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All Journal Journal of Mathematical and Fundamental Sciences BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Informatika (JUTIF) Jambura Journal of Biomathematics (JJBM) Journal of Applied Data Sciences
Hertono, Gatot Fatwanto
Mathematics Department, Faculty Of Mathematics And Science, Universitas Indonesia, Indonesia
Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province) Cindy; Cynthia; Valentino Vito; Devvi Sarwinda; Bevina Desjwiandra Handari; Gatot Fatwanto Hertono
Journal of Mathematical and Fundamental Sciences Vol. 53 No. 3 (2021)
Publisher : Institute for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.math.fund.sci.2021.53.3.9

Abstract

In Indonesia, Dengue incidence tends to increase every year but has been fluctuating in recent years. The potential for Dengue outbreaks in DKI Jakarta, the capital city, deserves serious attention. Weather factors are suspected of being associated with the incidence of Dengue in Indonesia. This research used weather and Dengue incidence data for five regions of DKI Jakarta, Indonesia, from December 30, 2008, to January 2, 2017. The study used a clustering approach on time-series and non-time-series data using K-Medoids and Fuzzy C-Means Clustering. The clustering results for the non-time-series data showed a positive correlation between the number of Dengue incidents and both average relative humidity and amount of rainfall. However, Dengue incidence and average temperature were negatively correlated. Moreover, the clustering implementation on the time-series data showed that rainfall patterns most closely resembled those of Dengue incidence. Therefore, rainfall can be used to estimate Dengue incidence. Both results suggest that the government could utilize weather data to predict possible spikes in DHF incidence, especially when entering the rainy season and alert the public to greater probability of a Dengue outbreak.
CLASSIFICATION OF COAL MINE PILLAR STABILITY USING EXTREME LEARNING MACHINE AND PARTICLE SWARM OPTIMIZATION ADAPTIVE WEIGHT DELAY VELOCITY Farhana, Nadhilah; Hertono, Gatot Fatwanto; Handari, Bevina Desjwiandra
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2403

Abstract

In underground mining, pillars are prime structural parts for supporting the overburden. Precise prediction of pillar stability is necessary because pillar failure might cause catastrophic events that could endanger mining personnel and equipment. This research aims to classify the stability of underground coal mining pillars using the Extreme Learning Machine model with Particle Swarm Optimization Adaptive Weight Delay Velocity that used to optimize the model's input weights and bias. Extreme Learning Machine is a model for training artificial neural networks using a single-layer feedforward Network architecture. Performance comparison is presented between the proposed model and the Particle Swarm Optimization-Extreme Learning Machine. The dataset originated from South African coal mining with two pillar stabilities: failed and intact. The pillar stability of the dataset expanded into five categories: failed upper, failed lower, intact upper, intact lower slender, intact lower not-slender. Out of the five pillar stability categories, the failed lower category is the most dangerous pillar category, with the rest are non-dangerous pillar category. The expanded categories are according to the Probability of Failure of the pillar and the type of pillar (slender, intermediate, and squat). The results showed that the AUC 91,4%; 74,3%; 72,6%, and G-mean 82,2% were all at least 10% higher in the proposed model. The proposed model successfully classified 91.24% of non-dangerous pillar stability category, but only 36% of the most dangerous pillar stability category. The results of this study are expected could give assistance to provide information as a consideration in predicting pillar.
Development of Gamification-Based Learning Management System (LMS) with Agile Approach and Personalization of FSLSM Learning Style to Improve Learning Effectiveness Saputra, Jeffri Prayitno Bangkit; Prabowo, Harjanto; Gaol, Ford Lumban; Hertono, Gatot Fatwanto
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.486

Abstract

This research focuses on designing a Learning Management System (LMS) that incorporates gamification elements while addressing student learning styles based on the Felder-Silverman Learning Style Model (FSLSM). Using Agile methodology in the development process, the LMS is designed to deliver a more personalized learning experience, with features tailored to students' unique learning style preferences. The research process began with a comprehensive user needs analysis, followed by iterative design and development in accordance with Agile principles. System evaluation involved user feedback and performance analysis, revealing that the developed LMS increased student engagement by 25% and improved learning motivation by 30% compared to the previous system. Furthermore, 88% of users reported a positive experience with the personalized features, and the system achieved an overall satisfaction score of 85% in usability testing. These results demonstrate that the LMS effectively enhances student motivation and engagement in the learning process while providing a more individualized learning experience. This research contributes to the advancement of adaptive and responsive learning systems that better meet the diverse needs of students.
DESIGN OF STUDENT SUCCESS PREDICTION APPLICATION IN ONLINE LEARNING USING FUZZY-KNN Kharis, Selly Anastassia Amellia; Hertono, Gatot Fatwanto; Wahyuningrum, Endang; Yumiati, Yumiati; Irawan, Sam Rizky; Danial, T Ahmad; Saputra, Dimas Septian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0969-0978

Abstract

Effective evaluation of student performance is crucial. Hence, many kinds of techniques are used such as statistics, physical examination and currently data mining techniques to evaluate student performance. Data mining techniques as known as Educational Data Mining (EDM) collect, process, report and used to find the unseen patterns in the student dataset. EDM uses machine learning techniques to dig out useful data from multiple levels of meaningful hierarchy. Various data from intelligent computer tutors, classic computer based educational systems, online classes, academic data in educational institution, and standar assesment can be process for EDM. This led universities include open and distance learning (ODL) to collect large volume of student and learning data in their learning management systems (LMS). Students in ODL are relatively familiar with LMS and many learning activities such as number of accessing materials, student participation in discussion forum recorded in LMS. The processes of using EDM to improve the quality of educational policy maker with data-based models have become a challange that institutions of higher education face today. Therefore, this study aims to design applications that predict student performance in online learning using machine learning techniques based on EDM. The machine learning technique used in this research is Fuzzy-KNN. Testing using Fuzzy-KNN produces an accuracy of 92.5%.
Implementation of Moving Average Filter in SARIMA-ANN and SARIMA-SVR Methods for Forecasting Pneumonia Incidence in Jakarta Musyaffa, Muhammad Majid Rafi; Hertono, Gatot Fatwanto; Handari, Bevina Desjwiandra
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.30558

Abstract

In this study, we implemented a moving average filter in SARIMA-ANN and SARIMA-SVR to predict Pneumonia incidence in Jakarta. Pneumonia is one of the highest causes of death in children throughout the world. Forecasting pneumonia incidence in the future can help to reduce the spread of cases, so that the number of deaths due to pneumonia can be reduced. In general, time series data consists of linear and nonlinear patterns, which cannot be properly modeled by linear or nonlinear models alone. One way to solve this issue is to use a hybrid model that combines several models to overcome the limitations of each component model and improve predicting performance. SARIMA-ANN and SARIMA-SVR methods combine a linear seasonal autoregressive integrated moving average (SARIMA) model and a nonlinear artificial neural network (ANN) or support vector regression (SVR) model to capture the linear and nonlinear characteristics of the data. Parameter estimation in SARIMA uses Gaussian Maximum Likelihood Estimation. Initially, the time series will be transformed by a moving average (MA) filter, so SARIMA can model the data well. Meanwhile, the remaining components separated from the transformation will be modeled with a nonlinear model such as ANN in the SARIMA-ANN method, or SVR in the SARIMA-SVR method. The simulation results show that the SARIMA-ANN method is superior to the SARIMA-SVR method in predicting incidences in West Jakarta and East Jakarta, with a MAPE difference ranging from 0.6% to 0.75%. Meanwhile, in North, South, and Central Jakarta, the SARIMA-SVR method is superior to the SARIMA-ANN method, with MAPE differences ranging from 1.6% to 3.99%. The SARIMA-SVR model achieves better results across the majority of municipalities, indicating that the SARIMA-SVR model generally provides better result for predicting Pneumonia incidence in Jakarta.
Co-Authors Bangkit, Jeffri Prayitno Bevina D Handari Cindy Cynthia Danial, T Ahmad Devvi Sarwinda Endang Wahyuningrum Farhana, Nadhilah Ford Lumban Gaol, Ford Lumban Harjanto Prabowo Irawan, Sam Rizky Musyaffa, Muhammad Majid Rafi Saputra, Dimas Septian Selly Anastassia Amellia Kharis Valentino Vito Yumiati