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Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Published by Universitas Respati Yogyakarta
ISSN : 29856914     EISSN : -     DOI : https://doi.org/10.35842/icostec
Core Subject : Science,
Computer Science & IT
ICoSTEC is an annual forum for international researchers and students to exchange ideas on current studies and research topics. The international conference will discuss several sub-topics, including innovation in information science and technology and leveraging globalization.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 57 Documents
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Integration of Motor Vehicle Testing Service System with BLUE Smart Card Ni Nyoman Harini Puspita
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.21

Abstract

The integration of the Motor Vehicle Testing Service System in the regions with the application of the Electronic Test Pass (Bukti Lulus Uji elektronik /BLUe) from the Ministry of Transportation is carried out to improve the quality of motor vehicle testing services. The purpose of this application is to support real-time reporting of motor vehicle testing carried out by the Regions to the Center. This application is made partial, it only accepts test result data without an operational system for the testing process, because of that, motor vehicle testing application that is integrated with the application must be provided. This study aims to build a vehicle testing system that can manage test results automatically and is integrated with BLue. The results of this study are based on the implementation process for three months, the real-time integrated testing service system runs well, which is 94,4 %. the factors that affect the value of 5,6% are influenced by the existing infrastructure and manual payment processes. The calculation of the success of this implementation is determined based on the service target which previously reached 60 minutes per vehicle to 30 minutes, the efficiency of using paper is 66,6%.
Simulation on the effects of the Arduino PID controller parameters using the WOKWI online simulator Djoko Untoro Suwarno
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.22

Abstract

PID controllers are known in the industrial world as reliable controllers and are studied in universities. The explanation of the ideal PID mathematically often makes it difficult for students to understand the computational process and the implementation that occurs. Often there is difficulty in selecting the correct controller parameters, for example, the Kp, Ki, and Kd parameters. In this study, PID parameters were selected in the form of Kp, Ti, and Td and observed controller output. The Ti parameter as the time integral is easier to understand than the Ki parameter as the gain integral. The Arduino simulator used is wokwi which is an online Arduino simulator. The PID library used is the Arduino Brett Beauregard PID. The results obtained are the effect of changes in the parameters of Kp, Ti, and Td on the controller output. For larger Kp, the controller output is proportional to the amount of input. The larger the Ti, the slower the system output, while the effect of Td is used when the input changes frequently
MODELLING AND ANALYSIS OF PVSC TYPE BUCK BUCK-BOOST DC-DC CONVERTER T Ram Manohar Reddy; Shaik Hussain Vali; Phanindra Thota; Kamaraju Kamaraju
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.23

Abstract

In the era of modern industrial development, power electronics equipment has been developed aggressively and brought dc system again in power utilization to use clean energy resources like solar array, fuel cell, wind generator, etc. Since the past decade, power electronics equipment has become very popular; hence, the switch-mode converter requirement is increasing rapidly day by day in applications like communication power supply, space crafts, hybrid electric vehicles, micro-grid and nano-grids. Among the various available configurations of converters, Multi-Input DC/DC converters became more and more popular in power electronics field, especially, for provide interface of various renewable energy sources and deliver regulated power to several loads. In this article, a PVSC type Buck Buck-Boost Dual-Input DC- DC Converter (DIDC) is designed and modelled for DC grid application. The proposed converter is driven with two renewable energy sources PV cell and a battery having different amplitudes which can able to deliver the power from source to load individually or simultaneously. DIDC tropology is simply configured with two passive elements L, C, diodes D1 D2 and switches S1, S2. The Dual-Input DC-DC Converter suitability is validated by carrying out simulations in different modes of operation. The de-centralized PID controller is designed for voltage and current loop controller to ensure the DC output voltage of 48 V, load current of 4.8 A and power of 230W. The Stability of the closed-loop converter is also verified under all possible source and load disturbance conditions. The simulations and analysis of the proposed converter are carried out using MATLAB and PSIM software
Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron Ichsan Firmansyah; Rika Rosnelly; Wanayumini Wanayumini
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.24

Abstract

Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre-trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.
Classification of Shape Bean Coffee Using Convolutional Neural Network P.P.P.A.N.W. Fikrul Ilmi R.H. Zer; Rika Rosnelly; Wanayumini Wanayumini
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.25

Abstract

Deep Learning is a sub-field of Machine Learning in addressing the development of an image classification. This study uses a Deep Learning algorithm to classify the shape of coffee beans which consist of 4 types, namely defect, longberry, peaberry and premium. We use the Convolutional Neural Network to classify the shape of the coffee beans. This study combines the Convolutional Neural Network algorithm with Adam's optimization to get the best results. The research dataset uses training data of 4800 images and testing data of 1600 images with four classes. The results of this study get an accuracy result of 90,63%, a precision result of 88,23%, and a recall result of 95,74%. Based on the results obtained that the Convolutional Neural Network with Adam's optimization can be applied to the classification of coffee bean shapes with good results.
COMPARISON OF K-NEAREST NEIGHBOR (KNN) AND LINEAR DISCRIMINANT ANALYSIS (LDA) ALGORITHMS FOR MATURE AJWA DATE FRUIT CLASSIFICATION Risna Risna; Fadila Amanda; Shofwatul Uyun
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.26

Abstract

Currently, many applications of artificial intelligence in various fields of life, especially in image data, require digital image processing. One example of the use of digital images often encountered is image processing of fruit ripeness. Dates are a fruit in great demand by the people of Indonesia, and one of the most popular dates is the Ajwa date. The author is interested in developing previous research regarding identifying the ripeness of Ajwa Dates, where previous research used RGB color image processing with the HIS method. Therefore, the authors want to apply a different method, namely the K-Nearest Neighbor (K-NN) method and Linear Discriminant Analysis (LDA), in classifying the ripeness of the Ajwa Dates by applying a statistical feature algorithm. This research aims to develop a classification model for the maturity level of Ajwa Dates. Furthermore, it is expected to provide better classification results than the previous algorithm. The test results using the KNN method can produce higher accuracy than the LDA, where the KNN method is obtained from the calculation of the Euclidean distance k = 1 100% and Manhattan with a value of k = 1 and k = 2 worth 100%, but the minimum accuracy value is 53.33 % is found at k = 9 in the Euclidean distance calculation, while the LDA accuracy value can reach 93.33%
Transfer Learning for Feral Cat Classification Using Logistic Regression Fazli Nugraha Tambunan; Rika Rosnelly; Zakarias Situmorang
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.27

Abstract

Machine learning is an alternative tool for classifying animal species, especially feral cats. In this research, we use a machine learning algorithm to classify three species of feral cats: American Wildcat, Black-footed Cat, and European Wildcat. We also use a transfer learning model using the VGG-19 network for extracting the features in the feral cat images. By combining the VGG-19 and logistic regression algorithm, we build six models and compare which one is the best to solve the problem. We evaluate and analyze all models using a 5-fold, 10-fold, and 20-fold cross-validation, with accuracy, precision, and recall as the base performance value. The best result obtained is a model with a lasso regularization and cost parameter value of 1, with an accuracy value of 0.846667, a precision value of 0.845389, and a recall value of 0.846667. We also tune the C parameter in each LR model with values such as 0.1, 0.5, and 1. The most optimum C value for the lasso and ridge regularization is one, resulting in an average value of accuracy = 0.813, precision = 0.812, and recall = 0.813.
Combination of Pre-Trained CNN Model and Machine Learning Algorithm on Pekalongan Batik Motif Classification Masri Wahyuni; Rika Rosnelly; Wanayumini Wanayumini
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.28

Abstract

Pekalongan is a region in Indonesia well-known for its batik production. The Pekalongan batik is rich in varieties of motifs, such as the Jlamprang, Liong, Terang Bulan, and Tujuh Rupa. The difficulty of distinguishing Pekalongan batik motifs for ordinary people causes the need for a model that can help recognize these motifs automatically based on input from digital images. This research aims to classify the Pekalongan batik motifs using a pre-trained Convolutional Neural Network (CNN), the Inception V3, and machine learning, the K-Nearest Neighbors (K-NN) algorithm. First, we extract the features from the digital image using the Inception V3 model, resulting in m x 2048 features, where m is the number of images. The extracted features generated from the Inception V3 model will be used as the dataset for the motif classification. We build models to classify the features using the K-Nearest Neighbors (KNN) with a K value of 5. In the classification process, we employ two distance metrics, the Euclidean and Manhattan distance, and analyze their performance using the 10-fold and 20-fold crossvalidation. The results of this study are the highest overall performace of accuracy (0.987), precision (0.987), and recall (0.987) produced by the Euclidean model.
Bulldog Breed Classification Using VGG-19 and Ensemble Learning Abwabul Jinan; Zakarias Situmorang; Rika Rosnelly
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.29

Abstract

In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933
A Combination Of Support Vector Machine And Inception-V3 In Face-Based Gender Classification Doughlas Pardede; Wanayumini Wanayumini; Rika Rosnelly
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.30

Abstract

Differences in human facial structures, especiallythose recorded in a digital image, can be used as an automaticgender comparison tool. This research utilizes machine learning using the support vector machine (SVM) algorithm to perform gender identification based on human facial images. The transfer learning technique using the Inception-v3 model is combined with the SVM algorithm to produce six models that implement polynomial, radial basis function (RBF), and sigmoid kernel functions. The results obtained are models with excellent performance, as seen from the lowest values of accuracy = 0.852, precision = 0.856, recall = 0.852, and the highest values of 0.957, 0.957, and 0.957. This combination also produces a model with excellent reliability, where the probability of overfitting or underfitting obtained is below 1%.

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