Indonesian Journal on Computing (Indo-JC)
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia).
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<![CDATA[Lung Cancer Prediction Model using Logistic Linear Regression with Imbalanced Dataset ]]>
<![CDATA[Priscilia Lovita Paelongan]]>;
<![CDATA[Irma Palupi]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.616
<![CDATA[Cancer is one of the leading causes of death worldwide. Cancer cases in Indonesia have now reached 4.8 million in 2018. Most cases are breast, cervix, and lung. Furthermore, we need to note that 43 percent of these cancer cases are preventable. This study uses a linear logistics regression model. Linear logistic regression models can be used for categoric datasets. The appropriate model is obtained after parameter assessment, test the significance of each affecting attribute, and test the suitability of the model. This is done to obtain prediction models and risk factors at the level of correlation of disease size. This method is relatively easy and conceptually practical, so it is possible to apply it to diagnose early symptoms of lung cancer. The results include a linear logistics regression model for early prediction of lung cancer patients based on symptoms, habits, and history of health diseases to see the likelihood that someone with a certain level of risk could have lung cancer. The factors that affect a person with lung cancer are difficulty swallowing, coughing, chronic diseases, fatigue, and age.]]>
<![CDATA[Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods ]]>
<![CDATA[Fazainsyah Azka Wicaksono]]>;
<![CDATA[Ade Romadhony]]>;
<![CDATA[Hasmawati]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.638
<![CDATA[Social media has become one of the most powerful platforms for information sharing. Colleges and universities now have official social media profiles to convey information about the campus and boost its branding and popularity. Instagram is a popular social networking website among college students. It is important for a university to comprehend its performance from the community's perspective, whether positive, negative, or indifferent toward the university. One solution is to examine the university's social media sentiment to establish the public's perception of the university. In this study, we will conduct a sentiment analysis on university social media based on public opinion or comments for each post on the university's Instagram to identify whether the comments are “Positive,” “Negative,” or “Neutral.” To classify posts on university Instagram, we use two methods: Support Vector Machine and Logistic Regression. The results suggest combining the Support Vector Machine approach with the TF-IDF feature yields the best F1-Score performance. In contrast, Logistic Regression with the FastText feature produces the worst performance of all models and feature extraction employed.]]>
<![CDATA[Energy Efficiency Analysis of Network Slicing Algorithm on WiFI Network ]]>
<![CDATA[Dimas Prakoso]]>;
<![CDATA[Hilal Hudan Nuha]]>;
<![CDATA[Rio Guntur Utomo]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.642
<![CDATA[The 5G network generation is a modern innovation after it was first introduced by the New Generation Mobile Network (NGMN). The rapid development of mobile devices is marked by the number of companies launching mobile devices with the latest network connection technology, namely the 5G network. In addition, the rapid development of technology has led to an increase in the number of network requirements that are increasingly current. The development of network virtualization and software network functions is proposed as Network Slicing technology. Network Slicing can integrate and distribute independent network resources so that users get services with low latency and high-reliability requirements. The Network Slicing algorithm can reduce energy wastage when used and aims to divide and allocate network resources into several parts in proportion to the expected resource ratio or priority.]]>
<![CDATA[Performance Analysis of PPG Signal Denoising Method Using DWT and EMD for Detection of PVC and AF Arrhytmias: Analisis Performansi Metode Denoising Sinyal PPG Menggunakan DWT dan EMD untuk deteksi Aritmia PVC dan AF ]]>
<![CDATA[Muhammad Aniq Wafa]]>;
<![CDATA[Satria Mandala]]>;
<![CDATA[Miftah Pramudyo]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.648
<![CDATA[In the cardiac arrhythmia detection system using a Photoplethysmography (PPG) sensor, noise is often found in the PPG signal due to internal and external factors in the signal retrieval process. So it is necessary to do a denoising process to remove noise before the signal is used. This study aims to test the Discrete wavelet transform (DWT) and Empirical Mode Decomposition (EMD) methods in removing noise from the PPG signal and to test the denoising signal on the Premature Arrhythmia Verticular Contractions (PVC) and Atrial Fibrillation (AF) detection systems. The parameters used to compare the performance of the denoising method are Mean Square Error (MSE), Signal to Noise Ratio (SNR), Accuracy, F1, Precision, and Recall. The method with the highest SNR, Accuracy, F1, Precision, and Recall values and the lowest MSE values is the best denoising method.]]>
<![CDATA[Music Recommender System Using K-Nearest Neighbor and Particle Swarm Optimization ]]>
<![CDATA[Randika Dwi Maulana Rasyid]]>;
<![CDATA[ZK Abdurahman Baizal]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.649
<![CDATA[In this day, users can listen to music anytime digitally and access them through the already available applications. A music recommender system is needed to help users choose music according to their interests and find music to listen to. K-Nearest Neighbor (KNN) is a popular method used in Collaborative Filtering (CF). In many studies, CF with the KNN method has been widely used, but it does not provide good performance. Thus, in this study, we use KNN, which will be optimized using Particle Swarm Optimization (PSO), which can improve the performance of the results obtained against the method used. System testing is done by comparing the performance of the KNN algorithm with the optimization results of KNN-PSO with several variables being observed, including the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. The results of these recommender will predict the rating value where the KNN method gives MSE 4.48 and RMSE 2.54 while the KNN-PSO method gives MSE 1.70 and RMSE 1.30.]]>
<![CDATA[E-Commerce Recommender System on the Shopee Platform Using Apriori Algorithm ]]>
<![CDATA[Rachmi Helfianur]]>;
<![CDATA[ZK Abdurahman Baizal]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.650
<![CDATA[The development of E-Commerce continues to increase every year, and all online shopping platforms continue to increase competition. Shopee as an online shopping platform offers various product categories that users need. To make it easier for users when shopping online, it is necessary to implement a product recommender system in E-Commerce. Therefore, in this study, we will build a recommender system using the a priori algorithm. The apriori algorithm is very widely used to find out the buying pattern of each user by looking at a combination of itemset. many recommender systems in e-commerce use various methods used, and provide recommendation results that display popular products, and based on the query results obtained. From the results of previous studies, there are similarities between products that have been liked by customers, so they do not have the best recommendations. Therefore, in this study we apply an apriori algorithm to add user confidence to the given recommendations, and to avoid overspecialization. In this research, we take the domain of electronics goods. In this study, the system produces the best value for association rules with a support value of 0.01, confidence 1.00, and lift 97.35.]]>
<![CDATA[Video Based Fire Detection Method Using CNN and YOLO Version 4 ]]>
<![CDATA[Muhammad Salman Farhan]]>;
<![CDATA[Febryanti Sthevanie]]>;
<![CDATA[Kurniawan Nur Ramadhani]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.654
<![CDATA[Fire detection is one of the technological efforts to prevent fire incidents. This is very important because the damage caused by fires can be minimized by having a fire detector. There are two types of fire detection, namely traditional-based and computer vision-based. Traditional-based fire detection has many shortcomings, one of which requires a close fire distance for activation. Hence, computer vision-based fire detection is made to cover the shortcomings of traditional-based fire detection. Therefore, in this study, we propose a video-based fire detection using a Convolutional Neural Network (CNN) Deep Learning approach supported by You Only Look Once (YOLO) object detection model version four. This study uses a dataset of various fire scenarios in the form of images and videos. The fire detection built in this study has an accuracy of above 90% with an average detection speed of 34.17 Frame Per Second (FPS).]]>
<![CDATA[Pengembangan Media Pelaporan Kegiatan Asisten Laboratorium Berbasis Web di Prodi Pendidikan Multimedia ]]>
<![CDATA[Alifiandi Nursanni Wiriadikusumah]]>;
<![CDATA[Feri Hidayatullah Firmansyah]]>;
<![CDATA[Fahmi Candra Permana]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.658
<![CDATA[Keperluan administrasi pelaporan kegiatan asisten laboratorium di Prodi Pendidikan Multimedia UPI Kampus Cibiru masih menggunakan media konvensional. Kasus pandemi virus Covid-19 yang terjadi menyebabkan permasalahan yang berimplikasi pada penundaan pencairan honor asisten laboratorium. Penelitian ini adalah sebuah pengembangan media pelaporan kegiatan asisten laboratorium di program studi pendidikan multimedia upi cibiru berbasis web dengan tujuan untuk mengatasi masalah yang berkaitan dengan keperluan administrasi pelaporan kegiatan asisten laboratorium. Pada penelitian ini menggunakan metode Waterfall sebagai metode pengembangan, dalam implementasinya penulis memanfaatkan ekosistem Node.js dan layanan firebase, serta membuat desain antarmuka yang responsif sehingga meningkatkan aspek aksesibilitas media. Hasil pengujian blackbox menunjukan sistem yang dibuat sudah berjalan dengan baik, sedangkan pengujian aksesibilitas seluruh halaman media yang dibuat dengan menguji di 2 tipe perangkat yang berbeda yaitu desktop dan mobile menggunakan tools Chrome Lighthouse menunjukan hasil dengan tingkat yang sangat baik dengan nilai rata rata 96,2 dari 10 halaman yang diuji. Pengembangan media pelaporan kegiatan berbasis web dapat membantu asisten laboratorium dalam mengatasi masalah keperluan administrasi konvensional.]]>
<![CDATA[Forecasting Fuel Consumption Based-On OBD II Data ]]>
<![CDATA[Satrio Nurcahya]]>;
<![CDATA[Bayu Erfianto]]>;
<![CDATA[Setyorini Setyorini]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.659
<![CDATA[Cyber Physical System consists of computing devices that communicate with each other by interacting with the physical world assisted by sensors and actuators with an iterative response. Intelligent Transportation System which aims to apply information and communication technology in every transportation area. Applying ITS to vehicles, especially in the aspect of fuel consumption, vehicles must begin to be able to analyze the use of fuel that is being used to provide users so that users can be more effective. Regarding the analysis of fuel consumption, several researchers have done this with several existing methods such as ANN, SVM and the like. The use of the Multivariate time series method is used as a solution to the forecast analysis of vehicle fuel consumption. In this study, data from vehicles obtained from OBD-II will be processed using the Multivariate time series method with output in the form of analysis and visual data from the forecast with parameters related to RPM, TPS and fuel consumption. So the expected result is the relationship between RPM, TPS and fuel consumption as well as the formation of a system model to obtain sample data related to RPM, TPS and fuel consumption.]]>
<![CDATA[Perancangan Aplikasi Pembelajaran Pemrograman Dasar Bahasa C untuk Kelas X Multimedia SMKN 1 Majalaya ]]>
<![CDATA[Rizki Cahya Iskandar]]>;
<![CDATA[Fahmi Candra Permana]]>;
<![CDATA[Feri Hidayatullah Firmansyah]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.2.660
<![CDATA[Penggunaan media sangat dibutuhkan untuk mengatasi media pembelajaran yang terbatas di sekolah, mengatasi kejenuhan dan kebingungan belajar tanpa alat untuk melakukan coding secara langsung. penggunaan media yang tepat dan bervariasi dalam proses pembelajaran dapat meningkatkan motivasi belajar dan dapat mengurangi sikap pasif peserta didik. Berdasarkan hal tersebut peneliti bermaksud mengembangkan media pembelajaran yang dapat digunakan oleh siswa di kelas X SMKN 1 Majalaya dengan tujuan dari penelitian ini ialah menghasilkan suatu produk media pembelajaran aplikasi Pembelajaran Pemrograman C pada mata pelajarangan Pemrograman Dasar dengan menggunakan dengan model penelitian ADDIE (Analysis, Design, Develoment, Implementation and Evaluation). Hasil dari produk tersebut berupa aplikasi yang dipasang di perangkat android dengan pembuatan aplikasi menggunakan perangkat lunak Construct 2 dan desain dengan Adobe Illustrator dibantu Canva. Hasil dari validasi kelayakan media pembelajaran yaitu validasi materi meperoleh rata-rata 91,75%, dan validasi media memperoleh rata-rata 90,11%, secara keseluruhan hasil validasi berpredikat sangat layak. Untuk uji coba kepada peserta didik memperoleh presentase 91,98% dengan predikat sangat layak. Dari hasil tersebut dapat disimpulkan bahwa aplikasi Pembelajaran Pemrograman C layak digunakan dalam mata pelajaran Pemrograman dasar pada program multimedia.]]>
