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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) International Journal of New Media Technology Jurnal ULTIMA InfoSys Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Instrumentasi dan Teknologi Informasi (JITI)
Mitra, Aditya Rama
Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pelita Harapan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Ekspansi Layanan Mimio® pada Jaringan Nirkabel 802.11 Aditya Rama Mitra; Welly Kamarudin; Liana Tirtasendjaja
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2008
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Perangkat bantu ajar mimio® sebagai salah satu perekam tulisan tangan dalam bentuk digitalmenyediakan fasilitas koneksi point-to-multipoint hingga 30 pengguna yang dapat secara simultan membangunkoneksi dengan komputer sumber tanpa menggunakan titik akses nirkabel (wireless access point). Namunfasilitas ini tidak dapat mengakomodasi setting kelas paralel yang melibatkan hingga 200 peserta ajar.Tulisan ini menyajikan bagaimana ekspansi layanan untuk banyak pengguna simultan dilakukan dalamsetting kelas paralel. Eksplorasi didukung dengan pengukuran yang dilakukan dalam lingkungan terbatas untukmemperoleh gambaran teoritis mutu layanan (quality of service) aplikasi mimio® yang dijalankan pada jaringannirkabel 802.11. Pengukuran ini hanya melibatkan metrik lebar pita (bandwidth) yang mencakup prosespengunduhan (download) dan pemunggahan (upload).Pengujian menunjukkan bahwa kebutuhan ekspansi banyak pengguna dapat dipenuhi melalui duametode. Metode pertama menerapkan konfigurasi satu VLAN untuk satu kelas; sedangkan metode keduamelibatkan konfigurasi banyak VLAN (multiple VLAN) dengan dukungan server Macromedia Breeze.Kata Kunci: mutu layanan, mimio®, WLAN, VLAN, koneksi banyak pengguna.
METODE BUDIDAYA TAUGE DALAM SMART GREEN HOUSE DENGAN SISTEM PENYIRAMAN OTOMATIS Kevin Arie Sandy; Arnold Aribowo; Alfa Satya Putra; Aditya Rama Mitra
JURNAL FASILKOM (teknologi inFormASi dan ILmu KOMputer) Vol 11 No 1 (2021): Jurnal Fasilkom
Publisher : Fakultas Ilmu Komputer, Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (777.839 KB) | DOI: 10.37859/jf.v11i1.2470

Abstract

There are many techniques to cultivate mung bean sprout, however the process requires many human involvements which can cause problems, such as forgetting to water the mung bean sprout, cutting the tail which must be cut one by one, finding the ideal location, and detecting water leakage. In this research, mung bean sprout will be grown with an automatic watering system in a Smart Green House system, which is able to monitor the mung bean sprout using technologies such as light, temperature and humidity sensors to determine the ideal place of cultivation, water level sensor to measure remaining water level in the container, and water sensor to detect water leakage from the planting medium. Arduino Mega 2560 is used as the microcontroller in this system. The sensors and components used in the system includes Light Dependent Resistor GL5506, Water Sensor Funduino, buzzer, Ultrasonic Ping HC-SR04, Humidity and Temperature Sensor DHT11, potentiometer, servo motor, relay with 12V pump, Knee and Nipple Watering System, and Fogger Head Sprayer. The system is able to cultivate mung bean sprout with length of 3-5 cm, without losing their color in the process, and can be harvested without tail.
Perancangan Aplikasi Sistem Pengenalan Wajah Dengan Metode Convolutional Neural Network (CNN) Untuk Pencatatan Kehadiran Karyawan Efanntyo; Aditya Rama Mitra
Jurnal Instrumentasi dan Teknologi Informatika (JITI) Vol. 3 No. 1 (2021): November
Publisher : Prodi D3 Teknik Elektronika Politeknik Gajah Tunggal

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Abstract

Dalam situasi menghadapi pandemi novel coronavirus (COVID-19), pemakaian masker wajah dan menjaga jarak antar sesama menjadi suatu kewajiban dalam beraktivitas. Hal ini diperkuat dengan saran yang diberikan oleh badan dunia yang berkecimpung untuk urusan kesehatan, yaitu WHO (World Health Organization), agar penggunaan masker dilakukan secara kontinu selama beraktivitas dengan menjaga jarak antar individu minimal 1 meter. Dengan mengacu pada hal tersebut, terjadi perubahan yang mencakup juga sistem pencatatan kehadiran (presensi) secara khusus karyawan perusahaan X. Bila sebelumnya sistem presensi perusahaan X menggunakan sidik jari (fingerprint) kini beralih ke sistem presensi berbasis pengenalan wajah (face recognition) dengan memanfaatkan salah satu pendekatan dalam deep learning, yaitu metode Convolutional Neural Network (CNN) untuk identifikasi wajah seseorang. Keunggulan sistem ini adalah memungkinkan orang bisa menjaga jarak saat melakukan presensi. Berdasarkan hasil observasi sistem presensi yang berjalan dan hasil studi kepustakaan dilakukan perancangan dan pengembangan aplikasi pencatatan kehadiran berbasis pengenalan wajah mengikuti RAD (Rapid Application Development). Bahasa pemrograman yang digunakan adalah bahasa Python dengan model FaceNet yang dapat digunakan untuk pengembangan sistem pengenalan wajah disediakan TensorFlow. Aplikasi sistem pengenalan wajah yang telah dibangun memiliki tingkat akurasi pengenalan wajah yang dipengaruhi jarak antara kamera dengan wajah karyawan pada tingkat pencahayaan 24 lux. Pengukuran pada jarak 30 cm memberi hasil rata-rata tingkat akurasi sebesar 81%; sementara pengukuran pada jarak 60 cm, 90 cm, dan 120 cm memberikan hasil rata-rata untuk masing-masing jarak secara berurutan adalah 81%, 72%, 69%. Sebagai kesimpulan, aplikasi yang dibangun memungkinkan terhindarinya kontak langsung antara karyawan dengan perangkat presensi. Notifikasi bagi karyawan yang belum melakukan presensi telah ditunjukan berfungsi sebagaimana yang diharapkan.
The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring Hery Hery; Calandra Alencia Haryani; Aditya Rama Mitra; Andree Emmanuel Widjaja
IJNMT (International Journal of New Media Technology) Vol 9 No 1 (2022): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v9i1.2405

Abstract

Fire is a type of disaster that can occur anytime and anywhere as a result of any accidental or intentional causes. Without exception, houses are also very vulnerable to fire. To anticipate the catastrophic effects of fire that can destroy houses, advanced technology, such as the Internet of Things (IoT) can be utilized to detect the smoke and fire. This study aims to design an early warning fire detection system for home monitoring using smoke detection sensors based on Arduino microcontroller together with NodeMCU ESP8266. This early warning fire detection system is expected to function by notifying homeowners when detecting the presence of smoke in their homes. With the aid of this detection system, the issue of potential damage, death, or material loss caused by fire can be significantly reduced. The results and testing of the designed system will be discussed in the paper.
From Tradition to Innovation: Mind Map Generation in Higher Education Mitra, Aditya Rama; Samosir, Feliks Victor Parningotan; Hudi, Robertus; Tarigan, Riswan Effendi
ULTIMA InfoSys Vol 14 No 2 (2023): Ultima Infosys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v14i2.3432

Abstract

In higher education, effectively delivering complex topics to students with diverse learning preferences remains a pressing challenge. This comprehensive survey delves into the utilization of mind maps as an innovative instructional tool to navigate this challenge. From 2003 to 2022, we review the implementation of mind maps in higher education, highlighting aspects such as the year of the paper, the techniques employed for mind mapping, target audiences, objectives, and outcomes. Mind maps, centralized and radial visual techniques, have been recognized for their capacity to enhance memory retention, comprehension, and active student engagement. We identify two primary scenarios: Learner-Driven and Lecturer-Driven Development. While students employ mind maps for revision and understanding, lecturers utilize them as visual aids to structure content and elucidate intricate relationships. The paper underscores the need for inclusivity, accommodating varied learning styles, and integrating mind maps into a broader educational toolkit. Through this study, we uncover research gaps and propose future avenues to further amplify the potential of mind maps in academia.
Harnessing the Power of Prophet Algorithm for Advanced Predictive Modeling of Grab Holdings Stock Prices Hery, Hery; Haryani, Calandra A.; Widjaja, Andree E.; Mitra, Aditya Rama
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.181

Abstract

This study investigates the effectiveness of the Prophet algorithm in predicting Grab Holdings' stock prices dataset from Kaggle. By meticulously analyzing historical closing, high, low, and volume data, the research aims to uncover market patterns and gain insights into investor sentiment based on short-term forecasting. The findings reveal a dynamic trajectory for Grab Holdings' stock, characterized by significant fluctuations and evolving investor confidence. The stock reached a peak of $14 in early 2022, indicating optimism, but subsequently experienced a decline to $4 by late 2023, reflecting a shift in sentiment. Notably, 2023 witnessed heightened volatility compared to 2022, evident in more significant price swings and increased trading volume. The Prophet algorithm demonstrated promising potential for prediction better than traditional methods, which overlook the presence of seasonality or fail to adapt to evolving market conditions, leading to less accurate forecasts. The excellent performance of Prophet is indicated by a Mean Absolute Percentage Error (MAPE) of 10.45511%, a Mean Absolute Error (MAE) of 3.112026, and a Root Mean Squared Error (RMSE) of 3.516969. Compared to the traditional ARIMA, MAE and RMSE resulting from Prophet are much lower than their counterparts, which are 14.49675 and 16.079898, respectively. These widely used metrics suggest moderate accuracy in predicting future stock prices. This research offers valuable insights for investors that they can use to understand the trend of Grab Holdings' stock price and make more informed investment decisions regarding buying or selling opportunities. However, it is crucial to acknowledge the inherent limitations of such models and interpret results cautiously, considering the ever-changing dynamics of the financial market.
Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method Widjaja, Andree E; Harjono, Nathanael Joshua; Hery, Hery; Mitra, Aditya Rama; Haryani, Calandra Alencia
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.133

Abstract

Over the past few years, face recognition has been widely used to help human activities in various sectors, including the education sector. By using facial recognition, the class attendance system at universities can be significantly improved. For example, students are no longer asked to sign attendance sheets manually, but attendance can be taken, recorded, and managed automatically through an integrated class attendance management system using facial recognition. The recorded data can then be further analysed to produce useful information for instructors and administrators. In turn, this arrangement will assist them in making decisions about matters relating to student attendance. The main objective of this research is to develop an automatic class attendance management system using facial recognition. In particular, the system we propose was developed using a prototyping software development approach and was modelled using UML version 2.0. As a choice of methods and tools, we used the Viola-Jones method as a face detection algorithm, Python and PHP as programming languages, OpenCV as the computer vision library, and MySQL as the DBMS. The results obtained from a number of black box tests carried out were a fully functional automatic class attendance system prototype using facial recognition.
Co-Authors Arnold Aribowo Efanntyo Harjono, Nathanael Joshua Haryani, Calandra A. Haryani, Calandra Alencia Hery Hery Hudi, Robertus Kevin Arie Sandy Liana Tirtasendjaja Putra, Alfa Satya Samosir, Feliks Victor Parningotan Tarigan, Riswan Effendi Welly Kamarudin Widjaja, Andree E.