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All Journal Jurnal Gaussian Jurnal Matematika Sains dan Teknologi International Journal of Science, Engineering and Information Technology Jurnal Statistika dan Aplikasinya Xplore: Journal of Statistics Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Sains dan Kesehatan Indonesian Journal of Statistics and Its Applications
Akbar Rizki
Department Of Statistics And Data Science, IPB University, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Development of Situ Gede Lake Tourism and Local MSMEs through Web-Based Digital Promotion and Data Analysis Meavi Cintani; Zamrah Mutmainah; I Gusti Ngurah Sentana Putra; Sabrina Adnin Kamila; Lisa Amelia; Sachnaz Desta Oktarina; Anang Kurnia; Agus Mohamad Soleh; Akbar Rizki
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2026): May 2026
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v10i2.2161

Abstract

Background: The development of tourism at Situ Gede Lake and local MSMEs through a web-based digital promotion and data analysis approach addresses the need for local tourism to compete more effectively. This community service focuses on empowering local MSMEs within the tourism ecosystem using digital technology. Purpose of the Study: This study aims to develop a web-based platform that integrates digital promotion and data analysis to support tourism digitalization at Situ Gede Lake, enhance MSME empowerment, and introduce data-driven visitor trend analysis using the Long Short-Term Memory (LSTM) deep learning method. Methods: The platform was developed using daily visitor data from the Situ Gede Village Office. The LSTM model was applied to forecast tourist numbers through 2026. Socialization and training were conducted on July 3, 2025, with 31 participants from POKDARWIS, MSME actors, village officials, and the community. The web application (https://situgede-ssmi.ipb.ac.id/) integrates a visitor statistics dashboard, MSME catalog, interactive map, event schedule, and waste management education. Results: The dashboard reveals an average of 53 visitors per day, 17 active communities, and estimated daily revenue of 2.5 million rupiah. LSTM predictions indicate a seasonal surge in mid-2026 potentially exceeding 400 visitors per day. MSME actors showed readiness to utilize the digital catalog, and participants responded positively to improved information access. Early findings demonstrate that combining web-based digital promotion and data analysis enhances MSME visibility, supports sustainable tourism development, and strengthens environmental awareness at Situ Gede Lake.
KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL Khusnul Khotimah; Kusman Sadik; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.502

Abstract

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.
PENGGEROMBOLAN TWEET BADAN NASIONAL PENANGGULANGAN BENCANA INDONESIA PERIODE AGUSTUS 2018 FEBRUARI 2019 MENGGUNAKAN TEXT MINING Windyana Pusparani; Agus M Soleh; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i4.525

Abstract

Twitter is a popular social media platform for communicating between its users by writing short messages in limited characters, called tweets. Extracting data information that has non-structured form and huge-sized, usually known as text mining. Badan Nasional Penanggulangan Bencana Indonesia (@BNPB_Indonesia) is the official twitter account of the government agency in the field of disaster management that uses twitter to share much information about disasters that have occurred in Indonesia. This study aims to determine the characteristics of all tweets and to group the types of tweets that they shared based on the similarity of its content. The data used in the study came from BNPB Indonesia's tweets with the period of taking tweets 6th of August 2018 to 16th of February 2019. The cluster result obtained by the k-Means method was 4 groups. The characteristics of the first cluster contained information about the weather conditions in Yogyakarta, the second cluster was about the source and magnitude of an earthquake, and the third group was about the occurrence of earthquakes in Lombok. However, the fourth group characteristic couldn’t be specifically identified because there was no clear distinction between other tweets in its members.
Analysis of Covid-19 Risk Perception Survey Result Using Generalized Structured Component Analysis: Analisis Hasil Survei Persepsi Risiko Covid-19 Menggunakan Generalized Structured Component Analysis Zahira Rahvenia Robert; Akbar Rizki; Budi Susetyo; Sulfikar Amir
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p336-347

Abstract

The capital city of Indonesia, Jakarta, became the province with the highest number of Covid-19. Response this situation, LaporCovid-19 collaborate with the Social Resilience Lab, Nanyang Technological University conducted a survey to measure how Jakarta residents perceive the risk of Covid-19 from May 29 to June 20 2020. Factors of risk perception are variables that cannot be measured directly, so they are analyzed used a Structural Equation Modeling (SEM) approach, namely Generalized Structured Component Analysis (GSCA). The Likert scale used can be considered as interval or ordinal depending on the point of view of the theory built. Therefore, this study will compare the GSCA method with the nonlinear GSCA and evaluate six variables, namely risk perception, knowledge, information, health behavior , social capital, and economy. Evaluation of the overall model showed that the nonlinear GSCA model can explain the diversity of qualitative data better than the GSCA model with FIT > 0.9. Based on GSCA nonlinear model, information has significantly influence of knowledge, economy and social capital have a real reciprocal relationship, along knowledge and risk perception have significantly influence of health behavior.
OPEC Crude Oil Price Forecasting Using ARIMA with Ensemble Empirical Mode Decomposition Tiara Lutfiah Adisti; Agus M Soleh; Aam Alamudi; Septian Rahardiantoro; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p230-239

Abstract

World crude oil prices fluctuate every day. One source of crude oil traded is oil from crude oil exporting countries that are members of the Organization of the Petroleum Exporting Countries (OPEC). In the total of 40% of world crude oil is produced by OPEC. This makes forecasting the price of crude oil OPEC’s policy very necessary in order to maintain world oil market stability. Fluctuating oil price data is made simpler and easier to interpret by applying the Ensemble Empirical Mode Decomposition (EEMD) method. The EEMD method decomposes the data into a number of Intrinsic Mode Functions (IMF) and residual of the IMF. In this study, the ARIMA forecasting model is compared using the original data and the decomposition results in the form of IMF components and IMF residuals. The comparison of the two methods is seen based on the overall and average MAPE value of the forecasting results in five time ranges. The EEMD-ARIMA method has an average MAPE value of 9.09% and standard deviation MAPE value of 7.39%. OPEC crude oil price forecast in January-August 2021 ranges from $42.22 to $60.6 per barrel. The final result of the analysis in this study shows that the ARIMA method with decomposition data (EEMD-ARIMA) is better than the ARIMA method using original data
The Impact of Data Splitting on ANN Performance in Predicting Foreign Tourist Visits to Inodnesia Akbar Rizki; Muhammad Dzakwan Alifi; Haidar Ramdhani; Lilis Indra Purnama; Shalma Kaisya Candradewi; Farid Yafi Suwandi; Adelia Putri Pangestika
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.11104.2025

Abstract

The data sharing stage is an important step in model building using Artificial Neural Network (ANN) methods to avoid the risk of overfitting and underfitting that can affect model performance. Proper data division aims to ensure that the model can generalize well to data that has never been seen before. Generally, data sharing is done by dividing the dataset into two main parts, namely training and testing data. However, to better address overfitting, there are also those who divide the data into three parts, namely training, testing, and validation. This study aims to evaluate the performance of ANN modelling using these two ways of dividing data. The model is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics to measure prediction error. The data used is data on foreign tourist arrivals to Indonesia, which has a fluctuating pattern and is influenced by calendar effects. The results show that the data division type with two groups generally produces a smaller MAPE value than the data division into three groups. However, the model with two parts of data is not able to capture the seasonal pattern in the data. On the other hand, the model with three parts of data can overcome this problem better. The best model was obtained with the proportion of training data, validation data, and test data of 80%, 10%, and 10%, respectively, which resulted in a MAPE value of 24.45%.
Co-Authors Aam Alamudi Abdul Aziz Nurussadad Adelia Putri Pangestika Agus M Soleh Agus M Soleh Agus Mohamad Soleh Anang Kurnia Andika Putri Ratnasari Auzi Asfarian Bagus Sartono Barokaturrizkia Ameliani Berliana Apriyanti Budi Susetyo Christin Halim Farid Yafi Suwandi Gemala Aleida Fitri Gilang Juniar Azmi Haidar Ramdhani I Gusti Ngurah Sentana Putra Itasia Dina Sulvianti Kenia Maulidia Khusnul Khotimah Kusman Sadik Lilis Indra Purnama Lisa Amelia M. Taqy Abiyu Dzakwan Mahesa Ahmad Rahmawan Meavi Cintani Mohammad Abror Gustiansyah Mohammad Masjkur Muhammad Dzakwan Alifi Muhammad Hafiz Fazli Muhammad Rizky Nurhambali Nabila Destyana Achmad Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nada Ardelia Nickyta Shavira Maharani Nouval Habibie Oksi Al Hadi Oktaviani Aisyah Putri Pika Silvianti Putri Qoonitah Dewi Putri Zainal Raffael Julio Roger Roa Ragsa Endahas Ahmad Rahardiantoro, Septian Sabrina Adnin Kamila Sachnaz Desta Oktarina Shalma Kaisya Candradewi Siti Yuditha Cahaya Anugerah Steven Kurniawan Sulfikar Amir Tiara Lutfiah Adisti Tias Amalia Safitri Utami Dyah Syafitri Windi Pangesti Windyana Pusparani Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yenni Angraini Zahira Rahvenia Robert Zamrah Mutmainah