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All Journal JURNAL SISTEM INFORMASI BISNIS International Journal of New Media Technology
Bambang Dwi Wijanarko
Bina Nusantara University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Warehouse Management System for Smart Digital Order Picking Systems Dina Fitria Murad; Widya Ratnasari; Bhumyamka Yala Saputra; Bambang Dwi Wijanarko
IJNMT (International Journal of New Media Technology) Vol 6 No 2 (2019): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (813.332 KB) | DOI: 10.31937/ijnmt.v6i2.1215

Abstract

The purpose of this paper is to identify problems in the identified picking process that lead to consumer dissatisfaction and provide solutions to problems that exist in the company, especially in warehousing, supported by analysis of the running system to obtain the information needed. The decision-making system is used to be able to produce information regarding picking orders in adjusting the number of orders, availability of pickers and distribution to consumers. The research method that uses the PIECES analysis and technology acceptance model method to determine the user's acceptance of the system being built. The smart digital order picking system was able to significantly accelerate the order picking business process from the ones that previously took a long time after implementation could meet consumer needs quickly.
Teknologi Baru Pada Pendidikan Tinggi Menuju Revolusi Industri 4.0: Studi Kasus Indonesia dan Malaysia Dina Fitria Murad; Silvia Ayunda Murad; Rosilah Hassan; Yaya Heryadi; Bambang Dwi Wijanarko; Titan Titan
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 11, No 2 (2021): Volume 11 Nomor 2 Tahun 2021
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol11iss2pp139-145

Abstract

IoT with E-learning is intended to support data collection from devices and share to other devices in use for effective E-learning applications from Smart Campus. This study aims to conduct studies related to online learning models by utilizing Internet of Everything (IoE) technology to support Revolution 4.0. This study aims to support the latest communication paradigm in which the objects of everyday life will be equipped with a series of appropriate protocols and enable them to communicate well with each other as part of the internet. IoE will help improve learning by leveraging the large subject data generated by these objects to provide dynamic services to educators, learners, and even content developers. Using qualitative research methods This research uses a questionnaire to find out the views and assessments of the community in this case online learners regarding online learning as one of the impacts of the Covid-19 pandemic and produces an online learning model that is supported by an integrated system between learning media such as LMS and devices. others use IoE. The results of this study support the implementation of Smart campuses that allow the use of IoE methodologies to make them always ready in certain network areas.
Pengukuran Prestasi Belajar Mahasiswa Berdasarkan Prediksi Nilai Menggunakan General Linear Model Dina Fitria Murad; Bambang Dwi Wijanarko; Silvia Ayunda Murad; Vina Septiana Windyasari
Jurnal Sistem Informasi Bisnis Vol 13, No 2 (2023): Volume 13 Nomor 2 Tahun 2023
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol13iss2pp135-142

Abstract

The Covid-19 pandemic is an international disaster experienced by almost all countries in the world. Several research results reveal the special impact of the pandemic on the education sector. Not only lecturers and students, but higher education providers also experience the same thing. Various adjustments were made so that all parties involved were able to adapt properly. It's been two years since the pandemic among us and during that time the learning process has continued. Based on this, several institutions began to take steps that raised questions about whether the learning achievement targets in each subject could still be achieved. This study aims to predict student grades using several machine-learning algorithms. The prediction results are a measure to find out whether learning outcomes have been achieved or not, if not achieved then additional steps need to be taken to help students. The results of this research are expected to help UNIS to prepare appropriate learning models for its students and ensure that all learning achievement targets are achieved. The research method used is a technique of machine learning. The results of this study indicate that the General Linear Model is a classification model with the best accuracy, which can be used to predict student achievement in certain classes based on the evaluation scores of the first structured activity (EKT1), midterm exams, grades (UTS), and second structured activity evaluation scores (EKT2). And it turns out that the UTS score has the greatest influence between EKT1 and EKT.
Co-Authors Bhumyamka Yala Saputra Dina Fitria Murad Dina Fitria Murad Rosilah Hassan Silvia Ayunda Murad Silvia Ayunda Murad Titan Titan Vina Septiana Windyasari Widya Ratnasari Yaya Heryadi