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Benthix VR: A Virtual Reality Simulation Application to Preserve Traditional Benthik Game Setiawan, Abas; Nugraha, Alvin Satria; Haryanto, Hanny; Gamayanto, Indra
ComTech: Computer, Mathematics and Engineering Applications Vol 8, No 4 (2017): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v8i4.4036

This research aimed to preserve Benthik traditional game using Benthix VR. Benthix VR used the Virtual Reality Interface Design (VRID) development model. The development phase of the VRID model started from High Level to Low-Level phase. The High-Level Design (HLD) phase consisted of identifying data elements and multiple objects, and modeling component objects. The output from the HLD phase would be input to the Low-Level Design (LLD) phase. The LLD phase was a phase of repetition and fine-tunes from the modeling of several component objects thoroughly. Testing of Benthix VR was conducted on 34 respondents with five assessment aspects. Those were enjoyment, realism, interactivity, usability, and impact. The average result of the questionnaire assessment of all aspects is 3,18824. These results indicate that users feel that Benthix VR is comfortable, realistic, interactive, and fascinating. Moreover, they are also interested in playing Benthik in the real world after using the application.

Playing the SOS Game Using Feasible Greedy Strategy Setiawan, Abas
CommIT (Communication and Information Technology) Journal Vol 14, No 1 (2020): CommIT Vol. 14 No. 1 Tahun 2020
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.6167

The research aims to make an intelligent agent that can compete against the human player. In this research, the feasible greedy strategy is proposed to make an intelligent agent by checking all possible solutions in the limited tree levels to find effective movement. Several matches are conducted to evaluate the performance of the feasible greedy agent. The board size for the evaluation consists of 33, 44, 55, 66, 77, and 88 squares. From the result, the feasible greedy agent never loses against the random agent and the pure greedy agent. In 3 3 squares match, the agent can compensate against the human player, so the game always ends with a draw. In 44, 55, 66, 77, and 88 squares matches, the feasible greedy agent slightly outplays the human player.

Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada Convolutional Neural Network Haksoro, Elok Iedfitra; Setiawan, Abas
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol 5 No 2 (2021)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v5i2.428

Not all mushrooms are edible because some are poisonous. The edible or poisonous mushrooms can be identified by paying attention to the morphological characteristics of mushrooms, such as shape, color, and texture. There is an issue: some poisonous mushrooms have morphological features that are very similar to edible mushrooms. It can lead to the misidentification of mushrooms. This work aims to recognize edible or poisonous mushrooms using a Deep Learning approach, typically Convolutional Neural Networks. Because the training process will take a long time, Transfer Learning was applied to accelerate the learning process. Transfer learning uses an existing model as a base model in our neural network by transferring information from the related domain. There are Four base models are used, namely MobileNets, MobileNetV2, ResNet50, and VGG19. Each base model will be subjected to several experimental scenarios, such as setting the different learning rate values for pre-training and fine-tuning. The results show that the Convolutional Neural Network with transfer learning method can recognize edible or poisonous mushrooms with more than 86% accuracy. Moreover, the best accuracy result is 92.19% obtained from the base model of MobileNetsV2 with a learning rate of 0,00001 at the pre-training stage and 0,0001 at the fine-tuning stage.

Pengenalan Jamur yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning pada Convolutional Neural Network Haksoro, Elok Iedfitra; Setiawan, Abas
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 5 No. 2 (2021)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v5i2.428

Not all mushrooms are edible because some are poisonous. The edible or poisonous mushrooms can be identified by paying attention to the morphological characteristics of mushrooms, such as shape, color, and texture. There is an issue: some poisonous mushrooms have morphological features that are very similar to edible mushrooms. It can lead to the misidentification of mushrooms. This work aims to recognize edible or poisonous mushrooms using a Deep Learning approach, typically Convolutional Neural Networks. Because the training process will take a long time, Transfer Learning was applied to accelerate the learning process. Transfer learning uses an existing model as a base model in our neural network by transferring information from the related domain. There are Four base models are used, namely MobileNets, MobileNetV2, ResNet50, and VGG19. Each base model will be subjected to several experimental scenarios, such as setting the different learning rate values for pre-training and fine-tuning. The results show that the Convolutional Neural Network with transfer learning method can recognize edible or poisonous mushrooms with more than 86% accuracy. Moreover, the best accuracy result is 92.19% obtained from the base model of MobileNetsV2 with a learning rate of 0,00001 at the pre-training stage and 0,0001 at the fine-tuning stage.

Adaptive Difficulty in Earthquake Mitigation Game Using Fuzzy Mamdani Ardiadna, Rika Jane; Setiawan, Abas
Recursive Journal of Informatics Vol 1 No 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v1i1.66543

Abstract. Earthquake disasters cause a lot of casualties. Therefore, needs to be education on earthquake disaster mitigation to minimize losses. In addition to counseling and teaching in schools, mitigation education can also be through games. Some education games for earthquake disaster mitigation have circulated quite a lot but have disadvantages, namely the difficulty level that hasn't been adaptive. A game requires an adaptive level of difficulty that can adjust between the ability and playing experience of the player with the level of difficulty so that players do not feel bored or frustrated.Purpose: This study aims to provide earthquake disaster mitigation education and discuss making the level of difficulty in the game adaptive to suit the abilities and experience of the player.Method: From the research carried out by applying the Mamdani Fuzzy Logic, the game's difficulty level for each player becomes more adaptive or different for each player according to the ability and experience of each player in the previous stage measured from 6 input parameters.Result: The level of difficulty that is obtained becomes adaptive. It changes according to conditions or is adjusted based on the player's ability. It is from the playtesting experiment conducted on 20 players. The minimum difficulty level's score is five, and the difficulty level's score is 28.36.Novelty: This paper's purpose is an educational game for earthquake mitigation with the feature of adaptive level based on fuzzy Mamdani.

Peran Kecerdasan Buatan Generatif Bagi Peningkatan Kompetensi Guru di SMA Muhammadiyah 2 Semarang Setiawan, Abas; Arifudin, Riza; Sugiharti, Endang; Abidin, Zaenal; Al Hakim, M. Faris; Choirunnisa, Rizkiyanti; Subarkah, Agus
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2952

Tantangan yang saat ini dibutuhkan oleh guru SMA adalah menciptakan inovasi pembelajaran berbasis teknologi. Saat ini kecerdasan buatan (AI) telah muncul sebagai solusi potensial untuk meningkatkan kualitas pembelajaran di tengah pesatnya kemajuan teknologi. Namun, geografi Indonesia yang luas membuat fasilitas teknologi pendidikan belum merata. Oleh karena itu, guru di SMA Muhammadiyah 2 Semarang perlu meningkatkan kompetensi literasi digitalnya, terutama untuk teknologi terkini. Program ini telah berhasil membuka wawasan guru terhadap teknologi baru dan memberikan keterampilan praktis dalam mengintegrasikan teknologi Kecerdasan Buatan Generatif ke dalam proses pembelajaran. Para guru diberikan pembekalan penggunaan teknologi ChatGPT dan Gemini untuk mempersiapkan bahan ajar. Hasil evaluasi menunjukkan bahwa guru mampu memahami dan mulai menerapkan teknologi AI dalam pembuatan materi ajar, serta merasa termotivasi untuk terus menggunakannya secara berkelanjutan dalam pembelajaran.

Pengenalan Gambar Braille Menggunakan Depthwise Separable Convolutional Neural Network Afriyanto, Kevin Maulana; Setiawan, Abas
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12445

Huruf Braille adalah huruf yang memiliki sistem tulisan yang menggunakan indra peraba untuk menggunakannya dan umum digunakan oleh penyandang tunanetra. Pada orang normal yang sering berinteraksi dengan para penyandang tunanetra terkadang mengalami kesulitan dalam menggunakan huruf braille, sehingga diperlukan suatu pendekatan teknologi sistem cerdas untuk dapat membantu mengenali huruf braille. Penelitian ini bertujuan untuk menggunakan metode depthwise separable convolutional neural network untuk melakukan pengenalan terhadap gambar braille. Dataset yang digunakan memiliki ukuran 28x28 dengan total dataset sebanyak 1560 citra. Arsitektur yang digunakan adalah input layer, yaitu sebesar 28x28x3 dan layer depthwise separable convolution pertama, kedua, dan ketiga menggunakan masing masing 3x3x128, 3x3x256, dan 2x2x512 dengan fungsi aktivasi rectified linear unit (ReLu) dan setiap konvolusi diikuti dengan lapisan-lapisan maxpooling dan batch normalization. Setelah itu, dilanjutkan dengan dua fully connected layer dengan jumlah neuron 256 dan 128 serta diakhiri dengan output layer sejumlah 26 neuron. Arsitektur tersebut menghasilkan model terbaik dengan akurasi sebesar 98.40%. Model tersebut diterapkan pada aplikasi berbasis website. Hasil temuan penelitian ini diharapkan dapat membantu masyarakat dalam mengenali huruf braille dengan mudah. Kata kunci: tunanetra, braille, depthwise separable convolution, model, akurasi

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