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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Solang, Efraim William
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Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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SMALL ROAD MONITORING SYSTEM FOR FOUR WHEEL VEHICLES Solang, Efraim William; Suarjaya, I Made Agus Dwi; Pratama , I Putu Agus Eka
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.139

Abstract

The application of the vehicle regulation system at intersections in rural areas does not yet have a good regulatory system compared to the vehicle control system in urban areas. The lack of equitable distribution of road widening in rural areas makes people often have problems accessing roads when using four-wheeled vehicles. People often experience problems when accessing small roads, especially when more than one vehicle passes at the same time. This problem was also experienced in DesaTanjung Sari, Kecamatan Cijeruk, Kabupaten Bogor. This study aims to design and build a small road monitoring system so that car drivers get a warning if there are four-wheeled vehicles that have passed the small road. The object recognition method used in the small road monitoring system for four-wheeled vehicles is the HC-SR04 ultrasonic sensor which is placed on the left, right, and top sides. Arduino Uno R3 microcontroller will be used as the main system device. The communication between points used is LoRa Ra-02 as the sender and receiver. The results of this study show the results of the system prototype running well in accordance with the planned functions.
A Systematic Literature Review of Machine Learning for Endurance Running Performance Prediction Solang, Efraim William; Linawati, Linawati; Manuaba, Ida Bagus Gede; Setiawan, I Nyoman
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15743

Abstract

This study systematically reviews the application of machine learning methods for predicting running performance, with particular emphasis on short-middle distance events such as the 5 km. Although machine learning based performance prediction has been widely explored in endurance sports, a comprehensive review synthesizing models, predictors, and pipelines across running distances remains limited. The review followed the PRISMA 2020 framework. Articles published between 2020 and 2025 were retrieved from ScienceDirect, Google Scholar, and PubMed using predefined keyword combinations related to machine learning and running performance. Studies were included if they focused on running (excluding cycling, triathlon, or other sports), applied predictive modeling, and reported model evaluation metrics. A total of 26 studies met the inclusion criteria and were assessed using quality appraisal criteria inspired by TRIPOD and QUADAS-2. The analysis identified four main research themes: (1) application of machine learning models for running performance prediction, (2) physiological and anthropometric predictors, (3) non-physiological and contextual factors, and (4) personalized athlete training and monitoring. Ensemble learning models (Random Forest, XGBoost, LightGBM) consistently outperformed traditional linear regression by capturing non-linear interactions, while deep learning approaches (LSTM, GRU) demonstrated strong capability in modeling temporal training dynamics. A generalized machine learning pipeline for running performance prediction was also synthesized. This review contributes a structured framework that integrates modeling approaches, predictor categories, and evaluation strategies, and highlights research opportunities for explainable and personalized prediction systems, particularly for 5 km running performance.
Evaluasi Machine Learning untuk Prediksi Pembatalan Hotel dengan Threshold Adjustment dan Cost-Based Evaluation: Machine Learning Evaluation for Hotel Cancellation Prediction with Threshold Adjustment and Cost-Based Evaluation Solang, Efraim William; Adu, Franco Xander; Dharma, Agus; Gunantara, Nyoman
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2466

Abstract

Pembatalan pemesanan hotel merupakan permasalahan krusial yang berdampak langsung pada pendapatan dan perencanaan operasional. Penelitian ini mengevaluasi penerapan threshold adjustment dan cost-based evaluation untuk meningkatkan kualitas pengambilan keputusan bisnis. Penelitian ini melibatkan perbandingan beberapa jenis model machine learning menggunakan dataset hotel booking demand. Kinerja model dinilai menggunakan metrik F0.5-Score, precision, ROC AUC, dan pendekatan cost-based evaluation berbasis net revenue. Hasil penelitian menunjukkan bahwa Random Forest memberikan kinerja terbaik dengan F0.5-Score 0.8279, precision 0.878 dan ROC AUC 0.9165. Model lain seperti Logistic Regression (baseline) dengan F0.5-Score 0.7816, XGBoost dengan F-.5-Score 0.8108 dan ANN dengan F0.5-Score 0.8091 menunjukan performa lebih relatif lebih rendah, mengindikasikan bahwa dataset ini lebih cocok menggunakan pendekatan ensamble learning. Temuan penting mengungkapkan bahwa penyesuaian threshold berdasarkan F0.5-Score tidak selalu menghasilkan keuntungan ekonomi maksimum. Penggunaan threshold (0.52) terbukti menghasilkan nilai net revenue lebih tinggi dibandingkan threshold optimal berbasis F0.5-Score. Pendekatan ini diharapkan dapat meningkatkan kualitas pengambilan keputusan bisnis bagi manajer hotel dalam pengelolaan risiko finansial.
Co-Authors Adu, Franco Xander Agus Dharma I Made Agus Dwi Suarjaya I Nyoman Gunantara I Nyoman Setiawan Linawati Linawati Manuaba, Ida Bagus Gede Pratama , I Putu Agus Eka