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All Journal Proceeding Information Technology JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Intelligent Decision Support System (IDSS)
Ito Wasito
Universitas Pradita, Indonesia
Computer Science & IT

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Fractal Dimension Approach for Clustering of DNA Sequences Based on Internucleotide Distance Sadikin, Mujiono; Wasito, Ito; Veritawati, Ionia
Proceeding Information Technology 2013
Publisher : Proceeding Information Technology

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Abstract – Recently, the volume of biological data increasesexponentially. Problem of utilization of this kind of data is notonly concerning to the volume but also to its various format andstorage distribution. To solve this kind of problems, someapproaches require new methods, algorithms or tools to assisthuman being in getting beneficial from the biological data. Thispaper presents the usage of fractal dimension approach based oninter nucleotide distance to cluster DNA sequences. Internucleotide distance is a numerical representation of DNAsequences which is transformed to time series signal spectrum.Higuchi Fractal Dimension (HFD) is one of methods to estimatefractal dimension which it can be utilized to reduce time seriesdimension. HFD estimation then is applied to the signal spectrumand it is treated as input to clustering method. The result of thisclustering shows that HFD approach can be considered as analternative method for dimensional reduction purposes.Compared with previous study result as ground truth, the HFDapproach clustering provides some similarities in certain degree.Tested with two kinds of data test sample, this approach results 6and 7 group similarities of 10 groups. Keywords: DNA Sequences, Fractal, Inter Nucleotide Distances
FRACTAL DIMENSION AS A DATA DIMENSIONALITY REDUCTION METHOD FOR ANOMALY DETECTION IN TIME SERIES Sadikin, Mujiono; Wasito, Ito
Proceeding Information Technology 2013
Publisher : Proceeding Information Technology

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ABSTRACT -- Many researches show that time series data is a kind of data which has biggest volume and growth compared to the others kind of data. Parallel with its huge volume and rapid growth, it is always needed new method, technique or approach to explore knowledge contained in time series data. One of many goals in data mining of time series is anomaly detection. By definition fractal is an object that has such self similarities in certain degree. This paper presents the results study of HFD (Higuchi Fractal Dimension) approach for clustering to detect the existence of an anomaly or deviation in time series data. This proposed method is applied to PT.PGN daily stock trade year 2004 to 2012 as test data. The results show that for value of discrete interval k = 5, 10, 15 their HFD tend to diverge and there are tend to converge to 2 for the greater value of k. Based on HFD time series clustering results, this approach can be used to divide the normal data and the other data that contain certain anomaly. In this study is also shown that this approach provides a better result compared to adjustment method which fill unbalance time series data with a zero value. Keywords: time series, anomaly, dimensionality reduction, fractal dimension, clustering
Prototype temperature monitoring system for medicine refrigeration in the pharmaceutical installation Willy Willy; Haryono Haryono; Handri Santoso; Ito Wasito
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i3.159

Abstract

Patient safety in a hospital is a healthcare service that is safe and non-harmful to patients. All components of healthcare services (doctors, nurses, and other health teams) in hospitals must be aware of and care about patient safety while in the hospital. One of the most important components is monitoring the temperature of the medicine refrigeration. Human resources or work overload often leads to the need for more monitoring of the temperature of medicine refrigeration. Therefore, IoT technology can be the solution to assist in monitoring the temperature of the medicine refrigerator. The method used in this study is observation. Based on the conducted research, it is proven that the IOT system for temperature monitoring can reduce missed temperature records. The results of this study indicate that the DHT22 sensors have good accuracy as they remain within the accuracy range of the room thermometer used as a reference,  with a temperature reading accuracy of ±1oC and a maximum temperature measurement limit of 70oC. The data collection process uses the ESP8266 as the microcontroller, which is then connected to the DHT22 module as a temperature and humidity sensor and sends a database every 30 seconds. The real-time temperature and humidity measurement results can be viewed through mobile apps using the Flutter programming language and the website. If the temperature exceeds 8oC, the fan LED will automatically turn on and send notifications to WhatsApp registered using Python and Twilio. Furthermore, the existing data can be analyzed using a machine learning model, enabling the prediction of when the refrigerator will be damaged as a preventive measure
Transfer Gaya Gambar Batik Menggunakan Neural Style Transfer dan Convolutional Autoencoder Celvyn Yulian; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5573

Abstract

The challenge of neural networks to process visual art judgments like humans inspired Gatys et al and in 2015 they succeeded in creating neural style transfer (NST) that can transfer European artistic image styles to other images. At present, research related to NST has been widely conducted, but its use with a convolutional autoencoder (CAE) as one of the NN architectures capable of compressing NST output is still rare. This research intends to design an NST system with CAE as an additional architecture in charge of the compression process while maintaining the force transferred. As a substitute for European-style artistic images, batik is used as an original Indonesian artistic work. NST and compression images will be measured using structural similarity index measure (SSIM) evaluation metrics. The evaluation results showed that the system designed managed to get an average SSIM score of 0.67 out of 1 and an average value of storage size reduction ratio of 37.43% from the original size. Then, the survey showed that the quality of the compressed image was quite good with a score of 64.09% and the compressed image was quite usable in the field of work of each respondent with a score of 49.09%.
Sistem Pengenalan Emosi Menggunakan Autoencoder + CNN + Attention Mikhail Aresa Latumahina; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5576

Abstract

In the Digital Transformation era, many businesses use technology in the form of Deep Learning which is used to change the way business is run, one of the methods used is Emotion Recognition. Emotion Recognition itself is part of Computer Vision, and computer vision tasks are usually done using the CNN algorithm. Accuracy is important in Emotion Recognition where many studies use various methods, both Transfer and Hybrid learning to try to improve this aspect, so this research intends to design a Autoencoder + CNN + Attention that can be used for Emotion recognition, which is made by combining Encoder, CNN, and Attention Mechanisms. this model is circumspect by using FER2013 and compared to the CNN + Attention model which is shutting down in the same way. Even though the Autoencoder + CNN + Attention managed to get 64% Accuracy in Evaluate Test_Model compared to CNN + Attention which got 55%, it should be noted that adjustments still have to be treated because of the 43% sensitivity of testing on external data such as tuning, layer adjustments, and FER2013 data augmentation.
Co-Authors Celvyn Yulian Handri Santoso Handri Santoso Haryono . Haryono Haryono Ionia Veritawati Mikhail Aresa Latumahina Mujiono Sadikin Willy Willy