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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI)
Samuel Ady Sanjaya
Universitas Multimedia Nusantara, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Machine Learning Approach of Obesity Level Classification: A Systematic Literature Review of Methods and Factors Steven Marcelino Tandiono; Samuel Ady Sanjaya
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3604

Abstract

The high prevalence of obesity over the years has become a global concern, as obesity contributes to an increased risk of many deadly diseases, such as diabetes, heart disease, and some cancers. This condition has become a serious concern for public health authorities, researchers, and the general public. Therefore, a comprehensive and effective approach is needed to tackle this obesity problem. Machine learning can be the answer to the required approach as it offers a method to predict the risk level of obesity through identifying the risk causes quickly and accurately. Through this approach, the most influential factors in obesity risk can be identified to aid in the development of more effective prevention and intervention strategies. Understanding the correlation between risk factors and obesity will hopefully lead to better solutions in addressing global obesity.
Utilization of Online Village Administration Services: Training on the use of Cihuni Village Website Features Jansen Wiratama; Rudi Sutomo; Raymond Sunardi Oetama; Samuel Ady Sanjaya; Santo Fernandi Wijaya
I-Com: Indonesian Community Journal Vol 3 No 4 (2023): I-Com: Indonesian Community Journal (Desember 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i4.3486

Abstract

Public administration services are important for urban and rural communities. With a large population, it is difficult for administrative service processes using conventional methods to run optimally, so they need to be transformed into digital form. Current technology should support optimizing administrative services from village officers to the community. However, limitations related to the use of technology still need to be solved by village officers. Therefore, training is needed for village officers in using administrative service features via the Cihuni village website resulting from previous research. This training activity was carried out in Cihuni Village, a village supported by Multimedia Nusantara University (UMN). The training process involved the UMN Lecturer Team as community outreach members and Cihuni Village officers. This community outreach activity increases the understanding of village officials regarding using the Cihuni village website, which has various features for public administration services.
Perancangan Dasbor Pra-Bencana Tsunami di Gugus Mitigasi Lebak Selatan: Visualisasi dan Mitigasi di Panggarangan Richard Faustine Wong; Samuel Ady Sanjaya; Christopher Darren; Howard Yahya; Muhammad Fikri Riyadi
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.4763

Abstract

Tsunami telah meninggalkan tragedi di Indonesia, salah satunya tsunami Selat Sunda pada Lampung dan Banten. Diperlukan mitigasi bencana, tetapi kurangnya sinergi Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dengan pelaporan erupsi Pusat Volkanologi dan Mitigasi Bencana Geologi (PVMBG) menghambat upaya mitigasi dampak tsunami Selat Sunda. Untuk meningkatkan sinergi, dirancang dasbor pra-bencana tsunami dengan tujuan memaksimalkan pengurangan risiko bencana (PRB) di Panggarangan, Lebak. Perancangan dilakukan dengan metodologi Prototype, yang dilakukan secara sistematis mulai dari analisis kebutuhan GMLS, perancangan dengan menggunakan business intelligence tool bernama Tableau, pengujian dengan kuesioner System Usability Scale, kemudian revisi hingga dasbor sesuai kebutuhan. Harapannya, rancangan dasbor dapat meningkatkan sinergi antar-stakeholders, terutama di Panggarangan, serta mendukung pengambilan keputusan baik pihak yang berkepentingan dalam perencanaan tata kota maupun mitigasi bencana.
Intelligent LSTM-based deforestation prediction model and dashboard for Kalimantan using global forest change and night-time light data Jansen Wiratama; Samuel Ady Sanjaya; Thomas Januardy; Reyhan Arya Hermawan; Santo Fernandi Wijaya
Jurnal Ilmu Komputer dan Sistem Informasi |JIKSI| Vol. 7 No. 2 (2026): Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI) [in Progress]
Publisher : Institute of Information Technology and Social Science (IITSS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61346/jiksi.v7i2.333

Abstract

Forest assessment based on predictions and evidence is an appropriate step toward environmental conservation. This study developed a Long Short-Term Memory (LSTM) based prediction model to monitor and predict forest cover and light intensity in Kalimantan. Using Global Forest Change (GFC) and Night-Time Light data, the prediction results are applied to a web-based dashboard. The data were available for the period from 2001 to 2023 and has been processed and aggregated to the provincial level. The LSTM model was trained using five optimizers, namely Adam, RMSprop, Stochastic Gradient Descent (SGD), Adadelta, and Adamax. Model performance was evaluated using Root Mean Squared Error (RMSE). The results showed that the Adam and RMSprop optimizers produced lower RMSE values compared to the other optimizers on the GFC dataset, making them more effective at learning patterns and forest cover change. On the NTL dataset, Adam, RMSprop, and Adamax demonstrated relatively similar performance due to the data’s more stable characteristics. Overall, Adam provided the most accurate and consistent predictions. The prediction results are presented via a web-based dashboard that allows for the visualization of historical trends as well as predictions of change in forest cover and nighttime light intensity. These findings suggest that the combination of the LSTM model and an interactive dashboard can support more effective deforestation monitoring.
Co-Authors Christopher Darren Howard Yahya Jansen Wiratama Muhammad Fikri Riyadi Raymond Sunardi Oetama Reyhan Arya Hermawan Richard Faustine Wong Rudi Sutomo Santo Fernandi Wijaya Steven Marcelino Tandiono Thomas Januardy