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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Pattimura International Journal of Mathematics (PIJMath) PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Lexy Janzen Sinay, Lexy Janzen
Jurusan Matematika, Fakultas MIPA Universitas Pattimura Ambon Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PEMODELAN LAJU PERTUMBUHAN EKONOMI DI PROVINSI MALUKU MENGGUNAKAN REGRESI SPASIAL DATA PANEL Sampulawa, Zulfikar Ilham; Sinay, Lexy Janzen; Djami, Ronald John
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/variancevol6iss1page113-122

Abstract

Laju pertumbuhan ekonomi merupakan suatu tingkat perkembangan agregrat pendapatan untuk setiap tahun yang dapat dibandingkan serta dapat memberikan gambaran inti mengenai kinerja dari setiap wilayah kabupaten atau kota dalam pemanfaatan wilayahnya. Laju pertumbuhan ekonomi di Provinsi Maluku mengalami peningkatan pada tahun 2022 sebesar 4,81%, walaupun pernah mengalami penurunan pada periode 2020 sebesar -0,92%. Adapun penelitian ini bertujuan untuk mendeskripsikan karakteristik dan memetakan sebaran data serta dapat memodelkan laju pertumbuhan ekonomi di Provinsi Maluku dari periode 2017 sampai dengan 2021. Dengan menggunakan metode analisis spasial data panel dengan pembobot spasial queen contiguity, yang merupakan pembobot yang bertujuan sebagai komponen penting dalam pembentukan model karena dalam hal ini menunjukkan hubungan keterkaitan antar lokasi sehingga diperoleh model terbaik ialah SAR-fixed effect. Dari penelitian ini didapatkan hasil bahwa variabel independen yang terdiri atas variabel Indeks Pembangunan Manusia (IPM), Dana Alokasi Umum (DAU), dan Jumlah Pengangguran Terbuka (JPT) berpengaruh signifikan secara simultan terhadap laju pertumbuhan ekonomi di Provinsi Maluku, dengan koefisien determinasi (R-Square) sebesar 0,6323 atau 63,23%, yang menunjukan bahwa kemampuan variabel independen menjelaskan variabel dependen sebesar 63,23%.
Forecasting The Composite Stock Price Index Using Autoregressive Integrated Moving Average Hybrid Model Artificial Neural Network Jaariyah, Muhidin; Sinay, Lexy Janzen; Lewaherilla, Norisca; Lesnussa, Yopi Andry
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.178 KB) | DOI: 10.30598/pijmathvol1iss2pp89-100

Abstract

A stock index is a statistical measure that reflects the overall price movement of a group of stocks selected based on certain criteria and methodologies and evaluated regularly. JCI is included in the composite index, which is the Headline index. The Headline Index is an index that is used as the main reference to describe the performance of the capital market. The JCI is very important in describing the current condition of the capital market because the JCI measures the price performance of all stocks listed on the Main Board and Development Board of the IDX. This study aims to predict JCI data using the time series method. The hybrid Autoregressive Integrated Moving Average–Artificial Neural Network (ARIMA-ANN) model combines the linear ARIMA model and the non-linear ANN model. The best models are the ARIMA model (2,1,1) and the ANN Backpropagation model with one input layer, one hidden layer with 20 neurons, and one output. The ARIMA-ANN hybrid model accurately predicts JCI data because it produces a MAPE value of less than 1%, with the level of forecasting accuracy from testing results being smaller than the level of accuracy during training. In addition, the forecast for the next five days is very accurate because it produces a very small RMSE and a MAPE below 1%, respectively, namely 56.99 and 0.72%.
The Influence of Macroeconomic Factors on Credit Risk of Banks in Indonesia using ARDL Model Sinay, Lexy Janzen; Kembauw, Esther
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp79-88

Abstract

One of the efforts to maintain economic stability during the Covid-19 pandemic is to reduce the risk of in the banking sector. One of the risks in the banking sector that must be anticipated is credit risk. Non-Performing Loan (NPL) is one of the indicators used to detect credit risk. There are various factors that can affect credit risk, both from internal and external banking. One of the external factors that can affect NPL is macroeconomic conditions. This study aims to identify macroeconomic factors that affect banking NPLs in Indonesia using the autoregressive distributed lag (ARDL) model. The data used is time series data from January 2015 – August 2020, which period describes the condition of the Indonesian economy before and during the Covid-19 pandemic. The data consists of six variables, namely the NPL ratio of commercial banks and macroeconomic factors in Indonesia such as gross domestic product (GDP), inflation rate, USD-IDR exchange rate, benchmark interest rates [BI 7-Day (Reverse) Repo Rate], and credit growth. The results of the data analysis show that the NPL ratio and macroeconomic variables are experiencing shocks due to the COVID-19 pandemic. The results of the ARDL model analysis show that these macroeconomic variables are able to explain the NPL of 66.61%
Classification of Poverty in Maluku Province using SMOTE-Random Forest Algorithm Damamain, Ferina L; Sinay, Lexy Janzen; Latupeirissa, Sanlly J; Bakarbessy, Lusye
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp17-28

Abstract

Poverty is a complex issue. According to BPS publications, in 2023, the poverty line in Indonesia has reached 9.57%. Maluku is one of the provinces with a high poverty rate, reaching 16.23%. This research aims to classify poverty status in Maluku Province using the SMOTE-random forest algorithm. This research uses SUSENAS 2022 data, where the data is not balanced. SMOTE is used to overcome this problem. The best model obtained has an accuracy rate of 85.8%. The model is based on a training data proportion of 75% and testing 25%, with parameters m=4 and r=100. The critical factor that influences poverty status in Maluku Province is the number of households.
Co-Authors Arifin, Hasmia Melati Borel, Aryanto Damamain, Ferina L Damamain, Ferina Lestari Ega, L Esther Kembauw, Esther Ferdinandus, Stenly J Haumahu, Gabriella Ilwaru, Venn Yan Ishak Jaariyah, Muhidin Latupeirissa, Sanlly J Lewaherilla, Norisca Lusye Bakarbessy Meilin Imelda Tilukay Muhammad Faisal Muspida, Muspida Pelu, Shelma M RAHAKBAUW, DORTEUS LODEWYIK Regar, Erfendi Restia Christianty Rijoly, Monalisa E. Ronald John Djami Salmon Notje Aulele, Salmon Notje Sampulawa, Zulfikar Ilham Sopaheluwakan, Marsa Souhoka, Semuel Wenno, Meiske Yopi Andry Lesnussa Yudistira Yudistira Yunizar, Mahdayani Putri