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All Journal Faktor Exacta InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Tekno Insentif Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sains Indonesia Engagement: Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Unimus
Riady, Sasmitoh Rahmad
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Nursing Public Health Social Sciences Other

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Penerapan Absensi Kuliah Berbasis QR Code dengan Modul Raspberry Pi3 Menggunakan Metode Arsitektur Zachman Framework Implementation of Lecture Absence Based on QR Code with Raspberry Pi3 Modul Using Zachman Framework Architecture Method Bei Harira Irawan; Sasmitoh Rahmad Riady; Khalis Sofi
Prosiding Seminar Nasional Unimus Vol 1 (2018): Hilirisasi & Komersialisasi Hasil Penelitian dan Pengabdian Masyarakat untuk Indonesia
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengefektifkan sistem absensi perkuliahan dan menanggulangi adanya indikasikecurangan atas absensi mahasiswa dalam proses perkuliahan di kelas. Daftar absen manual menyebabkan losttime perkuliahan dan pemborosan kertas, maka dari itu perlunya adanya sistem absensi untuk mengurangipermasalahan tersebut. Dalam perancangan sistem ini peneliti menggunakan metode model Arsitektur ZachmanFramework, kemudian diuraikan berdasarkan abstraksi yang dilihat dari sudut pandang dariPlannerPerspectiveyaitu dosen dan OwnerPerspective yaitu mahasiswa. Penelitian ini menghasilkan sebuah rancangan alat sebagaialternatif absensi perkuliahan yaitu menggunakan QR Code. Dimana QR Code tersebut akan di generateolehmahasiswa untuk absen pada sebuah portal atau sistem dosen berbasis online, lalu di-scan oleh camera yangterhubung ke Raspberry Pi3. Data hasil scanner ter-generateke system di website dan tersimpan dalam databasedan dapat diakses kapanpun dan dimanapun dosen mengajar. Sistem ini menjamin keakuratan data yang didapat,serta mengefisiensikan proses rekapitulasi absensi mahasiswa bagi dosen pengampu mata kuliah yangbersangkutan, Kata kunci: absensi, arsitektur Zachman Framework, QR Code, Raspberry Pi3
Water Quality Monitoring System with Parameter of pH, Temperature, Turbidity, and Salinity Based on Internet of Things Adityas, Yazi; Ahmad, Muchromi; Khamim, Moh; Sofi, Khalis; Riady, Sasmitoh Rahmad
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.965

Abstract

This research aims to monitor the quality of water used for aquariums. The physical parameters used are water pH, water temperature, water turbidity, and water salinity. Using a pH sensor, temperature sensor, turbidity sensor, and salinity conductivity sensor with Arduino as the controller. The prototype method used in this research, starting from the formulation, research, building stages to testing and evaluating the results of the research. The working process of the system is when the system is activated, the sensors will detect and capture the amount of value contained in the water, then the data from the sensor is sent to a database in the cloud using an ethernet shield that is connected to the media router as a liaison for the internet network then displayed on the website dashboard in the form of graphs and monitoring record tables in real time. The sensors function to detect water quality, where quality standards have been set in this system, namely temperature standards of 27-30°C, pH standards of 7.0-8.0, turbidity standards of 2.5-5 ntu, and salinity of 20-28 ppt. If the sensor detects non-compliance with water quality standards, the buzzer in this system will sound. From the results of system testing, sensors can detect water quality in real time within 5-10 seconds. Based on the research results, this water quality monitoring system is effective to help ensure the quality of the water in the aquarium so that it always meets the standards.
Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique Riady, Sasmitoh Rahmad; Sen, Tjong Wan
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.904

Abstract

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519. 
Co-Authors Adityas, Yazi Agung Nugroho Agus Susanto Agus Suwarno, Agus Ahmad, Muchromi Antika Zahrotul Kamalia Ardi Gunawan Aswan Supriyadi Sunge Bei Harira Irawan Donny Maulana Jafar Shadiq Khalis Sofi Khamim, Moh Nawangsih, Ismasari R. R. Wening Ken Widodasih Rianti Kinasih Rika Apriani Rila Mandala Rita Wahyuni Arifin Rita Wahyuni Arifin Shita Dwi Ranti Tjong Wan Sen