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All Journal JOIV : International Journal on Informatics Visualization IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Serambi Engineering
Yudi Widhiyasana
Politeknik Negeri Bandung
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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STUDI KOMPARASI UNITY3D DAN UNREAL 3D BERDASARKAN KUALITAS PERANGKAT LUNAK MODEL ISO 9126 Widhiyasana, Yudi
IKRA-ITH INFORMATIKA : Jurnal Komputer dan Informatika Vol 3 No 1 (2019): IKRA-ITH INFORMATIKA Vol 3 No 1 Bulan Maret 2019
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (768.767 KB)

Abstract

Pada setiap proses pengembangan perangkat lunak dipastikan ada tahapan implementasi. Proses implementasi biasanya menggunakan alat bantu berupa perangkat lunak lain yang spesifik. Pengembangan perangkat lunak berupa game biasanya menggunakan 3D engine. Pada saat ini 3D engine yang banyak digunakan adalah Unity3D dan Unreal Engine. Studi komparasi pada 3D engine ini akan melihat kualitasnya sebagai perangkat lunak. Tolak ukur kualitas perangkat lunak yang digunakan adalah ISO 9126, yang dibuat oleh International Organization for Standarization (ISO) dan International Electrotechnical Commission (IEC). ISO 9126 mendefinisikan kualitas produk perangkat lunak, model, karakteristik mutu, dan metrik terkait yang digunakan untuk mengevaluasi dan menetapkan kualitas sebuah produk software.
Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia Yudi Widhiyasana; Transmissia Semiawan; Ilham Gibran Achmad Mudzakir; Muhammad Randi Noor
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1150.961 KB) | DOI: 10.22146/jnteti.v10i4.2438

Abstract

Text classification is now a well-studied field, particularly in Natural Language Processing (NLP). The text classification can be carried out using various methods, one of which is deep learning. Deep learning methods such as RNN, CNN, and LSTM are the most frequent methods used for text classification. This research aims to analyze the implementation of two deep learning methods combination, namely CNN and LSTM (C-LSTM), to classify Indonesian news texts. News texts used as data in this study were collected from Indonesian news portals. The obtained data were then divided into three categories based on their scope: "National," "International," and "Regional." Three research variables were tested in this study: the number of documents, the batch size value, and the learning rate value of the built C-LSTM. The experimental results showed that the F1-score obtained from the classification results using the C-LSTM method was 93.27%. The F1-score value generated by the C-LSTM method was higher than that of CNN (89.85%) and LSTM (90.87%). In summary, the combination method of two deep learning methods, namely CNN and LSTM (C-LSTM), outperforms CNN and LSTM.
Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games Yudi Widhiyasana; Maisevli Harika; Fahmi Faturahman Nul Hakim; Fitri Diani; Kokoy Siti Komariah; Diena Rauda Ramdania
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.832

Abstract

Controlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly.
Enkripsi SMS dengan Menggunakan One Time Pad (OTP) dan Kompresi Lempel-Ziv-Welch (LZW) Fitri Diani; Yudi Widhiyasana
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Short Message Service (SMS) is one of the features on a mobile phone. This feature is widely used because it is easy to use and does not require the latest telecommunications network connection. Short messages in the form of SMS consistsof 140 characters at the most.The message is sent through infrastructure in telecommunications providers. Using this process, there is a possibility that the sent message is leaked. Therefore, data encryption is required to maintain the message confidentiality. Unfortunately, encryption mechanism uses cipher to encrypt data, which causes another problem. The type of cipher is symmetric or asymmetric, and both cipher mechanism will increase the length of the sent messages. In this paper, One Time Pad encryption method and LZW compression method is used to optimize the message length.
Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya Nurjannah Syakrani; Yudi Widhiyasana; Abid Arinu Efendi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 1: Februari 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Liver is one of the most important organs in the human body. One of the dangerous diseases of the liver is tumor. In the CT scan image, the tumor has different texture, color, shape, and position, according to patient's condition. In this study, a tumor detection was carried out by tree stages: firstly some steps of preprocessing, such as filtering, edge detection, and erotion; secondly, finding the liver among organs in abdomen using segmentation and checking the liver position in the right abdomen; and thirdly performing the tumor detection in the liver using graph cut and push relabel algorithm. Usually, segmentation using graph cut needs two interactive inputs, namely sample of object area and sample of background area. In this paper, the interactive inputs on graph cut were replaced by deviation standard calculation. Testing using three sets of CT image and the ground truth produces average of the dice similarity coefficient (DSC), volumetric overlap error (VOE), and absolute volume difference (AVD) parameters of 78.15%, 25.72%, 19.30%, respectively. Furthermore, volume of liver tumor is approximated by utilizing area of tumor in each slice of CT image, then displayed in 3D view.
Perbandingan Model U-Net dan ELU-Net untuk Segmentasi Semantik Citra Medis Kanker Pankreas Algi Fari Ramdhani; Yudi Widhiyasana; Setiadi Rachmat
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.15262

Abstract

Medical image analysis for semantic segmentation using deep learning technology has been extensively developed. One of the notable architectures is U-NET, which has demonstrated high accuracy in segmentation tasks. Further advancements have led to the development of ELU-NET, which aims to enhance model efficiency. ELU-NET achieves relatively good accuracy; however, further comparative analysis of both models is necessary. The comparison between these models is based on accuracy, storage usage, and processing time in performing semantic segmentation of pancreatic cancer images. The pancreatic cancer images utilized in this study are sourced from the PAIP 2023 Challenge, consisting of hematoxylin and eosin (H&E)-stained images. Experiments were conducted by varying the number of filters and model depth for both architectures. The evaluation was performed using a dataset of 57 pancreatic cancer images. The experimental results indicated that U-NET achieved the highest accuracy at 92.8%, slightly outperforming ELU-NET, which attained 89.7%. However, ELU-NET is significantly more efficient in terms of storage usage (8.1 MB for ELU-NET compared to 93.31 MB for U-NET) and processing time (4.0 s for ELU-NET and 5.3 s for U-NET). Although ELU-NET exhibited slightly lower accuracy than U-NET, it surpassed U-NET considerably in terms of storage efficiency (by 85.21 MB) and processing speed (by 1.3 s). These findings suggest that ELU-NET is not superior to U-NET in accuracy. However, given the storage size ratio of 1:11.51 and the processing time ratio of 1:1.325 between ELU-NET and U-NET, the 3.1% accuracy difference represents a reasonable trade-off.
Iron Ball Launcher Platform Control System for Impact Test at Glass Testing Laboratory Iwan Awaludin; Muhammad Rizqi Sholahuddin; Yudi Widhiyasana; Sofy Fitriani
Jurnal Serambi Engineering Vol. 10 No. 4 (2025): Oktober 2025
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

This study presents the digital transformation of a glass impact test system at the Center for Standardization and Services for the Ceramic and Non-Metallic Mineral Industry (BBK) through Industry 4.0 integration. The legacy system faced challenges including manual distance measurement, outdated safety components, mechanical momentum causing positioning inaccuracy, and inability to sequentially launch multiple iron balls. To address these, a phased approach was implemented: analysis, design, implementation, and testing of a digital control system. Key upgrades included LIDAR-based wireless distance sensing (up to 9 meters), RS-485 communication for reliable data transfer, replacement of 1980s-era fuses with modern Mini Circuit Breakers, and algorithmic compensation for mechanical delay. A microcontroller-based control system enabled automated height adjustment, mode selection per national standards, and sequential ball release. The system was tested across six height settings with five trials each, achieving an error rate below 1% in all cases. Results confirm enhanced precision, safety, and efficiency. This targeted digitalization demonstrates how Industry 4.0 technologies can modernize legacy testing equipment without full replacement, offering a cost-effective, scalable model for industrial laboratories undergoing digital transformation.
Co-Authors Abid Arinu Efendi Algi Fari Ramdhani Diani, Fitri Diena Rauda Ramdania Efendi, Abid Arinu Fahmi Faturahman Nul Hakim Fitri Diani Ilham Gibran Achmad Mudzakir Iwan Awaludin Kokoy Siti Komariah Maisevli Harika Muhammad Randi Noor Muhammad Rizqi Sholahuddin Nurjannah Syakrani Setiadi Rachmat Sofy Fitriani Syakrani, Nurjannah Transmissia Semiawan