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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Journal of Information Systems Engineering and Business Intelligence Info-Teknik KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jukung (Jurnal Teknik Lingkungan) JTERA (Jurnal Teknologi Rekayasa) Jurnal Komtika (Komputasi dan Informatika) ILKOM Jurnal Ilmiah Astonjadro Jurnal Ilmu Komputer dan Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Buletin Profesi Insinyur Jurnal Sains Komputer dan Teknologi Informasi Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) JUSTE (Journal of Science and Technology) Journal of Advanced Health Informatics Research Jurnal Jalan Jembatan INSTALL: Information System and Technology Journal
Yuslena Sari, Yuslena
Dosen Program Studi Teknologi Informasi Fakultas Teknik Universitas Lambung Mangkurat, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Ground Coverage Classification in UAV Image Using a Convolutional Neural Network Feature Map Erika Maulidiya; Chastine Fatichah; Nanik Suciati; Yuslena Sari
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.206-216

Abstract

Background: To understand land transformation at the local level, there is a need to develop new strategies appropriate for land management policies and practices. In various geographical research, ground coverage plays an important role particularly in planning, physical geography explorations, environmental analysis, and sustainable planning. Objective: The research aimed to analyze land cover using vegetation density data collected through remote sensing. Specifically, the data assisted in land processing and land cover classification based on vegetation density. Methods: Before classification, image was preprocessed using Convolutional Neural Network (CNN) architecture's ResNet 50 and DenseNet 121 feature extraction methods. Furthermore, several algorithm were used, namely Decision Tree, Naí¯ve Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Results: Classification comparison between methods showed that using CNN method obtained better results than machine learning. By using CNN architecture for feature extraction, SVM method, which adopted ResNet-50 for feature extraction, achieved an impressive accuracy of 85%. Similarly using SVM method with DenseNet121 feature extraction led to a performance of 81%. Conclusion: Based on results comparing CNN and machine learning, ResNet 50 architecture performed the best, achieving a result of 92%. Meanwhile, SVM performed better than other machine learning method, achieving an 84% accuracy rate with ResNet-50 feature extraction. XGBoost came next, with an 82% accuracy rate using the same ResNet-50 feature extraction. Finally, SVM and XGBoost produced the best results for feature extraction using DenseNet-121, with an accuracy rate of 81%.   Keywords: Classification, CNN Architecture, Feature Extraction, Ground Coverage, Vegetation Density.
Pemanfaatan Konfigurasi Layer Pada Metode CNN Untuk Peningkatan Kinerja Klasifikasi Penyakit Daun Tomat Sari, Yuslena; Firmansyah, Muhammad Ilham; Pramunendar, Ricardus Anggi
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.13953

Abstract

Tomat adalah salah satu komoditas hortikultura dengan nilai ekonomi yang tinggi, tantang yang dihadapi oleh petani salah satunya dalah kerentanan penyakit tomat terhadap penyakit. Identifikasi secara visual pada daun sulit diuraikan dengan sekali pandang, sehingga menyebabkan asumsi yang tidak akurat tentang penyakit tersebut. Akibatnya, mekanisme pencegahan yang dilakukan petani menjadi tidak efektif dan berdampak merugikan. Penelitian ini mengusulkan identifikasi penyakit tomat secara automatis menggunakan metode Convolution Neural Network. Dalam makalah ini kami melakukan evaluasi pada metode CNN dengan arsitektur Alexnet dengan konfigurasi layer untuk mencari hasil kinerja terbaik dari penggunaan parameter tersebut pada architektur Alexnet. Pada penelitian ini juga melakukan analisis yang diperoleh dari hubungan antara parameter yang digunakan terhadap kinerja akurasi, dan analisis terhadap dampak penggunaan parameter dengan jumlah dataset daun tomat dari dataset PlantVillage.
Prediksi Harga Emas Menggunakan Metode Neural Network Backropagation Algoritma Conjugate Gradient Sari, yuslena
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 1 No. 2 (2017)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v1i2.21

Abstract

Artificial Neural Network Backpropagation is known as one of the most reliable methods of predicting. The algorithm used in this research is Conjugate Gradient algorithm, with gold data data of input data for training data. The price of gold becomes an issue in the market, as a precious metal that can be used for investment is very interesting to make a gold price prediction application. Gold prices continue to increase in the world market, making investors interested to invest in this precious metal. The application of gold price prediction will be very useful for investors of precious metals. Gold price data used in this research is daily data, taken 3 (three) last year and divided into test data and data testing. Test data is used to generate new weights for data testing. The parameters used in the measurement of evaluation of predicted results from Conjugate Gradient algorithm Artificial Neural Network Backpropagation method is Meant Square Error (MSE), where the result of MSE from this research is 0.0313651
Challenges and Opportunities: Integration of Data Science in Cancer Research Through A Literature Review Approach Purwono, Purwono; Ariefah Khairina Islahati; Yuslena Sari; Dewi Astria Faroek; Muhammad Baballe Ahmad
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.167

Abstract

Several research articles in this journal relate to various aspects of cancer, such as treatment, patient outcomes, caregiver responsibilities, and the use of AI and liquid biopsy in cancer research. Covers a wide range of topics, including valuable insights into the latest developments in cancer research as well as potential future opportunities and issues. Several articles discuss the impact of non-coding RNA on gastric cancer, machine learning decision support systems for cancer survival factors, economic impact of cancer mortality, nausea in children diagnosed with cancer, protein-RNA variations in cancer clinical analysis, integration and proteomic data analysis in the context of cancer genomics, personalized cancer medicine, mass spectrometry-based clinical proteomics, cancer proteogenomics, subtype-based This journal provides an in-depth overview of various aspects of current cancer research and future research prospects
The Regression Analysis Data for E-Sport Athletes Prediction using OSEMN Framework: Analisis Regresi Data Prediksi Atlet E-Sport Menggunakan Kerangka OSEMN Septyan Eka Prastya; Musyfia Adla; Bayu Nugraha; Yuslena Sari
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.542

Abstract

In the fast-growing E-Sports industry, athlete performance is the key to achieving success and winning. Therefore, analyzing the factors that contribute to the performance of E-Sports athletes is essential in order to optimize their performance in competition. This study aims to analyze the relationship between age, number of training hours, and experience playing in competition with rank, kill death ratio (KDA), and the number of wins of E-Sports athletes using the OSEMN approach (Obtain, Scrub, Explore, Model, Interpret, and Communicate). The data was obtained from 300 professional or non-professional E- Sports athletes, over the past three years who were involved in various competitions. Independent variables included age, number of training hours, and experience playing in competitions, while the dependent variables included rank, KDA, and number of wins. Data was collected, processed and explored and then analyzed using multiple linear regression methods. This study succeeded in applying the regression analysis method using the OSEMN framework, identifying relevant variables, and developing effective data collection and processing methods. This model has the potential to provide accurate predictions of E- Sport athlete performance data. However, it is still important to consider other factors such as business context, comparison with other models, and cross- validation to confirm the reliability of the prediction results.
EFFECTIVENESS OF APPLYING BIM BASED COST ESTIMATION IN DEVELOPMENT OF THE SYAMSUDIN NOOR AIRPORT PROJECT BANJARMASIN Khatimi, Husnul; Fardian, Muhammad Reza; Sari, Yuslena
ASTONJADRO Vol. 10 No. 1 (2021): ASTONJADRO
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/astonjadro.v10i1.4200

Abstract

Development of The Syamsudin Noor Airport Project in Banjarmasin is one of the largest projects in Banjarmasin, South Kalimantan. This project applied BIM-based cost estimation on a steel roof structure. However, the cost estimation for this steel roof structure is applied conventionally. The BIM-based cost estimation could have been applied in collaborating a building information becomes unity in one model. This research will raise the issue of applying BIM-based cost estimation at The Syamsudin Noor Airport Project to find out the effectiveness calculation of cost estimation conventionally and BIM-based cost estimation. The report result by 3D modeling of Tekla is quantity take-offs using as a data for processing the cost analysis conventionally. Whereas the 3D model made by Tekla will be exported to Revit through the interoperability of IFC or application of extention of Tekla warehouse that is "Export to Revit Geometry” for the processing the BIM-based cost estimation analysis. The unit price for the cost calculation is acquired by list price (AHSP or subcontractor value). The result of these both cost calculation, there are large enough difference in cost of these both calculations. Difference of conventional calculations and BIM-based cost estimation using Revit worth Rp 3,690,741,474 - Rp 5,047,206,780 with a percentage of 14% - 20%. Cause of these large enough differences in cost due to the model exported is only 90% succeeded. It happened due to difference thing in the mapping of object profile and difference in shape BREP geometry conditions.
Co-Authors Ahmad Rivaldy Ahmad Shalludin Akhmad Rivaldy Andreyan Rizky Baskara Ariefah Khairina Islahati ARIF RAHMAN, MUHAMMAD Bambang Lareno, Bambang Bayu Nugraha Chastine Fatichah Dany Primanita Kartikasari Dewi Astria Faroek Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Endi Gunawan Fardian, Muhammad Reza Ferry Pratama Finki Dona Marleny Firmansyah, Muhammad Ilham Gusti Nizar Syafi'i Halimah Halimah Hani Pratiwi Husnul Khatimi Irfan Prasetia Johan Wahyudi Johan Wahyudi Mambang Maulidia Arafah Maulidiya, Erika Maya Amalia Meila Izzana, Meila Muhammad Adetya Ashari Muhammad Alkaff Muhammad Andri Firdaus Muhammad Arif Rahman Muhammad Baballe Ahmad Muhammad Faidhorrahman Muhammad Syauqi Al Fath Musyfia Adla Mutia Maulida Muti`a Maulida Nanik Suciati Noor Razikin Noorhanida Royani Novitasari Novitasari Novitasari Novitasari Nurul Fathanah Mustamin Nurul Qamaria Prakoso, Puguh Budi Pratiwi, Hani Puguh Budi Prakoso Puguh Budi Prakoso Puja Darmawan Purwono, Purwono Rahman, Muhammad Arif Rakhmadhany Primananda Rama Aria Megantara Ricardus A P, Ricardus A Ricardus Anggi Pramunendar Rico Silas Dwi Kasanda Rudy Ansari Rusiana, Novi Septyan Eka Prastya Subhan Panji Cipta Syarifah Soraya Wenny Puspita Wijaya, Eka Setya