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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Mandiri IT Indonesian Community Journal Jurnal INFOTEL
Shinta Puspasari
Universitas Indo Global Mandiri, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Library & Information Science Mathematics Public Health Social Sciences Other

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Forecasting a museum visit post pandemic using exponential smoothing model Shinta Puspasari; Rendra Gustriansyah; Ahmad Sanmorino
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.949

Abstract

This paper aims to evaluate the performance of a machine learning model for predicting the number of visitors to a museum after the COVID-19 pandemic. The easing of policies that began to be implemented by the Palembang city government after the end of the pandemic at the end of 2022 became a momentum in predicting the number of visits to the SMBII museum. During the pandemic the museum experienced a very drastic decline due to closures and restrictions on activities at the museum and had an impact on achieving the museum's targets in the fields of tourism and education. Museum managers need to establish a strategy as an effort to achieve the targets set during the post-pandemic period. This study predicts the number of visits to the SMBII museum in post-pandemic years by applying the double exponential smoothing (ESM) model. The dataset used is SMBII museum visit data which is divided into three categories of visitors, namely students, local and foreign. The evaluation results show that the double ESM model has the best performance with MSE = 3.8 and a = 0.9. The phenomena that occurred in the student visitor category affected ESM's performance in predicting visits where MSE in the post-pandemic period had a 200% higher value than before the pandemic which was influenced by the implementation of post-pandemic policies in museums. With the forecasting results in this study, it is hoped that it can become information in developing strategies and improving the performance of post-pandemic museums
Pendampingan Pemanfaatan Mikroskop Digital dalam Konservasi Koleksi Kain Songket Museum Sultan Mahmud Badaruddin II Shinta Puspasari; Rendra Gustriansyah; Ahmad Sanmorino; Ditho Hersilava; Ade Fathurahman
I-Com: Indonesian Community Journal Vol 4 No 2 (2024): I-Com: Indonesian Community Journal (Juni 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i2.4234

Abstract

Conservation of Palembang Songket woven cloth is carried out at SMBII Museum. The thread fibers woven into Songket cloth are small in size and require a microscope to see the thread fibers clearly before determining the appropriate conservation mechanism for fabric damage. Community service activities are carried out by assisting in the use of digital microscopes in the conservation of Songket cloth collections. A digital microscope is used to magnify the appearance of the gold thread fibers of the Songket cloth, whether there is damage or not. The activity began with setting up a digital microscope device to be used in the study of Songket cloth conservation and continued with assistance in using the microscope by participants. The results of the assistance show that users can easily use digital microscope devices to display Songket fabric fibers that are damaged and require conservation. Hopefully, this community service will make it easier for the SMBII museum to carry out conservation and learning tasks at the museum and have an impact on ensuring the resilience of the Palembang Songket cloth cultural heritage in the digital era.
Metode Pembelajaran Mesin untuk Memprediksi Status Gizi Balita Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.988

Abstract

Malnutrition is one of the leading health problems experienced by toddlers in various countries. Based on the 2022 Indonesian Nutritional Status Survey results, malnutrition in children under five in Indonesia is higher than the average malnutrition in Africa and globally. Therefore, a way is needed to predict the nutritional status of children under five early and quickly so that the Government (through District Health Office) can immediately provide the necessary treatment. This study aims to predict or classify the toddlers' nutritional status based on age, body mass index (BMI), weight, and body length using various machine learning (ML) methods, namely naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbor, random forest, and support vector machine. The predictive performance of each ML method was evaluated based on accuracy, sensitivity, specificity, the area under curve, and Cohen's Kappa coefficient. The test results show that the RF method is the most recommended for predicting toddlers' nutritional status. The study's contribution is to obtain information about toddlers' nutritional status easier.
Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim Gasim; Rudi Heriansyah; Shinta Puspasari; Muhammad Haviz Irfani; Evi Purnamasari; Indah Permatasari; Samsuryadi Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.
Attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari
Jurnal Mandiri IT Vol. 15 No. 1 (2026): Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i1.552

Abstract

Skin cancer is one of the most prevalent cancers worldwide, making early and accurate diagnosis essential for improving patient outcomes and reducing mortality. However, automated skin lesion classification remains challenging due to high inter-class similarity, class imbalance, and variations in lesion appearance. This study proposes an attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion. The proposed framework integrates channel and spatial attention with hierarchical feature fusion to enhance discriminative feature learning and improve classification robustness. Experiments were conducted on the PAD-UFES-20 dataset using image preprocessing and data augmentation. The proposed model achieved approximately 98% training accuracy, 95% validation accuracy, and a validation loss below 0.5, outperforming DRMv2Net, DenseNet201, ResNet101, and MobileNetV2 while demonstrating faster convergence and stronger generalization. These findings demonstrate the potential of hybrid attention and hierarchical feature fusion to improve the reliability and robustness of artificial intelligence-based diagnostic systems in dermatology, supporting more effective clinical decision-making and early skin cancer screening.
Comparative Evaluation of Naïve Bayes and Support Vector Machine for Human Development Index Classification Dwi Asa Verano; Eka Putri Apriliani; Zahratul Aliah; Sarah Nur Arna Cholifah; Amanda Dwi Tio Agustin; Thomas; Shinta Puspasari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9162

Abstract

The Human Development Index (HDI) is one of the most widely used indicators for evaluating the quality of human development through health, education, and living standards. Accurate HDI classification is essential for supporting evidence-based public policy and regional development planning. Recent advances in machine learning provide opportunities to improve classification performance compared with conventional statistical approaches. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms for classifying the Human Development Index across Indonesian provinces and to identify the more effective classification model. The study employed a quantitative experimental approach using the 2024 Human Development Index dataset published by Statistics Indonesia (BPS), covering all 38 provinces. Four predictor variables were used: life expectancy at birth, expected years of schooling, mean years of schooling, and adjusted expenditure per capita. The dataset was preprocessed before being divided into training and testing sets using an 80:20 ratio. Both algorithms were implemented using Python's Scikit-learn library and evaluated through confusion matrix analysis, accuracy, precision, recall, and F1-score. Experimental results indicate that both algorithms successfully classified provincial HDI categories. However, SVM consistently outperformed Naïve Bayes, achieving an accuracy of 100% on the held-out test set (5-fold cross-validated mean accuracy of 86.4%), compared with 87.5% (cross-validated mean of 59.6%) obtained by Naïve Bayes. SVM also produced higher precision, recall, and F1-score, indicating stronger classification capability for multidimensional socioeconomic data. The findings demonstrate that Support Vector Machine provides superior performance for HDI classification compared with Naïve Bayes. This study contributes empirical evidence regarding the application of machine learning techniques in socioeconomic indicator classification and offers practical insights for decision support in regional development policy.
Co-Authors Ade Fathurahman Ahmad Sanmorino Amanda Dwi Tio Agustin Ditho Hersilava Dwi Asa Verano Eka Putri Apriliani Evi Purnamasari Gasim Gasim Indah Permatasari Muhammad Haviz Irfani Nazori Suhandi Rendra Gustriansyah Rudi Heriansyah Samsuryadi Samsuryadi Sarah Nur Arna Cholifah Thomas Zahratul Aliah