Garuda - Garba Rujukan Digital

Go-Ethereum for electronic voting system using clique as proof-of-authority Basilius Bias Astho Christyono; Moeljono Widjaja; Arya Wicaksana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20415

Current advances in information technology have brought about significant changes, including ways to carry out elections using computer technology known as e-voting. Blockchain underlies the popularity of the digital currency Bitcoin and some other digital money, sparking the start of a new era of Internet use, including electronic voting (e-voting) system. In this work, we proposed designing and implementing an evoting system powered by the Ethereum blockchain. We used real-world data from the Indonesian Election Commission (KPU) with 26 candidates and 15,725 voters from the Jelupang sub-district. The testing and evaluation results using three miners in the blockchain show that the system could commit around 23 transactions per second. All 15,725 votes are committed to the blockchain successfully within 12.75 minutes. The total time required for creating all blockchain accounts is 13.4 hours.

APLIKASI MOBILE LAYANAN ADMINISTRASI DESA/KELURUHAN UNTUK LAYANAN DI ERA PANDEMI COVID 19 Yaman Khaeruzzaman; Adhi Kusnadi; Marlinda Vasty Overbeek; Moeljono Widjaja; Dennis Gunawan; Ni Made Satvika Iswari
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 5 No 2 (2022): APTEKMAS Volume 5 Nomor 2 2022
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v5i2.4721

Administrative services are a routine activity carried out by village and/or sub-district offices andallow for mobility and crowding of residents which should be avoided during the current pandemic.The administrative service procedure requires a series of approvals from the head of the RT, headof the RW, the apparatus and the head of the village/kelurahan so it takes time. Administrativeservices will be very good if they can be carried out electronically using devices that are commonlyowned by citizens such as smart cell phones so that they can be carried out faster, complying withrecommendations to avoid mobility and crowds and the slogan "stay at home" can be implemented.The stages of community service activities carried out started from needs analysis and systemdesign using the waterfall model development method. This activity will be continued at theimplementation stage so that it can be used widely for the community. The development andinstallation of village administration service applications is planned to be based on Android withcentralized data in the cloud. The system design is then verified and validated by a number of users,especially from the kelurahan because those parties are the ones who know the existing serviceprocess. The verification test states that the service process is in accordance with what is neededand the validation test states that the design is in accordance with the user's needs.

Peningkatan Kemampuan Membuat Surat dan Laporan Menggunakan Ms.Word pada Siswa MA Raudatul Irfan Adhi Kusnadi; Marlinda Vasty Overbeek; Yaman Khaeruzzaman; Moeljono Widjaja; Alethea Suryadibrata
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol 1 No 2 (2020)
Publisher : Politeknik Dharma Patria

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v1i2.341

Kegiatan pelatihan ini bertujuan untuk meningkatkan kemampuan siswa/siswi MA Raudatul Irfan dalam penggunaan MS.Office, terutama MS.Word. Peningkatan kemampuan siswa/siswi akan meningkatkan peluang mereka untuk memasuki jenjang pendidikan lebih tinggi dan dunia kerja. Kegiatan ini dilaksanakan selama dua hari dimulai dari pagi jam 8.00 WIB sampai selesai. Banyaknya jumlah siswa/siswi yaitu berjumlah 80 orang ditambah staf dan guru sebanyak 10 orang, maka untuk meningkatkan efektifitas dalam pelatihan di laboratorium Kampus UMN, peserta dibagi dua kelompok sebanyak 40 orang untuk setiap kelasnya. Metode yang digunakan dalam pelatihan ini adalah demontrasi dan contoh. Pada awal sesi peserta akan diperkenalkan pada komputer, sistem operasi, dan MS.Office, kemudian diakhiri dengan evaluasi dan latihan. Dengan mengikuti pelatihan ini, siswa/siswi akan meningkat kemampuannya dalam menggunakan komputer terutama MS.Office dengan tujuan utama membuat surat dan laporan menggunakan MS.Word. Secara keseluruhan kegiatan ini berlangsung dengan lancar dan baik, dan hasil latihan/evaluasi yang baik dengan rata-rata indeks prestasi 2,7 dari skala 1-4

The Development of an IoT-based Indoor Air Monitoring System Towards Smart Energy Efficient Classroom Moeljono Widjaja; Dareen Kusuma Halim; Rahmi Andarini
Ultima Computing : Jurnal Sistem Komputer Vol 14 No 1 (2022): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v14i1.2565

Indoor air quality has become a crucial issue, specifically during COVID 19 pandemic. The good indoor air quality will lead to occupants’ comfort condition, thus affecting their productivity. Indoor air temperature and relative humidity are two essential components of thermal comfort. This paper presents the development of a temperature and relative humidity monitoring system for the classroom using the Internet of Things (IoT). This system consists of three main components: logger nodes, a gateway logger, and an interconnected cloud server. The logger node (ESP8266 / ESP32 microcontroller and DHT22 sensor) is a device at the edge of the IoT system and is placed at the monitoring location. The logger gateway is built on a Raspberry Pi 4, which serves as an intermediate server. It receives periodic data (temperature and humidity) from the logger nodes through the publish-subscribe MQTT protocol and sends it to the MongoDB Atlas cloud database. The logger gateway saves all received logs into the SQLite database as temporary local storage and then uploads the data to the MongoDB Atlas cloud at a predefined interval. The MongoDB data is then displayed on a monitoring dashboard using MongoDB charts. The logger node with the DHT22 sensor has been adjusted using a linear model and successfully tested to monitor indoor and outdoor air conditions with satisfactory results. The recorded data has also been successfully modeled using the Gaussian Mixture Model and a simple Fuzzy model. These models can capture the dynamic of air condition in the room and predict the state of the cooling system.

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

Automatic detection of dress-code surveillance in a university using YOLO algorithm Tantra, Benjamin Jason; Widjaja, Moeljono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1568-1575

Dress-code surveillance is a field that utilizes an object detection model to en- sure that people wear the proper attire in workplaces and educational institutions. The case is the same within universities, where students and staff must adhere to campus clothing guidelines. However, campus security still enforces univer- sity student clothing manually. Thus, this experiment creates an object detection model that can be used in the campus environment to detect if students are wear- ing appropriate clothing. The model developed for this research has reached an f1-score of 45% with an overall 51.8% mean accuracy precision. With this, the model has reached a satisfactory state with room for further improvements.

Beyond Traditional Methods: The Power of Bi-LSTM in Transforming Customer Review Sentiment Analysis Tjiptadjaja, Casey; Widjaja, Moeljono
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): 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.v12i1.3775

In the current generation, many large and small companies compete fiercely to create things better than those on the market, such as smartphones, TVs, and many other things. One way they can do this is by guaranteeing the quality of services or goods that are better than others. The provider must investigate the feedback of their users or customers to improve the quality of service of the goods or services offered. Most medium and small companies, such as Micro, Small, and Medium enterprises (MSMEs), online stores, and so on, conduct research on customer feedback manually by looking at one-by-one feedback from customers, which is very ineffective and inefficient if a lot of customer feedback is obtained. Therefore, this research is conducted with the intention and purpose of helping medium and small companies analyze their customer sentiment, as well as trends over a certain period. This research will apply the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm to perform sentiment analysis on customer feedback. This research also compares other deep learning methods with the proposed method, namely the Uni-LSTM, GRU, CNN, and Simple-RNN algorithms. After testing, the accuracy results of the Uni-LSTM, Bi-LSTM, GRU, CNN, and Simple-RNN algorithms are 52.2%, 92.4%, 52.2%,

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

Fine-tuning convolutional neural network for artificial intelligence generated image detection enhancement Steven Vincent Hendrawan; Moeljono Widjaja; Alethea Suryadibrata
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2238-2246

The relationship between art and technology has changed how people engage with creativity, leading to the industrialization of the field. Various digital media have been utilized in the endeavour of art creation, such as artificial intelligence (AI) generation for images. The utilization of AI-generated art has yielded negative reactions due to its exploitative nature on pre-existing artworks without the creator’s consent, which raises plagiarism concerns. This research utilized convolutional neural network (CNN) to help detect such images to reduce public concerns on the abuse of AI images. The algorithm is proposed to detect such images as it involves spatial convolution within two-dimensional spaces, matching the nature of images. The model was developed from pre-existing architectures, namely EfficientNetB1 and Xception, which was pre-trained on ImageNet classification task with the modification of inclusion or exclusion of dropout in the top layer. After assessing the models, removing top layer dropout from EfficientNetB1 model improved it to reach the F1-score of 97.66% compared to 97.44% in the base model and Xception with a dropout layer yields lower F1-score of 95.56% compared to 97.07% in the base model.

Enhancing Energy Visibility: A Real-Time Power Monitoring System via Web Scraping and NoSQL Moeljono Widjaja; Rahmi Andarini
ULTIMA Computing Vol 18 No 1 (2026): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v18i1.4647

Modern industrial power meters are often equipped with Ethernet ports, providing web-based interfaces for real-time monitoring. However, automating data extraction from these interfaces for long-term analysis can be challenging without costly proprietary software. This paper presents a systematic design for an energy logging system that utilizes a web scraping approach to systematically retrieve data from power meter web APIs. The system is implemented using a Python-based scraping script deployed on a Raspberry Pi and utilizes a MongoDB database for scalable data storage and subsequent load profile visualization. Experimental results from an installation at the Universitas Multimedia Nusantara campus demonstrate the system’s ability to effectively monitor high-load Mechanical Ventilation and Air Conditioning (MVAC) systems, providing critical insights into peak consumption periods and operational efficiency. This low-cost, automated solution facilitates data-driven energy management and supports institutional energy-saving initiatives.

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