Article Per Year (5 Year)
p-Index From 2021 - 2026
4.774
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Informatika Komputer Jurnal Elemen Sistemasi: Jurnal Sistem Informasi JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) Informatik : Jurnal Ilmu Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal GERVASI: Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi G-Tech : Jurnal Teknologi Terapan Aiti: Jurnal Teknologi Informasi REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Information Technology (JIfoTech) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Pengabdian Masyarakat Bhinneka Jurnal Informatika dan Multimedia Jurnal Komunikasi, Sains dan Teknologi (JKST) Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer Blantika : Multidisciplinary Journal Infact: Jurnal Sains dan Komputer Jurnal Pengabdian Masyarakat Indonesia (JPMI) Jurnal Pengabdian Kepada Masyarakat Indonesia
Cahyaningtyas, Christian
Institut Shanti Bhuana
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
Implementasi Metode CNN Berbasis Transfer Learning dengan Arsitektur MobileNetV2 dalam Klasifikasi dan Pemetaan Tempat Wisata Mira; Cahyaningtyas, Christian; Sari, Maya; Yuliana
Jurnal Ilmiah Informatika Komputer Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2025.v30i3.56

Abstract

The growth of tourism in the digital era encourages the use of social media as a source of visual data for destination analysis. This study aims to classify and map tourist attractions in West Kalimantan using a transfer learning-based Convolutional Neural Network (CNN) method with the MobileNetV2 architecture. A total of 454 images were collected through web scraping from the Instagram account @enjoykalbar, then through a process of elimination, augmentation, normalization, and manual labeling based on the West Kalimantan Disporapar tourism categories, namely Hills, Beaches, Cascades, Culture, Lakes, Rivers, Caves, and Forests. The dataset was divided into training data (70%), validation (20%), and test (10%). The model was built by freezing the initial layers of MobileNetV2 and adding a classification head, then drilled for 20 epochs using the Adam Optimizer and EarlyStopping and ReduceLROnPlateau callbacks. The training results showed a training accuracy of 95.8%, validation accuracy of 88.1%, and test accuracy of 80%. Further evaluation using the classification report yielded an overall accuracy of 89%, with an average precision of 0.93, a recall of 0.86, and an F1-score of 0.88. The model was then integrated into a category- and coordinate-based interactive mapping system to display the distribution of tourist attractions across 12 districts and 2 cities. The results demonstrate that the CNN transfer learning approach is effective for tourism image classification and supports spatial visualization in tourism promotion and planning.
Pelatihan Pemanfaatan Aplikasi Canva dalam Pembuatan Desain Konten untuk Media Sosial Christian Cahyaningtyas; Mira Mira; Yuliana Yuliana; Maya Sari
Jurnal Pengabdian Masyarakat Indonesia (JPMI) Vol. 2 No. 2 (2024): Desember
Publisher : Publikasi Inspirasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/jpmi.v2i2.2655

Abstract

Media sosial menjadi salah satu sarana komunikasi yang efektif pada saat ini bagi instansi dan organisai. Penyampaian informasi ini juga dapat dilakukan melalui media sosial dengan membuat konten-konten yang menarik dan informatif. Desain konten yang menarik juga akan berpengaruh pada efektifitas pemasaran digital dan berpengaruh dalam pengambilan keputusan. Namun dalam pembuatan konten yang menarik perhatian membutuhkan kreatifitas dan imajinasi yang tinggi. PDAM Bengkayang sebagai salah satu instansi pelayanan publik, kualitas desain konten media sosial ini sangat penting karena hal ini untuk memperkuat citra instansi dan mendukung layanan pelanggan. Namun terdapat kendala pada instansi ini yaitu keterbatasan pengetahuan dan keterampilan staff atau karyawan dalam menghasilkan desain yang profesional dan sesuai dengan kebutuhan. Sehingga sangat dibutuhkan pelatihan untuk staff maupun karyawan dalam mendesain konten yang kreatif dan inovatif. Maka dilakukan kegiatan pelatihan pemanfaatan aplikasi canva ini guna menjawab kebutuhan instansi. Adapun hasil dari kegiatan ini peserta mampu membuat desain yang lebih kreatif dan inovatif. Peserta juga dapat membuat vidio pendek untuk konten-konten media sosial mereka dengan sangat menarik. Peserta dapat mempunyai keterampilan atau meningkatkan keterampilan dalam membuat konten-konten yang lebih kreatif, inovatif dan informatif.
Forest Fire Detection Based on Digital Imagery Using Convolutional Neural Network (CNN) Model Candra Gudiato; Aditya Pratama; Christian Cahyaningtyas
G-Tech: Jurnal Teknologi Terapan Vol 10 No 2 (2026): G-Tech, Vol. 10 No. 2 April 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i2.9422

Abstract

This study explores the implementation of a Convolutional Neural Network (CNN) for automated forest fire identification using digital image processing. Utilizing the USTC 'Forest Fire' dataset, the research framework involved systematic data preprocessing, including a 70:30 training-validation split and the application of image augmentation techniques to enhance model robustness. The proposed architecture features a sequential design with dual convolution and pooling layers, integrated with ReLU and Sigmoid activations. Although initial training over seven epochs yielded a deceptive validation accuracy of 99%, granular performance analysis exposed critical limitations. Evaluation via a Confusion Matrix revealed that while the model excelled at identifying 'non-fire' scenarios, it struggled significantly with actual fire detection, failing to recognize 301 out of 331 fire instances. These results highlight a severe class imbalance issue, suggesting that standard accuracy metrics are insufficient for this application and emphasizing the need for more balanced sampling or advanced architectural adjustments in future fire detection systems.
Flood Disaster-Induced Water Inundation Potential Monitoring System in Bengkayang Regency Based on Remote Sensing Imagery and Machine Learning Cahyaningtyas, Christian; Eligia Monixa Salfarini; Egi Saputra
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2597

Abstract

Based on data from the Bengkayang Regency Regional Disaster Management Agency (BPBD), in early 2025, 11 sub-districts were affected by flooding, with 12,023 affected people and 3,468 homes submerged. Efforts to minimize the impact of this flood disaster require an effective data-driven monitoring system. A floodwater monitoring system in Bengkayang Regency is essential for effective disaster management, reducing losses and damage, and providing early warnings to the surrounding community. One approach that can be used is remote sensing technology, which can be a solution, especially when combined with machine learning algorithms that can accelerate and improve the accuracy of data analysis. One such machine learning algorithm is the Support Vector Machine (SVM) algorithm. This study has produced a final dataset of five variables: rainfall, slope gradient, land use, VV, and NDWI. This dataset is used for the classification process using the Support Vector Machine algorithm. After preprocessing and dividing the training data by 75% and the test data by 25% of the total 512 data sets. The image classification results using SVM demonstrated quite good performance. The resulting accuracy was 80%, with precision and recall values ​​ranging from 0.67 to 0.98. Based on these results, the model demonstrated excellent ability to identify waterlogging points. The classification results were then integrated into a web-based geographic information system that displays an interactive map of the distribution of waterlogging points.
Internet Bandwidth Management using a MikroTik Router at Shanti Bhuana Institute Sari, Maya; Noviyanti P, Noviyanti P; Cahyaningtyas, Christian; Mira, Mira; Dedy, Dedy
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6218

Abstract

This study aims to evaluate the effectiveness of bandwidth management using a MikroTik router in optimizing network capacity distribution at Shanti Bhuana Institute. The methods employed include designing a bandwidth allocation scheme based on the priority needs of each unit, implementing the configuration on a MikroTik router, and measuring network performance parameters such as throughput, delay, packet loss, and jitter before and after the implementation of bandwidth management. The testing was conducted in a campus environment involving active users from various academic and administrative units. The results show that the implementation of bandwidth management increased the average throughput from 412 kbps to 1,105 kbps, reduced the average delay from 22.4 ms to 5.2 ms, and decreased jitter from 8.7 ms to 2.1 ms. Packet loss remained below the 1% threshold both before and after implementation. All units received proportional bandwidth allocation according to their operational needs, enabling activities such as web browsing, email communication, and lecture material downloads to run without mutual interference. This study concludes that MikroTik-based bandwidth management is more effective than relying solely on high bandwidth availability, as it is capable of preventing network congestion in multi-user campus environments.
Co-Authors Aditya Pratama Danny Manongga dedy dedy, dedy Dima, Yansensius Reins Egi Saputra Eko Sediyono Eligia Monixa Salfarini Eligia Monixa Salfarini Fransiska Ria Fransiska Ria Gregorius Aryo Prasojo Gudiato, Candra Harmoni, Grecia Putri Indrastanti Ratna Widiasari Irwan Sembiring Joss, Mille Kusnanto Kusnanto Manggara, Angga Domeos Maya Sari Maya Sari Maya Sari Mira Mira Mira Mira Mira Pedeng Novando, Kristian Noviyanti P, Noviyanti P Noviyanti. P Nurcahyo, Azriel Christian Oktoberrikho, Stevanus P, Noviyanti Pery Jayanto Pranata, Devon Surya Prasojo, Gregorius Aryo Purnasari, Pebria Dheni Rendy Sari, Yessi Fatika Silvester Silvester Silvester Silvester, Silvester Sonia Dara Fortunata Sonia Sri Yulianto Joko Prasetyo Tessa Januarti Tesso, Revaldo thomas, santi Totok Victor Didik Saputro Vika Yeremia Niaga Atlantika Yessica Nataliani Yosua Damas Sadewo Yuliana Yuliana Yuliana Yuliana Yuliana