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All Journal Journal of Indonesian Management Scientific Journal of Informatics
Ginanjar, Teguh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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The Effect Of Work Discipline And Work Motivation On The Performance Of Courier PT. Ninja Express Tasikmalaya Ginanjar, Teguh; Rahwana, Kusuma Agdhi; Sutrisna, Arga
Journal of Indonesian Management Vol. 2 No. 3 (2022): September
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jim.v2i3.900

Abstract

The purpose of this study is determine the effect simultaneously and partially between Work Discipline and Work Motivation on Courier Performance PT. Ninja Express Tasikmalaya. The research method used is a survey method with a quantitative approach. The sampling technique used the saturated sample method with a total sample of 40 peoples. Data collection techniques using online questionnaires. The data used is primary data. The analytical tool used in this study is multiple regression using SPSS 26. The results show that work discipline and work motivation have a significant effect on performance simultaneously. Partially, work discipline has a significant effect on performance. Partially, work motivation has a significant effect on performance.
Development of Segmentation Method to Localize Epileptic Symptoms in EEG Signal Praja, Reval Bima; Nugroho, Hertog; Ginanjar, Teguh
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40414

Abstract

Purpose: Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide, where early detection through EEG signal analysis is crucial for proper management. However, the quality of EEG signals is often affected by noise and artifacts, which can lead to diagnostic errors of up to 30% in the early stages. This study aims to develop an EEG signal preprocessing method to improve the classification performance of epileptic symptoms through preprocessing, segmentation, and seizure interval analysis approaches. Methods: The preprocessing stage involved applying a 50 Hz notch filter and a 0.5–60 Hz bandpass filter. The contribution of this work is in the development of  hybrid segmentation based on frequency and amplitude analysis, while seizure intervals were identified using distances criteria between consecutive spikes detected on signals. The method was tested using the CHB-MIT dataset consisting of 23 EEG channels. Result: The results showed that the system successfully identified seizure segments with an average accuracy of 62.09%, and 9 out of 23 channels achieved accuracies above 70%. Channels Ch08 (86.60%), Ch09 (86.36%), and Ch19 (80.51%) achieved the highest accuracies. The results also showed high specificity(99.85%) and low False Positive rate(0.15%) indicating the system’s effectiveness to reduce falase positive. Novelty: This method proved effective in detecting epileptiform activity and shows potential as an EEG-based early detection tool for epilepsy, although further optimization is needed to improve accuracy on channels with low signal-to-noise ratio (SNR).
Co-Authors Kusuma Agdhi Rahwana Nugroho, Hertog Praja, Reval Bima Sutrisna, Arga