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All Journal Jurnal Sains dan Seni ITS BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Jurnal Abdi Masyarakat Indonesia Jurnal Pemberdayaan Masyarakat Indonesia (JPMI) DEDIKASI : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Mathematics and Applications Indonesian Actuarial Journal
Mey Lista Tauryawati
Business Mathematics Department, School Of Applied STEM, Universitas Prasetiya Mulya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Perbandingan Metode Fuzzy Time Series Cheng dan Metode Box-Jenkins untuk Memprediksi IHSG Mey Lista Tauryawati; M Isa Irawan
Jurnal Sains dan Seni ITS Vol 3, No 2 (2014)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.887 KB) | DOI: 10.12962/j23373520.v3i2.7985

Abstract

Proses peramalan sangat penting pada data time series karena diperlukan dalam proses pengambilan keputusan. Pada bidang finansial peramalan dapat digunakan untuk memantau pergerakan Indeks Harga Saham Gabungan (IHSG) yang akan datang. Perkembangan metode peramalan data time series yang cukup pesat mengakibatkan terdapat banyak pilihan metode yang dapat digunakan untuk meramalkan data sehingga perlu membandingkan metode yang satu dengan metode lainnya untuk mendapatkan hasil ramalan dengan akurasi yang tinggi. Pada tugas akhir ini dilakukan perbandingan peramalan untuk memperoleh metode yang terbaik diantara metode Fuzzy Time Series Cheng dan metode Box-Jenkins dalam memprediksi IHSG dengan akurasi yang tinggi berdasarkan MAE, MSE dan MAPE. Diantara kedua metode peramalan diperoleh metode yang terbaik adalah Fuzzy Time Series Cheng.
Pemberdayaan UMKM Mili Milk: Strategi Pemasaran dan Target Pasar yang Tepat untuk Keberhasilan Bisnis Mey Lista Tauryawati; Ilham Ramadhan Nur Ahmad; William Stevanno; Jessica Cheri T Palloan; Kyla Kanyaka Purbosatrio; Phillip Fabian; Shabrina Amira; Glorya Ichaisa G R; Glorify Megumi T W
Jurnal Abdi Masyarakat Indonesia Vol 3 No 5 (2023): JAMSI - September 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.876

Abstract

Pemasaran produk, atau yang sering disebut sebagai Marketing, merupakan tantangan utama yang dihadapi oleh Usaha Mikro Kecil Menengah (UMKM) bernama Mili Milk. Hal ini disebabkan oleh penjualan UMKM yang lebih banyak berfokus pada bisnis ke bisnis, dengan sedikit penjualan langsung kepada konsumen. Oleh karena itu, tim Community Development Prasetiya Mulya memiliki tujuan untuk mengembangkan Branding dan bisnis ini melalui pengelolaan media sosial, iklan daring, serta melakukan survei di lingkungan Mahasiswa Prasetiya Mulya dan sekitarnya. Hasil survei menunjukkan bahwa rasa susu Mili Milk mendapat respon positif dari masyarakat sekitar dan mahasiswa di Prasetiya Mulya. Selain itu, aspek terbesar yang perlu ditingkatkan berdasarkan tanggapan dari survei adalah desain kemasan produk. Selama proses survei, tim berhasil meningkatkan interaksi melalui akun media sosial UMKM. Kesimpulannya, melalui kegiatan ini, tim berhasil meningkatkan interaksi di media sosial UMKM dan memberikan masukan melalui survei untuk meningkatkan citra produk di masyarakat.
Comparison of Random Forest, XGBoost, and LightGBM Methods in Estimating Airbnb Accommodation Rental Prices Based on Customers in New York City Tauryawati, meylista
Indonesian Journal of Mathematics and Applications Vol. 1 No. 2 (2023): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.02.5

Abstract

The research data was taken from a dataset provided by Inside Airbnb, and the analysis was conducted using machine learning techniques with Random Forest, XGBoost, and LightGBM algorithms. The research stages include data understanding, data preprocessing, data analysis, modeling, model evaluation, and rental price estimation. It is expected that the results of this study can help customers estimate the rental price of Airbnb rooms.
Peningkatan Efektivitas Pengelolaan Keuangan dan Strategi Pemasaran untuk Mengoptimalkan Penjualan Gandawijaya, Carissa; Leffiana, Rachelle Viensisca; Pandita, Dominicus Hanschen; Arleta, Chelsea; Dior, Daido Van; Keane, Roy; Deven, Deven; Tauryawati, Mey Lista
Journal Pemberdayaan Masyarakat Indonesia Vol 7 No 1 (2025): Jurnal Pemberdayaan Masyarakat Indonesia (JPMI)
Publisher : Pusat Pengabdian kepada Masyarakat (PPKM) Universitas Prasetiya Mulya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21632/jpmi.7.1.41-54

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in Indonesia's economy, particularly in terms of employment generation and national economic resilience. However, many MSMEs face challenges in financial management and marketing, especially in the era of digitalization. This research examines the implementation of the Community Development Program KUM-017 in Sangkanerang Village, Kuningan, West Java, focusing on empowering the Wedang Jahe Binangkit MSMEs. The program aims to enhance financial performance, operational efficiency, and marketing strategies through educational approaches and the implementation of Standard Operating Procedures (SOPs). Methods include field studies, data analysis, and direct mentoring. Results show significant improvements in financial record-keeping, SOP development, and digital marketing strategies, despite challenges in adapting to change and human resources. The study concludes by emphasizing the importance of effective financial management and marketing as key drivers for enhancing competitiveness and growth of MSMEs. Programs like this contribute positively and can be widely applied to support MSME growth in Indonesia, alongside efforts to strengthen the local economy and improve community welfare.
Exploring Women Empowerment: Dried-grass Weavers in South Kalimantan Sari, Faizah; Yudianto, B. Realino; Tauryawati, Mey Lista; Sampe, Maria Z.
Dedikasi : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024): Dedikasi : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah III DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53276/dedikasi.v3i2.194

Abstract

The paper underscores challenges and opportunities in the community of female dried-grass basket weavers in Purun Village, Banjarbaru, South Kalimantan, Indonesia. A training workshop based on how to improve the branding effort using online shops was conducted through a community classroom-type presentation with a simple step-by-step procedure, a practice for taking a product photograph from a smartphone, and a demonstration of setting up an online shop. Fourteen female weavers of Purun Village, Banjarbaru, South Kalimantan, Indonesia participated in the workshop. Results show a set of characteristics of a female basket weaver that uncovers both challenges and opportunities for improvement, not only for the individual woman but also the community and product sustainability of dried-grass basket weaving. Implications for continuous interaction and future research were discussed, including (1) the importance of understanding a variety of women’s roles in private and public spheres within the community context that could complement each other, and (2) the inevitability of introducing technology to the community to help furthering the participant’s skills and of small business community.
Comparison of Random Forest, XGBoost, and LightGBM Methods in Estimating Airbnb Accommodation Rental Prices Based on Customers in New York City Tauryawati, meylista
Indonesian Journal of Mathematics and Applications Vol. 1 No. 2 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.02.5

Abstract

The research data was taken from a dataset provided by Inside Airbnb, and the analysis was conducted using machine learning techniques with Random Forest, XGBoost, and LightGBM algorithms. The research stages include data understanding, data preprocessing, data analysis, modeling, model evaluation, and rental price estimation. It is expected that the results of this study can help customers estimate the rental price of Airbnb rooms.
PREDIKSI NILAI KURS MATA UANG DOLLAR AMERIKA (USD) DAN YUAN CHINA (CNY) DENGAN RUPIAH (IDR) MENGGUNAKAN METODE ARIMA Teja, Satya Dhira Alfa; Derick, Luigi; Tauryawati, Mey Lista; Zainuddin, Ahmad Fuad
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page99-112

Abstract

Bonus demografi yang sedang terjadi di Indonesia tidak sebanding dengan lapangan pekerjaan yang tersedia. Hal ini menyebabkan banyak dari mereka untuk mencari sumber pendapatan dari berbagai tempat, salah satunya dengan berinvestasi atau trading. Trading forex juga merupakan sumber pendapatan yang potensial jika dilakukan dengan benar. Analisa dengan menggunakan metode yang benar dapat membantu untuk sukses dalam dunia trading forex. Dalam dunia forex, mata uang Dollar Amerika (USD) dan Yuan China (CNY) merupakan mata uang yang sering dipilih karena paling berpotensi menghasilkan keuntungan. Penelitian ini bertujuan untuk melakukan prediksi harga kurs Dollar Amerika (USD) dan Yuan China (CNY) terhadap Rupiah (IDR) menggunakan metode Autoregressive Integrated Moving Average (ARIMA). Data kurs mata uang USD dan CNY akan dibagi menjadi data train dan test untuk memprediksi secara long term (10 hari), dan short term (5 hari). Dari hasil analisa tersebut, diperoleh bahwa model ARIMA (2,0,2) adalah model terbaik untuk memprediksi kurs USD terhadap IDR, sedangkan model ARIMA (3,0,2) adalah model terbaik untuk prediksi kurs CNY terhadap IDR. Model terbaik diperoleh berdasarkan nilai AIC terendah dan signifikansi parameter. Setelah mendapatkan model terbaik untuk nilai tukar kurs mata uang USD dan CNY terhadap IDR, selanjutnya dilakukan prediksi untuk jangka waktu short term dan long term. Hasil menunjukkan bahwa untuk meramalkan secara short term, model ARIMA yang telah diperoleh, cocok untuk digunakan. Namun, untuk meramalkan secara long term, model ARIMA tersebut masih kurang akurat untuk digunakan, karena keterbatasan data train.
MAPPING DISASTER-PRONE AREAS ON JAVA ISLAND USING THE K-PROTOTYPES ALGORITHM Tauryawati, Mey Lista; Zainuddin, Ahmad Fuad
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/barekengvol20iss1pp0179-0196

Abstract

Clustering in disaster areas is often implemented as a disaster mitigation effort with the aim of minimizing risk. Determining the appropriate clustering method based on the data set will influence the clustering results. K-Prototypes is a clustering method that is capable of handling mixed data, numerical and categorical data, so this method is suitable to clustering disaster prone area with mixed data of disaster factors such as incident intensity, type of disaster, population density, and level of infrastructure vulnerability. This research focuses on disaster prone areas on Java Island and clustering using K-Prototypes to group and map areas that have the highest to lowest levels of disaster vulnerability based on the number of incidents, number of victims, and the amount of damage to facilities and the type of disaster. The clustering results obtained mapping of cities in the province into cluster groups based on the level of vulnerability and calculated potential losses based on disasters in each province. Afterward, the clustering results are used to determine priority areas for disaster mitigation to minimize losses.
Forecasting Rupiah-to-US Dollar Exchange Rate 2020 - 2025 Using a Fuzzy Time Series Markov Chain Model Rahmadani, Tiur Masayu; Maeni, Rosa; Hwa, Camelia Miftahur Rizki Kiem; Khasanah, Iftha Nikmatul; Yeo, Winner; Alfarisi, Kgs. M. Rifat; Kurniawan, Yohana Joevanca; Juwono, Adriano Fadlan; Tauryawati, Mey Lista
Indonesian Actuarial Journal Vol. 1 No. 1 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

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

Abstract

The exchange rate of the Indonesian Rupiah against the US Dollar experiences frequent fluctuations, making economic forecasting and financial planning more difficult. This study aims to enhance exchange rate prediction accuracy by combining Fuzzy Time Series with Markov Chain probability transitions. The approach is grounded in the idea that probabilistic modeling of state changes improves the representation of dynamic currency behavior. Using daily IDR/USD data from April 2020 to March 2025, the methodology involves two main steps: fuzzifying historical exchange rate data into linguistic variables, and applying a Markov Chain to compute transition probabilities between these fuzzy states. The model’s forecasting accuracy is evaluated using mean absolute percentage error. Results show that the hybrid model achieves a lower error rate of 0.50%, compared to 0.61% using conventional Fuzzy Time Series alone. This demonstrates the hybrid model’s ability to capture both sudden market changes and stable patterns effectively. The findings suggest that the integration of Markov Chain transitions significantly improves the predictive performance of fuzzy-based models. In conclusion, this hybrid method provides a practical and reliable forecasting tool for financial analysts and policymakers. Future research could include additional economic indicators and explore alternative probability weighting methods to further enhance model accuracy.
Co-Authors Alfarisi, Kgs. M. Rifat Arleta, Chelsea Derick, Luigi Deven, Deven Dior, Daido Van Gandawijaya, Carissa Glorify Megumi T W Glorya Ichaisa G R Hwa, Camelia Miftahur Rizki Kiem Ilham Ramadhan Nur Ahmad Jessica Cheri T Palloan Juwono, Adriano Fadlan Keane, Roy Khasanah, Iftha Nikmatul Kurniawan, Yohana Joevanca Kyla Kanyaka Purbosatrio Leffiana, Rachelle Viensisca Maeni, Rosa Mohammad Isa Irawan Pandita, Dominicus Hanschen Phillip Fabian Rahmadani, Tiur Masayu Sampe, Maria Z. Sari, Faizah Shabrina Amira Teja, Satya Dhira Alfa William Stevanno Yeo, Winner Yudianto, B. Realino Zainuddin, Ahmad Fuad