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All Journal Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Evi Septiana Pane, Evi Septiana
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Computer Science & IT Education Engineering Mathematics Social Sciences

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ICT UNTUK MEREDUKSI BIAYA LOGISTIK PADA TRANSPORTASI MULTIMODA Pane, Evi Septiana
Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Vol 7, No 1 (2016): Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan
Publisher : Kementerian Komunikasi dan Informatika R.I.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (865.067 KB) | DOI: 10.17933/mti.v7i1.28

Abstract

Penerapan ICT pada angkutan barang telah dikenal sejak lama. Namun, penerapan ICT pada logistik dalam transportasi multimoda di Indonesia terjadi sangat lamban. Padahal, bermacam aplikasi ICT tersedia di luaran untuk mendukung efisiensi biaya dan peningkatan kinerja logistik. Oleh sebab itu, studi ini bertujuan melakukan pemetaan aplikasi ICT yang dapat diterapkan pada transportasi multimoda, khususnya untuk mereduksi biaya logistik yang timbul. Dengan melakukan eksplorasi dan studi kualitatif terhadap berbagai sumber data sekunder, dimunculkan beberapa aplikasi ICT yang mungkin diadopsi di Indonesia. Pembahasan dari studi ini memuat pemetaan implementasi aplikasi ICT, stakeholders yang terlibat, serta hambatan dan tantangan yang muncul ketika akan mengadopsi aplikasi ICT tersebut di Indonesia
Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain Sulistyono, Marcelinus Yosep Teguh; Pane, Evi Septiana; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85550

Abstract

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.
Co-Authors Adhi Dharma Wibawa, Adhi Dharma Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Sulistyono, Marcelinus Yosep Teguh