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All Journal JUITA : Jurnal Informatika Kubik Jurnal Sains Matematika dan Statistika Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Journal of Science Innovare EKOLOGIA : Jurnal Ilmiah Ilmu Dasar dan Lingkungan Hidup BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Statistika dan Matematika (Statmat) KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Eurekamatika Jurnal Karya Abdi Masyarakat Jurnal Indonesia Sosial Sains Jurnal Riset Mahasiswa Matematika Jurnal Matematika Integratif Pelita: Jurnal Pengabdian kepada Masyarakat JMT (Jurnal Matematika dan Terapan) Jurnal Jendela Matematika Limits: Journal of Mathematics and Its Applications E-Jurnal Matematika
Embay Rohaeti
Universitas Pakuan
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Comparison of Missing Data Handling Methods and Forecasting of Broiler Egg Prices Using Autoregressive Integrated Moving Average : (Case Study: Bogor Regency/City) Dion, Shelly Selgiant; Rohaeti, Embay; Widyastiti, Maya
Jurnal Indonesia Sosial Sains Vol. 5 No. 12 (2024): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v5i12.1540

Abstract

Fluctuations in the price of broiler eggs can have an impact on decreasing people's purchasing power, so a form of price control through forecasting is needed. The existence of missing data in broiler egg price data can interfere with the accuracy of forecasting results. This research is carried out in two stages. The first stage is handling missing data. Missing data handling is done by comparing two methods, namely the linear interpolation method and the simple moving average (SMA) method. The second stage is forecasting with the autoregressive integrated moving average (ARIMA) method. The objectives of this study are to handle missing data on the data of broiler egg prices with linear interpolation and SMA methods, evaluate the results of the comparison of missing data handling methods, forecast future broiler egg prices, and evaluate the results of forecasting. The data used is daily data on the price of broiler eggs in Bogor Regency / City in the period January 1, 2019, to December 31, 2023, as much as 1,826 data. The results of the comparison of missing data handling methods showed that the linear interpolation method is declared better with an accuracy value using MAPE of 0.005%. The results of forecasting the price of broiler eggs show that the forecasting results with the ARIMA (1,1,3) model follow the actual data pattern, with a MAPE accuracy value of 0.601%; it is stated that the forecasting performance has performed well.
Klasifikasi Obesitas Menggunakan Domain-Based Decision Tree, XGBoost, dan SHAP Embay Rohaeti; Ani Andriyati; Muhammad Edy Rizal
Limits: Journal of Mathematics and Its Applications Vol. 23 No. 1 (2026): Limits: Journal of Mathematics and Its Applications Volume 23 Nomor 1 Edisi Ap
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v23i1.7970

Abstract

Obesity is a complex public health issue that requires effective early identification strategies to mitigate its long-term health impacts. This study aimed to classify obesity levels, categorized as Underweight, Normal, Overweight, and Obese, using 14 predictors grouped into three domains: biological, dietary, and physical activity. In addition to developing an accurate predictive model, the study investigated which domain contributes most to obesity classification. Two complementary modeling strategies were applied: domain-specific decision tree models to evaluate the predictive strength of each domain independently, and a comprehensive model using eXtreme Gradient Boosting (XGBoost) trained on all predictors. To address class imbalance, SMOTENC oversampling was applied to the training set, and hyperparameter tuning was performed via cross-validation for both approaches. Evaluation on the test set showed that the XGBoost model outperformed the domain-based decision trees across all performance metrics, including balanced accuracy, precision, recall, specificity, and F1-score. While decision trees offered domain-level interpretability, they lacked the predictive power of the integrated model. SHAP (SHapley Additive Explanations) analysis revealed that influential features spanned all domains, with Age, Vegetable consumption, and Transportation type emerging as top predictors. These findings demonstrate that integrating multi-domain behavioral data enhances both the accuracy and interpretability of obesity classification models, supporting the use of interpretable machine learning for personalized health risk assessment and prevention strategies
Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors Embay Rohaeti; Ani Andriyati
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21491

Abstract

Analysing pre-COVID-19 unemployment in West Java is vital for comprehending and tackling Indonesia’s economic challenges. This significance arises not only due to the region’s high unemployment rate, but also from the need to understand unemployment patterns before COVID-19, which has become more relevant now during the country’s post-pandemic recovery phase. This study evaluates four machine learning models (Random Forest, Linear SVM, RBF SVM, and Polynomial SVM) to classify employment status using demographic and job-related variables. The objective is to find the most suitable model, particularly considering the imbalanced nature of the study-case data. Data from the National Labor Force Survey (SAKERNAS) in August 2019 is utilized, comprising 54,429 respondents across districts in West Java. The four models are evaluated using holdout validation with a 70:30 stratified proportion, repeated for 100 times. Results indicate that the random forest model outperforms others in balanced accuracy, F1-score, and computational time. The random forest model also underscores the importance of gender and age in classifying employment status in West Java, suggesting a need for targeted intervention, especially for female citizens and individuals in productive age groups.
EVALUASI KINERJA PORTOFOLIO SAHAM OPTIMAL DENGAN LONG SHORT-TERM MEMORY BERBASIS MODEL MARKOWITZ PADA INDEKS SMINFRA18 ANNISA FITRIANI; BANDAR ANZARI; MUHAMMAD RAFLI; FAUZAN ISMAIL MULYADI; EMBAY ROHAETI
E-Jurnal Matematika Vol. 15 No. 3 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i03.p510

Abstract

This study aims to determine the optimal stock portfolio within the SMinfra18 index using the Markowitz model and forecast the dominant stock price using the Long Short-Term Memory (LSTM) method. Monthly closing prices of 18 SMinfra18 stocks from January 2024 to December 2025 were analyzed. The study consisted of three main stages: (1) calculating stock returns and selecting stocks with positive expected returns; (2) optimizing the portfolio using the Markowitz model to construct an Equal Weight Portfolio, a Minimum Variance Portfolio, and an Optimal Portfolio based on the maximum Sharpe Ratio; and (3) forecasting the dominant stock price using LSTM. Seven stocks met the selection criteria: WIFI, SSIA, PGAS, UNTR, ELSA, MEDC, and PGEO. The optimal portfolio achieved a monthly expected return of 5.04% with a risk of 6.26% and a Sharpe Ratio of 0.8044, comprising UNTR (37.50%), PGAS (29.85%), SSIA (13.92%), WIFI (12.51%), and ELSA (6.21%). The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 1.36%, while a 5-day forecast for UNTR indicated stable prices between Rp 29,421.87 and Rp 29,371.21. These results demonstrate the effectiveness of integrating the Markowitz model and LSTM for portfolio optimization and stock price forecasting.
Model Indeks Tunggal Dalam Optimalisasi Portofolio Saham dan Long Short Term Memory dalam Peramalan Harga Saham Optimal Kurnia' Aeni; Embay Rohaeti; Maya Widyastiti
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

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

Abstract

Abstrak Dalam berinvestasi, berbagai pilihan saham dihadapkan oleh investor. Strategi yang diperlukan oleh investor dapat meminimalkan risiko dan memaksimalkan potensi keuntungan. Salah satu yang diperlukan dengan pendekatan Model Indeks Tunggal untuk dapat mengoptimalkan saham dalam portofolio, sehingga dapat memberikan keputusan saham yang optimal. Pada saham yang telah optimal dapat dilanjutkan untuk diramalkan harga saham terpilih. Harga saham memiliki pergerakan ketidakpastian, sehingga diperlukan pendekatan Long Short-Term Memory (LSTM). Pendekatan LSTM untuk menangani kompleksitas harga saham. Pendekatan LSTM ini dapat memberikan hasil peramalan harga saham yang akurat pada periode mendatang. Tujuan penelitian untuk mengoptimalisasi portofolio saham serta meramalkan harga saham optimal terpilih untuk periode mendatang. Data penelitian menggunakan data saham Blue Chip. Pada tahapan pertama dilakukan dengan Model Indeks Tunggal dan tahapan kedua meramalkan harga saham yang telah optimal terpilih dengan LSTM. Hasil tahapan pertama diperoleh 13 saham yang termasuk ke dalam portofolio optimal. Pada hasil tahapan kedua diperoleh peramalan kinerja harga saham dengan performa terbaik. Akurasi peramalan kinerja harga saham diperoleh dengan MAPE sebesar 4,23%. Hasilnya dapat dikatakan peramalan dari kinerja harga saham memiliki akurasi sangat baik. Kata kunci: Portofolio Saham, Model Indeks Tunggal, , Long Short-Term Memory (LSTM)
Implementasi Rolling Forecast untuk Meningkatan Akurasi Peramalan Arima pada Saham Unilever Bandar Anzari; Alaika Luthfi; Qori Fadilah Widiastuti; Gitsa Laili Agisti; Embay Rohaeti
Saintifik: Jurnal Matematika, Sains, dan Pembelajarannya Vol 12 No 1 (2026): Saintifik: Jurnal Matematika, Sains, dan Pembelajarannya
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/saintifik.v12i1.619

Abstract

PT Unilever Indonesia Tbk (UNVR) menghadapi tantangan volatilitas harga saham yang tinggi akibat dinamika pasar dan faktor eksternal yang berfluktuasi, sehingga menurunkan akurasi model peramalan konvensional. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga saham UNVR dengan menerapkan metode rolling forecast pada model ARIMA. Data yang digunakan merupakan data harga penutupan mingguan dari Januari 2015 hingga Januari 2025. Berdasarkan proses identifikasi Box–Jenkins dan pemilihan model menggunakan Akaike Information Criterion (AIC), model terbaik yang diperoleh adalah ARIMA(0,1,1). Hasil evaluasi menunjukkan bahwa penerapan rolling forecast secara signifikan meningkatkan kinerja model dengan penurunan MAPE dari 58,12% menjadi 3,44% dan MAE dari 1.481,51 menjadi 105,01, yang berarti peningkatan akurasi sebesar lebih dari 94%. Peningkatan ini menunjukkan bahwa pembaruan parameter model secara berkala efektif dalam menangkap perubahan tren harga terkini dan mengurangi bias prediksi. Secara praktis, hasil penelitian ini memberikan kontribusi bagi investor dan manajer portofolio dalam mengoptimalkan strategi pengambilan keputusan berbasis data dengan sistem peramalan yang lebih adaptif terhadap dinamika pasar saham.
Evaluasi Kinerja ARMA-GARCH dalam Pemodelan Volatilitas Pasar dan Peramalan Return Saham BBCA Muhammad Rafli; Adisty Sungkar; Fijriyah Fitriyani; Embay Rohaeti
Saintifik: Jurnal Matematika, Sains, dan Pembelajarannya Vol 12 No 1 (2026): Saintifik: Jurnal Matematika, Sains, dan Pembelajarannya
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/saintifik.v12i1.620

Abstract

Pergerakan harga saham yang tidak stabil mencerminkan adanya ketidakpastian pasar yang perlu dipahami oleh investor. Saham BBCA dipilih karena memiliki pengaruh besar di pasar modal Indonesia serta menunjukkan pola return yang tidak stabil, sehingga memerlukan pendekatan pemodelan volatilitas yang lebih tepat. Penelitian ini memanfaatkan data return harian periode November 2022 hingga November 2025. Tahapan analisis meliputi uji stasioneritas, penentuan model ARMA untuk memodelkan komponen mean, langkah analisis yang dilakukan mencakup pemeriksaan sifat stasioner, penentuan model ARMA sebagai representasi komponen mean, pengujian keberadaan efek ARCH, pemilihan model GARCH yang paling optimal berdasarkan kriteria informasi, serta penilaian akurasi peramalan melalui ukuran error. Hasil penelitian menunjukkan bahwa return bersifat stasioner dan terdapat efek ARCH yang signifikan. Model ARMA(1,3) GARCH(1,1) dipilih berdasarkan perbandingan kriteria AIC dan BIC yang menghasilkan nilai terendah. Model ini mampu memberikan estimasi volatilitas yang akurat dengan nilai MAE sebesar 0,0122 dan RMSE sebesar 0,0164, mampu menangkap perilaku volatilitas yang bersifat persisten. Nilai galat peramalan yang rendah mengindikasikan bahwa model ini dapat menghasilkan estimasi volatilitas yang cukup akurat. Temuan ini memberikan wawasan penting mengenai tingkat risiko pada pergerakan saham BBCA dan dapat membantu investor dalam mengambil keputusan investasi yang lebih tepat.
Perbandingan Metode Discounted Cash Flow dan Net Discounted Cash Flow dalam Analisis Nilai Wajar Saham Alya Sekar Dwianto; Trexivia Arrumaisya Ayu Rayagung; Nala Kheni Salsabilla; Embay Rohaeti
Jurnal Riset Mahasiswa Matematika Vol 5, No 6 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i6.44714

Abstract

Penelitian ini bertujuan membandingkan metode Discounted Cash Flow (DCF) dan Net Discounted Cash Flow (NDCF) dalam menentukan nilai wajar saham PT Adaro Energy Tbk (ADRO) periode 2019-2023. Metode DCF mengukur nilai intrinsik berdasarkan perkalian Earning Per Share (EPS) dengan Price Earning Ratio (PER) tanpa mempertimbangkan nilai waktu uang, sedangkan metode NDCF mendiskontokan Future Value arus kas dengan tingkat diskonto 9% untuk merefleksikan nilai kini dari arus kas masa depan. Data sekunder bersumber dari laporan keuangan ADRO dan Bursa Efek Indonesia (BEI). Hasil penelitian menunjukkan bahwa kedua metode memberikan sinyal investasi yang berbeda pada beberapa periode. Nilai intrinsik DCF lebih tinggi secara konsisten dibandingkan NDCF karena tidak memperhitungkan depresiasi nilai waktu uang. Perbandingan akurasi menggunakan Root Mean Square Error (RMSE) menunjukkan bahwa metode NDCF memperoleh nilai RMSE sebesar 8.013,95, lebih rendah dibandingkan DCF sebesar 11.953,79, mengindikasikan bahwa NDCF memberikan estimasi nilai wajar yang lebih akurat terhadap harga pasar saham ADRO. Penelitian ini memberikan kontribusi bagi investor dalam memilih metode valuasi yang tepat untuk pengambilan keputusan investasi di sektor energi Indonesia.
Co-Authors Adisty Sungkar Aji Hamim Wigena Alaika Luthfi Alya Sekar Dwianto Amar Sumarsa Andriyati, Ani Ani Andriyati ANNISA FITRIANI ASEP SAEFUDDIN Bagus Sartono BANDAR ANZARI Dion, Shelly Selgiant Else As Syavira FAUZAN ISMAIL MULYADI Fernanda Caesa Putri Fijriyah Fitriyani Gitsa Laili Agisti Hagni Wijayanti I Made Sumertajaya Isti Kamila Kurnia' Aeni Kusman Sadik Maya Widyastiti Maya Widyastiti, Maya Miasih, Sekar Muhammad Edy Rizal MUHAMMAD RAFLI Mulyati , Nala Kheni Salsabilla Ningsih, Wiwik Andriyani Lestari Oktaviani, Nita Prilia Ditasari Qori Fadilah Widiastuti Sekar Miasih Sri Yuningsih Trexivia Arrumaisya Ayu Rayagung Triyanto, Muhammad Virgantari, Fitria Yasmin Eka Faridhan