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All Journal Indonesian Community Journal Jurnal INFOTEL
Shinta Puspasari
Universitas Indo Global Mandiri, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science 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
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.
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.
Feature Extraction vs Fine-tuning for Cyber Intrusion Detection Model Ahmad Sanmorino; Suryati Suryati; Rendra Gustriansyah; Shinta Puspasari; Nining Ariati
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.996

Abstract

This study investigates the effectiveness of feature extraction and fine-tuning approaches in developing robust cyber intrusion detection models using the Network-based Security Lab - KDD dataset (NSL-KDD). The role of cyber intrusion detection is pivotal in securing computer networks from unauthorized access and malicious activities. Feature extraction, involving methods such as PCA, LDA, and Autoencoders, aims to transform raw data into informative representations, while fine-tuning leverages pre-trained models for task-specific adaptation. The study follows a comprehensive research method encompassing data collection, preprocessing, model development, and experimental evaluation. Results indicate that LDA and Autoencoders excel in the feature extraction phase, demonstrating precision, high accuracy, F1-Score, and recall. However, fine-tuning a pre-trained Multilayer Perceptron model surpasses individual feature extraction methods, achieving superior performance across all metrics. The discussion emphasizes the complexity and flexibility of these approaches, with fine-tuned models showcasing higher adaptability. In conclusion, this study provides valuable insights into the comparative effectiveness of feature extraction and fine-tuning for cyber intrusion detection. The findings underscore the importance of leveraging pre-trained knowledge and adapting models to specific tasks, offering a foundation for further advancements in enhancing network security through advanced machine learning techniques.
Co-Authors Ade Fathurahman Ahmad Sanmorino Ditho Hersilava Nazori Suhandi Nining Ariati Rendra Gustriansyah Suryati Suryati