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All Journal JOIV : International Journal on Informatics Visualization International Journal Of Science, Technology & Management (IJSTM) Malcom: Indonesian Journal of Machine Learning and Computer Science
Henry, Amir Acalapati
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Modify The Led Lights By Adding Pir Sensor As A Motion Sensor Permana, Angga Aditya; Pradipta, Allan; Henry, Amir Acalapati; Sufyan, Ammar; Maskurudin, Muhamad; Syafiq, Zahra
International Journal of Science, Technology & Management Vol. 4 No. 5 (2023): September 2023
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v4i6.989

Abstract

Motion sensor lights have become a popular solution to optimize lighting usage in various spaces and environments. This article explores the concept and advantages of motion sensor lights, which are capable of detecting human motion and automatically controlling illumination. Motion sensor lights use PIR sensors to detect human movement, triggering a relay module as a switch to turn the lights on or off. The primary benefit of motion sensor lights is energy efficiency, reducing unnecessary power consumption. The article also explains how to create motion sensor lights using simple tools and easy-to-follow steps. Consequently, readers can grasp the fundamental concept of motion sensor lights and have a practical guide to make them on their own. Through the research conducted, a modification of regular LED lights into LED lights that can function with a PIR sensor has been achieved through several experiments.
Performance Improvement for Hotspot Prediction Model Using SBi-LSTM-XGBoost and SBi-GRU-XGBoost Sukmana, Husni Teja; Aripiyanto, Saepul; Alamsyah, Aryajaya; Henry, Amir Acalapati; Nandaputra, Riandi
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3047

Abstract

Forest fires damage ecosystems and harm all living beings, often triggered by low rainfall that worsens fire spread. Climatic factors such as the El Nino–Southern Oscillation (ENSO) also contribute to reduced rainfall and prolonged dry seasons. This study aims to enhance the performance of fire prediction models to support forest fire mitigation. Modified artificial neural network algorithms—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with bidirectional stacked layers—are employed as baseline models. An experimental approach was used to compare the performance of LSTM and GRU models with their ensemble versions, where XGBoost was added to improve prediction accuracy. The results show that the proposed ensemble algorithms significantly outperform the baseline models in multivariate fire prediction. The SBi-LSTM-XGBoost and SBi-GRU-XGBoost models demonstrated more than a 40% performance improvement compared to the original SBi-LSTM and SBi-GRU models. In multivariate modelling, the ensemble models achieved an R-value of 1.0000, with an average MAE of 0.0007, RMSE of 0.0009, and MAPE of 0.0008. This study also identified limitations of the LSTM and GRU models in processing ENSO data due to their non-linearity and weak correlation with hotspot data. As a contribution, our experiments show that integrating XGBoost into LSTM and GRU models effectively overcomes these limitations, significantly improving hotspot prediction accuracy and supporting better forest fire mitigation strategies.
Hybrid Learning for Automated E-commerce Churn Prediction with XGBoost and SHAP Henry, Amir Acalapati; Arribathi, Abdul Hamid; Henderi, Henderi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2833

Abstract

Customer retention is a critical challenge in the e-commerce industry, yet many platforms frequently suffer from a lack of explicit labels to identify potential defectors (churn). This research proposes a hybrid unsupervised-supervised learning framework to perform automated churn classification using the high-dimensional Olist Brazilian E-commerce dataset. The first stage employs K-Means Clustering to objectively generate churn labels based on Recency, Frequency, and Monetary (RFM) features. The second stage applies an Extreme Gradient Boosting (XGBoost) model, augmented by the Synthetic Minority Over-sampling Technique (SMOTE), to address the inherent class imbalance typical of transactional data. Experimental results demonstrate that the proposed model achieved a robust accuracy of 72% with a churn recall rate of 69%. Interpretability analysis using SHapley Additive exPlanations (SHAP) revealed that delivery duration and customer review scores are the most dominant predictors of churn, significantly outweighing financial metrics. These findings contribute a novel integration of automated labeling and model transparency, enabling e-commerce managers to implement proactive, data-driven customer retention strategies.
Co-Authors Abdul Hamid Arribathi Alamsyah, Aryajaya Angga Aditya Permana Henderi . Husni Teja Sukmana Maskurudin, Muhamad Nandaputra, Riandi Pradipta, Allan Saepul Aripiyanto Sufyan, Ammar Syafiq, Zahra