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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization IJAIT (International Journal of Applied Information Technology) Journal of Appropriate Technology for Community Services JOURNAL of LEGAL RESEARCH Yayasan Cita Cendikiawan Al Khwarizmi JUSTIN (Jurnal Sistem dan Teknologi Informasi) NERO (Networking Engineering Research Operation) International Journal of Applied Information Systems and Informatics
Rachman, Andi Nur
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia Gufroni, Acep Irham; Hoeronis, Irani; Fajar, Nur; Rachman, Andi Nur; Sidik Ramdani, Cecep Muhamad; Sulastri, Heni
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1949

Abstract

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.
K-Nearest Neighbor and Weight K-Nearest Neighbor Classification of Cork Fish Using Gray-Level-Co-Occurrence Matrix Algorithm Approach Fitriani Dewi, Euis Nur; Rachman, Andi Nur; Nur Shofa, Rahmi; Tarempa, Genta Nazwar
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2745

Abstract

Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.
Implementation of Convolutional Neural Network and Long Short-Term Memory Algorithms in Human Activity Recognition Based on Visual Processing Video Rachman, Andi Nur; Mubarok, Husni; Fitriani Dewi, Euis Nur; Edwinda Putra, Rama
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1504

Abstract

Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.
Co-Authors Acep Irham Gufroni Al Husaini, Muhammad Alam Rahmatulloh Aldya, Aldy Putra Alwin, Muhammad ANWAR, FAHMI Azim, Muhammad Fauzan Azzahra, Berliana Dewi , Euis Nur Fitriani Dewi, Euis Nur Fitriani Edwinda Putra, Rama Fiitriani Dewi, Euis Nur Fitriani Dewi, Euis Nur Genta Nazwar Tarempa Gumilah, Dena Surya Guna, Nandana Surya Harahap, Rizky Maulana Hidayat, Eka Wahyu Husni Mubarok Insani, Muhammad Hanif Irani Hoeronis Iswanto, Muhammad Edi Latifah, Azzahra Putri Lukmana, Hen Hen Mujar Ibnu Syarif Noorsyabani, Fauzi Nugraha, Muhammad Bisma Nur Fajar Nur Shofa, Rahmi Purwayoga, Vega Rahmat, Biki Zulfikri Ramdani, Cecep Muhamad Sidik Rindiawan, Selvina Zalfa Shofa, Rahmi Nur Sidik Firmadi, Sidik Sidik Ramdani, Cecep Muhamad Siti Yuliyanti, Siti Sulastri , Heni Sulastri, Heni