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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Telematika JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH MIND (Multimedia Artificial Intelligent Networking Database) Journal CSRID (Computer Science Research and Its Development Journal) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Manajemen, Ekonomi Sains Jurnal Varian JURNAL PIONIR INFOKUM Journal of Applied Data Sciences Jurnal Ipteks Terapan : research of applied science and education International Journal of Research in Vocational Studies (IJRVOCAS) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Jurnal Pengabdian Masyarakat dan Riset Pendidikan SISFOTENIKA Proceeding Of International Conference On Education, Society And Humanity
Wanayumini
Universitas Asahan
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Journal : Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)

 1 
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.
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.
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%.
COMPARISON OF SGD, RMSProp, AND ADAM OPTIMATION IN ANIMAL CLASSIFICATION USING CNNs Desi Irfan; Teddy Surya Gunawan; 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.v2i1.32

Abstract

Many measures have been taken to protect endangered species by using "camera trap" technology which is widespread in the field of technology-based nature protection field research. In this study, a machine learning-based approach is presented to identify endangered wildlife images with a data set containing 5000 images taken from Kaggle and some other sources. The Gradient Descent optimization method is often used for Artificial Neural Network (ANN) training. This method plays a role in finding the weight values that give the best output value. Three optimization methods have been implemented, namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam on the Artificial Neural Network system for animal data classification. In some of the studies reviewed there are differences in the results of SGD and ADAM, which on the one hand SGD is superior, and on the one hand ADAM is superior with the appropriate learning rate. The results of this study show that the CNN method with the Adam optimization function produces the highest accuracy compared to the SGD and RMSprop optimization methods. The model trained using Adam's optimization function achieved an accuracy of 89.81% on the test, showing the feasibility of the approach.
Sentiment Analysis on Hotel Ratings Using Dynamic Convolution Neural Network Novendra Adisaputra Sinaga; Teddy Surya Gunawan; 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.v2i1.36

Abstract

Currently, the role of information technology is very important in everyday life because heavy workloads can become easier, communication time can be made shorter and data processing can be faster and more accurate. Hotel ranking sentiment analysis can provide important information for hotel owners and managers to improve the quality of service and guest experience. It can also be used by prospective guests to make the right booking decisions. Sentiment analysis can identify positive or negative feelings from guest reviews. There are 694,213 data reviews about hotels using English which are used as training data. The data was preprocessed and 76,905 vocabularies were obtained by utilizing Word2Vec. The training data was carried out at the encoding stage. The DCNN model is given a K-Max-Polling value of 2. The model is trained for 20 epochs. The model that has been formed is tested with 173,554 data and obtained an accuracy rate of 95%.
Predicting Children's Talent Based On Hobby Using C4.5 Algorithm And Random Forest Sugeng Riyadi; Hartono Hartono; 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.v2i1.54

Abstract

A person's talent is closely related to intelligence, hobbies, and interests. These factors are the best features to be used in a dataset to predict a children's talent, such as in an academy, arts, or sports. This research uses the C4.5 and random forest algorithms in 8 different models to predict a children's talent based on a dataset gained from a survey involving 1601 parents. Each model contains four training-testing data ratios, such as 50:50, 60:40, 70:30, and 80:20. We calculate each model prediction performance using 10-fold and 20-fold crossvalidation, with the accuracy, f-score, precision, and recall values as a comparison. The best result for the training evaluation we get is 91.5% for each comparison value from the random forest model (70:30 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 92.7%, 92.8%, 92.8%, and 92.7% from the random forest model (50:50 ratio). The worst testing evaluation we get is 81.7% for each comparison value from the C4.5 model (50:50 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 89.2%, 89.2%, 89.3%, and 89.2% from the C4.5 model (50:50 ratio).
Co-Authors Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Al Ayyub, Muhammad Azwar Alfitra, Andra Amanda, Windi Winona Andi Zulherry Annas Prasetio Annas Prasetio Ardana, Abdul Aziz Arjuna Ginting ayadi, B. Herawan H B. Herawan Hayadi Dedy Hartama Dedy Hartama Desi Irfan Desi Irfan Devy Pratiwi Dini Farhatun Doughlas Pardede Elisabeth S, Noprita Erica Rian Safitri Erlina Erlina Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Harahap, Sarwedi Hartama, Dedy Hartono Hartono Hasibuan, Cici Cahyati Husin Sariangsah Ichsan Firmansyah Indra Mawanta Indra Swanto Ritonga Irfan Sudahri Damanik Ismail, Juni isnaini, fitri JAKA KUSUMA Juni Ismail Karina Andriani Khoirunsyah Dalimunthe Lili Tanti Lili Tanti Lubis, Cindy Paramitha lvindra, Farhan A M yoggi saputra M. Ari Iskandar Margolang, Khairul Fadhli Masri Wahyuni Mhd Fauzan Yafi Miftahul Jannah Muhammad Fachrurrozi Nasution Muhammad Nasri Gea Muhammad Sadikin Muhammad Sayid Amir Ali Lubis Muhammad Zarlis Mutiara S. Simanjuntak Nasution, Ammar Yasir Novendra Adisaputra Sinaga NURLIANA NURLIANA P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Prasetya, Hardi Rahma, Intan Dwi Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly RIKA ROSNELLY Rika Rosnelly, Rika Roesnelly, Rika Rohima, Rohima Roslina Roslina, Roslina Roslina, Roslina Sartika Mandasari Selase, Septinur Sihombing, Rotua Simangunsong, Dame Lasmaria Sri Ayu Rosiva Srg Sugeng Riyadi Sugeng Riyadi Sumantri, Ekoliyono Wahyu T S Gunawan Tambunan, Fazli Nugraha Tammamah Lubis, Hartati Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Triana Puspa handayani Triwanda, Eri Vicky Rolanda Wardana, Revo Wulandari, Wulandari Yuni Franciska Br Tarigan Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.