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All Journal International Journal of Evaluation and Research in Education (IJERE) Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics The Indonesian Journal of Computer Science Research Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Afrig Aminuddin
Universitas AMIKOM Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Journal : joiv international journal on informatics visualization

 1 
Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia Agastya, I Made Artha; Aminuddin, Afrig
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1819

Abstract

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.  
Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms Kusnawi, Kusnawi; Ipmawati, Joang; Asadulloh, Bima Pramudya; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Rahardi, Majid
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2453

Abstract

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.
Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth Abdulloh, Ferian Fauzi; Aminuddin, Afrig; Rahardi, Majid; Harianto, Fetrus Jari
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2567

Abstract

The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.
Optimization and Collaboration of Fuzzy C-Mean, K-Mean, and Naïve Bayes Algorithms Using the Elbow Method for Micro, Small, and Medium Enterprises Norhikmah, -; Nurastuti, Wiji; Aminuddin, Afrig; Sidauruk, Acihmah; Gunawan, Puguh Hasta
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3292

Abstract

Micro, Small, and Medium Enterprises (SMEs) have a vital role in Indonesia’s economy. However, IT-based marketing strategies among SMEs receive limited support from the government due to the lack of sufficient data to inform policy. This study aims to (1) identify the needs of SMEs for social media promotion training as part of their digital capacity building, (2) develop and compare the effectiveness of classification models that combine Fuzzy C-Means and K-Means clustering algorithms with the Naïve Bayes algorithm to group SMEs based on business characteristics, (3) analyze the relationships between business variables—such as business type, marketing media, funding sources, and financial aspects—and SME performance through regression analysis, and (4) provide data-driven foundations for designing targeted digital interventions and policy strategies to support SME development in Indonesia. This study used UPPKS data from 133 SMEs in seven districts in the Special Region of Yogyakarta. Data analysis covered business types, marketing platforms used, funding sources, and financial performance indicators. Data pre-processing involved cleaning, normalization, and integration to ensure consistency and readiness for analysis. The researcher used the Elbow method to determine the optimal number of clusters. Then, it also used Fuzzy C-Means (FCM) and K-Means to categorize SMEs into three groups: high, medium, and low. The classification was based on the Naïve Bayes algorithm. The evaluation of the model performance used a confusion matrix, cross-validation, and regression analysis to examine inter-variable relationships. The results showed that the combination of FCM and Naïve Bayes achieved an accuracy of 85% based on the confusion matrix and 97% based on cross-validation. Meanwhile, the combination of K-Means and Naïve Bayes respectively achieved an accuracy of 96% and 94.7%. These findings demonstrate the effectiveness of the proposed approaches in classifying SMEs based on their characteristics and performance. This research provides important insights for policymakers and SME development agencies in designing more targeted digital training and support programs. Future studies should explore the integration of other algorithms, such as Support Vector Machines (SVM) and Decision Trees, while incorporating market trends and customer engagement to enhance SME classification and provide ongoing support.
Co-Authors Abdullah, Khairul Hafezad Abdulloh, Ferian Fauzi Acihmah Sidauruk Agastya, I Made Artha Alam Miah, Mohammad Badrul Apitiadi, Satyo Dwi Asadulloh, Bima Pramudya Ashadi, Diki Bety Wulan Sari Bety Wulan Sari, Bety Wulan Dhanar Intan Surya Saputra Diki Ashadi Donni Prabowo, Donni Ghozali, Bahrun Gunawan, Puguh Hasta Hairani Hairani Hammood, Waleed A. Harianto, Fetrus Jari Haryoko, Haryoko I Nyoman Switrayana Joang Ipmawati Kusnawi Kusnawi Kusuma, Velizha S LILIS DWI FARIDA Majid Rahardi Mohammad Iskandar Dzulkurnain Mustofa, Dinar Norhikmah, - Wiji Nurastuti, Wiji Wirasto, Anggit Yoga Pristyanto