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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

Author-ID : 3379361

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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Penggerombolan Hasil Ujian Nasional Menggunakan K-Rataan Samar Nouval Habibie; Akbar Rizki; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

National examination scores can be a basis for the government to make a mapping of education quality in order to increase it. The mapping can be done by using fuzzy cluster analysis. The objective of this experiment is to cluster districts/cities in Indonesia based on national examination score in natural and social science in 2014/2015 until 2017/2018 school year by using the fuzzy c-means method. The evaluation criteria that will be used are the standard deviation ratio, silhouette coefficient, and Xie Beni index. The best cluster size is two clusters, A and B. The clustering result shows cluster A has a higher mean from each subject than cluster B. Therefore, cluster A will be categorized as good, whereas cluster B as bad. The proportion of districts/cities that belong to cluster A decreased each year. The final cluster result can be determined by the mean of its degree of membership from those four school years. The analysis results show that the distribution of education quality is dominated in Java Island and squatter cities. East Nusa Tenggara, West Sulawesi, Central Sulawesi, and North Kalimantan donâ€™t have any districts/cities belong to cluster A.

IDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI PRESTASI MAHASISWA PROGRAM SARJANA DI INSTITUT PERTANIAN BOGOR MENGGUNAKAN METODE CHAID Ragsa Endahas Ahmad; Akbar Rizki; Mohammad Masjkur
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

IPB University (IPB) is one of the best universities in Indonesia, based on the Ministry of Education and Culture (Kemendikbud) clustering in 2020. As the best university, IPB requires efforts to improve the quality of its education. One of these efforts is to improve student achievement. This study aims to identify the factors that influence the competition and non-competition achievements of undergraduate students at IPB. The data used are achievement data (academic year 2016/2017 to 2020/2021) from the Directorate of Student Affairs and Career Development (Ditmawa) of IPB and demographic data of undergraduate level IPB students (entry year 2016/2017 to 2019/2020) from the Directorate of Administration and Education (Dit-Ap) IPB. The analytical method used in this study is the Chi-square Automatic Interaction Detection (CHAID) classification method. There was an imbalance of data on the Student Achievement response variable. Therefore, in this study, unbalanced data handling was also carried out by resampling in the form of oversampling, undersampling, and over-undersampling methods. The results showed that the classification using CHAID analysis with resampling in the form of oversampling with a balance accuracy of 73.7% resulted in the best classification performance. The factors that influence student achievement are 11 variables, and the 3 most influential variables are variables of year of admission, department, and last GPA.

Perbandingan Perbandingan Pengklasifikasian Metode Support Vector Machine dan Random Forest (Kasus Perusahaan Kebun Kelapa Sawit) Nabila Destyana Achmad; Agus M Soleh; Akbar Rizki
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Palm oil is one of the leading commodities that support the economy in Indonesia. One of the companies engaged in the oil palm plantation sector has 146 units of oil palm plantations. It is very important to optimize oil palm production, so it is necessary to classify the status of plantation units. Classification aims to predict new plantation units and find the most important variables in the modeling process. The variables used were the status of the garden as a response variable and nine explanatory variables, namely harvested area, rainfall, percentage of normal fruit, fresh fruit bunches production, oil palm loose fruits, production, harvest job performance, harvesting rotation, and farmers. The classification process is carried out using the Support Vector Machine and Random Forest methods to find which method is the best. The data is divided into 80% training data and 20% test data with ten iterations so that ten models are produced for each method. Comparing accuracy value, F1 score, and Area Under Curve (AUC) to evaluate the model. The modeling results show that the random forest method has better performance than the SVM method. The random forest has an average occuracy, F1 score, and AUC, respectively, 90%, 86%, and 89%. Variables of harvest job performance, oil palm loose fruits, harvested area, rainfall, and harvesting rotation are important variables that contribute more than 10% of the model. The results of the research are used for the evaluation and development process of oil palm companies by taking into account the result of important variables that affect productivity and predictive results of new plantation units.

Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia Putri Zainal; Yenni Angraini; Akbar Rizki
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) model. After modeling, it is proven that the world crude oil price data for the period January 2002 to June 2022 has a heteroscedasticity effect that cannot be overcome if only using the ARIMA model. The results of data processing show that the ARIMA (0,1,2) followed by the ARCH (2) is the best model with a MAPE value of 5,32%. The accuracy values obtained are classifield as very good for forecasting world crude oil prices.

APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022 Nickyta Shavira Maharani; Yenni Angraini; Mahesa Ahmad Rahmawan; Oktaviani Aisyah Putri; Steven Kurniawan; Tias Amalia Safitri; Akbar Rizki; Wiwik Andriyani Lestari Ningsih; Nabila Ghoni Trisno Hidayatulloh; Andika Putri Ratnasari
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 1 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i1.4875.2023

The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting the rupiah exchange rate is important to do and is one way to assess government policy. The data series to be used here are IDR exchange rate from the Yahoo Finance. It consists of 271 data taken from August 2017 to October 2022. This study aims to use the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modeling method using the R-studio software and predict the IDR exchange rate. The ARIMA method describes the data based on a certain time series. ARCH-Lagrange Multiplier (ARCH-LM) was applied on the residuals of the best ARIMA model to test whetoer the data is heteroscedasticity. The testing result shows that the residual of the IDR exchange rate is heteroscedasticity. Therefore, the GARCH model can be used to handle it. The results of this study are obtained for the ARIMA(2,1,3) GARCH(3,6) model as the best and describe the actual data pattern with a mean absolute percentage error (MAPE) forecasting value is 1,99%.

Aplikasi Model ARIMA dalam Peramalan Data Harga Emas Dunia Tahun 2010-2022 Mohammad Abror Gustiansyah; Akbar Rizki; Berliana Apriyanti; Kenia Maulidia; Raffael Julio Roger Roa; Oksi Al Hadi; Nabila Ghoni Trisno Hidayatulloh; Wiwik Andriyani Lestari Ningsih; Andika Putri Ratnasari; Yenni Angraini
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07108

Gold investment is one of the favorite investments during the Covid-19 pandemic because the price of gold is relatively volatile but shows an increasing trend. Savvy investors investing in gold need to be able to predict future opportunities. Therefore, price estimation is needed to develop a buying and selling strategy to maximize profits. The Autoregressive Integrated Moving Average (ARIMA) model is a suitable method for predicting time series data, so the best ARIMA model will be applied for forecasting world gold prices. The best ARIMA model is selected based on the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) criteria. Monthly world gold price data for 146 periods are applied in this study and will be used to predict gold prices for the following six periods. ARIMA (0,1,1) is the best model obtained from the analysis results, with AIC and MAPE values of 1264.731 and 11.972%, respectively. Forecasting results show that world gold prices will increase for the next periods.

Hand Load Analysis Using Text Mining Based on Letter Frequency of Indonesian Language Theses Documents Akbar Rizki; Barokaturrizkia Ameliani; Abdul Aziz Nurussadad; Bagus Sartono; Itasia Dina Sulvianti; Auzi Asfarian
International Journal of Science, Engineering, and Information Technology Vol 7, No 02 (2023): IJSEIT Volume 07 Issue 02 29 July 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/ijseit.v7i02.19078

Scientific documents contain valuable knowledge which can be discovered using text analysis. Meanwhile, the act of typing the text may discover the psychological and health condition. The hand load of the typer is essential information for designing a better keyboard, which is relevant to students' well-being in higher education institutions. This study aims to analyze the hand load of students when typing their theses in the Indonesian language. We calculate letter frequency in the Indonesian language theses documents, examine the hand load balance based on letter position on a QWERTY keyboard, identify letter hotspots, and examine hand alternation using circular visualization. The results are that the left hand has the higher load hand, indicated by the most frequent letter appearing in the documents and the letter W as the hotspot, located on the keyboard's left side. Moreover, hand alternations based on the sequence of Indonesian text identify a significant high alteration of letters from the left to the left side when typing Indonesian documents using the QWERTY keyboard. This result confirmed that the left hand has more load and less time to take a break than the right hand.

Identifying Factors Influencing the Number of Diarrhea Cases in Children Under Five in West Java Using Negative Binomial Regression Akbar Rizki; Utami Dyah Syafitri; Christin Halim
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.7582.2024

The WHO states that diarrhea is the leading killer of children under five worldwide, and Indonesia is no exception, where 10.3% of under-five deaths are caused by diarrhea. West Java Province, with the largest population in Indonesia, has the highest diarrhea cases under five. The potential for diarrhea to become an extraordinary event, which is often accompanied by death, is very likely to occur because diarrhea is an endemic disease in West Java. Therefore, analyzing the factors influencing the children under five diarrhea cases in West Java is essential. Negative binomial regression was used in this study because the response was to count data on the incidence of diarrhea in children under five in West Java. The analysis results show that an increase in the percentage of public premises (PPP) meeting health requirements and population density per km2 will increase the number of diarrhea cases under five in West Java. However, an increase in the percentage of Community-Based Total Sanitation (CBTS), percentage of the population living in poverty, and percentage of households practicing Clean and Healthy Behavior (CHB) will decrease the number of diarrhea cases in West Java.

Analisis Faktor yang Memengaruhi Tingkat Diabetes Melitus pada Masyarakat di Kota/Kabupaten di Provinsi Jawa Timur Tahun 2018: Analysis of Factors Affecting Diabetes Melitus Prevalence in Cities/Districts of East Java in 2018 Gilang Juniar Azmi; Putri Qoonitah Dewi; Siti Yuditha Cahaya Anugerah; Sachnaz Desta Oktarina; Akbar Rizki; Muhammad Rizky Nurhambali
Jurnal Sains dan Kesehatan Vol. 5 No. 5 (2023): J. Sains Kes.
Publisher : Fakultas Farmasi, Universitas Mulawarman, Samarinda, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25026/jsk.v5i5.1896

Among the non-communicable diseases, diabetes mellitus is one of the most significant contributors to mortality. This condition is a silent killer because its signs are hard to identify, and its prevention efforts are strenuous. Diabetes mellitus cases in Indonesia are not without attention, especially in the East Java province, which is in the top five of the region with the highest incidence of diabetes mellitus in Indonesia. This study aims to raise awareness among the government and the public about preventing and controlling diabetes. The research method used is quantitative. The data used in this study are secondary data obtained from the East Java Basic Health (Riskesdas) Research in 2018. Based on the results of the study found that the consumption of fatty foods, the population that has never checked blood sugar levels, the nutritional status based on the body mass index (BMI) category with the characteristics of obesity, as well as the population who has never consumed fruit and vegetables were the most influential factors in the increase in the prevalence of diabetes mellitus in East Java in 2018 with an adjusted R-squared value of 79%. Keywords: Diabetes Melitus, East Java, 2018, Stepwise Linear Regression, Robust Linear Regression Abstrak Di antara penyakit tidak menular, diabetes melitus menjadi salah satu penyumbang angka kematian terbesar. Penyakit ini juga disebut silent killer karena tanda-tandanya sulit diketahui dan upaya pencegahannya sulit untuk dilakukan. Kasus penyakit diabetes melitus di Indonesia pun tidak luput dari perhatian, khususnya di Provinsi Jawa Timur yang berada pada posisi lima besar dari keseluruhan provinsi dengan penyandang penyakit diabetes melitus tertinggi di Indonesia. Tujuan penelitian ini adalah sebagai penambah wawasan baik kepada pemerintah maupun masyarakat mengenai seberapa penting upaya pencegahan dan pengendalian diabetes melitus. Metode penelitian yang digunakan merupakan metode kuantitatif. Data yang digunakan pada kajian ini adalah data sekunder yang diperoleh dari Riset Kesehatan Dasar (Riskesdas) Jawa Timur pada 2018. Berdasarkan hasil penelitian, diperoleh bahwa konsumsi makanan berlemak, masyarakat yang tidak pernah memeriksa kadar gula darah, status gizi berdasarkan kategori Indeks Massa Tubuh (IMT) dengan karakteristik obesitas, serta masyarakat yang tidak pernah mengkonsumsi buah dan sayur merupakan faktor yang paling berpengaruh terhadap peningkatan prevalensi penyakit diabetes melitus di Jawa Timur pada tahun 2018 dengan nilai adjusted R-squared sebesar 79%. Kata Kunci: Diabetes Melitus, Jawa Timur, 2018, Stepwise Linear Regression, Robust Linear Regression

PREDIKSI PENUMPANG LRT JAKARTA MENGGUNAKAN SARIMAX DAN XGBOOST DENGAN EFEK KALENDER Muhammad Hafiz Fazli; M. Taqy Abiyu Dzakwan; Nada Ardelia; Gemala Aleida Fitri; Akbar Rizki; Windi Pangesti
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.188-199

The daily passenger volume of Jakarta’s LRT fluctuates significantly due to weekly seasonality and calendar variations, making accurate forecasting important for operational planning and decision-making. This study aims to determine the most effective model for forecasting daily passenger demand by comparing the SARIMAX and XGBoost methods on transportation data characterized by strong seasonal patterns and external influences. SARIMAX was selected because it models seasonal and autoregressive structures alongside exogenous variables, while XGBoost captures nonlinear relationships between temporal features and external factors. The dataset covers the period from 1 January 2024 to 31 August 2025 and includes variables such as weekends, national holidays, and special events. Model evaluation was conducted using walk-forward cross-validation and hyperparameter tuning. The results show that the SARIMAX(1,0,1)(0,1,1)7 model achieved the best performance, with a validation MAPE of 11.26% and a test MAPE of 8.64%, outperforming XGBoost. SARIMAX also reproduced weekly fluctuation patterns more consistently, indicating that it is more suitable for forecasting transportation demand with strong seasonal characteristics and relatively stable external influences.

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

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