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p-Index From 2021 - 2026
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All Journal Journal of Industial Engineering and Technology Jurnal Teknologi dan Manajemen Sistem Industri
Heru Prastiyono
Universitas Bojonegoro
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Model Smart Glasses Untuk Penyandang Tuna Netra Berbasis Computer Vision Faisal Ashari; Heru Prastiyono; Widyo Nugroho; Regar Rea Saputra
Journal of Industrial Engineering and Technology Vol. 6 No. 2 (2026): 30 Juni 2026
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/jointech.v6i2.17399

Abstract

This study proposes and implements a smart glasses model specifically designed to assist blind people in identifying people around them in real-time. This system utilizes computer vision technology, specifically the Local Binary Patterns Histograms (LBPH) algorithm implemented using the OpenCV library. The research process starts from facial dataset acquisition, model training, to dynamic facial recognition using an integrated camera. Test results show that the system is able to recognize faces with a certain level of confidence using a confidence threshold parameter of 80. System performance evaluation is carried out using Accuracy, Precision, Recall, and F1-Score metrics. Based on the results of experiments on three test objects, the system obtained an accuracy level of 90%–100%, a precision value of 94.7%–100%, a recall of 94.7%–95%, and an F1-score of 94.7%–97.4%. These results indicate that the LBPH algorithm has a fairly good ability to perform real-time facial recognition on smart glasses devices. This smart glasses model is expected to increase the independence, mobility, and social interaction of blind people in their daily lives.
Peramalan Jumlah Kunjungan Pengguna Pelayanan Statistik Terpadu menggunakan Metode Time Series di Badan Pusat Statistik: Nayla Farikha Zahra, Heru Prastiyono, Agung Firdausi Ahsan Nayla Farikha Zahra; Heru Prastiyono; Agung Firdausi Ahsan
Jurnal Teknologi dan Manajemen Sistem Industri Vol. 4 No. 1 (2025): Jurnal Teknologi dan Manajemen Sistem Industri (JTMSI) - MARET
Publisher : Universitas Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56071/jtmsi.v4i1.2078

Abstract

This study addresses the forecasting of visitor numbers to the Pelayanan Statistik Terpadu (PST) unit at the Badan Pusat Statoistic (BPS) Bojonegoro office using a time series approach. Forecasting visitor volume is essential for supporting service capacity planning, resource allocation, and enhancing the operational efficiency of data-driven public services. The study compares three forecasting methods—Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing, and Prophet—using monthly visitor data from January 2020 to May 2025. The dataset was divided into a training set (January 2020–December 2024) and a test set (January–June 2025). Model evaluation was conducted using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The results indicate that the Prophet model delivered the best performance, achieving an RMSE of 1.44, MAE of 1.34, and MAPE of 0.91—outperforming both Holt-Winters and SARIMA. Prophet's superior performance is attributed to its ability to simultaneously model long-term downward trends and annual seasonal patterns. The findings demonstrate that an adaptive, time series-based forecasting approach can support data-driven decision-making for regional public statistical services.
Co-Authors Agung Firdausi Ahsan Faisal Ashari Nayla Farikha Zahra Regar Rea Saputra Widyo Nugroho