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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika INFORMAL: Informatics Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal RESISTOR (Rekayasa Sistem Komputer) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Sistem Cerdas Jurnal Mantik Journal of Electrical, Electronics and Informatics Jurnal Teknik Informatika C.I.T. Medicom Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing INSERT: Information System and Emerging Technology Journal Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat KOMET
Asana, I Made Dwi Putra
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences

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Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System Asana, I Made Dwi Putra; Oka, I Dewa Gede Ari; Widyantara, I Made Oka; Sandhiyasa, I Made Subrata
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21990

Abstract

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.
Performance Comparison of MobileNet and EfficientNet Architectures in Automatic Classification of Bacterial Colonies Wahyudi, I Putu Alfin Teguh; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Radhitya, Made Leo; Asana, I Made Dwi Putra
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.218

Abstract

Bacterial colony classification is crucial in microbiology but remains labor-intensive and time-consuming when performed manually. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automated classification, improving accuracy and efficiency. This study compares MobileNetV2 and EfficientNet-B0 for bacterial colony classification, evaluating the impact of data augmentation on model performance. Using the Neurosys AGAR dataset, preprocessing techniques such as histogram equalization, gamma correction, and Gaussian blur were applied, while data augmentation (rotation, noise addition, luminosity adjustments) improved model generalization. The dataset was split (80% training, 20% testing), and models were trained with learning rates (0.0001, 0.001) and epochs (100, 150, 200). Results show EfficientNet-B0 outperforms MobileNetV2, achieving higher validation accuracy and stability, with optimal performance at 150–200 epochs and a lower learning rate (0.0001). Data augmentation significantly improved accuracy and reduced overfitting. While MobileNetV2 remains a lightweight alternative, its performance is heavily reliant on augmentation. These findings highlight EfficientNet-B0 as the superior model, supporting the automation of microbiological diagnostics. Future research should explore hybrid CNN architectures, Vision Transformers (ViTs), and real-time implementation for improved classification efficiency.
Pengembangan Website Profil Dusun untuk Mendukung Pembangunan Desa yang Informatif dan Terintegrasi Radhitya, Made Leo; Atmaja, Ketut Jaya; Wijaya, Bagus Kusuma; Asana, I Made Dwi Putra; Sudipa, I Gede Iwan; Gunawan, I Komang Agus Bryan
Jurnal KOMET Vol 2 No 1 (2025): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 2 Nomor 1, Juni 2
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v2i1.63

Abstract

Keterbatasan akses terhadap informasi dan minimnya transparansi dalam penyampaian kegiatan sosial serta struktur organisasi di tingkat komunitas masih menjadi tantangan bagi banyak lingkungan masyarakat. Salah satu solusi yang dapat diterapkan untuk mengatasi masalah tersebut adalah melalui pengembangan website profil komunitas berbasis Content Management System (CMS) yang mudah diakses dan dikelola. Kegiatan pengabdian ini bertujuan untuk meningkatkan transparansi, memperkuat partisipasi masyarakat, dan mendigitalisasi pengelolaan informasi komunitas melalui pembuatan dan pemanfaatan website. Metode pelaksanaan dilakukan melalui pendekatan Transfer Knowledge, Technology Transfer, dan Difusi IPTEKS, yang meliputi pelatihan kepada pengurus dan masyarakat, pembuatan konten, dan pendampingan teknis. Website dirancang dengan fitur-fitur utama seperti informasi profil, dokumentasi kegiatan, pengumuman penting, layanan pengurus, dan fasilitas kontak masyarakat. Hasil kegiatan menunjukkan bahwa keberadaan website mampu meningkatkan efisiensi komunikasi, memperluas jangkauan informasi, serta membangun budaya digital yang partisipatif di kalangan warga. Sosialisasi dan pengenalan website mendapat antusiasme tinggi dari masyarakat, dengan banyaknya masukan yang konstruktif mengenai kebutuhan informasi yang relevan. Dengan demikian, platform digital ini menjadi sarana strategis untuk mendukung tata kelola komunitas yang lebih transparan, informatif, dan adaptif terhadap perkembangan teknologi.
Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI) Asana, I Made Dwi Putra; Widyantara, I Made Oka; Linawati, Linawati; Wiharta, Dewa Made; Wikananda, I Gusti Ngurah Satya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15106

Abstract

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.
Comparison of Automation Testing On Card Printer Project Using Playwright And Selenium Tools Melyawati, Ni Luh Putu; Asana, I Made Dwi Putra; Putri, Ni Wayan Suardiati; Atmaja, Ketut Jaya; Sudipa, I Gede Iwan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4362

Abstract

The quality of the software is greatly determined by the testing phase, which involves various test cases that can be conducted through manual testing and automation testing. Manual testing is performed manually without using automation scripts, whereas automation testing is conducted using automation scripts. ABC is a company that operates globally in the field of access control, with the Card Printer being one of the menus used in access control. In the development process of this software, both manual and automation testing phases are carried out. The automation testing process employs the Selenium tool, which has proven to be time-consuming and poses challenges when running numerous test cases. This research aims to develop automation testing using Playwright to address the long execution time issue encountered with Selenium. The research utilizes the Card Printer project in the development of automation testing and adopts the Agile methodology. The result of developing automation testing using Playwright was successfully applied to 12 test cases. Additionally, the time analysis between Playwright and Selenium showed that Playwright has a total execution time of 4.9 minutes, which is faster compared to Selenium's total execution time of 8.3 minutes. With faster execution times, Playwright can be considered a tool in the development of automation testing.
Naïve Bayes-based Student Graduation Prediction Model: Effectiveness and Implementation to Improve Timely Graduation Atmaja, Ketut Jaya; Indrawan, I Putu Yoga; Asana, I Made Dwi Putra; Wawan, I Kadek; Udayanie, Ayu Gde Chrisna
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4408

Abstract

Studies in an educational institution, when the lack of timely graduation of students in each batch and the number of students in each batch, causes an imbalance between incoming students and outgoing students and causes a decrease in accreditation from the campus, this should not continue to happen, the solution to dealing with this problem as an early detection of students who graduate on time is to predict the length of the student study period they have. Therefore, researchers will discuss the design of a prediction system for graduating on time using the Naïve Bayes method, to predict student graduation so that there is no imbalance of incoming and outgoing students. The construction of this system also uses the Naïve Bayes method and the CRISP-DM (Cross Industry Standard Process Data Mining) development method. In this case study, the Naïve Bayes method predicts data into 2, namely 1 (graduated on time) and 0 (did not graduate on time) by labeling the data used. In this model using 3247 data with the selection of features, namely semester achievement index 1 (ips1), ips2, ips3, ips4, ips5, semester credit units1 (credits1), credits2, credits3, credits4, credits5, semester credit units not passed 1 (skstidaklulus1), skstidaklulus2, skstidaklulus3, skstidaklulus4, skstidaklulus5 and labels. Using these feature variables results in model performance with 80% accuracy, with 80% accuracy it can be said that the model works well.
Pelatihan dan Pendampingan Media Sosial dalam Mendukung Promosi dan Penjualan Produk UMKM Ukiran Kayu Wiguna, I Komang Arya Ganda; Semadi, Ketut Ngurah; Asana, I Made Dwi Putra; Putra, Putu Satria Udyana; Radhitya, Made Leo
Jurnal KOMET Vol 1 No 1 (2024): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 1 Nomor 1, Juni 2
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v1i1.8

Abstract

Promosi dan Penjualan digital menjadi suatu proses bisnis yang harus dapat diterapkan pada setiap usaha, tidak terkecuali pada Usaha Mikro, Kecil, dan Menengah (UMKM). Perkembangan UMKM saat ini telah mengantarkan pada UMKM Goes Digital, sehingga adopsi teknologi dan pemanfaatan media promosi dan penjualan digital menjadi kebutuhan. Namun pada realitanya tentunya pemerataan dari pemanfaatan promosi dan penjualan digital disesuaikan dengan kemampuan dari setiap pemilik UMKM. Contohnya pada UMKM Kasari Ukir, Gianyar, bali yang memiliki permasalahan utama yang dihadapi dalam upaya mereka untuk bisa aktif di media sosial untuk menjalankan strategi promosi. Permasalah dalam pemasaran digital yang dialami pada usaha kasari ukir ini adalah belum mampu memanfaatkan sosial media untuk melakukan promosi serta pemasaran produk ukiran pada usaha kasari ukir.  Sehingga pada kegiatan Pengabdian Kepada Masyarakat (PKM) ini memfokuskan pada tranfer knowledge terkait pemanfaatan media sosial bagi pengelola UMKM dalam mendukung promosi dan penjualan.  Hasil kegiatan pelatihan dan pendampingan berupa pendampingan pembuatan konten Instagram, foto produk UMKM dalam mengasah keterampilan dalam bidang sosial media berupa Instagram, selain melatih cara copywriting dan teknik foto produk kegiatan ini diharapkan dapat mengasah keterampilan soft skill dan hard skill dalam usaha kasari ukir dalam bentuk pengaplikasian di era digital.
Classification of Gamelan Selonding Music Using Convolutional Neural Network Savitri, Ni Putu Diah Pradnya; Ariana , Anak Agung Gde Bagus; Pande, Ni Kadek Nita Noviani; Asana, I Made Dwi Putra; Indrawan , I Gusti Agung
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.358

Abstract

Introduction: Balinese Selonding gamelan is an endangered sacred repertoire, and automatic recognition of its musical pieces can support documentation and preservation. Method: This study investigates the automatic classification of Selonding gamelan music using a Convolutional Neural Network (CNN). The dataset consists of 10 traditional Selonding compositions. Recordings were segmented into fixed 15-second excerpts, converted to WAV, normalized, and transformed into time–frequency features using two approaches: Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform (CQT). A CNN-based classifier was trained and evaluated using 5-fold cross-validation for each feature representation. Results: The MFCC-based model achieved stable high performance, with mean accuracy of 94.67% (±2.11%), mean precision of 94.97% (±1.90%), mean recall of 94.67% (±2.11%), and mean F1-score of 94.63% (±2.12%) across folds. In contrast, the CQT-based model performed notably worse, reaching only 58.04% mean accuracy and 53.28% mean F1-score, with large variance across folds. These results indicate that MFCC features capture the discriminative timbral characteristics of Selonding more effectively than CQT under the current experimental setting. Conclusion: Overall, the findings show that a CNN trained on MFCC features can reliably distinguish Selonding compositions using only short (15-second) audio segments, despite limited data. This suggests that deep learning is a feasible strategy for indexing, retrieval, and long-term preservation of Balinese gamelan repertoires.
Co-Authors A.A. Tri Wulandari Mayun Ariana , Anak Agung Gde Bagus Astari, Gusti Ayu Shinta Dwi Atmaja, Ketut Jaya Devi Valentino Waas Dewa Made Wiharta Dewi, Ni Putu Wahyuni Dewi, Ni Wayan Jeri Kusuma Dirgayusari, Ayu Manik Gede Aldhi Pradana Gunawan, I Komang Agus Bryan I Gede Iwan Sudipa I Komang Arya Ganda Wiguna I Made Angga Wijaya I Made Deni Kurniadi I Made Oka Widyantara I Putu Anjas Sanjaya I Putu Susila Handika I Putu Yoga Indrawan I Wayan Krishna Sangging Wiguna Ida Bagus Putu Adnyana Ida Bagus Putu Adnyana Indrawan , I Gusti Agung Kadek Ari Prayoga Putra Krismentari, Ni Kadek Bumi Libraeni, Luh Gede Bevi Linawati Linawati Meinarni, Ni Putu Suci Melyawati, Ni Luh Putu N.M.A.E.D Wirastuti NI LUH KARTIKA DEWI Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Ary Esta Dewi Wirastuti Ni Putu Della Tirta Yanti Ni Putu Dita Ariani Sukma Dewi Ni Putu Suci Meinarni NI PUTU SUCI MEINARNI Ni Putu Widantari Suandana Ni Wayan Suardiati Putri Nirwana, Ni Kade Ayu Nirwana, Ni Kadek Ayu Oka, I Dewa Gede Ari Pande, Ni Kadek Nita Noviani Putra, I Putu Satria Udyana Putra, Putu Satria Udyana Putu Gede Surya Cipta Nugraha Putu Praba Santika Putu Wirayudi Aditama Radhitya, Made Leo Rini Komalasari Sandhiyasa, I Made Subrata Santi Ika Murpratiwi Savitri, Ni Putu Diah Pradnya Semadi, Ketut Ngurah Sugihya Artha Dwipayani Sugihya Artha Dwipayani Sutriasih, Ni Kadek Udayanie, Ayu Gde Chrisna Wahyudi, I Putu Alfin Teguh Wawan, I Kadek Wayan Gede Suka Parwita Wiguna, I Komang Arya Ganda Wijaya, Bagus Kusuma Wikananda, I Gusti Ngurah Satya