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All Journal Jurnal Matematika International Journal of Science and Engineering (IJSE) Media Statistika SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Science and Technology E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Riset Teknologi Pencegahan Pencemaran Industri STATISTIKA
Hasbi Yasin
Department Of Statistics, Faculty Of Sciences And Mathematics, Diponegoro University
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Jurnal Gaussian

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ESTIMASI PARAMETER DAN PENGUJIAN HIPOTESIS MODEL GEOGRAPHICALLY WEIGHTED GENERALIZED GAMMA REGRESSION Hasbi Yasin; Purhadi Purhadi; Achmad Choiruddin
Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i1.33990

Abstract

Each location has unique characteristics, which are different from other locations which give rise to spatial effects between locations. Therefore, the Generalized Gamma Regression (GGR) model is not suitable to be applied to this problem. The solution is to use a Geographically Weighted Generalized Gamma Regression (GWGGR) model which produces different parameters for each observation location. This study aims to estimate GWGGR parameters using the Berndt-Hall-Hall-Hausman (BHHH) algorithm. After parameter estimation is performed, the hypothesis testing procedure is used to test the similarity of parameters between the generalized gamma regression and GWGGR and to test the significance of the independent variables in the model, either simultaneously using the Maximum Likelihood Ratio Test (MLRT) or partially using the Z-test. Keywords: BHHH, Generalized Gamma, GGR, GWGGR, MLRT.
PEMODELAN TRANSFORMASI FAST-FOURIER PADA VALUASI OBLIGASI KORPORASI (Studi Kasus: PT. Bank Danamon Tbk, PT. Bank CIMB Niaga Tbk, dan PT. Bank UOB Indonesia Tbk) Ubudia Hiliaily Chairunnnisa; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30937

Abstract

The basic assumption that is often used in bond valuations is the assumption on the Black-Scholes model. The practical assumption of the Black-Scholes model is the return of assets with normal distribution, but in reality there are many conditions where the return of assets of a company is not normally distributed and causing improperly developed bond valuation modeling. The Fast-Fourier Transform model (FFT) was developed as a solution to this problem. The Fast-Fourier Transformation Model is a Fourier transformation technique with high accuracy and is more effective because it uses characteristic functions. In this research, a modeling will be carried out to calculate bond valuations designed to take advantage of the computational power of the FFT. The characteristic function used is the Variance Gamma, which has the advantage of being able to capture data return behavior that is not normally distributed. The data used in this study are Sustainable Bonds I of Bank Danamon Phase I Year  2019 Series B, Sustainable Bonds II of Bank CIMB Niaga II Phase IV Year 2018 Series C, Sustainable Subordinated Bonds II of Bank UOB Indonesia Phase II 2019. The results obtained are FFT model using the Variance Gamma characteristic function gives more precise results for the return of assets with not normal distribution.  Keywords: Bonds, Bond Valuation, Black-Scholes, Fast-Fourier Transform, Variance Gamma
ANALISIS MODEL ANTREAN NON-POISSON DAN UKURAN KINERJA SISTEM BERBASIS GUI WEB INTERAKTIF MENGGUNAKAN R-SHINY (Studi Kasus: Bus di Terminal Penggaron Kota Semarang) Devi Wijayanti; Sugito Sugito; Hasbi Yasin
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29010

Abstract

Since September 1, 2018, The Semarang City Government has diverted intercity bus stop within the province from Terboyo Terminal to Penggaron Terminal, resulting in an imbalance of movement and capacity of the Penggaron Terminal which causes queue of bus. Non-Poisson queue is a queue model in which the arrival and service distribution do not have a Poisson distribution or do not have an Exponential distribution. The study was conducted on buses entering the Penggaron Bus Station with the destination of Jepara, Kedungjati, Juwangi, Yogyakarta, Kudus/Pati/Lasem, Pekalongan/Tegal, and Purwokerto/Purworejo. The main goal of this project is to identify the queue model of Non-Poisson and calculate the measure of system performance using the GUI R. One of the programs in R that can create an interactive web-based GUI (Graphical User Interface) is R-Shiny. R-Shiny is a toolkit of R programs that can be used to create online programs. The distribution test obtained using the EasyFit program. The bus queue model of Jepara is (DAGUM/GEV/4):(GD/∞/∞), the queue model of Kedungjati is (GPD/ DAGUM/1):(GD/∞/∞), the queue model of Juwangi is (GEV/ GEV/1):(GD/∞/∞), the queue model of Yogyakarta is (DAGUM/ DAGUM/1) : (GD/∞/∞), the queue model of Kudus/Pati/Lasem is (DAGUM/GEV/1):(GD/∞/∞), the queue model of Pekalongan/Tegal is (GEV/DAGUM/1):(GD/∞/∞), and the queue model of Purwokerto/Purworejo is (GPD/DAGUM/1) : (GD/∞/∞). 
PENERAPAN METODE WAVELET NEURO-FUZZY SYSTEM (WNFS) DALAM MEMPREDIKSI HARGA BERAS DUNIA (Studi Kasus: Harga Beras Thailand sebagai Harga Acuan Dunia) Sri Endah Moelya Artha; Hasbi Yasin; Budi Warsito
Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v6i4.30381

Abstract

Rice trade is one of the food resistance components in terms of its availability. The comprehensive integration between international commodity rice prices and domestic prices encourage the prediction of world rice prices, using the Thai rice price as the world's reference price. In this study, the wavelet neuro-fuzzy system which combines the wavelet transform and the neuro-fuzzy technique has been applied to monthly predict the world rice price. The observed monthly rice price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), and then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for monthly predicting world rice prices for six months in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno zero-order model. Obtained results showed that the WNFS method can be used to predict the world rice price, with the error value resulted from learning process of MSE 20,69097 and MAPE 0,65584%. While the error measurement results for the six months in advance prediction shows the acquisition of MSE 3610,14847 and MAPE 13,62334%. Keywords : Prediction of Monthly World Rice Price, Maximal Overlap Discrete Wavelet Transform, Neuro-fuzzy System.
ANALISIS SENTIMEN DATA ULASAN APLIKASI RUANGGURU PADA SITUS GOOGLE PLAY MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER DENGAN NORMALISASI KATA LEVENSHTEIN DISTANCE Hindun Habibatul Mubaroroh; Hasbi Yasin; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i2.35472

Abstract

One form of technological development in education is the increasing number of online based learning. More than that, during this period of Covid-19 pandemic distance education was tried by the government that requires learning are done online. The online learning application that is the implementation of this technological development continues to show its existence. Many non-formal educational companies are available, one of which is the Ruangguru, getting a nickname as a number one learning application requires the Ruangguru to continue and improve the performance. Users of the Ruangguru application can communicate a response to Ruangguru through the review feature available on the google play site. The reviews that have been written can be analyzed how the user sentiment is whether positive or negative using Multinomial Naïve Bayes. This method is used because it is easy to use with simple structures and gives high accuracy values. The model will be selected using 10-fold cross validation method to get the model with the best accuracy. The normalization phase of words was also perfected using Levenshtein Distance method that was proven to add accuracy value. Performance result using Multinomial Naïve Bayes by adding Levenshtein Distance method to fix the words gives an average accuracy value of 88,20% with the 8th fold as the fold with the best accuracy value of 94%.
PEMODELAN JUMLAH WISATAWAN DI JAWA TENGAH MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE - SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR) Innosensia Adella; Dwi Ispriyanti; Hasbi Yasin
Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i2.35473

Abstract

Space-time model is a model that can explain data with spatial and time characteristics. The Generalized Space Time Autoregressive (GSTAR) model is one of the generalized space-time models from the Space Time Autoregressive (STAR) model. The GSTAR model is more flexible when dealing with areas that have heterogeneous characteristics than the STAR model. The GSTAR model models time series data in multiple regions at once. This model can then be used to model data on the number of tourists in four regions in Central Java, namely Semarang, Jepara, Magelang and Semarang district for the 2014 to 2019 period. in Central Java. On the residual model, the Lagrange Multiplier Test is carried out and it is known that there is a correlation between the residuals. The modeling was continued by using the Generalized Space Time Autoregressive – Seemingly Unrelated Regression (GSTAR-SUR) model. GSTAR-SUR is one of the more efficient models used to model GSTAR with correlated residuals. Residual through the white-noise assumption test, it is found that the appropriate model is the GSTAR-SUR(2,1) model. This model can then be used in forecasting data on the number of tourists in Semarang, Jepara, Magelang and Semarang district in the next period
Co-Authors Abdul Hoyyi Achmad Choiruddin Adi Waridi Basyiruddin Adi Waridi Basyirudin Arifin Agus Rusgiyono Ajeng Arum Sari Alan Prahutama Alvita Rachma Devi Amanda Lucky Berlian Andreanto Andreanto Anggun Perdana Aji Pangesti Arief Rachman Hakim Arief Rachman Hakim Baluk, Andreas Pedo Bens Pardamean Budi Warsito Budi Warsito Danang Chandra Pradana, Danang Chandra Dani Al Mahkya Darwanto Darwanto Devi Wijayanti Dewi Setya Kusumawardani Dharmawan, Bagus Dwiky Dhea Kurnia Mubyarjati Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Dwi Hasti Ratnasari Dwi Ispriyanti Eko Siswanto Fadhilla Atansa Tamardina Felinda Arumningtyas Fiqria Devi Ariyani Gera Rozalia Hanien Nia H Shega Hari Susanta Nugraha Hendrian, Jody Hidayatul Musyarofah Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Inas Hasimah Inayati, Syarifah Indah Suryani Innosensia Adella Intan Monica Hanmastiana Isna Wulandari Ispriyansti, Dwi Johanes Roisa Prabowo Kadi Mey Ismail Kurniawan, Isma Dwi Lutfia Septiningrum Maghfiroh Hadadiah Mukrom Maria Odelia Mas'ad, Mas'ad Maulana Taufan Permana Mega Fitria Andriyani Meilia Kusumawardani, Meilia Moch. Abdul Mukid Mochammad Iffan Zulfiandri MUHAMMAD HARIS Muhammad Mujahid Muhammad Tahmid Muryanto Muryanto Muryanto, Muryanto Mustafid Mustafid Mutiara, Dinar Nova Delvia Nur Azizah Nur Indah Yuli Astuti, Nur Indah Yuli Pandu Anggara Purhadi Purhadi Puspita Kartikasari Ragil Saputra Rahmasari Nur Azizah Reza Dwi Fitriani Rezzy Eko Caraka Riama Oktaviani Samosir, Riama Oktaviani Rifki Adi Pamungkas, Rifki Adi Rita Rahmawati Rita Rahmawati Riza Fahlevi Rizki Brendita Br Tarigan Rose Debora Julianisa, Rose Debora Rukun Santoso Rung Ching Chen Saepudin, Yunus Sakhinah Abu Bakar Salma Farah Aliyah Sari, Ajeng Arum Sari, Indri Puspita Satriyo Adhy Setiawan Setiawan Setyoko Prismanu Ramadhan Siahaan, Rina Br Siska Alvitiani Siti Maulina Meutuah Sri Endah Moelya Artha Sudarno Sudarno Sudarno Sudarno Sugito Sugito - Sugito Sugito Suhartono Suhartono Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Tsania Faizia Ubudia Hiliaily Chairunnnisa Via Risqiyanti Wahyu Sabtika Wawan Sugiarto, Wawan Wulandari, Heni Dwi Wulandari, Isna Youngjo Lee Yuciana Wilandari Yudha Subakti, Yudha Zulfa Wahyu Mardika, Zulfa Wahyu