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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer Networks, Architecture and High Performance Computing Internet of Things and Artificial Intelligence Journal
Manalu, Andi Setiadi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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PENERAPAN INTERNET OF THINGS (IOT) UNTUK OPTIMASI PEMELIHARAAN TANAMAN RUMPUT RAJA SECARA REAL-TIME Manalu, Andi Setiadi; Siregar, Victor Marudut Mulia; Sugara, Heru
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1709

Abstract

This study aims to optimize the maintenance of King Grass plants by utilizing Internet of Things (IoT) technology for real-time monitoring. King Grass is known as a source of livestock feed with high productivity, but its maintenance often faces challenges such as environmental fluctuations and inefficient irrigation patterns. This study uses an ESP32 microcontroller integrated with a DHT22 sensor, soil moisture sensor, and automatic water pump, and utilizes the Thingsboard platform to monitor data in real time. The data collected includes air temperature, air humidity, and soil moisture, which are displayed through interactive widgets. The system was tested by simulating various environmental conditions, showing success in automating irrigation based on soil moisture thresholds. The test results show that IoT technology is able to improve plant maintenance efficiency, reduce manual intervention, and ensure optimal conditions for plant growth. The use of the Thingsboard platform facilitates data monitoring and analysis for further planning. This study concludes that the application of IoT to King Grass maintenance provides an effective solution in increasing agricultural productivity through accurate and efficient monitoring and control.
PENGGUNAAN TEKNOLOGI AUGMENTED REALITY SEBAGAI ALAT UNTUK MEMPROMOSIKAN PARIWISATA BERKELANJUTAN Siregar, Victor Marudut Mulia; Damanik, Erikson; Manalu, Andi Setiadi; Siringo-ringo, Eko Deswin; Parapat, Eka Pratiwi
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1558

Abstract

This study aims to develop interactive tourism promotion media using Augmented Reality (AR) technology to increase the attractiveness of Sipolha Tourism Village as a leading destination in the Lake Toba area. Sipolha Village has great tourism potential but is not widely known compared to the surrounding areas. The research method used is design and development research which includes the design, development, implementation, and evaluation of an application called PAR Sipolha. This application presents 3D visualizations of local tourist attractions in an immersive manner and allows tourists to obtain information interactively. The prototype was developed using Blender for 3D object creation and Unity 3D with Vuforia Engine for AR integration. Implementation and testing were carried out with local communities and tourists to evaluate the effectiveness of the application. The results of the study show that the PAR Sipolha application is able to improve the tourism experience, provide interesting information, and support the promotion of sustainable tourism in Sipolha Village. This application is expected to be a model for the development of technology-based tourism in other tourist villages.
Perancangan Dan Implementasi Private Cloud Storage Dengan Owncloud Pada Jaringan Lokal Menggunakan Virtualbox Manalu, Andi Setiadi; Sitanggang, Sahat Sonang
Journal of Computer Networks, Architecture and High Performance Computing Vol. 1 No. 2 (2019): Computer Networks, Architecture and High Performance Computing
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v1i2.244

Abstract

Teknologi Cloud Computing dengan layanan Private Cloud Storage merupakan dan menjadi sebuah jawaban untuk permasalahan yang sering terjadi dikehidupan kita sehari-hari yaitu pada permasalahan penggunaan perangkat penyimpanan fisik seperti memory card, flashdisk dan harddisk dimana data-data yang ada didalamnya sering terjadi kerusakan seperti kerusakan fisik perangkat, terkena bad sector, terkena virus, perangkat hilang dan lain sebagainya seperti yang sering terjadi pada mahasiswa dan mahasiswi Politeknik Bisnis Indonesia Murni Sadar Pematangsiantar. Teknologi Cloud Storage yang akan dirancang bersifat private hanya untuk kalangan kampus dan pada jaringan lokal kampus yang tersedia. Perancangan sistem yang akan dilakukan menggunakan virtualisasi dengan Oracle VM VirtualBox. Sistem operasi menggunakan Ubuntu Server 14.04.6 LTS, Web Server menggunakan Apache2, DBMS (Database Management System) menggunakan Mariadb, Interpreter menggunakan PHP dan Content Management System (CMS) menggunakan Owncloud. Setelah perancangan sistem dilakukan kemudian sistem tersebut diimplementasikan pada VirtualBox setelah itu dilakukan pengujian sistem terhadap akses data ke sistem dengan Smartphone dan komputer sehingga didapatkan sebuah sistem yang dapat berjalan dan berfungsi dengan baik agar dapat meningkatkan efisiensi perkuliahan.
Classification of Customer Satisfaction Through Machine Learning: An Artificial Neural Network Approach Siregar, Victor Marudut Mulia; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi; Yunus, Muhammad
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.643

Abstract

This study aims to classify customer satisfaction data from Café Alvina using Machine Learning, specifically by implementing the Backpropagation Artificial Neural Network. The data used in this study consists of 70 training data and 30 testing data, with the input layer of the Artificial Neural Network having 5 neurons and the output layer having 2 neurons. The tested Artificial Neural Network models include the 5-5-2 model, 5-10-8-8-2 model, 5-5-10-2 model, and 5-8-10-2 model. Among the four models used in the testing process of the Backpropagation Artificial Neural Network system using Matlab, the 5-10-8-8-2 architecture model performed the best, achieving an MSE (Mean Squared Error) of 0.000999932 during training with 2920 epochs and a testing MSE of 0.000997829. After conducting the testing, the performance of the Artificial Neural Network models was as follows: the 5-5-2 model achieved 81%, the 5-10-8-8-2 model achieved 100%, the 5-5-10-2 model achieved 98%, and the 5-8-10-2 model achieved 96%. Through the implementation of Backpropagation Artificial Neural Network, the classification of customer satisfaction can be effectively performed. The trained and tested data demonstrate that the Artificial Neural Network can accurately recognize the input data in the system.
Decision Support System for Selecting Social Assistance Recipients using The Preference Selection Index Method Parapat, Eka Pratiwi Septania; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i4.662

Abstract

This research aims to solve the problem of selecting social assistance recipients in the Nagori Moho area, Java Marajah Bah Subdistrict, Jambi, Simalungun District; in order to obtain the right targeted recipients of social assistance, the Nagori office carries out the selection of its residents, this selection is carried out by implementing a computer-based decision support system (DSS). The decision support system uses the PSI method. The criteria used in this method consist of economic condition, income, jobs, age, and dependents of the school children. The results obtained from this research are recommendations for the population receiving aid with results consisting of rank 1 with the alternative value S_Purba with a value of 0.9286, then rank two with the alternative F_Azhar with a value of 0.7599, and rank 3 is Jumiati with a value of 0.7163. This decision support system can make it easier for the Nagori office to select residents worthy of assistance.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MAHASISWA PKL TERBAIK MENGGUNAKAN METODE MOOSRA Purba, Arifin Tua; Manalu, Andi Setiadi; Sirait, Erwin
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2137

Abstract

Politeknik Bisnis Indonesia is a vocational higher education institution committed to producing graduates who are not only academically competent but also equipped with practical skills required in the workforce. One of the essential programs in its curriculum is the Internship (PKL), designed to allow students to apply their knowledge in real-world work environments. However, the selection of the best internship students has been conducted manually, leading to inefficiencies and potential subjectivity in the evaluation process. This study aims to design a Decision Support System (DSS) using the MOOSRA (Multi-Objective Optimization on the Basis of Simple Ratio Analysis) method to support a more objective and systematic selection process. The evaluation involves five main criteria: discipline, teamwork, skill, work quality, and attendance, with six student candidates as alternatives. The research stages include problem identification, criteria and weight determination, data collection, data processing with the MOOSRA method, system design, and system testing. The results show that the MOOSRA method effectively ranks the students, with student A4 selected as the best internship participant with the highest Yi score of 6.12347. This research demonstrates that the MOOSRA method can significantly improve decision-making accuracy and fairness in multi-criteria selection processes.
Co-Authors Erikson Damanik Erwin Sirait, Erwin Muhammad Yunus Parapat, Eka Pratiwi Parapat, Eka Pratiwi Septania Purba, Arifin Tua Sinaga, Kalvin Siregar, Victor Marudut Mulia Siringo-ringo, Eko Deswin Sitanggang, Sahat Sonang Sugara, Heru