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All Journal International Journal of Information Techonology and Education (IJITE) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Rekayasa elektrika
Alex C. Andaria
Universitas Trinita
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Other

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Development of Electronic Business Management Information System At Trinita Elektro Manado Store Alex Andaria; Hiskia K Manggopa; Mario T Parinsi
International Journal of Information Technology and Education Vol. 1 No. 3 (2022): June 2022
Publisher : JR Education

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Abstract

The purpose of the study was to determine the results of the development of an electronic business management information system and to produce a valid, practical, and efficient electronic business management management information system at the Trinita Elektro Store. The type of research used is research and development or Research and Development (R&D) using the Borg & Gall development model whose research and development steps consist of data collection and initial planning, product development (initial product), product validation, product revision. , product trial 1, product revision 2, product trial 2 and (8) final product (dissemination and implementation). The subjects in this study were business owners, technicians, cashiers, and customers at the Trinita Elektro Store. Data collection techniques using literature studies, interviews and questionnaires / questionnaires. The instruments used are expert validation sheets and product trial questionnaires. While the data analysis technique used is quantitative descriptive analysis. The results showed that the electronic business management information system was feasible to be tested after being validated by design experts and materials experts at the address https://trinitaelektro.website/. Keywords : research and development, management information system, electronic business.
Pengembangan Aplikasi Sistem Informasi Kecerdasan Moral (SICEMOR) Berbasis Android: Android-Based Moral Intelligence Information System (SICEMOR) Application Development Yuliana Mose; Alex C. Andaria; Prasetyo Y. Damongi; Henry Hendrawan; Petra Egeten; Injilia Rindorindo
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1013

Abstract

Selalu ada oknum yang ingin mendapatkan nilai akhir mata kuliah bahkan gelar akademik melalui jalan pintas untuk beragam alasan. Memilih untuk menolak melakukan tindakan seperti itu merupakan sebuah keputusan etis yang dilakukan oleh seseorang karena dorongan moral bukan intelektualnya. Artinya kecerdasan intelektual harus dilengkapi dengan kecerdasan moral. Disinilah urgensinya mengukur kecerdasan moral seseorang. Pengukuran kecerdasan moral dapat dilakukan dengan menggunakan kuesioner atau skala yang dirancang untuk mengukur tingkat pemahaman, kesadaran, dan penilaian moral individu terhadap situasi atau dilema moral. Moral Competency Inventory (MCI) yang dikembangkan oleh Lennick, Kiel & Jordan dapat digunakan sebagai alat ukur untuk mengetahui kekuatan dan kelemahan kecerdasan moral. MCI merupakan instrumen yang terdiri dari 40 item yang terbagi dalam 10 kompetensi moral. Penelitian ini bertujuan membuat inovasi untuk mengukur level kecerdasan moral seseorang melalui program aplikasi Sicemor berbasis Android dengan mengadopsi instrumen MCI. Metode penelitian yang digunakan yaitu riset dan pengembangan (R&D) dengan pendekatan ADDIE. Aplikasi SiCeMor telah berhasil dirancang dengan bahasa pemrograman Kotlin dan arsitektur MVVM.  Aplikasi Sicemor melalui tahapan pengujian fungsionalitas, performa, keamanan data dan kompatibilitas perangkat dengan indikator ketepatan dan keakuratan dalam perhitungan skor akhir, menampilkan data secara live, navigasi antar tampilan, penggunaan sumber daya seperti CPU dan memori. 
Streamlining Deep Learning Network for Real-time Sea Turtle Detection Muhamad Dwisnanto Putro; Yuliana Mose; Alex Copernikus Andaria; Jane Litouw; Vecky Canisius Poekoel; Xaverius Najoan
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.35236

Abstract

Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed.
Co-Authors Dedi Sorongan, Yuliana Mose, Reonaldy A Berikang Fradel R. Sigar, Verry R. Palilingan, Mario T. Parinsi Henry Hendrawan Hiskia Kamang Manggopa Injilia Rindorindo Jane Litouw Muhamad Dwisnanto Putro Petra Egeten Prasetyo Y. Damongi Selvi Marcellia Vecky Canisius Poekoel Xaverius Najoan