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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 140 Documents
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Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation Kusuma Adi Achmad; Lukito Edi Nugroho; Achmad Djunaedi; Widyawan
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.858

Abstract

The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label to suggest context was uHijos, uCuisine, uAmbience, and uTransport. The accuracy of the socio-user context aware-based recommender system in suggesting the context of uHijos, uAmbience, and uTransport was 100% with an error rate of 0%. It was found that only the level of recognition of the model in suggesting uCuisine was less accurate (below 30%) with a classification error for more than 70%. Performance evaluation of the socio-user model context-based recommender system was considered efficient, particularly for the evaluation of the level of accuracy, completeness (recall/sensitivity), precision, and a harmonic average of precision and recall (F-score), mainly for label/context of uHijos, uAmbience, and uTransport.
Reducing Lending Risk: SVM Model Development with SMOTE for Unbalanced Credit Data Josya Ryan Alexandro Purba; Qilbaaini Effendi Muftikhali; Bony Parulian Josaphat
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.860

Abstract

Lending is an important activity for banks in managing available funds. However, lending is also an activity that has a high risk, because not all customers who borrow funds can fulfill the responsibilities of the existing agreement. Because of this, it is necessary to have a method that can predict creditworthiness to customers in order to minimize the risks that arise. This research uses machine learning method, namely Support Vector Machine (SVM) in predicting creditworthiness. This method is applied and compared before and after the Synthetic Minority Oversampling Technique (SMOTE) on historical bank credit data BPR NBP 16 Rantau Prapat, North Sumatra and find the best parameters with grid search. According to the results of the analysis based on Area Under the Receiver Operating Characteristic Curve (AUC-ROC), SVM with SMOTE shows better results, namely 96%, than SVM without SMOTE, namely 56%.
XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire Nur Ghaniaviyanto Ramadhan; Aji Gautama Putrada
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.864

Abstract

Customer satisfaction can be created through a well-crafted service quality strategy, which forms the cornerstone of a successful business-customer relationship. Establishing and nurturing these relationships with customers is vital for long-term success. Within the airline industry, a persistent challenge lies in enhancing the passenger experience during flights, necessitating a comprehensive understanding of customer demands. Addressing this challenge is crucial for airlines aspiring to thrive in a competitive landscape, thus underlining the significance of providing top-notch services. This study addresses this issue by leveraging predictive airline customer satisfaction data analysis. We forecast customer satisfaction levels using a powerful Extreme Gradient Boosting (XGBoost) ensemble-based model. An integral aspect of our methodology involves handling missing values in the dataset, for which we utilize mean-value imputation. Furthermore, we introduce a novel logistic Pearson Gini (Log-PG) score to identify the factors that significantly influence airline customer satisfaction. In our predictive model, we achieved notable results, showing an accuracy and precision of 0.96. To ascertain the efficiency of our model, we conducted a comparative analysis with other boosting-type ensemble prediction models, such as gradient boosting and adaptive boosting (AdaBoost). The comparative assessment established the superiority of the XGBoost model in predicting airline customer satisfaction.
Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression irma palupi; bambang ari wahyudi; Naila AL Mamuda; Ayu Shabrina
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.865

Abstract

This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.
Performance of Time-Based Feature Expansion in Developing ANN Classification Prediction Models on Time Series Data Sri Suryani Prasetiyowati; Arnasli Yahya; Aniq Atiqi Rohmawati
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The prediction problem in most research is the main goal, to estimate future events related to the field under study. Research on classification that involves the prediction process in it, with spatial-time data and influenced by many features, such as the problem of disease spread, climate change, regional planning, environment, economic growth, requires methods that can predict while solving the problem of features and time. To obtain a time-based classification prediction model using many features, this research uses machine learning methods, one of which is Artificial Neural Network (ANN). The scenario carried out is to develop a t+r classification prediction model by expanding features based on the time t-r of the previous period. The performance of feature expansion in the development of ANN classification prediction models is determined based on the optimal accuracy value of the combination of t-r classification prediction models for the previous time period. By implementing the model on the data, it is found that the performance of time-based feature expansion in ANN classification ranges from 3.5% to 11%. While the optimal accuracy value is obtained from the feature expansion scenario of 3 to 5 time periods earlier.
Application of Singular Spectrum Analysis (SSA) Decomposition in Artificial Neural Network (ANN) Forecasting Annisa Martina; Irwan Girana
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.870

Abstract

Over time, various forecasting methods have been introduced. An example is the Hybrid model. This model can enhance the forecast accuracy compared to a single model. The Hybrid Singular Spectrum Analysis (SSA)-Artificial Neural Network (ANN) model combines the concepts of decomposition and forecasting. The Hybrid SSA-ANN forecasting works through two stages. Firstly, SSA decomposes the data into trend, seasonal, noise, and residue components. Secondly, the decomposed data is predicted using the ANN model, specifically the LSTM and GRU models. The Hybrid SSA-ANN model has been proven to improve forecasting accuracy. The Hybrid SSA-LSTM model improves the forecast accuracy by 78% compared to the single LSTM forecasting model. This can be seen from the respective RMSE values of 4.36 changing to 0.97 and MAPE values of 5.2% changing to 1.16%. Similarly, the Hybrid SSA-GRU model improves the forecast accuracy by 79% compared to the single GRU forecasting model. This can be observed from the respective RMSE values of 4.86 changing to 1.01 and MAPE values of 6.33% changing to 1.36%. In a case study using weekly data of crude oil's opening prices, the application of SSA decomposition can enhance the forecast accuracy by 78-79% in ANN forecasting
Hoax Detection of Covid-19 News on Social Media using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) Arvia Dwi Cahyani; Andi Kholik Ramdani
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.872

Abstract

It is undeniable that nowadays news spreads very quickly on social media. The ease of getting news on social media has resulted in some users using and spreading news without knowing the authenticity of the news. Twitter (X) users play an important role in spreading news on social media. In early 2020, cases of Covid-19 started to occur in Indonesia and some people spread news about Covid-19 without knowing the real information. The news is increasingly spreading through Twitter media which is shared by irresponsible people. This research builds a system that can detect hoax news on social media. The stages in this study started from crawling data, data preprocessing, word embedding, data separation, modeling process, and model evaluation. The methods used are Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The dataset used is news of Covid-19 in X Social media. The experiment showt that the use of the N-Gram Unigram + Bigram + Trigram combination on CNN produces an accuracy value of 75.8%, meanwhile in the SVM modeling produces 77.9%. It can be concluded that SVM has better performance than CNN in detecting hoax news,
Web-Based Formaldehyde Detection System in Chickens using IOT and Fuzzy Logic Azizurahman Arafah Mufti; Siti Amatullah Karimah, S.T., M.T.; Hilal Hudan Nuha; Endang Rosdiana
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.885

Abstract

Chicken is a widely consumed source of animal protein globally, valued for its high protein content and essential nutrients. Ensuring the freshness of chicken meat is crucial to guarantee its healthiness and prevent harm to consumers. Unfortunately, there are concerns about the use of hazardous substances like formaldehyde by some traders for meat preservation. Formalin, a clear liquid with a pungent odor, is commonly utilized as a food preservative. To address the misuse of formaldehyde in broiler chickens, an innovative solution is proposed involving IoT technology and Fuzzy Logic. The developed formaldehyde detection system employs an ESP8266 microcontroller and a TCS3200 sensor to assess color variations in chicken meat samples mixed with Schiff's reagent. The TCS3200 sensor detects color changes, and the ESP8266 Microcontroller converts measurements into RGB basic colors. Calibration of the sensor yielded a 98.30% relative accuracy at a 3 cm distance. Fuzzy Logic is then applied to determine formaldehyde levels, displayed on an LCD screen. The tool exhibits a 95% reliability for achieving a 0 ppm level, 93% for 40 ppm, 92% for 80 ppm, and 100% for 200 ppm
Analysis of Factors Affecting the Use of Digital Paylater Transactions Using the Hedonic-Motivation System Adoption Model (HMSAM) Muhammad Zahwan Latif; Rio Guntur Utomo
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.896

Abstract

The use of paylater digital transaction methods is a major trend in the current era of digitalization. This study aims to analyze the factors of Continuance Intention of paylater digital transactions. Employing a quantitative approach with Partial Least Square Structural Equation Modeling (SEM-PLS), the research focuses on individuals aged 18 and above who have made digital paylater transactions. The sampling technique chosen was a purposive sampling technique, while data collection was conducted through questionnaires. This research proposes a modified Hedonic-Motivation System Adoption Model (HMSAM) and formulates hypotheses to test the relationship between variables. Data analysis was conducted by measuring the validity and reliability of the model and applying SEM-PLS to analyze variable relationships and test hypotheses. This model integrates elements from HMSAM developed by previous researchers. The six main variables include perceived ease of use, curiosity, joy, control, satisfaction, and Continuance Intention. The results revealed that the hypothesis testing conducted for 6 from 7 hypotheses shows the value of T-Statistics> 1.96, the value of P-Values <0.05, and the value of R-Square in low and moderate indicates moderate and small classification of the influence of Factors Affecting the Use of Digital Paylater Transactions Using the Hedonic-Motivation System Adoption Model (HMSAM).
Sentiment Analysis on Social Media Using Word2Vec and Gated Recurrent Unit (GRU) with Genetic Algorithm Optimization Syafa Fahreza; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.903

Abstract

The evolution of information technology has changed the function of social media from a mere information repository to a platform for expressing opinions and aspirations. One of the most used social media is Twitter. Twitter users can express opinions according to their conscience. Therefore, a sentiment analysis process is needed to classify the opinion as positive or negative. Sentiment analysis on social media is important to understand user opinions, monitor public perception, measure campaign performance, identify trends and opportunities, and improve customer service. This research builds a model to perform sentiment analysis on the topic the president election with a total dataset of 39,791 with GRU method, TF-IDF feature extraction, Word2Vec feature expansion with 142,545 corpus from IndoNews, and Genetic Algorithm optimization. The test results show that the highest accuracy achieved is 83.39%, which shows an improvement of 1.42% compared to the baseline. This performance was achieved when combining of TF-IDF with a 5,000 maximum features, applying Word2Vec at top 1 similarity, and applying Genetic Algorithm for feature optimization. This study proves the relationship between the use of Word2Vec feature expansion and Genetic Algorithms as optimization in improving the accuracy of the model created.

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