, exercise management, active workout sessions, workout history recording, and workout statistics visualization. on was conducted using Black Box Testing to evaluate all major application features. The testing results demonstrated that every functional requirement operated successfully without significant defects. The integration of Flutter, Cubit, and SQLite produced an application with responsive performance, modular architecture, and reliable offline capability. The developed application provides an effective solution for individuals seeking a simple and practical workout planner while also serving as a reference for implementing Flutter-based mobile applications using Cubit state management and local database technology. on was conducted using Black Box Testing to evaluate all major application features. The testing results demonstrated that every functional requirement operated successfully without significant defects. The integration of Flutter, Cubit, and SQLite produced an application with responsive performance, modular architecture, and reliable offline capability. The developed application provides an effective solution for individuals seeking a simple and practical workout planner while also serving as a reference for implementing Flutter-based mobile applications using Cubit state management and local database technology.

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