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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Amali Amali
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Sistem Monitoring dan Kontrol Taman Pintar Berbasis IoT (Internet of Things) dengan NodeMCU ESP8266 Syahrul Gunawan; Abdul Halim Anshor; Amali Amali
Bulletin of Computer Science Research Vol. 3 No. 4 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v3i4.270

Abstract

Parks are recreational areas that are often visited by people to relax and unwind. However, sometimes parks are not well-maintained and become less attractive to visitors. The lack of park maintenance is often due to a shortage of human resources and inefficiencies in monitoring park conditions. Therefore, this research aims to address this issue by developing a Internet of things-based Smart Park monitoring and control system using NodeMCU ESP8266. This system allows park managers to monitor and control park conditions in realtime from a distance using parameters such as soil moisture, air humidity, temperature, and watering. This research uses a prototype method with stages of requirement analysis, system design, implementation, and evaluation. The Smart Park system was successfully implemented using NodeMCU ESP8266 and integrated with a telegram bot as a means of remote monitoring and control. This telegram bot allows park managers to check park environmental parameters. The integration of the Smart Park system with telegram bot has been proven effective in increasing park maintenance efficiency. The results of the study show that park managers can monitor park conditions more easily, thus reducing the burden on park managers.
Analisis Komparatif Decision Tree C4.5 dan Neural Network pada Prediksi Kanker Payudara Amali Amali; Edy Widodo
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.295

Abstract

Breast cancer is one of the diseases with the highest incidence and mortality rates among women, requiring methods that can support fast and accurate detection. This study aims to compare the performance of the Decision Tree C4.5 and Neural Network algorithms in breast cancer classification using the Breast Cancer Wisconsin dataset obtained from the UCI Machine Learning Repository. The research method adopts the CRISP-DM approach, which includes data collection, preprocessing, model development, testing, and evaluation stages. The preprocessing stage was carried out through data cleaning, data transformation, and data reduction to improve dataset quality before the modeling process. The testing process used split validation and evaluation based on accuracy, precision, recall, and Area Under Curve (AUC) metrics. The results indicate that the Neural Network algorithm achieved better performance than Decision Tree C4.5. Neural Network obtained an accuracy of 96.17%, precision of 95.80%, recall of 96.50%, and an AUC value of 0.989, which is categorized as excellent classification. Meanwhile, Decision Tree C4.5 achieved an accuracy of 93.50% and an AUC value of 0.945, categorized as very good classification. ROC Curve analysis demonstrates that Neural Network is more effective in distinguishing benign and malignant classes. Therefore, Neural Network is recommended as the best model to support early breast cancer detection based on machine learning, while Decision Tree C4.5 remains relevant for conditions requiring simpler and more interpretable models.
Prediksi Curah Hujan Jawa Barat Menggunakan Algoritma Machine Learning: Analisis Komparatif Berbasis Data Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) 2024 Alif Fahmi; Amali Amali; Aceng Badruzzaman
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9018

Abstract

West Java Province exhibits high vulnerability to hydrometeorological disasters due to dynamic rainfall variability, necessitating an accurate weather prediction system for effective disaster mitigation.1 This study aims to conduct a comparative performance analysis of Machine Learning algorithms, specifically Support Vector Machine (SVM), Naïve Bayes, Random Forest, and XGBoost, in predicting rainfall events based on 2024 daily meteorological data sourced from BMKG. Through computational experiments utilizing three data splitting scenarios 80:20, 75:25, and 70:30, and Recursive Feature Elimination (RFE), the results demonstrate that Naïve Bayes, Random Forest, and XGBoost consistently achieved a perfect accuracy of 100% across all scenarios, whereas SVM exhibited stable but more conservative performance with an average accuracy of 95.4%. In-depth analysis indicates that the absolute accuracy achieved under specific data conditions was significantly influenced by the dominance of the daily rainfall feature (RR), leading to indications of data leakage where ensemble and probabilistic models exploited deterministic relationships much more effectively than SVM. Consequently, this study recommends a rigorous re-evaluation of input features, prioritizing atmospheric leading indicators, to develop a more realistic and adaptive early warning system in the future.
Co-Authors Abdul Halim Anshor Aceng Badruzzaman Alif Fahmi Edy Widodo Syahrul Gunawan