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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) JURNAL PENDIDIKAN TAMBUSAI TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Abdimas Maduma LOGIC : Jurnal Ilmu Komputer dan Pendidikan OKTAL : Jurnal Ilmu Komputer dan Sains BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika Bulletin of Engineering Science, Technology and Industry Research Center Journal of Informatics and Information Systems
Yuda Samudra
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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COMPARISON OF DECISION TREE AND NAIVE BAYES METHODS FOR RAINFALL CLASSIFICATION USING A WEATHER DATASET WITH A WEB-BASED APPLICATION Yuda Samudra; Amin Hidayat; Nanang
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/fxnw2631

Abstract

Rainfall prediction is an important component of weather analysis as it provides valuable information to support decision-making in sectors such as agriculture, transportation, and environmental management. Although various studies have compared machine learning algorithms for rainfall classification, many of them lack detailed discussion on dataset characteristics and practical system implementation. Therefore, this study aims to evaluate and compare the performance of Decision Tree and Naive Bayes algorithms for rainfall classification while considering dataset characteristics and implementing the model in a web-based application. The dataset used in this study consists of 2,500 records with meteorological parameters including temperature, humidity, wind speed, cloud cover, and atmospheric pressure. The data underwent preprocessing, including data cleaning and label encoding, where rainfall was represented as 1 and no rainfall as 0. The dataset was divided into training and testing sets, and both algorithms were applied to build classification models. Model performance was evaluated using confusion matrix, accuracy, and ROC curve analysis. The results show that the Decision Tree algorithm achieved an accuracy of 1.00 (100%), while the Naive Bayes algorithm achieved 0.972 (97.2%). Although Decision Tree shows superior performance, the perfect accuracy may indicate potential overfitting, and therefore the results should be interpreted carefully. Furthermore, the developed models were successfully implemented into a web-based application that enables users to perform rainfall prediction interactively. This study demonstrates that Decision Tree provides better performance for rainfall classification in the given dataset, while also highlighting the importance of considering dataset characteristics and evaluation methods. The integration of machine learning models into a web-based system provides a practical contribution for real-world weather prediction applications.   Keywords: Rainfall Classification, Decision Tree, Naive Bayes, Machine Learning, Weather Dataset, Web-Based Application
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.
Implementation of UI/UX Design for Goods Delivery System With One Way To Go Model - SiCepat Hackathon Challenge Yuda Samudra; Santi Rahayu; Nanang; Addelia Aldha Kharisma
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1588

Abstract

The SiCepat Hackathon Challenge is a collaborative start-up activity in building an application ecosystem that aims to support system updates to become more efficient and support the growth of digital businesses in accordance with market needs in the field of logistics technology to accelerate the delivery process of goods based on consumer needs. The main problem in the traditional delivery system is the lack of ability to determine the optimal delivery and pick-up location. Many couriers have to travel long distances simply because the system is unable to find the closest pickup point from the courier's position. As a result, delivery times are inefficient, and operational costs increase. In addition, an unclear commission system often causes dissatisfaction among couriers because it does not represent the effort and time they put in. Problem solving using Design Thinking method approach to study user needs. In the Design Thinking process, there are several stages such as Empathize, Define, Ideate, Prototype, and Test. There is also a Design System process to help determine the component requirements in the design interface that can be used for reusable purposes with the Atomic's Design approach. In the validation or testing stage of the feature development results, the result of this development plan is to provide features for selecting a warehouse for picking goods, destination locations, a commission system, deposits, withdrawals, and shipping history. The results of the Usability Testing with Usability Metrics using SEQ (Single Ease Question) obtained a score of 6 (out of 7 points), this is interpreted as Passing for each testing criterion carried out by the user.
IPLEMENTATION OF UI/UX DESIGN FOR A ONE-WAY-TO-GO DELIVERY SYSTEM - SICEPAT HACKATON CHALLENGE Yuda Samudra; Santi Rahayu; Nanang; Addelia Aldha Kharisma
Bulletin of Engineering Science, Technology and Industry Vol. 4 No. 3 (2026): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The SiCepat Hackathon Challenge is a collaborative start-up activity in building an application ecosystem that aims to support system updates to become more efficient and support the growth of digital businesses in accordance with market needs in the field of logistics technology to accelerate the delivery process of goods based on consumer needs. The main problem in the traditional delivery system is the lack of ability to determine the optimal delivery and pick-up location. Many couriers have to travel long distances simply because the system is unable to find the closest pickup point from the courier's position. As a result, delivery times are inefficient, and operational costs increase. In addition, an unclear commission system often causes dissatisfaction among couriers because it does not represent the effort and time they put in. Problem solving uses the Design Process with a Design Thinking method approach to study user needs. In the Design Thinking process, there are several stages such as Empathize, Define, Ideate, Prototype, and Test. There is also a Design System process to help determine the component requirements in the design interface that can be used for reusable purposes with the Atomic's Design approach. In the validation or testing stage of the feature development results, the final result of this development plan is to provide features for selecting a warehouse for picking goods, destination locations, a commission system, deposits, withdrawals, and shipping history. The final results of the Usability Testing with Usability Metrics using SEQ (Single Ease Question) obtained a score of 6 (out of 7 points), this is interpreted as Passing for each testing criterion carried out by the user.
Co-Authors Addelia Aldha Kharisma Ade Putra Prima Suhendri Ade Putra Prima Suhendri Amin Hidayat Amin Hidayat Andriyanto, Lely Panca Darmawati Fadilah, Muhammad Farhan Fizram, Muhammad Herwis Gultom Hizkia Bayu Wijaya Lely Panca Andriyanto Lely Panca Andriyanto Meidy Fajar Wahyu Meidy Fajar Wahyu Muhammad Fiqi Eyriska Rokhim Nanang Nanang Nanang nanang nanang Nardiono Oke Hariansyah Rahmat Hidayat Santi Rahayu Sartika Lina Mulani Sitio Suhendri, Ade Putra Prima Suryaningrat Wiwin Winarti Zurnan Alfian