cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota bandung,
Jawa barat
INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 8 Documents
 1 
Search results for , issue "Vol. 8 No. 1 (2022): June 2022" : 8 Documents clear
Java Island Health Profile Clustering using K-Means Data Mining Muhammad Andryan Wahyu Saputra; Sri Harini
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.606

Abstract

Health is the best gift in life, because with health humans can carry out daily activities. Administratively, Java Island consists of 85 administrative regions and 34 cities. Therefore, it is very important to understand the health level of each area. The main objective of this research is to divide each region (district and city) into several groups and use the K-means method to determine health status based on 8 data parameters into certain groups. Algorithm in groups, will place the data based on the similarity of characteristics between groups. The results showed that there were 4 clusters of health profiles in Java, with 1 high health quality cluster in Central Jakarta, 55 regencies/municipalities with low health quality, 52 regencies/cities with low health quality. and the quality of health is quite low there are 13 districts/cities, it can be concluded that the health indicators in Java
Performance Analysis of the Hybrid Voting Method on the Classification of the Number of Cases of Dengue Fever arief rahman; Sri Suryani Prasetiyowati
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.614

Abstract

Dengue hemorrhagic fever (DHF) is a health problem in Indonesia. The region in Indonesia that has the highest number of cases in West Java with the highest ranking with 10,772 cases. The city of Bandung is recorded to have the highest number of cases at this time, namely 4,424 cases. Dengue fever can be caused by high rainfall. Judging from the high number of cases and fluctuations that occur, it is necessary to predict the spread of the disease so that in the future it can be anticipated by the government. Prediction of the spread of dengue fever in the city of Bandung using various classification algorithms has been done. Therefore, the author wants to make a new breakthrough by using hybrid ensemble learning using a hard voting method from three classification methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Using the Bandung City DHF disease dataset from 2012 to 2018. The results obtained using the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) were 84%, 87%, 79%. to improve the classification accuracy of the three methods using a hybrid classification with the hard voting method to get 91% results.
Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE Aji Gautama Putrada; Irfan Dwi Wijaya; Dita Oktaria
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.622

Abstract

Delivery of justice with the help of artificial intelligence is a current research interest. Machine learning with natural language processing (NLP) can classify the types of sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes of sexual harassment classification on specific datasets. Data imbalance can cause a decrease in the classifier's performance because it usually tends to choose the majority class. This study proposes the implementation and performance evaluation of the synthetic minority over-sampling technique (SMOTE) to improve the QPQ and HWE harassment classifications in the sexual harassment experience dataset. The term frequency-inverse document frequency (TF-IDF) method applies document weighting in the classification process. Then, we compare naïve Bayes with K-Nearest Neighbor (KNN) in classifying sexual harassment experiences. The comparison shows that the performance of the naïve Bayes classifier is superior to the KNN classifier in classifying QPQ and HWE, with AUC values of 0.95 versus 0.92, respectively. The evaluation results show that by applying the SMOTE method to the naïve Bayes classifier, the precision of the minority class can increase from 74% to 90%.
Stock Portfolio Optimization on JII Index using Multi-Objective Mean-Absolute Deviation-Entropy Deni Saepudin; Dimas Rizqi Guintana
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.623

Abstract

Stock portfolio optimization is allocating stock assets from investors to manage return and risk. Investors need a high return portfolio with a given level of risk, and portfolio optimization can help to find the feasible one. The data used for this problem are stocks listed on the Jakarta Islamic Index (JII). The portfolio optimization methods are applied Mean-Absolute Deviation (MAD) and Entropy. MAD is used because it can solve the portfolio optimization problem for the nonnormal distribution of data. Meanwhile, entropy is used because it can better diversify the weight of stocks in the MAD portfolio. Experiment results in this study show that MAD-Entropy and Equal Weight portfolio outperform the MAD portfolio in Sharpe Ratio and Performance Ratio. MAD only excels in one period, influenced by a stock that has a fantastic return in a certain period.
General Depression Detection Analysis Using IndoBERT Method Ilham Rizki Hidayat; Warih Maharani
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.634

Abstract

Many of the tweets we discover on Twitter are concerning feelings of depression which will be caused by varied things. The amount of tweets additionally continues to increase. To be able to decide however depressed a user is, analysing tweets from users can facilitate with that. The method of analysing the detection of depression can help to supply applicable treatment for users who are detected to own depression. During this paper, the users to be analysed are users who have more than 1000 tweets and are Indonesian tweets. Then, crawling / retrieval of user tweet data is carried out. After that, data pre-processing is done. Once that done, using the IndoBERT method to classify the data obtained. In the end, this paper provides the accuracy value of this detection analysis using the IndoBERT method with an accuracy value of 51% and F1-Score of 31%.
Classification Analysis using CNN and LSTM on Wheezing Sounds Gustav Bagus Samanta
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.621

Abstract

Asthma is a public health problem in almost all countries in the world. One of the symptoms that exist in asthmatics is wheezing. In several studies, wheezing has been classified using classification algorithm. However, the implemented classification algorithm still has a low level of accuracy. This study aims to determine the accuracy of the results from wheezing classification of respiratory sounds by comparing the algorithm.
Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA) I Nengah Dharma Pradnyandita
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.632

Abstract

In today's digital era, the trend of payments with electronic money is rising. Some people have switched to do their way to the modern method such as electronic money. This is to improve the efficiency of the financial system. However, with the convenience and speed provided, if the use of electronic money is not being controlled properly, this can cause an unmanageable price of goods. In the context of controlling the risk of the use of electronic money, it is required to predict the use of electronic money in Indonesia. This paper, by using multivariate data analysis with the variable of electronic money transaction and Money supply (M1) as supporting variables in order to predict the nominal of electronic money transactions. The methods used are Vector Autoregressive Moving Average (VARMA) and Support Vector Regression (SVR). The results of the forecasting model will be compared using Mean Absolute Percentage Error (MAPE). According to the research that had been done, the SVR model had a better result compared to VARMA with a MAPE value of 3.577 %. This shows that the prediction data of the SVR model is close to actual data
Error Correction Codes Performance using Binary Phase Shift Keying over Fading Channel Hilal Hudan Nuha; Abdi T. Abdalla
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.646

Abstract

In a communication system, two main resources are used: transmission power and channel bandwidth. Transmission power is the average power of the transmitted signal. Channel bandwidth is defined as the frequency band allocated for the transmission of the message signal. A goal of general system design is to use these two resources as efficiently as possible. This scientific paper presents the experimental results of the Binary Phase Shift Keying (BSK) communication system on the additive white gaussian noise (AWGN) channel and the Fading channel. To improve system performance, error correction code (ECC) is used for encoding. ECC used include convolutional code (ConvCode) and Hamming code. Experimental results show that for BER=10^(-4) the coding gain of the ConvCode over Hamming code under AWGN is G=0.475dB. Whereas the coding gain of the ConvCode over unencoded BPSK is G=19.6dB.

Page 1 of 1 | Total Record : 8

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024 Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024 Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023 Vol. 9 No. 1 (2023): June 2023 Vol. 8 No. 2 (2022): December 2022 Vol. 8 No. 1 (2022): June 2022 Vol. 7 No. 2 (2021): December 2021 Vol. 7 No. 1 (2021): June 2021 Vol. 6 No. 2 (2020): December 2020 Vol. 6 No. 1 (2020): June 2020 Vol. 5 No. 2 (2019): December 2019 Vol. 4 No. 2 (2018): December 2018 Vol. 2 No. 2 (2016): December 2016 Vol. 2 No. 1 (2016): June 2016 Vol. 1 No. 1 (2015): December 2015 More Issue