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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
Universitas Kristen Satya Wacana
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SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL Nugroho, Didit Budi; Priyono, Agus; Susanto, Bambang
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.21-32

Abstract

The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.
Model Galat Spasial Untuk Jumlah Produksi Padi Tahun 2014 Di Jawa Barat Alicia Anggelia Lumbantoruan; Adi Setiawan; Bambang Susanto
Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika Vol 3, No 2 (2019)
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jm.v3i2.753

Abstract

Indonesia merupakan salah satu negara produksi padi oleh karena itu luas lahan yang merupakan faktor utama yang sangat berpengaruh dalam produksi padi. Pad studi ini mengusulkan Model Galat Spasial dalam penerapan untuk jumlah produksi padi dan jumlah produksi padi tetangga. Model ini mengasumsikan bahwa kesalahan dalam model memiliki hubungan spasial antara satu daerah dan daerah lain. Data yang diolah adalah luas lahan produksi dan jumlah produksi padi di setiap kabupaten, di Jawa Barat pada tahun 2014. Matriks Pembobot juga digunakan, dalam menggambarkan kedekatan antar wilayah. Hasil yang diperoleh dari model spasial diketahui bahwa konstanta dan juga jumlah total produksi daerah tetangga tidak dipengaruhi berdasarkan hasil konstanta negatif dan juga hasil uji t, yang menyatakan bahwa jumlah produksi tetangga daerah tidak mempengaruhi jumlah produksi padi. Regresi sederhana tanpa konstanta adalah model terbaik untuk produksi tanaman padi yang hanya didasarkan pada area produksi.
Word Cloud of UKSW Lecturer Research Competence Based on Google Scholar Data Suryasatriya Trihandaru; Hanna Arini Parhusip; Bambang Susanto; Carolina Febe Ronicha Putri
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.13123

Abstract

There is a need in the Universitas Kristen Satya Wacana (UKSW) to identify the research competence of their faculties at a study program and University level. To accomplish this requirement, we need to automate the analysis of research output and publications quickly. Research articles are scattered in many publisher systems and journals which may be reputable, unreputable, accredited, and unaccredited. We devised a computer code to quickly and efficiently retrieve publication titles recorded in Google Scholar using a machine learning algorithm. The result display is in the form of a word cloud so that dominant and frequent words will be prominent in the visualization. In determining scientific terms to display, we used a modified version of the word cloud Python module and unmodified Term Frequency - Inverse Document Frequency (TF-IDF) library. The algorithm was tested on publication titles of our study program in UKSW and confirmed directly. The system features the ability to produce a word cloud visualization for an individual faculty, for faculties in a study program, or in the University as a whole. We have not differentiated publication sources, whether they are reputable or unreputable, which might affect the accuracy of competence identification.
Estimation of Exchange Rate Volatility using APARCH-type Models: A Case Study of Indonesia (2010–2015) Didit B Nugroho; Bambang Susanto; Saragah R Pratama
Jurnal Ekonomi dan Studi Pembangunan Vol 9, No 1 (2017)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (887.684 KB) | DOI: 10.17977/um002v9i12017p065

Abstract

Volatiliy measurement and modeling is an important aspect in many areas of finance. The main purpose of this study is to apply seven APARCH-type models with (1,1) lags to investigate the behavior of exchange rate volatility for the EUR, JPY, and USD selling exchange rates to IDR for the duration from January 2010 to December 2015. The competing models include ARCH, GARCH, TARCH, TS-ARCH, GJR-GARCH, NARCH, and APARCH used with Gaussian normal distribution. In order to estimate the model parameters, this study applies the Bayesian inference using the adaptive random walk Metropolis method in the MCMC algorithm. Empirical results based on the deviance information criterion indicate that the GARCH (1,1), APARCH (1,1), and TARCH (1,1) models provide the best fit for the EUR, JPY, and USD data, respectively. In those models, both the JPY and USD data have significant negative leverage effect at the 99% credible level. Moreover, the JPY returns also have significant Taylor effect in return volatility at the 99% credible level.Keywords: APARCH, ARWM, IDR exchange rate, MCMC, volatilityJEL Classification: C30, F31
Analisis Indeks Pembangunan Manusia Provinsi-Provinsi di Indonesia Menggunakan Indeks Baru Berdasarkan Metode Resampling Elsa Septyana; Adi Setiawan; Bambang Susanto
Jurnal Sains dan Edukasi Sains Vol. 4 No. 1 (2021): Jurnal Sains dan Edukasi Sains
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/juses.v4i1p9-16

Abstract

Pada penelitian ini dibahas tentang penggunaan Indeks Baru untuk mengukur autokorelasi spasial secara global. Kontribusi utama dari karya ini adalah untuk mengetahui korelasi antara Indeks Geary dan Indeks Moran. Selain itu penelitian ini bertujuan untuk mengetahui pengaruh spasial terhadap IPM Provinsi-Provinsi di Indonesia. Data yang digunakan adalah data IPM provinsi-provinsi di Indonesia tahun 2010 – 2018. Prosedur yang dilakukan adalah dengan mengukur data IPM menggunakan Indeks Geary dan Indeks Moran. Selanjutnya nilai Indeks Geary dan Indeks Moran digunakan untuk mencari korelasi yang terjadi antara Indeks Geary dan Indeks Moran. Korelasi yang terbentuk antara Indeks Geary dan Indeks Moran sangat kuat sehingga Indeks Baru dapat dirumuskan. Untuk memvalidasi keakuratan Indeks Baru dilakukan uji autokorelasi spasial terhadap Indeks Baru menggunakan Metode Bootstrap dan resampling berdasarkan estimasi densitas Kernel. Berdasarkan hasil resampling menggunakan metode bootstrap dan resampling berdasarkan estimasi densitas Kernel dengan pengulangan sebanyak 10000 kali ditemukan bahwa nilai-p tahun 2010 – 2018 berkisar antara 0,001 – 0,006 yang berarti terdapat autokorelasi spasial atau terdapat keterkaitan erat antara IPM provinsi dengan IPM provinsi-provinsi yang bertetangga. Dengan kata lain, bahwa antar provinsi satu dengan provinsi yang berbatasan memiliki kemiripan nilai IPM atau saling berkorelasi. Dengan didapatkannya hasil analisis ini, maka dapat menjadi acuan pemerintah dalam melakukan pemerataan pembangunan disetiap provinsi di Indonesia dengan mempertimbangkan keterkaitan antar provinsi di Indonesia.
Desain S-Box Fleksibel: Regenerasi Konstanta dan Koefisien Fungsi Linier Berbasis CSPRNG Chaos Bambang Susanto; Alz Danny Wowor; Vania Beatrice Liwandouw
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1281.002 KB)

Abstract

The substitution process in block ciphers usually uses static s-boxes, where the value of each s-box entry is always fixed and the functions are one-to-one. Visually, the dataset pattern in the input is also seen in the output. This will make it easier for cryptanalysts to look at patterns and can predict behavior based on the input. This study designs a flexible s-box where each entry contains a linear function, with constants and coefficients generated by CSPRNG chaos from the key input. The strength of a flexible s-box can be seen when changing key inputs (although 1 bit different) will produce different s-boxes. In addition, the same input dataset values will produce different outputs, so flexible s-boxes are one-to-many. Statistical tests show that the average correlation is in a low category. Tests on the cipher block also fulfill the Shannon Principle. The principle of Iterated Cipher with n-round is filled with n > 5 and 46n random numbers. The Avalanche Effect test on the algorithm gives better results than Two fish even though it is still below the DES.
Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations Didit Budi Nugroho; Bambang Susanto; Kezia Natalia Putri Prasetia; Rebecca Rorimpandey
Jurnal Akuntansi dan Keuangan Vol. 21 No. 1 (2019): MAY 2019
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.325 KB) | DOI: 10.9744/jak.20.1.12-20

Abstract

This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.
Penerapan Bilangan Kompleks untuk Menyelesaikan Soal-Soal Geometri Datar Dwi Tristianto; Lilik Linawati; Bambang Susanto
d'CARTESIAN:Jurnal Matematika dan Aplikasi Vol 7, No 1 (2018): Maret 2018
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (592.849 KB) | DOI: 10.35799/dc.7.1.2018.19548

Abstract

Soal-soal geometri datar pada umumnya diselesaikan dengan definisi, aksioma dan teorema-teorema yang ada. Tetapi, soal-soal geometri datar tertentu dapat diselesaikan dengan konsep dan sifat bilangan kompleks, hal ini dikarenakan setiap titik pada bidang dapat diwakili dengan sebuah bilangan kompleks, demikian pula sebaliknya. Dalam makalah ini sifat-sifat bilangan kompleks diterapkan untuk menyelesaikan beberapa soal geometri datar. Untuk itu, dipilih beberapa soal geometri datar yang diselesaikan menggunakan pendekatan ini. Studi ini dilakukan dengan cara mempelajari dan mensurvei teori-teori yang berkaitan dengan bilangan kompleks dan aplikasinya pada geometri datar. Soal-soal yang dipilih merupakan soal yang diambil dari buku, catatan kuliah, karya ilmiah para ahli dibidangnya dan soal Olimpiade Matematika tingkat SMA. Soal-soal yang dikaji berkaitan dengan dua segmen garis sejajar (parallel), syarat tiga titik segaris (collinear), dua segmen garis saling tegak lurus (perpendicullar) dan syarat empat titik membentuk suatu segiempat talibusur (concyclic). Hasil dari penelitian ini adalah pembuktian dua sifat istimewa dari jajar genjang, penentuan hasil pencerminan suatu titik terhadap garis tertentu, penentuan letak titik tinggi dari suatu segitiga jika diketahui ketiga titik sudutnya dan dua tipe soal terakhir yang dibahas adalah soal OSAMO 2015 no. 2 dan OSN SMA 2009 tentang pembuktian dua segmen garis tegak lurus dan 4 titik tertentu membentuk suatu segiempat talibusur.Kata Kunci:  Bilangan Kompleks, Dua Segmen Garis Saling Tegak Lurus, Dua Segmen Garis Sejajar, Geometri Datar, Syarat Empat Titik Membentuk Segiempat Talibusur, Syarat Tiga Titik Segaris.
Perbandingan Metode Random Forest dan Naïve Bayes dalam Email Spam Filtering Maria Anita; Bambang Susanto; Lenox Larwuy
KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v7i2.18933

Abstract

Email is an important tool not only for communicating and transferring files but also it can be used for advertising media over the Internet. Since the increase in email user numbers, many users send viruses, fraud, and even pornography contained emails. Those kinds of emails were called spam, where unexpected emails sent in bulk. Many email users are annoyed by the amount of time spent deleting individual spam messages. This study provides a comparison between the Random Forest and Naïve Bayes classification methods for email spam predicting. It aims for searching the most accurate method. The data used in this study is an email dataset totaling 2607 data with two variables, namely the body variable (which shows the contents of the email) and the label variable (which shows labeling) where 1 indicates spam and 0 indicates not spam. From the test result using the confusion matrix, it is known that the random forest method has the highest accuracy value, namely 98%, and Naïve Bayes 73%.
Perbandingan Kinerja Regresi Decision Tree dan Regresi Linear Berganda untuk Prediksi BMI pada Dataset Asthma Alfida Tegar Nurani; Adi Setiawan; Bambang Susanto
Jurnal Sains dan Edukasi Sains Vol. 6 No. 1 (2023): Jurnal Sains dan Edukasi Sains
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/juses.v6i1p34-43

Abstract

Penelitian ini menerapkan dua metode regresi yaitu Decision Tree dan linear berganda untuk memprediksi Body Mass Index (BMI) berdasarkan variabel-variabel lainnya pada dataset Asthma. Metode Decision Tree merupakan salah satu cara data processing dalam memprediksi masa depan dengan cara membangun klasifikasi dan regresi model dalam bentuk struktur pohon. Analisis regresi linear berganda merupakan model regresi yang melibatkan lebih dari satu variabel independen. Analisis regresi linear berganda dilakukan untuk mengetahui arah dan seberapa besar pengaruh variabel independen terhadap variabel dependen. Untuk mengukur tingkat keakuratan model peramalan, digunakan suatu ukuran keakuratan MAPE (Mean Absolute Percentage Error). Hasil MAPE menunjukkan nilai rata-rata prosentase kesalahan mutlak dari nilai sebenarnya dengan nilai peramalan. Pada penelitian ini, metode regresi linear berganda menunjukkan hasil yang lebih baik dengan nilai MAPE berturut-turut sebesar 12,737%; 12,76%; 12,89%; dan 12,99% untuk proporsi data uji berturut-turut 10%, 20%, 30%, dan 40% sedangkan nilai MAPE dari metode regresi Decision Tree sebesar 12,758%; 12,79%; 12,92%; dan 13,13%. Apabila digunakan ukuran kebaikan yang lain seperti MAE dan RMSE akan memberikan hasil yang analog, sedangkan ukuran kebaikan R2 berkebalikan. Penelitian ini dapat dilanjutkan dengan membandingkan hasil yang diperoleh dengan menggunakan metode lain dalam machine learning seperti Support Vector Machine (SVM), random forest, Artificial Neural Network (ANN), dan lain-lain.
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