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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Pangabdhi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal ABDINUS : Jurnal Pengabdian Nusantara G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Statistika dan Aplikasinya Elastisitas Jurnal Ekonomi Pembangunan Journal of Mathematics Education and Science Indonesian Community Journal Eksponensial Limits: Journal of Mathematics and Its Applications
Elly Pusporani
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS Maula, Sugha Faiz Al; Setiawan, Nicoletta Almira Dyah; Pusporani, Elly; Jannah, Sa'idah Zahrotul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp107-118

Abstract

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.
Peningkatan Kompetensi AKM Numerasi Guru SMAN 6 Surabaya Melalui Pembelajaran Interaktif sebagai Upaya Mendukung Kualitas Pembelajaran di Kelas Pusporani, Elly; Syahzaqi, Idrus; Sediono, Sediono; Ana, Elly; Melati, Adinda Tries; Salsabila, Ailsa Shafa; Riyanto, Aufa Muhammad Yogi; Ariyani, Azizah Dewi; Maulana, Bagas; Victoria, Deby; Ismi, Ferissa Maulida; Sangadji, Nurul Fajriah Deswani; Ibrahim, Rahmat Agung; Karima, Sasy Okti
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.8022

Abstract

Perubahan kurikulum di Indonesia belum memberikan dampak signifikan terhadap peningkatan kompetensi siswa, sehingga pemerintah meluncurkan Asesmen Kompetensi Minimum (AKM) dengan fokus literasi dan numerasi. Di SMAN 6 Surabaya, siswa mengalami kejenuhan dalam pembelajaran numerik sehingga diperlukan upaya pendukung melalui program pengabdian masyarakat. Kegiatan ini bertujuan meningkatkan kompetensi guru dalam merancang pembelajaran numerasi kontekstual berbasis AKM. Metode pelaksanaan meliputi sosialisasi, pelatihan, pendampingan, serta publikasi dan keberlanjutan program selama satu bulan dengan peserta 40 guru. Hasil evaluasi menunjukkan peningkatan skor rata-rata dari 45 (pre-test) menjadi 64 (post-test), serta tersusunnya modul pembelajaran interaktif dan soal AKM Numerasi. Kegiatan ini terbukti mampu meningkatkan kapasitas guru dalam mengimplementasikan strategi pembelajaran numerasi. Ke depannya, guru diharapkan terus mengembangkan kreativitas penyusunan soal, sekolah membentuk community of practice sebagai wadah berkelanjutan, serta dukungan pemerintah diperlukan melalui fasilitas dan kebijakan strategis.
Prediksi Harga Saham Big Four Banks di Indonesia Menggunakan Deret Fourier Multirespon Rasyid, Mochamad; Sediono, Sediono; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3379

Abstract

Temperature Forecast at Djuanda International Airport using ARIMA, ANN, and Hybrid ARIMA-ANN Elly Pusporani; Fitriana Nur Afifa; Fidela Sahda Ilona Ramadhina
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.13219

Abstract

This research evaluates the performance of Artificial Neural Network (ANN) models in forecasting temperature at Djuanda Airport, comparing them with the traditional Autoregressive Integrated Moving Average (ARIMA) model and a hybrid ARIMA–ANN approach. Although statistical models such as ARIMA are widely applied, their capacity to capture nonlinear dynamics in tropical climate conditions is limited, particularly when the data exhibit irregular fluctuations that linear models cannot adequately represent. Forecasting temperatures in tropical airport settings, which is crucial for flight planning, operational safety, and the reliability of aviation operations, remains relatively underexplored. This gap underscores the importance of alternative modeling techniques that can effectively address nonlinear relationships. Using one year of observed data, the models are evaluated with three accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The ANN model achieves the lowest error values (MAE 0.7630, MAPE 2.7067%, RMSE 1.0074) compared to both ARIMA and hybrid approaches. The metrics and the testing graph collectively indicate that ANN has a stronger ability to capture nonlinear temperature dynamics in tropical contexts. Nonetheless, the findings must be interpreted with caution due to the limited dataset and single case study. These limitations highlight the need for extended data and alternative architectures to improve forecasting accuracy and strengthen support for safer aviation operations.
Prediksi Inflasi, Tingkat Suku Bunga, dan Nilai Ekspor dengan Vector Autoregressive dan Estimator Deret Fourier Simultan Lu'lu'a, Na'imatul; Haq, Affan Fayzul; Fitri, Marfa Audilla; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Contemporary Mathematics and Applications (ConMathA) Vol. 6 No. 1 (2024)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v6i1.54128

Abstract

In the face of global economic uncertainty, predictions of the value of inflation, interest rates, and the value of exports are becoming increasingly crucial. This is also closely related to the SDGs in goals 8 and 9, namely on Decent Work and Economic Growth as well as Industry, Innovation, and Infrastructure. This study discusses the use of Vector Autoregressive (VAR) methods and Fourier series estimators to improve the accuracy of predictions of these economic variables. The data used are the inflation, export value, and BI Rate sourced from Bank Indonesia and Badan Pusat Statistik with a monthly period and starting from the beginning of 2010 to September 2023. After analysis, the best method was obtained, namely the Fourier series estimator which included cosine and sine components with oscillation parameters 6 with MAPE 1.51% on the inflation value, 1.65% on the interest rate, and 3.03% on the export value. By considering the interaction between economic variables, the prediction results are expected to provide deeper understanding, support decision-making at the macroeconomic level, and assist governments, central banks, and market participants in identifying risks and planning export strategies.
Generalized Space Time Autoregressive (GSTAR) Modeling in Predicting the Price of Bird’s Eye Chili in East Java, West Java, and Central Java Pusporani, Elly; Yuniar, Muhammad Alvito Dzaky Putra; Fajrina, Sofia Andika Nur; Alexandra, Victoria Anggia; Mardianto, Muhammad Fariz Fadillah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i2.25730

Abstract

Bird’s eye chili (Capsicum frutescens L.) is a major agricultural commodity in Indonesia that contributes to the economy through high market demand and its impact on inflation. In 2022, production reached 1,544,441 tons, with East Java, Central Java, and West Java being the top producing provinces. However, price fluctuations due to production and market mismatches are a concern for farmers and policy makers. The objective of this study was to model the price dynamics of bird’s eye chili in the provinces of East Java, Central Java, and West Java, given their substantial contribution to national production. To address this, the Generalized Space Time Autoregressive (GSTAR) method was applied to model the price of bird’s eye chili from February to November 2023 using data from the National Food Agency with 8:2 ratio between training and testing data. By utilizing different weighting schemes-uniform weight, inverse distance, and cross-correlation normalization, the GSTAR(2_1 )I(1) with uniform location weights performed best, showing high predictive accuracy with MAPE values of 2.021% for training data and 2.045% for test data. The model is recommended to stabilize the price of bird’s eye chili, with further validation recommended to improve reliability
ANALISIS BIPLOT PADA BERBAGAI FAKTOR KEMISKINAN DI INDONESIA BERDASARKAN PROVINSI Wieldyanisa, Ezha Easyfa; Ismi, Ferissa Maulida; Putri, Refa Berliana; Dwitya, Shabrina Nareswari; Elly Pusporani; Amelia, Dita
Elastisitas : Jurnal Ekonomi Pembangunan Vol. 7 No. 2 (2025): Elastisitas, September 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/e-jep.v7i2.09

Abstract

Kemiskinan merupakan permasalahan kompleks yang dipengaruhi oleh berbagai faktor sosial dan ekonomi. Berdasarkan hal tersebut, penelitian ini bertujuan untuk melihat hubungan antara provinsi di Indonesia dan berbagai faktor yang berpengaruh terhadap kemiskinan seperti pendidikan, kesehatan, dan infrastruktur dasar menggunakan analisis biplot. Data sekunder tahun 2024 dari BPS digunakan dengan delapan variabel utama, meliputi usia harapan hidup, produk domestik regional bruto (PDRB) per kapita, angka melek huruf, rumah tangga dengan sanitasi layak, akses air layak, akses listrik, angka partisipasi sekolah, dan rata-rata lama sekolah. Hasil analisis menunjukkan bahwa 81,772% keragaman data dapat dijelaskan oleh dua komponen utama dalam grafik biplot. Provinsi-provinsi dikelompokkan ke dalam empat kuadran berdasarkan kesamaan karakteristik kemiskinan. Faktor dengan keragaman tertinggi adalah rumah tangga dengan sanitasi layak, sedangkan faktor dengan keragaman terendah adalah PDRB per kapitaKorelasi antar variabel menunjukkan bahwa angka melek huruf dan akses listrik memiliki hubungan paling kuat, yang berarti semakin tinggi tingkat melek huruf suatu daerah, semakin besar pula kemungkinan masyarakatnya memiliki akses terhadap listrik. Sebaliknya, hubungan terlemah terdapat antara PDRB dan akses listrik. Penelitian ini menunjukkan bahwa memahami kemiskinan memerlukan pendekatan terhadap berbagai faktor yang saling berkaitan serta perlunya kebijakan pembangunan yang disesuaikan dengan karakteristik daerah masing-masing.
Pengelompokan Provinsi di Indonesia berdasarkan Ketimpangan Akses Layanan Kesehatan Tahun 2024 Menggunakan Pendekatan Cluster Hirarki Nabila Rahma Na’ifa, Ariza; Rohayah, Dewi; Yuliati, Intan; Tsabita Amalia Shofa, Nayla; Pusporani, Elly; Amelia, Dita
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/40w3md62

Abstract

Health disparities remain a major challenge in Indonesia, particularly in terms of access to healthcare services across provinces. This study aims to classify 38 Indonesian provinces based on inequality in healthcare access in 2024 using a hierarchical clustering approach. Three key indicators were used: the number of hospitals, the number of medical personnel, and the percentage of people experiencing health complaints who opted for self-medication. The analysis identified the average linkage method as the most suitable model, supported by the highest cophenetic correlation coefficient (0,911). The results revealed two distinct clusters. The first cluster includes most provinces outside Java Island, characterized by limited healthcare infrastructure and personnel. The second cluster comprises four provinces on Java Island with advanced healthcare facilities but a high rate of self-medication. These findings suggest that healthcare access inequality is influenced not only by infrastructure but also by social and behavioral factors. Therefore, policy recommendations should be tailored accordingly: infrastructure improvement and equitable distribution of medical personnel for the first cluster, and health education interventions for the second. This study contributes to evidence-based policy design in line with the Sustainable Development Goals (SDGs), particularly the goal of ensuring equitable healthcare access for all.
UNILEVER STOCK PRICES FORECASTING WITH ENSEMBLE AVERAGING APPROACH ARIMA-GARCH AND SUPPORT VECTOR REGRESSION Pusporani, Elly; Nitasari, Alfi Nur; Salsabila, Fatiha Nadia; Indrasta, Irma Ayu; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0137-0154

Abstract

Investment, mainly in stock prices, plays a significant role in the Indonesian economy. Accurate stock price forecasting can help investors make informed decisions. Unilever Indonesia Tbk (UNVR) exhibits high volatility in its closing stock prices, making it crucial to develop a reliable forecasting model. This study applies an ensemble averaging method that integrates the ARIMA-GARCH model and Support Vector Regression (SVR) to predict UNVR's closing stock prices from January 6, 2019, to November 5, 2023. The results indicate that the data can be modeled using ARIMA (0,2,1). However, the squared residuals of the model show heteroscedasticity, necessitating variance modeling using the ARCH-GARCH approach. The best combination of mean and variance modeling is achieved with ARIMA (0,2,1) – GARCH (1,1), yielding a Mean Absolute Percentage Error (MAPE) of 2.865%. Additionally, a nonparametric SVR model with parameters C = 4 and ε = 0 is applied, resulting in a MAPE of 2.94%. An ensemble averaging approach is implemented to optimize forecasting accuracy further, combining ARIMA-GARCH and SVR models. This ensemble approach improves predictive performance, achieving a final MAPE of 1.682%. These findings demonstrate that ensemble averaging effectively enhances stock price forecasting accuracy by leveraging linear and nonlinear modeling techniques.
PREDICTION OF THE INDONESIA COMPOSITE INDEX (ICI) USING THE ARCH GARCH APPROACH AND THE FOURIER SERIES Fadillah Mardianto, M. Fariz; Valida, Hanny; Putri, Farah Fauziah; Fauzi, Doni Muhammad; Pusporani, Elly
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0271-0286

Abstract

The Indonesia Composite Index (ICI) is a key indicator of stock market performance in Indonesia, often experiencing high volatility due to various domestic and global economic factors. In recent years, ICI has shown a significant upward trend, influenced by both local and international factors. In 2024, from June to October, the ICI saw a notable increase, reaching its highest value since 2020 at Rp 7,670. Despite fluctuations in stock prices, the rise in ICI reflects a positive outlook for the Indonesian stock market, attracting both domestic and foreign investors. This study aims to predict ICI movements using ARIMA-GARCH and Fourier Series approaches. The ARIMA model is employed to analyze time series data, while the ARCH-GARCH model addresses heteroskedasticity in residual variance. For comparison, the Fourier Series Estimator is applied to capture seasonal patterns in the data. Although ICI volatility is driven by a range of external macroeconomic and geopolitical factors, this study focuses on univariate modeling to evaluate the predictive capability of the index’s own historical movements, without involving exogenous variables. The data used comes from Investing.com. Weekly ICI data from March 2020 to June 2024 is used, split into training and testing sets. The analysis results indicate that the ARIMA-GARCH method provides higher accuracy, with a Mean Absolute Percentage Error (MAPE) of 5% (out-sample), compared to the Fourier Series method, which has a MAPE of 8.57%. This suggests that ARIMA-GARCH is more effective in predicting ICI trends, reflecting its ability to account for volatility and market changes more accurately.
Co-Authors Ain, Dzuria Hilma Qurotu Alexandra, Victoria Anggia Alfredi Yoani Ana, Elly Ariyani, Azizah Dewi Audilla, Marfa Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Carista, Nashwa Christopher Andreas Diana Nurlaily Dita Amelia Dwitya, Shabrina Nareswari Elly Ana Fadillah Mardianto, M. Fariz Fajrina, Sofia Andika Nur Farida Nur Hayati Farizi, Muhammad Fikry Al Fauzi, Doni Muhammad Fidela Sahda Ilona Ramadhina Fitri, Marfa Audilla Fitriana Nur Afifa Grace Lucyana Koesnadi Haq, Affan Fayzul I Kadek Pasek Kusuma Adi Putra Ibrahim, Rahmat Agung Idrus Syahzaqi Indrasta, Irma Ayu Irhamah - Ismi, Ferissa Maulida Jannah, Sa'idah Zahrotul Karima, Sasy Okti Koesnadi, Grace Lucyana Lu'lu'a, Na'imatul Lu’lu’a, Na’imatul M. Fariz Fadillah Mardianto Makkiyah, Afifah Nur Marcel Laverda Subiyanto Marcelena Vicky Galena Mardianto, M. Fariz Fadillah Mardianto, Muhammad Fariz Fadillah Maula, Sugha Faiz Al Maulana, Bagas Melati, Adinda Tries Nabila Rahma Na’ifa, Ariza Naura, Sheila Sevira Asteriska Nitasari, Alfi Nur Nurrohmah, Zidni ‘Ilmatun Permana, Made Riyo Ary Pratama, Bagas Shata Previan, Anggara Teguh Putri, Farah Fauziah Putri, Ferdiana Friska Rahmana Putri, Refa Berliana Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Rani, Lina Nugraha Rasyid, Mochamad Riyanto, Aufa Muhammad Yogi Rohayah, Dewi Sa'idah Zahrotul Jannah Salsabila, Ailsa Shafa Salsabila, Fatiha Nadia Sangadji, Nurul Fajriah Deswani Sari, Adma Novita Sari, Adma Novita Sediono, Sediono Setiawan, Nicoletta Almira Dyah Simamora, Antonio Nikolas Manuel Bonar Siregar, Naufal Ramadhan Al Akhwal Siti Maghfirotul Ulyah Siti Qomariyah Steven Soewignjo Toha Saifudin Trisa, Nadya Lovita Hana Tsabita Amalia Shofa, Nayla Valida, Hanny Victoria, Deby Wieldyanisa, Ezha Easyfa Yuliati, Intan Yuniar, Muhammad Alvito Dzaky Putra Zah, Alfian Iqbal Zuleika, Talitha Zuleika, Talitha