Garuda - Garba Rujukan Digital

Usability of Lampung Heritage Virtual Reality Tour Sony Ferbangkara; Mardiana Mardiana; F.X. Arinto; Sri Ratna Sulistiyanti; Khairudin Khairudin; Wahyu Eko Sulistiono; Meizano Ardhi Muhammad
Journal of Engineering and Scientific Research Vol. 4 No. 2 (2022)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v4i2.107

Lampung Heritage Virtual Reality Tour was developed as a tool to educate the importance of Lampung’s historical heritage. It represents eight Lampung historical heritage sites in the virtual reality world, namely the Lampung Museum, Thay Hin Bio Vihara, Al-Anwar Mosque, Lampung Siger Tower, Krakatau Monument, Kerti Bhuana Temple, Nuwo Sesat Traditional House, and the Japanese Caves. Functional features of the Lampung Heritage Virtual Reality Tour are visiting virtual tourist spots and viewing information on virtual tourist attractions. Digital tourists can select a virtual tourist spot with the location panel. The user perception and satisfaction require a qualitative measurement to understand its impact on educating Lampung's historical heritage. Using usability, we should understand the quality of the Lampung Heritage Virtual Reality Tour. The tools to measure the usability level of the application are the User Acceptance Test and the System Usability Scale. There were 15 questions User Acceptance Test (UAT) with a composition of five questions affordance, four questions signifier, and six questions feedback. According to the SUS standard, we asked ten questions on the System Usability Scale (SUS). The result for UAT was an average of 95.75%, which consist of 95.00% affordance, 94.79% signifier, and 97.45% feedback. The result of SUS was Good, based on a score of 83.39. The Lampung Heritage Virtual Reality Tour meets good usability standards, making the application suitable.

PELATIHAN TEKNOLOGI MIKROKONTROLER BERBASIS ARDUINO SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN SISWA DI SMA DARMA BANGSA BANDAR LAMPUNG Samosir, Ahmad Saudi; Ferbangkara, Sony; -, Sumadi; Muhammad, Meizano Ardhi; -, Ubaidah
Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Vol 8 No 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jss.v8i3.539

Pelatihan teknologi mikrokontroler berbasis Arduino bertujuan untuk meningkatkan pengetahuan dan keterampilan siswa di SMA Darma Bangsa Bandar Lampung. Saat ini, pemahaman dan penerapan teknologi mikrokontroler di kalangan siswa masih terbatas, yang menghambat kemampuan mereka untuk terlibat dalam proyek praktek dan inovatif. Oleh karena itu, pelatihan ini dirancang untuk memberikan pendidikan yang komprehensif tentang teknologi Arduino, memungkinkan siswa mengembangkan keterampilan praktek dan menerapkannya dalam proyek nyata. Tujuan jangka panjang dari inisiatif pengabdian ini adalah untuk mendorong pembelajaran mandiri dan inovasi di kalangan siswa melalui penguasaan teknologi mikrokontroler. Sasaran khususnya adalah meningkatkan kesadaran dan pemahaman tentang teknologi mikrokontroler serta membekali siswa dengan keterampilan praktek dalam menggunakan Arduino. Metode yang digunakan meliputi: koordinasi antara tim dosen dan pihak sekolah terkait kegiatan pelatihan, penyiapan bahan dan peralatan, termasuk kit Arduino, sesi teoretis, dan latihan praktek, diikuti dengan evaluasi kegiatan. Pelaksanaan pelatihan dimulai dengan koordinasi antara tim dosen dan pihak sekolah, dilanjutkan dengan penyiapan bahan dan peralatan dengan bantuan siswa, pelaksanaan sesi pelatihan dengan partisipasi staf sekolah dan siswa, dan diakhiri dengan evaluasi untuk menentukan keberlanjutan program. Para siswa menunjukkan minat besar dalam mengikuti pelatihan ini. Tantangan utama yang dihadapi selama pelatihan adalah penjadwalan, karena siswa memiliki berbagai kegiatan selama jam sekolah.

Design and Construction of Duku Sorting System Based on Size Using a Microcontroller on Conveyor Work Martinus, Martinus; Muhammad, Meizano Ardhi; Telaumbanua, Mareli; Andrianto, Rifqi Rhama; Ferbangkara, Sony
International Journal of Electronics and Communications Systems Vol. 1 No. 2 (2021): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v1i2.10612

Duku (Lansuim Domesticum Corr)fruit harvesting is generally done manually by farmers in Indonesia so that the quality of the duku fruit, especially the uniformity of size, is not considered. The impact of this harvesting is a decrease in fruit quality and a decrease in selling prices. It is necessary to develop a new sorting machine for duku so that the fruit size is accurate. This research aims to make a sorting system for duku fruit based on size using a microcontroller on conveyor work. The sorting system uses two sensors, VL53L0X and FC-51. The design has a servo actuator to separate the fruit classes. This study developed a correct sorting of duku fruit sizes up to 97.4 percent, counting accuracy up to 99.4 percent, system stability up to 96.65 percent, and transient response of 100 ms. The result of testing this tool is that the ability of the Duku fruit sorting system based on size has a stability value of 96.6 percent. The transient response obtained is 100ms. The accuracy of the perfect sorting results is 97.4 percent, and the calculation of the number of duku using the system is 99.4 percent. The conclusion is that the researchers can create a sorting system based on size using a microcontroller on conveyor work.

Development of Lampung Script Characters Recognition Model using TensorFlow Muhammad, Meizano Ardhi; Martinus, Martinus; Nurhartanto, Adhi; Mulyani, Yessi; Djausal, Gita Paramita; Achmad, Deni; Ferbangkara, Sony
International Journal of Electronics and Communications Systems Vol. 3 No. 2 (2023): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v3i2.19878

In the face of cultural erosion, particularly the dwindling proficiency in deciphering Lampung characters, this research pioneers an innovative approach to cultural preservation. The Lampung character recognition model was developed using TensorFlow, a robust computer vision and machine learning framework. Convolutional Neural Networks (CNN) are integrated to enhance the image processing capabilities. The research employs the Design Science Research methodology, emphasizing problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The dataset, comprising 3900 instances, is meticulously collected and features diverse Lampung script writing. Through preprocessing and classification, the model undergoes training with an 80:10:10 split for training, validation, and test data. The architecture includes CNN layers with ReLu activation functions, and transfer learning is employed using the MobileNet V2 network model. Demonstrating commendable performance, the model achieves an accuracy spectrum of 0.652 to 0.998. The research not only underscores the viability of the TensorFlow model but also establishes a foundation for future explorations in preserving Lampung cultural heritage. This intersection of advanced machine learning and cultural preservation signifies a promising synergy, ensuring the enduring legacy of Lampung characters amid societal and technological transformations.

Karakterisasi Dinamis Tegangan Primer dan Sekunder pada Transformator Auxiliary akibat Beban Motor yang Berubah Ubaidah; Fahrur Riza Priyana; Yudi Eka Putra; Sony Ferbangkara
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 19 No. 2 (2025)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v19n2.2815

Auxiliary transformers play a crucial role in maintaining the stability of electrical systems, especially when facing load changes caused by motor disconnection. This study aims to analyze the impact of current and voltage on Unit 3 auxiliary transformer due to the disconnection of vacuum pump motor A (500 kW) and vacuum pump motor B (500 kW) from the system. Simulations were conducted under three main conditions: normal operation without load disconnection, disconnection of vacuum pump motor A, and simultaneous disconnection of both vacuum pump motors A and B. The simulation results indicate that under normal conditions without load, the current and voltage on the primary side of the transformer are 283.9 A and 11 kV, while on the secondary side they are 495.7 A and 6.212 kV. When vacuum pump motor A is disconnected, transient current and voltage on the primary side range from 257.8 A to 250.1 A, with voltage fluctuating from 11 kV to 11.033 kV. In the case of both motors being disconnected, transient current and voltage on the primary side range from 231.8 A to 217.3 A, with voltage changing from 11 kV to 11.067 kV, while on the secondary side, the current drops from 404.7 A to 379.4 A, and voltage increases from 6.212 kV to 6.304 kV. These findings provide a clear depiction of the dynamic characteristics of the auxiliary transformer due to motor load variation and its impact on the stability of the electrical system.

PELATIHAN TEKNOLOGI MIKROKONTROLER BERBASIS ARDUINO SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN SISWA DI SMA DARMA BANGSA BANDAR LAMPUNG Samosir, Ahmad Saudi; Ferbangkara, Sony; -, Sumadi; Muhammad, Meizano Ardhi; -, Ubaidah
Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Vol. 8 No. 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jss.v8i3.539

Pelatihan teknologi mikrokontroler berbasis Arduino bertujuan untuk meningkatkan pengetahuan dan keterampilan siswa di SMA Darma Bangsa Bandar Lampung. Saat ini, pemahaman dan penerapan teknologi mikrokontroler di kalangan siswa masih terbatas, yang menghambat kemampuan mereka untuk terlibat dalam proyek praktek dan inovatif. Oleh karena itu, pelatihan ini dirancang untuk memberikan pendidikan yang komprehensif tentang teknologi Arduino, memungkinkan siswa mengembangkan keterampilan praktek dan menerapkannya dalam proyek nyata. Tujuan jangka panjang dari inisiatif pengabdian ini adalah untuk mendorong pembelajaran mandiri dan inovasi di kalangan siswa melalui penguasaan teknologi mikrokontroler. Sasaran khususnya adalah meningkatkan kesadaran dan pemahaman tentang teknologi mikrokontroler serta membekali siswa dengan keterampilan praktek dalam menggunakan Arduino. Metode yang digunakan meliputi: koordinasi antara tim dosen dan pihak sekolah terkait kegiatan pelatihan, penyiapan bahan dan peralatan, termasuk kit Arduino, sesi teoretis, dan latihan praktek, diikuti dengan evaluasi kegiatan. Pelaksanaan pelatihan dimulai dengan koordinasi antara tim dosen dan pihak sekolah, dilanjutkan dengan penyiapan bahan dan peralatan dengan bantuan siswa, pelaksanaan sesi pelatihan dengan partisipasi staf sekolah dan siswa, dan diakhiri dengan evaluasi untuk menentukan keberlanjutan program. Para siswa menunjukkan minat besar dalam mengikuti pelatihan ini. Tantangan utama yang dihadapi selama pelatihan adalah penjadwalan, karena siswa memiliki berbagai kegiatan selama jam sekolah.

Pemodelan AI dengan CNN Untuk Klasifikasi Tanaman Uvaria Grandiflora di Hutan Tropis Indonesia Martinus, Martinus; Ferbangkara, Sony; Annisa, Resty; Hidayatullah, Vezan; Pratama, Rama Wahyu Ajie; Makarim, Alvin Reihansyah
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5012

Purpose: This research aims to develop an artificial intelligence (AI) model based on the Convolutional Neural Network (CNN) to classify Uvaria plant species, a tropical genus native to Indonesia. The study addresses the challenge of limited datasets for automatic classification in tropical plant identification. Methodology/approach: Images of Uvaria plants were collected directly from their natural habitat and categorized into four primary classes: leaves, stems, twigs, and trees. The dataset comprises 400 labeled images, split into training (279 images, 70%), validation (40 images, 10%), and testing (81 images, 20%). The CNN model was trained for 200 epochs, using data preprocessing techniques such as normalization and augmentation to improve performance. Results/findings: The CNN model achieved an accuracy of 90% on the test set, indicating strong performance in classifying the four categories of Uvaria plant components. The model showed particularly consistent results in distinguishing between leaves and twigs. Conclusion: Despite the relatively small dataset, the results demonstrate that the CNN algorithm is capable of accurately classifying images of Uvaria species. The dataset is considered sufficient to build an effective classification model. Limitations: The main limitation of this study is the limited number of images, which may restrict the model’s ability to generalize to broader or more varied data in real-world conditions. Contribution: This research contributes to the development of AI-based tools for identifying tropical plant species. It offers a practical model and dataset that can support biodiversity monitoring, environmental research, and conservation efforts in Indonesia and similar tropical regions.

Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN Ferbangkara, Sony; Mulyani, Yessi; Mardiana, Mardiana; Pratama, Rama Wahyu Ajie; Putri, Renatha Amelia Manggala; Rafi'syaiim, Muhammad Afif
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5014

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.

Implementasi Convolutional Neural Network (CNN) untuk Identifikasi Spesifik Cassia fistula dalam Mendukung Pelestarian Hutan Muhammad, M A; Febrianto, F; Martinus; Mardiana; Annisa, R; Ferbangkara, S; Wijaya, P; Afif, M; Putri, R A M
Prosiding Seminar Nasional Ilmu Teknik Dan Aplikasi Industri Fakultas Teknik Universitas Lampung Vol. 7 (2024): Prosiding Seminar Nasional Ilmu Teknik dan Aplikasi Industi (SINTA) 2024
Publisher : Fakultas Teknik Universitas Lampung

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

Identifikasi spesifik tumbuhan hutan memiliki peran krusial dalam pelestarian, terutama untuk spesies seperti Cassia fistula, yang banyak ditemukan di Asia Selatan dan Asia Tenggara. Cassia fistula, dikenal dengan nama umum Golden Shower, adalah spesies yang penting dalam ekosistem hutan karena peran ekologisnya dan kegunaannya dalam pengobatan tradisional. Penelitian ini bertujuan untuk meningkatkan akurasi identifikasi spesies ini dengan menerapkan Convolutional Neural Network (CNN). Gambar dari berbagai bagian tubuh Cassia fistula seperti batang, bunga, daun, ranting, dan pohon diambil dan dikelompokkan ke dalam lima kelas, dengan total 345 citra. Model CNN kemudian dilatih dengan 10, 100, dan 200 epoch untuk mengukur efektivitas dalam identifikasi. Hasil menunjukkan bahwa akurasi meningkat dengan bertambahnya epoch: 73,53% untuk 10 epoch, 88,57% untuk 100 epoch, dan 94,12% untuk 200 epoch. Nilai loss juga berkurang secara signifikan, dari 61,16% pada 10 epoch, menjadi 44,31% pada 100 epoch, dan akhirnya 9,27% pada 200 epoch. Dengan hasil ini, CNN terbukti mampu mengidentifikasi Cassia fistula secara spesifik dan akurat, dengan tingkat keberhasilan yang tinggi. Penelitian ini mengindikasikan potensi besar penerapan CNN dalam mendukung efisiensi dan akurasi identifikasi tumbuhan hutan, yang pada gilirannya dapat mendukung upaya pelestarian hutan, terutama di kawasan Asia Tenggara di mana spesies ini banyak ditemukan.

Analisis Kinerja Layanan Cloud Computing dalam Sistem Cerdas Rekomendasi Tanaman Perkebunan Annisa, R.; Makarim, A. R.; Afif, M.; Sulistiono, W. E.; Ferbangkara, S.; Ubaidah
Prosiding Seminar Nasional Ilmu Teknik Dan Aplikasi Industri Fakultas Teknik Universitas Lampung Vol. 7 (2024): Prosiding Seminar Nasional Ilmu Teknik dan Aplikasi Industi (SINTA) 2024
Publisher : Fakultas Teknik Universitas Lampung

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

Penelitian ini bertujuan untuk mengukur dan menganalisa kinerja layanan cloud computing dalam sistem cerdas rekomendasi tanaman perkebunan sebagai adaptasi perubahan iklim. Penelitian dilakukan menggunakan layanan App Engine dan Compute Engine dari Google Cloud Platform (GCP) untuk menganalisis kinerja sistem melalui pengujian beban berdasarkan parameter Response Time, Error Rate, Throughput, dan CPU Utilization. Hasil pengujian menunjukkan bahwa App Engine memiliki performa yang lebih stabil dan responsif. Pada durasi pengujian 30 hingga 300 detik, waktu respons App Engine menurun secara konsisten dari 7238 ms menjadi 252 ms. Tingkat Error Rate tetap 0% sepanjang pengujian, dengan throughput meningkat dari 11.1 hingga 73.8 req/detik. CPU utilization pada App Engine berkisar antara 31.56% hingga 59.89%, menunjukkan manajemen sumber daya yang efisien di bawah beban tinggi. Sebaliknya, Compute Engine menunjukkan fluktuasi dalam waktu respons, yang mencapai puncak 7481 ms pada pengujian 300 detik, dengan Error Rate tetap 0% tetapi throughput yang stabil di sekitar 12 req/detik. CPU utilization Compute Engine mencapai nilai maksimum mendekati 90%, menunjukkan keterbatasan dalam pengelolaan beban. Hasil ini mengindikasikan bahwa App Engine lebih direkomendasikan dalam implementasi sistem cerdas berbasis cloud mendukung keberlanjutan dan efisiensi perkebunan dalam menghadapi perubahan iklim.

Title

Found 15 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 15 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 15 Documents
Search