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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Eduvest - Journal of Universal Studies Multifinance KISA INSTITUE : Journal of Economics, Accounting, Business, Management, Engineering and Society Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
Bambang Susanto
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Pemodelan Volatilitas Menggunakan Garch(1,1) dengan Volatilitas Lag-1 Ditransformasi Box–Cox Rorimpandey, Rebecca; Nugroho, Didit Budi; Susanto, Bambang
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (828.044 KB)

Abstract

Studi ini mengusulkan klas baru dari model GARCH dengan mengaplikasikan keluarga transformasi Box–Cox ke volatilitas lag-1. Model GARCH telah banyak digunakan untuk mendikripsikan tingkah laku volatilitas suatu runtun waktu keuangan, terutama pada kurs mata uang. Tingkah laku dari volatilitas return dipelajari berdasarkan model yang mengasumsikan distribusi normal untuk inovasi. Model diestimasi menggunakan alat bantu Solver Excel dan Matlab. Analisis empiris didasarkan pada data simulasi dan data kurs beli EUR, JPY, dan USD terhadap IDR atas periode harian dari 2010 sampai 2017. Dalam kasus data simulasi dan data riil, ditemukan bahwa Solver Excel memiliki kelemahan. Hasil empiris untuk data simulasi menunjukkan bahwa model BC(1)-GARCH(1,1) bisa dikatakan tidak lebih baik dari model GARCH(1,1). Sedangkan untuk kasus data riil dengan inovasi berdistribusi normal menunjukkan bahwa model BC(1)-GARCH(1,1) mengungguli model GARCH pada data kurs beli USD terhadap IDR.
Perbandingan Empiris antara Model Log-Garch dan Garch Kholil, Zaini; Nugroho, Didit Budi; Susanto, Bambang
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Studi ini berfokus pada studi empiris tentang perbandingan antar model Log-GARCH(1,1) dan model GARCH(1,1). Kedua model diaplikasikan pada data simulasi dan data riil, data rill yang digunakan berjumlah tiga jenis data yaitu indeks harga saham Dow Jones Industrial Average (DJIA), Standard and Poor’s (S&P 500), dan S&P CNX Nifty pada periode harian dari bulan Januari tahun 2000 sampai bulan Desember tahun 2017. Model diasumsikan mempunyai inovasi return dengan berdistribusi normal. Solver Excel digunakan untuk mengestimasi model Log-GARCH(1,1) dan model GARCH(1,1) dan diselidiki kemampuannya. Secara keseluruhan, studi ini menunjukkan bahwa Solver pada Microsoft Excel mampu mengestimasi parameter-parameter model dengan cukup akurat. Dalam kasus data simulasi, hasil dari perhitungan nilai estimasi total log-likelihood mengindikasikan bahwa model Log-GARCH(1,1) berpotensi mencocokkan lebih baik dibandingkan dengn model GARCH(1,1). Sementara itu, dalam kasus data riil, hasil perhitungan nilai estimasi pada model GARCH(1,1) lebih cocok digunakan untuk ketiga data return harian indeks harga saham dibandingkan dengan model Log-GARCH(1,1).
Determinants of the Underpricing Rate of Stocks: Study on Companies Conducting IPO on the IDX Rudianto, Dudi; Ratnawati, Aryanti; Susanto, Bambang; Susilo, Tri Pujadi
Adpebi International Journal of Multidisciplinary Sciences Vol. 1 No. 1 (2022)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/aijms.v1i2.227

Abstract

This article analyzes the determinants that affect the level of underpricing in property and real estate sub-sector companies that conduct Initial Public Offerings (IPOs) on the Indonesia Stock Exchange for the 2015-2019 period. The variables used in this study include financial factors consisting of Current Ratio (CR) for liquidity ratios, Debt to Equity Ratio (DER) for leverage ratios, Return On Assets (ROA) for profitability ratios, and Earning Per Share (EPS) for ratios. market, as well as non-financial factors consisting of Underwriter Reputation (UR) and Share Offering Percentage (SOP). The results show that simultaneously all financial and non-financial factors have a significant effect on the level of underpricing, with a very strong influence. While partially CR, ROA, UR and SOP are factors that have a significant influence on the level of underpricing. So it can be concluded that financial and non-financial factors have the same influence on the level of underpricing.
The Effect Of Impaired Loan And CAR To Banking Performance At Private National Bank : Listed On Indonesia Stock Exchange 2015-2019 Ratnawati, Aryanti; Susanto, Bambang; Saepudin, Saepudin; Herdiyanti, Gita; Rudianto, Dudi; Khalingga, M Ariq
Adpebi International Journal of Multidisciplinary Sciences Vol. 1 No. 1 (2022)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/aijms.v1i1.310

Abstract

The purpose of this study was to determine the effect of impaired loans and capital adequacy ratios on banking performance at Private Commercial Banks listed on Indonesia Stock Exchange for period 2015-2019. Research method uses a quantitative approach with multiple regression analysis. The source of this research uses secondary data. The sample of this study were 18 companies Private Commercial Banks using purposive sampling technique. Finding test results show that Impaired Loans have no significant effect on banking performance while the Capital Adequacy Ratio has a significant effect on banking performance. Simultaneously, it shows that Impaired Loans and the Capital Adequacy Ratio have a significant effect on banking performance. Value the determination coefficient of 0.075 indicates that the Impaired Loan and the Capital Adequacy Ratio provide a variation of 7.5% on banking performance, while the remaining 92.5% is influenced by other factors that are not observed.
FUNDAMENTAL AND TECHNICAL ANALYSIS AND EXTERNAL FACTORS ON FINANCIAL PERFORMANCE MODERATED BY DIVIDEND POLICY Susanto, Bambang; Dwijayanty, Rima; Ratnawati, Aryanti
Multifinance Vol. 2 No. 2 (2024): Multifinance
Publisher : PT. Altin Riset Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61397/mfc.v2i2.258

Abstract

Financial performance proxied by price to book value (PBV) will be greatly influenced by technical and fundamental factors as well as external factors. Technically for listed or public companies, it will be greatly influenced by the volume and frequency of transactions in the secondary market, while fundamentally it will be influenced by asset growth and the level of balance between the company's debt to equity ratio. External factors from the exchange rate and the Fed's interest rate will bring pressure on financial performance, especially if the exchange rate weakens and the Fed's interest rate rises, of course, financial performance will be affected by the increasingly high cost of capital. In this study, the dividend policy variable is included as a moderating variable to produce more comprehensive research outputs.
LSTM-IOT (LSTM-based IoT) untuk Mengatasi Kehilangan Data Akibat Kegagalan Koneksi Susetyo, Yosia Adi; Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Susanto, Bambang
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129157

Abstract

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif.   Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.
Prediksi Laju Inflasi dengan Metode Long Short-Term Memory (LSTM) Berdasarkan Data Laju Inflasi dan Pengeluaran Kota Ternate masipupu, Frangky Aristiadi; setiawan, Adi; Susanto, Bambang
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.30627

Abstract

Inflation is one of the main indicators that reflect the economic stability of a region. Ternate City, as one of the cities in North Maluku Province, exhibits fluctuating inflation dynamics from year to year. This study aims to forecast the inflation rate in Ternate using the Long Short-Term Memory (LSTM) method, which is a neural network architecture well-suited for processing time series data. The data used consists of monthly Consumer Price Index (CPI) figures for Ternate from 2016 to 2023, obtained from the Central Bureau of Statistics (BPS). The LSTM model was trained using monthly CPI changes as the basis for calculating inflation. The model evaluation results show a Root Mean Square Error (RMSE) of 0.9275, Mean Absolute Error (MAE) of 0.8369, and Mean Absolute Percentage Error (MAPE) of 20.13%. These results indicate that the LSTM model performs well in forecasting inflation in Ternate City and can be utilized as a decision-support tool in regional economic planning and policymaking.   
PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING SARI, EMMA NOVITA; SUSANTO, BAMBANG; SETIAWAN, ADI
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.9181

Abstract

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.
Improvement of Real-GJR Model using Jump Variables on High Frequency Data Nugroho, Didit Budi; Wulandari, Nadya Putri; Alfagustina, Yumita Cristin; Parhusip, Hanna Arini; Tita, Faldy; Susanto, Bambang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24294

Abstract

Volatility is a key indicator in assessing risk when making investment decisions. In the world of financial markets, volatility reflects the degree to which the value of a financial asset fluctuates over a given period. The most common way to measure the future loss potential of an investment is through volatility. Focusing on the Realized GJR (RealGJR) volatility model, which consists of return, conditional volatility, and measurement equations, this study proposes the RealGJR-CJ model developed by decomposing the exogenous variable in the volatility equation of RealGJR into continuous C and discontinuous (jump) J variables. The decomposition of exogenous variables makes the RealGJR-CJ model follow realistic financial markets, where the asset volatility is a continuous process with some jump components. As an empirical illustration, the models are applied to an index in the Japanese stock market, namely Tokyo Stock Price Index, covering from January 2004 to December 2011. The observed exogenous variable in the volatility equation of RealGJR models is Realized Volatility (RV), which is calculated using intraday data with time intervals of 1 and 5 minutes. Adaptive Random Walk Metropolis method was employed in Markov Chain Monte Carlo algorithm to estimate the model parameters by updating the parameters during sampling based on previous samples from the chain. From the results of running the MCMC algorithm 20 times, the mean of the information criteria of competing models is significantly different based on standard deviation and the result suggests that the model with continuous and jump variables can improve the model without jump. The best fit model is provided by RealGJR-CJ with the adoption of 1-minute RV data. 
INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK Haay, Happy Alyzhya; Trihandaru, Suryasatriya; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.962 KB) | DOI: 10.30598/barekengvol17iss1pp0211-0224

Abstract

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.
Co-Authors Adi Setiawan Adrianus Herry Heriadi Agus Priyono Alfagustina, Yumita Cristin Alfida Tegar Nurani Algunadi, Muhammad Alicia Anggelia Lumbantoruan Alz Danny Wowor Asido Saragih Awik Hidayati Carolina Febe Ronicha Putri Denny Indrajaya Didit B Nugroho Didit Budi Nugroho Dini Kurniawati Dudi Rudianto Dwi Tristianto Egidius Saut Poltak Situmorang Elsa Septyana Endang Ruswanti Eva Rachmawati Faldy Tita Haay, Happy Alyzhya Hanna Arini Parhusip Hanna Arini Parhusip Herdiyanti, Gita Hersusetiyati, Hersusetiyati Johanes Dian Kurniawan Kezia Natalia Putri Prasetia Khalingga, M Ariq Khalingga, Muhammad Ariq Kholil, Zaini Kurniawan, Johanes Dian Laurentia Nindya Sari Prameswara Lenox Larwuy Leopoldus Ricky Sasongko Lestari, Dina Aulia Lilik Linawati Maria Anita masipupu, Frangky Aristiadi Mince M. M. Rosely Modjo, Marchella Ellena Nugraha, Rian Nanda Nuryani, Dwi Panjaitan, Lam Peter Petrus Priyo Santosa Prasetia, Kezia Natalia Putri PURWANTI PURWANTI Putri, Zefania Sasongko Ratnawati, Aryanti Rebecca Rorimpandey Ricky Agusiady Rima Dwijayanty Rinadi, Geraldus Anggoro Rorimpandey, Rebecca Saepudin Saepudin Saragah R Pratama SARI, EMMA NOVITA Setiawan, Audita Sukadwilinda Suroto, Agung Suryasatriya Trihandaru Susetyo, Yosia Adi Syafi’i, Syafi’i Tanujaya, Lukhia Britanthia Christina Tri Pujadi Susilo Tundjung Mahatma Vania Beatrice Liwandouw Visher Laja Jaja wahyu ngestisari Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono, Yohanes Zaenal Aripin