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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) ILKOM Jurnal Ilmiah IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal E-Komtek JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Informatics and Communication Technology (JICT) Journal of Engineering, Electrical and Informatics (JEEI) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology SmartComp Journal of Technology Informatics and Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science Journal of Informatics and Communication Technology (JICT) Jurnal TAM (Technology Acceptance Model) Jurnal Abdi Rakyat
Enny Itje Sela
Master Program Of Information Technology, Universitas Teknologi Yogyakarta, Jl. Siliwangi (Ringroad Utara), Jombor, Sleman, D.I. Yogyakarta 55285|Universitas Teknologi Yogyakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation Edi Priyanto; Enny Itje Sela; Luther Alexander Latumakulita; Noourul Islam
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1676.373-381

Abstract

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.
Pemanfaatan Teknologi Augmented Reality Sebagai Media Navigasi Berbasis Aplikasi Android Ganda Sinarna; Enny Itje Sela
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4406

Abstract

The navigation media on the Universitas Teknologi Yogyakarta campus environment currently still uses conventional guidance media in the form of printed pictures and mock-up plans, so it is considered ineffective and inefficient in providing directions for students, staff, or campus visitors, especially in the outdoor areas where signage is rarely installed. This research aims to assist 3D virtual navigation services by utilizing augmented reality technology based on longitude and latitude coordinate point data of an area, which is implemented in the form of an Android mobile application. The research method used is the case study method, with a qualitative approach. This application development model uses the waterfall model. The stages used in this research include planning, data collection, analysis, design, implementation, and application testing using Black box testing with the test case method, which produces application testing that descriptively explains the application work process. Application test results show that route search navigation, information features, and application menus work well. The combination of augmented reality technology and a global positioning system can be implemented to recognize various navigation routes based on longitude and latitude numerical values very well.
Implementasi Extreme Learning Machine untuk Pengenalan Jenis Sepatu Muhammad Ilham Triwibowo; Enny Itje Sela
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.5958

Abstract

Sepatu adalah salah satu alas kaki yang sering digunakan oleh masyarakat saat ini. Sepatu belakangan ini bahkan sudah menjadi sangat populer dan menjadi salah satu kebutuhan primer bagi beberapa orang. Beberapa orang awam yang tidak tau tentang jenis-jenis sepatu dan sering kali salah dalam membeli sepatu. Ditambah hal tersebut diperburuk oleh oknum-oknum penjual di online shop yang sering kali memberikan judul barang tidak sesuai dengan produk yang dijual. Extreme Learning Machine merupakan metode pembelajaran baru dari jaringan syaraf tiruan dan salah satu metode dalam Machine Learning. Data yang digunakan pada penelitian ini berupa masing-masing 60 citra sepatu casual, sepatu formal dan sepatu sport untuk data latih. Sedangkan untuk data uji masing-masing 40 citra sepatu casual, sepatu formal dan sepatu sport untuk data latih. Hasil terbaik yang didapat adalah menggunakan 75 neuron dengan akurasi latih 70%, akurasi uji 60%, dan MAPE 27.16.
Analisis Sentimen Opini Mahasiswa Terhadap Aplikasi Portal Mahasiswa UTY Menggunakan Metode Naïve Bayes Classifier Agus Ardiyanto; Enny Itje Sela
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.508

Abstract

University Technology of Yogyakarta (UTY) has released the UTY Student Portal Application since 2020 which is an improvement in services to support student academic activities. The UTY Student Portal application was developed by Puskom from Yogyakarta Technology University. The UTY Student Portal application is a system designed and built to manage data related to academic information which includes student data, lecturer data, lecture results records, lecture schedules and so on. The presence of this Student Portal Application has given rise to various comments from its users, namely UTY students. Seeing this problem, the researchers conducted research on student opinions regarding the UTY Student Portal Application using the Naïve Bayes Classifier. This research uses the Python programming language. Based on the results of the discussion, it was found that the accuracy level was 93% in the training process and the testing accuracy was around 65.2% with a distribution of training and test data of 70%:30% from 150 opinion text data. This model creation experienced overfitting, because the resulting testing accuracy was much smaller than the training accuracy.
Analisis Sentimen Komentar Youtube Tentang Resesi Global 2023 Menggunakan LSTM Ari Hendrawan; Enny Itje Sela
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.526

Abstract

The COVID-19 pandemic that occurred in 2020 caused the economy to decline due to declining economic activity, making companies decide to lay off some workers so that the unemployment rate increased. This makes economic activists predict that there will be a global recession in 2023, Youtube as a video-sharing platform is one of the places to discuss through the comment’s column. The increasing number of YouTube users is one of the references for sentiment analysis using data taken from video comments. Long Short-Term Memory (LSTM) is used to perform sentiment analysis, with 500 data divided into training data and test data, resulting in the highest accuracy of 90% training data and 76% test data. This result is obtained from the configuration of the LSTM architecture with dense layers using sigmoid activation and 50 epochs.
Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation Fathul Am; Enny Itje Sela
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.532

Abstract

Batik is an Indonesian cultural heritage that is internationally recognized by UNESCO. However, knowledge about the types of batik, especially traditional Pekalongan batik, is increasingly forgotten due to globalization. This research aims to create a Pekalongan traditional batik image classification system through Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Artificial Neural Network (ANN) classification method. This system aims to make it easier for people to identify Pekalongan batik motifs without requiring special skills. The results showed that the GLCM and JST methods can be used to classify Pekalongan batik can predict correctly. The use of JST Backpropagation architecture with 3 hidden layers resulted in train data accuracy of 46.6% and test data accuracy of 55.5%. This system is expected to help preserve the cultural heritage of batik and increase public understanding of Pekalongan batik motifs.
Perancangan Aplikasi Quiz Sebagai Media Pembelajaran Sejarah Idham Kholed Rachmawan; Enny Itje Sela
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.575

Abstract

Mastery of historical concepts is important for students in increasing their understanding of country’s past dan history. However, conventional history learning metodhs still often experience difficulties in attracting and motivating students to learn. Therefore, we need an interactive and fun learning media to increase students’ learning history. One alternative interactive learning media is an interactive quiz application. This study aims to help teachers in the learning process so that students are more interested in learning history with learning media in the form of interactive quiz applications. The interactive quiz application developed in this final project is an android-based application that makes it easier for students to learn history in a fun way. This application provides quizzes rellated to history subject matter wich are presented interactively. In addition, this application also provides an evaluation feature to evaluate students’ ability to master historical concepts.
The Implementation of Artificial Neural Networks for Stock Price Prediction Akbar Maulana; Enny Itje Sela
Journal of Engineering, Electrical and Informatics Vol 3 No 3 (2023): Oktober: Journal of Engineering, Electrical and Informatics
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i3.2254

Abstract

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0. Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
Nutritional Status Classification Of Stunting In Toddlers Using Naive Bayes Classifier Method Risky Devandra Hartana; Enny Itje Sela
Journal of Technology Informatics and Engineering Vol 3 No 1 (2024): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i1.154

Abstract

Stunting in toddlers is one of the prevalent issues of malnutrition in Indonesia. The causes of Stunting are diverse, and one contributing factor is the insufficient nutritional intake required for toddlers. The categorization of Stunting nutritional status in toddlers is crucial to identify those experiencing Stunting, enabling appropriate interventions to prevent more serious health problems in the future. This research aims to develop a classification model for short nutritional status in toddlers using the Naive Bayes Classifier method. The data utilized in this study originate from anthropometric measurements of toddlers in the Malebo area, Kandangan, Temanggung, Central Java. The anthropometric data include weight, height, and age of the toddlers. This data is then processed using the Naive Bayes Classifier method to classify the nutritional status of Stunting in toddlers. The results of this research are expected to assist in identifying toddlers experiencing Stunting, facilitating appropriate interventions to prevent more serious health issues in the future. Additionally, the Naive Bayes Classifier method employed can be applied in similar studies to enhance the quality of life, especially for children in Indonesia, particularly in the Malebo area, Kandangan, Temanggung, Central Java.
Analisis Sentimen Opini Mahasiswa Terhadap Aplikasi Portal Mahasiswa UTY Menggunakan Metode Naïve Bayes Classifier Ardiyanto, Agus; Sela, Enny Itje
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.508

Abstract

University Technology of Yogyakarta (UTY) has released the UTY Student Portal Application since 2020 which is an improvement in services to support student academic activities. The UTY Student Portal application was developed by Puskom from Yogyakarta Technology University. The UTY Student Portal application is a system designed and built to manage data related to academic information which includes student data, lecturer data, lecture results records, lecture schedules and so on. The presence of this Student Portal Application has given rise to various comments from its users, namely UTY students. Seeing this problem, the researchers conducted research on student opinions regarding the UTY Student Portal Application using the Naïve Bayes Classifier. This research uses the Python programming language. Based on the results of the discussion, it was found that the accuracy level was 93% in the training process and the testing accuracy was around 65.2% with a distribution of training and test data of 70%:30% from 150 opinion text data. This model creation experienced overfitting, because the resulting testing accuracy was much smaller than the training accuracy.
Co-Authors Adidarma, Muhamad Bahru Agus Ardiyanto Agus Ardiyanto Agus Harjoko Agus Harjoko AKBAR MAULANA Akbar, Riolandi Algadrie, Saniah Evatri Alhafiz, Amirrul Dafa Anggara, Afwan Anindita Septiarini, Anindita Apriyaldi Lukman Ari Hendrawan Arif Amrulloh Asep Zainal Alfarizi Briyan Efflin Syahputra Edi Priyanto Fatah Nur Saifullah Fathul Am Fathul Am Ferryma Arba Apriansyah Ganda Sinarna Hendrawan, Ari Idham Kholed Rachmawan Julian Ega Prabowo Luther Alexander Latumakulita Luther Latumakulita M Ihsan Muhammad Ilham Triwibowo Munakhir Mudjosemedi Nathaniela Aptanta Parama Noourul Islam Noviania, Reski Nugroho, Faishal Tirto Nur Wening, Nur Olwin Kirab Novaldy Prawirdani, Adil Rachmawan, Idham Kholed Retantyo Wardoyo Rianto Rianto Risky Devandra Hartana Saucha Diwandari Senoaji, Muhammad Senoaji Wibowo Sri Hartati Steven Ray Sentinuwo Syahrul Gunawan Ramdhani Syamsu Windarti Totok Suprawoto Tundo, Tundo Utami, Wahyu Sri