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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
Arjuna Subject : -
Articles 471 Documents
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Sistem Cerdas Deteksi Tindak Kekerasan Untuk Pengawasan Perundungan Dengan Model Deep Learning Putri, Sukmawati Anggraeni; Rifai, Achmad; Nawawi, Imam
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6451

Abstract

Bullying in schools is a severe problem that has both short- and long-term harmful implications for victims. However, surveillance of bullying, particularly acts of violence such as kicking, pushing, and striking at school, remains inadequate. Using Artificial Intelligence is one of the recommended solutions for detecting incidents of aggression in video footage. Deep learning methods, specifically Convolutional Neural Network and Long Short-Term Memory, are used in this study to construct Artificial Intelligence for detecting acts of aggression. The model can achieve an average accuracy of up to 92%. Based on these accuracy results, the model can be implemented into online intelligent applications. It is envisaged that sophisticated software that detect such acts of aggression will be effective in monitoring bullying incidents and reducing the number of bullying cases in schools.
Penerapan Machine Learning untuk Mengetahui Kelangsungan Hidup Pasien Gagal Jantung (Kardiovaskular) Menggunakan Kreatinin Serum dan Fraksi Ejeksi abbas, irfan; Faisal Bisar
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6452

Abstract

Penyakit kardiovaskular menyebabkan kematian di seluruh dunia setiap tahunnya, yang sebagian besar bermanifestasi terutama sebagai serangan jantung atau gagal jantung. Gagal jantung (HF) terjadi ketika jantung tidak dapat memompa cukup darah untuk memenuhi kebutuhan tubuh.  Catatan kesehatan elektronik yang tersedia dapat digunakan untuk mengukur gejala, karakteristik fisik, nilai laboratorium, dan  melakukan analisis biostatistik untuk mengungkap pola dan hubungan yang tidak diketahui oleh dokter umum.  Secara khusus, Machine Learning dapat memprediksi kelangsungan hidup pasien berdasarkan data dan mempersonalisasi karakteristik utama rekam medis. Artikel ini menganalisis kumpulan data 299 pasien gagal jantung dengan menerapkan algorithma machine learning menggunakan algoritma artificial neural network berbasis adaboost untuk lebih meningkatkan akurasi pada algoritma artificial neural network (ANN). Pada hasil eksperiment pada penelitian ini didapatkan akurasi algorithma artificial neural network (ANN) berbasis adaboost menjadi sangat signifikan dengan hasil akurasi menjadi 81.01%
Identifikasi Ukuran Luas Tanah dengan Mengimplementasikan Metode Transformasi Watershed Yoga Saputra; Yovi Apridiansyah; Ardi Wijaya
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6459

Abstract

This study discusses the application of the watershed transformation method to measure land area in satellite images. The main objective is to improve the accuracy of land area measurement, which is important for various applications such as agriculture, urban development, and nature conservation. Given its often time-consuming and resource-intensive nature, this study aims to develop a more efficient and faster method of processing satellite image data. The method starts with the collection and pre-processing of satellite image data to improve its quality. Next, watershed transformation is applied for image segmentation and land area measurement. The results are evaluated through accuracy analysis and comparison with reference data. From testing 20 sample data, Precision of 100%, Recall of 80%, and Accuracy of 95% were obtained. It is expected that this research can contribute to the development of a more efficient and reliable land area measurement technique.
Sistem Deteksi Cacat Buah Tomat Menggunakan Deteksi Tepi SUSAN, Ekstraksi Ciri Statistik, dan CNN della, Putri Rahma Della; Yulia Darnita
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6463

Abstract

This research aims to address the problem of defect detection in tomatoes, which often compromises product quality in the agricultural industry. The difficulty in detecting defects automatically and accurately is a major challenge, so an efficient and effective method is needed. For this reason, a detection system was created by combining SUSAN edge detection method, statistical feature extraction, and Convolutional Neural Network (CNN). The SUSAN method was chosen for its reliability in detecting edges well, which is important for identifying defective areas in tomatoes. The process starts with edge detection using the SUSAN method, followed by statistical feature extraction such as mean value, standard deviation, minimum value, and maximum value of pixel intensity in tomato images. This data is then used to train the CNN model, which achieves a training accuracy of 97.50% and a test accuracy of 90%. From testing 50 tomato samples, CNN accuracy of 96%, precision of 96%, and recall of 100% were obtained. These results show that this system works well in detecting defects in tomatoes. Thus, this system is expected to improve the quality of tomato products and support the quality standards of the agricultural industry.
Pengambangan Sistem Informasi E-Commerce Untuk Pangkalan Gas LPG Dengan Metode Rapid Application Development (RAD) Probonegoro, Wishnu Aribowo; Sari, Lili Indah; Wijaya, Benny
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6466

Abstract

Gas is one of the fuels used by the community. The process of recording and managing transactions is still done manually, therefore the ABC 3kg LPG gas base faces challenges such as gas inventory, an inefficient ordering process, lack of information for customers, unstructured data management. To overcome this problem, the author conducts several stages starting from identifying existing problems, literature study, data collection is carried out by interviewing, observing customers, owners, analyzing needs, system design, implementation and system testing. This study aims to design and develop an e-commerce information system for the ABC 3 KG LPG gas base using the Rapid Application Development (RAD) method, because using this method can emphasize a fast and iterative development cycle, allowing continuous adjustment and improvement based on user feedback. Which has also been proven by the achievement of measurement results using blackbox techniques based on usability aspects where the results of this study reached a percentage of 97.8% success rate.
Pengelolahan Citra Digital Dalam Mengukur Kemiringan Menggunakan Metode Trigonometri Dan Numerik Liza nurpatmala; ardi wijaya
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6490

Abstract

A house is a basic human need as a place of activity, shelter, and rest. One of the problems that is often overlooked in house construction is the mismatch or slope in the size of the room. Wall misalignment due to inaccurate measurements can affect building comfort and safety. This research uses technological advances in room slope measurement with trigonometric and numerical methods. The results show that the trigonometric and numerical methods have precision, recall, and accuracy rates reaching 100%. This method is effective and reliable for measuring the slope based on the captured digital image, ensuring that the measurements taken are true and accurate.
Penerapan Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) Untuk Fitur Pencarian Dokumen Standar Nasional Indonesia Ani, Nur; Yosephine Sinaga, Desi; Junior, Nickolas; Doni Munggaran, Muhamad
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6504

Abstract

Information about the Indonesian National Standard (SNI) is already available on the Ministry of Industry's Pustand website and also the BSN website. The SNI search process that is less effective and must be done on different websites will make it difficult for users and business actors. Therefore, the SNI search website using keywords is a solution that will facilitate the SNI search process. SNI search with keywords where users enter the words to be searched on the website and with the TF-IDF algorithm the website will appear any SNI that matches the keyword. In its application, the keywords entered by users on the SNI search website will go through preprocessing first, namely tokenizing, filtering, then stamming, and the TF-IDF algorithm will combine two methods, namely the concept of the frequency of the appearance of terms in an SNI document and inverse back documents that have the same meaning from the keywords entered by users into the system. This application will make it easier for business actors who want to find out about SNI for the product to be produced so that when the product already exists, business actors only need to register the product to be standardized in accordance with the applicable SNI according to the product description.
Studi Literatur: Transfer Learning Untuk Analisis Penyakit COVID-19 Berdasarkan Dataset Chest X-ray Purba, Mariana
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6571

Abstract

The urgency of the impact of the COVID-19 disease that attacks people around the world encourages special research, especially in the field of artificial intelligence. This study aims to conduct a literature study related to the use of artificial intelligence, especially transfer learning in analyzing COVID-19 disease based on chest X-ray datasets. The research method of this research adapts the Preferred Reporting for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results of the analysis of this data to answer research questions regarding the transfer learning model for the analysis of COVID-19 disease based on the chest X-ray dataset, it is known that the models used are MobileNet, Inception, VGG and ResNet. MobileNetV2 can be optimized by adding a global average pooling layer, dropout layer and dense layer and get an accuracy of 98.65%. InceptionV3 can be combined with Xception and get 98.8% accuracy. VGG-16 can be combined with ResNet-50 Xception and get 98.93% accuracy. ResNet-50 can be optimized by adding a dropout layer and a dense layer and getting an accuracy of 97.65%.
Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Sentimen Analisis Teks Umpan Balik Tentang Evaluasi Pengajaran Dosen Setiawan, Hadiguna; Ariatmanto, Dhani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6572

Abstract

Evaluation of lecturer performance is very important because it helps in monitoring and ensuring that lecturers fulfill their duties effectively in maintaining integrity and teaching lecture material. By assessing lecturer performance based on criteria such as teaching, it can identify areas for improvement and provide support if needed. This study aims to determine the accuracy level of machine learning and deep learning combined with word-embedding for text analysis of lecturer teaching performance evaluation using preprocess techniques.The dataset consisted of 663 positive data, 552 negative data, and 465 neutral data. Successful in the results of the experiment, the training accuracy value for each classification method included KNN of 74.75%, SVM of 65.78%, RF of 98.58%, LSTM of 95.64% and Bi-LSTM of 95.91%. The test accuracy value for each classification method includes KNN of 59.82%, SVM of 62.88%, RF of 69.37%, LSTM of 70.81% and Bi-LSTM of 72.25%. The most superior method in processing data of 663 positive data, 552 negative data, and 465 neutral data by applying the word-embedding method, namely BiLSTM with a training accuracy of 95.91% and a testing accuracy of 72.25%.
Deteksi Jenis Tanaman Buah Tropis Indonesia Menggunakan Metode Transfer Learning Noprisson, Handrie
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6573

Abstract

This proposed research aims to determine the best algorithm performance among VGG16, ResNet and MobileNet in the process of image classification of fruit plant species. Based on the results of research experiments, the best fruit plant image classification results are the results of implementation using ResNet. The accuracy of ResNet in the training stage, evaluation stage and testing stage was 94.65%, 89.28% and 87.72% respectively. The VGG16 model obtained the lowest accuracy, with results at the training stage of 3.36%, the validation stage of 3.36% and the testing stage of 3.33%. The low accuracy of VGG16 in the classification of fruit species can be attributed to several factors. One reason is the use of weak algorithms in some cases, which limits the ability of models to accurately classify fruits. In addition, training on a small number of datasets can also contribute to lower accuracy, as models may not be able to achieve reliability.

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