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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
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).
Arjuna Subject : -
Articles 251 Documents
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Non-Negative Matrix Factorization Based Recommender System using Female Daily Implicit Feedback Hani Nurrahmi; Agung Toto Wibowo; Selly Meliana
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.599

Abstract

Recommender Systems is widely used by e-commerce to provide recommendations of products that are probably to be the interest to users. One of the recommender system algorithms that can be implemented is Non-negative Matrix Factorization (NMF) which receives explicit feedback in the form of user ratings. Although this method is effective, there are problems faced by explicit feedback as input, e.g. there are users who act as grey-sheep or black-sheep by providing dishonest ratings as explicit feedback. On the opposite, dishonest feedback least frequently occurs in implicit feedback. Therefore, in this study, we used implicit feedback to recommend products by taking the implicit feedback obtained from Female Daily’s mobile application as a case study. There are three types of implicit feedback: View Product Detail, View Review Detail, and Add to Wishlist. We experimented with the NMF algorithm provided by Surprise library using two implicit ratings weighting scenarios: accumulative weighting and maximum weighting. We combined several NMF parameters and run our experiment in 5-fold cross-validation. The best performance result in accumulative weighting is MSE = 1,2969, RMSE = 1,1388, MAE = 0,7909. Meanwhile, the best performance result in maximum weighting is MSE = 0,6742, RMSE = 0,8211, MAE = 0,5924.
Column-Level Database Encryption Using Rijndael Algorithm and Dynamic Key on Learning Management System Ariva Syam Mursalat; Ari Moesriami Barmawi; Prasti Eko Yunanto
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.609

Abstract

The course management system’s goal is to help learning activities. The system helps tomanage tasks, the grading process, and user communications. To avoid unauthorized dataaccess, the course management system needs a mechanism to protect the password that isused in the system’s login process. Database encryption using Rijndael algorithm is proposedby Francis Onodueze et al. to protect the data. A key is needed for the encryption process,and the key has to be kept secret. Thus, when the key is static, it is vulnerable against keyguessing attacks. To overcome the static key’s drawback, a dynamic key generation usingHash Messages Authentication Code - Deterministic Random Bit Generator (HMAC-DRBG)is proposed because it can generate keys periodically. Based on the evaluation, the probabilityof success key guessing attack using the proposed method is less than using the previousmethod, while the time complexity of those methods is similar.
Study of Denoising Method to Detect Valvular Heart Disease Using Phonocardiogram (PCG) Muhammad Yaumil Ihza Ihza; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.610

Abstract

Heart sound is a very weak acoustic signal, very susceptible to external acoustic signals and electrical disturbances, especially friction caused by the subject's breathing or body movements. The heart sound signal will be recorded in a phonocardiogram (PCG) and produce heart sounds, noise, and extra sounds. The purpose of this work is to denoise the signal from the heart sounds recorded on the PCG and determine valvular heart disease (VHD). Several methods have been proposed for denoising heart sound signals, both in the time domain and in the frequency domain. Most of these methods still have problems for denoising results. In this paper, the techniques used to denoise the heart sound signal are Discrete Wavelet Transform (DWT), Short Term Fourier Transform (STFT), and Low-Pass filter.
Job Vacancy Information System Based on SMS Gateway as Part of Tracer Study Alumni of UPI Cibiru Campus Fahmi Candra Permana; Sisilia Sylviani; Feri Hidayatullah Firmansyah; Intan Permata Sari
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.611

Abstract

The waiting period for graduates of an educational institution to get a job can be a benchmark for the quality of the institution in carrying out the educational process for its graduates. One thing that indicates the success of the educational process in an institution is the absorption of graduates from that institution in the world of work. To achieve this, an educational institution requires a system that can provide services and specific attention to its graduates in obtaining information related to work by their scientific fields quickly and sustainably. In this paper, an information system based on SMS Gateway technology is designed as a medium that can provide information directly to graduates rapidly and sustainably according to the needs of graduates. The method that we used in this research is the Rapid Application Development method as an information system design method, and Black Box Testing as a test of information system applications that have been developed. This system was built as part of the Tracer Study for alumni of the UPI Cibiru Campus, to provide information on job vacancies by the scientific fields of its graduates.
Stock Market Price Forecasting Using Recurrent Neural Network Pragya Bhardwaj; Jayant Kwatra
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.612

Abstract

A stock refers to the ownership of the organisation and its investors. A market where these stocks are sold or purchased is known as stock market. The prices of the stock is listed over National Stock Exchange or Bombay Stock Exchange for all Indian Companies. In this work, a machine learning approach is used to predict and forecast the prices of a company listed in NSE and BSE for 30 days using recurrent neural network known as stacked long-short term memory model. The results show that the model worked highly effective in performing the task. The model in the evaluation phase gave a root mean square error of 3.00 on the training data, 0.03 on testing data. R2 score for training data was 0.99 and 0.97 for the testing data. The prices when compared by the client organisation showed that they matched the predicted values to upto 90%. Thus, stacked LSTM models are one of the best models to make predictions of stock related data.
IT Asset Assessment Using Quantitative Risk Analysis (QRA) Method at XYZ Cafe Rahmat Yasirandi; Emiya Fefayosa Br Tarigan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.613

Abstract

Many companies are starting to invest in information technology (IT) assets to improve their business performance and services in this era. This includes modern cafes, which are becoming a promising business trend, especially if they have branches in various places. As with XYZ Cafe, IT Assets play an essential role in running the business. This research has succeeded in utilizing the Quantitative Risk Analysis (QRA) Method to perform calculations and tabulations for any potential risks. The results show the increase in the value of losses by 371.5% of the issued IT Asset investment capital, or 477,388,063 Rupiah. In detail, through Across Asset Analysis, Across Asset Value is at the top of the rank, namely Mini PC, with a loss value of 127,440,000 Rupiah. Through Across Risk Analysis, the Accidental Errors is in the first rank with a loss value of 184,038,000 Rupiah. This result implies that stakeholders can develop and plan mitigation actions to reduce potential losses for the company. Mitigation actions can be in the form of regulations, standard operating procedures (SOP), proposed monitoring applications, or strategic plan at top-level management so every threat and risk can be controlled and managed.
Lung Cancer Prediction Model using Logistic Linear Regression with Imbalanced Dataset Priscilia Lovita Paelongan; Irma Palupi
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.616

Abstract

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.
Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods Fazainsyah Azka Wicaksono; Ade Romadhony; Hasmawati
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.638

Abstract

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.
Energy Efficiency Analysis of Network Slicing Algorithm on WiFI Network Dimas Prakoso; Hilal Hudan Nuha; Rio Guntur Utomo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.642

Abstract

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.
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 Muhammad Aniq Wafa; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.648

Abstract

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.

Page 16 of 26 | Total Record : 251

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