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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Kreativitas PKM Jambura Journal of Mathematics Jurnal Riset Pembangunan : Media Hasil Penelitian dan Kajian Pembangunan Daerah Journal of Social Outreach VARIANSI: Journal of Statistics and Its Application on Teaching and Research Eksponensial STATISTIKA REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Meiliyani Siringoringo
Program Studi Statistika, Fakultas MIPA, Universitas Mulawarman
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan Model Seasonal Autoregressive Fractionally Integrated Moving Average Pada Data Inflasi di Indonesia Edy Fahrin; Memi Nor Hayati; Meiliyani Siringoringo
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.603 KB)

Abstract

Current inflation data is influenced by previous inflation data. Inflation data from time to time is indicated to have a long memory and seasonal pattern. The Seasonal Autoregressive Fractional Integrated Moving Average (SARFIMA) model is one of the models used to predict data that has a long memory and seasonal pattern. The purpose of this research was to find out the the best SARFIMA model and forecast inflation in 2018 using the best SARFIMA model. The sample in this research was Indonesian monthly inflation data for the period January 2008 to December 2017. There are four stages of SARFIMA modeling, namely model identification, parameter estimation, diagnostic checking, and application of models for forecasting. Based on the results of the analysis, the best SARFIMA model selected based on the AIC and MSE criteria is the SARFIMA model with d = 0.687. The results of inflation forecasting from January to December 2018 show a fluctuating value every month with the inflation rate at 3.30% - 3.65%.
Pendekatan Regresi Nonparametrik Kernel pada Data IHSG Periode Januari 2020 – Desember 2021 William Yulius Karimuse; Darnah Andi Nohe; Meiliyani Siringoringo
Statistika Vol. 23 No. 1 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i1.1628

Abstract

ABSTRAK Pendekatan regresi nonparametrik Kernel digunakan untuk memperkirakan harapan bersyarat dari variabel dependen terhadap variabel independen tanpa mengasumsikan bentuk parametrik tertentu. Pendekatan ini menggunakan fungsi Kernel sebagai alat untuk melakukan estimasi. Dalam penelitian ini, digunakan fungsi Kernel Gaussian dan estimator Nadaraya-Watson. Estimator Nadaraya-Watson adalah metode yang mengestimasi fungsi regresi sebagai rata-rata tertimbang secara lokal, dengan menggunakan fungsi Kernel sebagai pembobot. Pendekatan regresi nonparametrik Kernel ini juga efektif dalam mengestimasi data yang memiliki pola runtun waktu, seperti indeks Dow Jones dan Indeks Harga Saham Gabungan (IHSG). Tujuan penelitian ini adalah untuk melakukan estimasi regresi nonparametrik Kernel dan memprediksi IHSG yang dipengaruhi variabel indeks Dow Jones. Model terbaik adalah yang mempunyai bandwidth optimal yang ditentukan berdasarkan Generalized Cross Validation (GCV) minimum. Berdasarkan hasil penelitian diperoleh model regresi nonparametrik Kernel dengan nilai bandwidth optimal sebesar 0,332 dan nilai GCV minimum sebesar 0,2455519. Hasil prediksi mengalami fluktuasi pada interval Rp6.100,00 sampai dengan Rp6.400,00 yang terjadi pada bulan Maret 2021 hingga Desember 2021. ABSTRACT The Kernel nonparametric regression approach is used to estimate the conditional expectations of the dependent variable on the independent variable without assuming a particular parametric form. This approach uses the Kernel function as a tool for estimating. In this study, the Gaussian kernel function and the Nadaraya-Watson estimator are used. The Nadaraya-Watson estimator is a method that estimates the regression function as a locally weighted average, using the Kernel function as the weight. The Kernel nonparametric regression approach is also effective in estimating data that has a time series pattern, such as the Dow Jones index and the Jakarta Composite Index (JCI). The purpose of this study was to estimate the nonparametric regression of the Kernel model and predict the JCI which was influenced by the Dow Jones index variable. The best model is the one that has the optimal bandwidth determined based on the minimum Generalized Cross Validation (GCV). Based on the results of the research, the Kernel nonparametric regression model was obtained with the optimal bandwidth value of 0.332 and the minimum GCV value of 0.2455519. The prediction results fluctuated at the interval of Rp. 6,100.00 to Rp. 6,400.00 which occurred in March 2021 to December 2021.
Literasi Dasar Melalui Numerasi dan Keuangan Rito Goejantoro; Ika Purnamasari; Memi Nor Hayati; Meiliyani Siringoringo; Darnah Andi Nohe; Muhammad Fathurahman; Surya Prangga; Khairun Nida; Sekar Nur Utami; Dini Elizabeth
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 6, No 12 (2023): Volume 6 No 12 2023
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v6i12.12705

Abstract

ABSTRAK Gerakan Literasi Nasional (GLN) merupakan kegiatan yang saat ini diserukan sebagai bentuk penerapan dari peraturan KEMENDIKBUD untuk menumbuhkan budi pekerti masyarakat. Numerasi dan literasi keuangan merupakan dua jenis literasi yang saling terkait. Salah satu dimensi dari literasi keuangan yaitu keterampilan menghitung. Keterampilan ini terkait pemahaman numerik, lambang bilangan dan analisa kuantitatif yang berkenaan dengan statistika dasar dalam dimensi numerasi. Kegiatan ini memiliki tujuan yaitu memberikan informasi dan pengetahuan numerasi dan keuangan kepada peserta dengan cara sederhana, menyenangkan, dan mudah dipahami berdasarkan tema lingkungan sekitar. Hasil penilaian sebelum dan sesudah kegiatan, menunjukkan bahwa adanya peningkatan kemampuan dan pemahaman peserta terkait numerasi dan keuangan, yang terlihat dari kenaikan nilai rata-rata pada saat evaluasi. Untuk kegiatan literasi selanjutnya, materi yang disampaikan dapat ditingkatkan ke jenjang materi lanjutan, serta dapat mengkombinasikan antara numerasi, literasi keuangan, dan digital untuk lebih menarik. Kata Kunci: GLN, KEMENDIKBUD, Literasi, Numerasi, Literasi Keuangan ABSTRACT The National Literacy Movement (GLN) is an activity that is currently called for as a form of application of the regulation of KEMENDIKBUD to foster community ethics. Numeracy and financial literacy are two types of literacy that are interrelated. One dimension of financial literacy is counting skills. This skill is related to numerical understanding, number symbols and quantitative analysis related to basic statistics in the numeracy dimension. This activity has the following objectives is to provide numeracy and financial information and knowledge to participants in a simple, fun, and easy-to-understand way based on the theme of the surrounding environment. The results of the assessment before and after the activity showed an increase in the abilities of participants and understanding related to numeracy and finance, which can be seen through the increase in the average scores at the time of evaluation. For further literacy activities, the material delivered can be upgraded to an advanced level of material, and can combine numeracy, financial literacy, and digital to be more attractive. Keywords: GLN, KEMENDIKBUD, Literacy, Numerasi, Financial Literacy.
PERAMALAN HARGA MINYAK MENTAH INDONESIA JENIS SEPINGGAN YAKIN MIX MENGGUNAKAN MODEL HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE - NEURAL NETWORK Dwi Ayu Anggraini; Sri Wahyuningsih; Meiliyani Siringoringo
JURNAL RISET PEMBANGUNAN Vol 5, No 2 (2023)
Publisher : BADAN PENELITIAN DAN PENGEMBANGAN DAERAH PROVINSI KALIMANTAN TIMUR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36087/jrp.v5i2.138

Abstract

Peramalan merupakan salah satu bidang penelitian yang aktif yang artinya sampai saat ini masih terus dilakukan penelitian mengenai proses peramalan runtun waktu terkait dengan proses pengambilan keputusan. Metode peramalan berkembang menjadi semakin cepat mengikuti perkembangan zaman dan teknologi komputasi. Terdapat hal yang menarik dari perkembangan tersebut ialah perbaikan metode peramalan runtun waktu bersifat hybrid, dengan menggabungkan dua jenis metode atau lebih yang berbeda, diharapkan dapat menjadi cara yang efektif dalam meningkatkan akurasi peramalan dibandingkan hanya dengan menerapkan satu metode saja. Salah satu metode hybrid yang dapat digunakan adalah Autoregressive Integrated Moving Average (ARIMA) dan Neural Network (NN). Tujuan dari penelitian ini adalah untuk memperoleh perbandingan kinerja peramalan harga minyak mentah Indonesia jenis Sepinggan Yakin Mix antara model ARIMA dan model hybrid ARIMA-NN tahun 2022. Berdasarkan hasil peramalan menggunakan model ARIMA, tingkat akurasi yang diperoleh dari peramalan data harga minyak mentah Indonesia jenis Sepinggan Yakin Mix yaitu ARIMA(0,1,1) sebesar 7,9661% dan model ARIMA(2,1,0) sebesar 7,7816% dan tingkat akurasi yang diperoleh menggunakan model  hybrid ARIMA(0,1,1)-NN 1 neuron sebesar 7,0910%, 2 neuron sebesar 7,0696%, 3 neuron sebesar 7,0661% dan menggunakan model  hybrid ARIMA(2,1,0)-NN 1 neuron sebesar 6,8972%, 2 neuron sebesar 6,8767%, 3 neuron sebesar 6,8692%. Kedua model menghasilkan kinerja peramalan yang sangat akurat untuk data tersebut karena nilai MAPE kedua model 10%. Namun nilai MAPE dari 6 model hybrid ARIMA-NN  cenderung lebih kecil dibandingkan nilai MAPE dari model ARIMA. Dengan demikian model hybrid ARIMA-NN dapat digunakan sebagai alternatif pemodelan harga minyak mentah Indonesia jenis Sepinggan Yakin Mix yang bisa dimanfaatkan sebagai pertimbangan pengambilan keputusan atau kebijakan dalam bidang energi dan sumber daya mineral khususnya industri minyak mentah Indonesia
Peramalan Nilai Transaksi Uang Elektronik Di Indonesia Menggunakan Double Exponential Smoothing Brown Dengan Optimasi Nonlinier Anna Putri Aritonang; Meiliyani Siringoringo; Wiwit Pura Nurmayanti; Sri Wahyuningsih; Suyitno Suyitno
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/8hjsey81

Abstract

The Double Exponential Smoothing (DES) Brown method is one of the forecasting methods used for data that exhibited a trend pattern, in which the smoothing process was performed twice. The determination of the optimal smoothing parameter in the DES Brown method is usually carried out through a trial-and-error process. Another way to obtain the optimal smoothing parameter value more quickly and accurately is by using nonlinear optimization. In this study, two optimization methods were used: the Golden Section and the Levenberg-Marquardt methods. The objectives of this research were to obtain the optimal smoothing parameter of the DES Brown method using the Golden Section and Levenberg-Marquardt optimizations, to forecast the value of electronic money transactions in Indonesia for the period of January to March 2025 using the DES Brown method with the optimal smoothing parameter, and to identify the best optimization method for determining the optimal smoothing parameter of DES Brown method were obtained based on the MAPE value. The results of the study showed that the optimal smoothing parameter of the DES Brown method using the Golden Section optimization was 0.4634178 and the Levenberg-Marquardt optimization was 0.3498674. 
Application of Random Forest with SMOTE and Random Search for Food Security Classification in Indonesia Susi Hasriani; Nariza Wanti Wulan Sari; Pratama Yuly Nugraha; M Fathurahman; Meiliyani Siringoringo
REKADATA Vol. 2 No. 1 (2026): Rekayasa Data dan Kecerdasan Artifisial (REKADATA)
Publisher : CV Mazaya Cahaya Utama

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

Abstract

Food security is an important aspect of sustainable development and one of the key priorities of the Sustainable Development Goals (SDGs). Differences in food security conditions across regencies and cities in Indonesia require accurate classification to support the identification of vulnerable regions and appropriatepolicy-making. The random forest method is a classification method with good predictive performance, which can be improved through hyperparameter optimization using random search. However, its classification performance may decline when applied to imbalanced datasets. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance. This study aimed to classify the food security levels ofregencies and cities in Indonesia in 2024 using random forest with SMOTE and random search hyperparameter optimization and to evaluate model performance based on accuracy, precision, recall, and F1-score. The data were obtained from the Food Security and Vulnerability Atlas (FSVA), comprising 514 regencies and cities, with food security level as the response variable and eight predictor variables. The dataset was divided into training andtesting sets using proportions of 80:20 and 90:10. SMOTE was applied with K = 5, while random search was conducted using 20 hyperparameter combinations to determine the optimal model. The results showed that the 80:20 data partition outperformed the 90:10 partition, achieving an accuracy of 88.35%, precision of 83.33%, recall of 50.00%, and F1-score of 62.50%. The best model correctly classified 10 food-insecure and 81 food-secure regencies and cities. These findings provide information for identifying regions based on food security levels and support more targeted food security policy-making. 
Co-Authors Adelia Ramadhani Anak Agung Gede Sugianthara Anna Putri Aritonang Annisa Fitri, Annisa Darnah Andi Nohe Darnah, Darnah Dini Elizabeth Dwi Ayu Anggraini Edy Fahrin Fachry Abda El Rahman Fidia Deny Tisna Amijaya Ika Purnamasari Khairun Nida M Fathurahman M. Fathurahman Mahadi Muhammad Melisa Arumsari Memi Nor Hayati Nariza Wanti Wulan Sari Ni Made Shantia Ananda Novita Andriani Nuning Kusumaningrum Pratama Yuly Nugraha Rito Goejantoro, Rito Sekar Nur Utami Sri Wahyuningsih Surya Prangga Susi Hasriani Suyitno Suyitno Suyitno Suyitno Syamsiar, Syamsiar Syaripuddin Syaripuddin Wasono Wasono William Yulius Karimuse Wiwit Pura Nurmayanti Yoki Novia Nasution