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All Journal Jurnal Ilmu Dasar Statistika Journal of Oceanography Jurnal Gaussian Novum : Jurnal Hukum Jurnal Statistika Universitas Muhammadiyah Semarang Dia: Jurnal Administrasi Publik The Indonesian Biomedical Journal Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Bina Hukum Lingkungan KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Surya Kencana Satu : Dinamika Masalah Hukum dan Keadilan JURNAL USM LAW REVIEW Jurnal Darma Agung Legal Spirit MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur) Jurnal Hukum Lex Generalis Jurnal Idea Hukum Indonesian Journal of Librarianship Jurnal Ilmiah Wahana Pendidikan Decode: Jurnal Pendidikan Teknologi Informasi KARYA: Jurnal Pengabdian Kepada Masyarakat Jurnal Administrasi Pemerintahan Desa Bina Hukum Lingkungan Deposisi: Jurnal Publikasi Ilmu Hukum Kultura: Jurnal Ilmu Sosial dan Humaniora Jurnal Mediasas : Media Ilmu Syari'ah dan Ahwal Al-Syakhsiyyah Informasi interaktif : jurnal informatika dan teknologi informasi
Arief Rachman Hakim
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Religion Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Gaussian

 1 
PEMODELAN PERSENTASE PENDUDUK MISKIN DI KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hakim, Arief Rachman; Yasin, Hasbi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.43 KB) | DOI: 10.14710/j.gauss.v3i4.8068

Abstract

Regression analysis is a statistical analysis that models the relationship between the response variable and the predictor variable. Geographically Weighted Regression (GWR) is the development of linear regression with the added factor of the geographical location where the response variable is taken, so that the resulting parameters will be local. Mixed Geographically Weighted Regression (MGWR) has a basic concept that is a combination of a linear regression model and GWR, by modeling variables that are local and which are global variables. Methods for estimating the model parameters MGWR no different from the GWR using Weighted Least Square (WLS). Selection of the optimum bandwidth using the Cross Validation (CV). Application models MGWR the percentage of poor people in the district and town in Central Java showed MGWR models that different significantly from the global regression model. As well as models generated for each area will be different from each other. Based on the Akaike Information Criterion (AIC) between the global regression model, the GWR and MGWR models, it is known that MGWR models with Gaussian kernel weighting function is the best model is used to analyze the percentage of poor in the counties and cities in Central Java because it has the smallest AIC value.Keywords: Akaike Information Criterion, Cross Validation, Kernel Gaussian function, Mixed Geographically Weighted  Regression, Weighted Least Square.
PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON Ridho, Wahyu Anwar; Wuryandari, Triastuti; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.372-381

Abstract

The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.
PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE MEDIAN VARIANCE PADA SAHAM JAKARTA ISLAMIC INDEX (JII) SEKTOR CONSUMER GOODS Faadillah, Muhamad Nabil; Maruddani, Di Asih I; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.487-498

Abstract

Investment is an activity to place owned assets or funds in a product hoping that there will be profits in the future. This case study was conducted by calculating the optimal portfolio using the median variance and calculating Value at Risk (VaR) using the historical simulation method. Median Variance in portfolio optimization is more suitable to be used as an investment guide because the method is not fixated on the normality distribution of the data. The data used is the Jakarta Islamic Index (JII) daily stock price data for 1 year period, which start from April 23th 2021 until April 23th 2022. The stock price used in this research is the closing price data each day during the period. The return data is used to find the weight using Median Variance method so that an optimal portfolio is formed. it is known that the Value at Risk with a confidence level of 95% and the next 1-day time period is -0,024088232 or -2,41% by investing 1% of the funds into UNVR.JK shares., by 58 % to shares of ICBP.JK, by 57% to shares of INDF.JK, by 1% to shares of JPFA.JK, and the last -17% to KLBF.JK shares is 2.41%. 
ANALISIS KEPUASAN TERHADAP LAYANAN APLIKASI DOLTINUKU DENGAN MENGGUNAKAN METODE STRUCTURAL EQUATION MODELING-PARTIAL LEAST SQUARE (SEM-PLS) Ul Haq, Hasna Faridah Dhiya; Hakim, Arief Rachman; Suparti, Suparti
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.605-615

Abstract

Doltinuku is an application that is used to buy and sell products and services online from MSME in Gedawang, Banyumanik Sub-District, Semarang City which was just launched in June 2021. Because this application is still new, this application needs to be developed by looking at users' satisfaction. This study wants to determine Doltinuku customer satisfaction using Structural Equation Modeling-Partial Least Square (SEM-PLS) method. PLS is an alternative method of SEM that is able to handle variance-based problems. In this study, customer satisfaction is measured through variables such as Quality, Information, Reputation, and Trust. Based on the results of the analysis, variables that have a significant effect on customer satisfaction are variable Quality and Reputation and have influence of 70.9% on satisfaction whose value is obtained from the R2 value. Then the variables that have no significant effect are variable Information and variable Trust.
METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG Jannah, Berliana; Utami, Iut Tri; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.445-452

Abstract

The purpose of this research is to group PTS that have performance achievements in five years, through the quality of Human Resources and Students (Input), the quality of Institutional Management (process), the quality of Short-Term Performance Achievements (Output) and the quality of Long-Term Performance Achievements (Outcome). In addition, it can also be seen from the form of PTS, PTS Accreditation and PTS Research Performance. This PTS grouping uses mixed data, namely numerical data and categorical data. The method used for grouping mixed data is the ROCK ensemble method (Robust Clustering Using Links). The results of clustering numerical data obtained the optimum number of groups 3, on categorical data obtained the optimum group 4. After clustering each type of data and merging and clustering obtained the optimum group 3 with a threshold (θ) is 0.2. The results of each group are: low quality consist of 29 PTS, medium quality consist of 7 PTS, and high quality there is 1 PTS. The results of this research can be used to cluster private universities in Semarang City, so that it can be used as a reference for prospective students in choosing private universities in Semarang, and can be referenced to the Central Java LLDIKTI in determining the quality of private universities in Semarang City.
PENGGUNAAN MIXTURE MODEL KERNEL-GENERALIZED PARETO DISTRIBUTION DAN D-VINE COPULA DALAM MENGANALISIS UKURAN PELANGGARAN DATA Dzikra, Fathiyyah Yolianda; Wilandari, Yuciana; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.392-402

Abstract

The research conducted on the 2015-2021 Data Breach Report in the U.S. Department of Health and Human Services is a study related to the estimation and modeling of the breach sizes each type of entity using the Kernel-Generalized Pareto Distribution Mixture Model method, as well as the estimation of the dependence of breach sizes between years with the D-Vine Copula. The D-Vine Copula can accommodate the complex dependencies demonstrated by data breach reports across all enterprise categories. Before researching with D-Vine Copula, we will first model and estimate breach size parameters for each type of entity using the Mixture Model Kernel-Generalized Pareto Distribution (GPD). The Mixture Model can accommodate large data breach sizes via GPD and also allows the use of non-parametric kernel distributions to model smaller data breach sizes. The data resulting from the logarithmic transformation of entity data in the Business Associate and Healthcare Provider types has a right short-tail with Weibull distribution, while the Health Plan category has a right heavy-tail with Frechet distribution. The three types of entity were estimated using the maximum likelihood Cross-Validation method. Dependency estimation with D-Vine Copula shows that the breach sizes between years measure has a positive dependency.
Co-Authors Abdul Hoyyi Afrianda, Charlina Retno Puteri Ahdi, Iwal Reza Alan Prahutama Aliansa, Wahyu Aljabar, Muhammad Isa Almuharrom, Fazar Ambarini , Shera Tri Aselina Pratidina Wrediningsih Asep Saepulrohman Baihaqi, Wiga Maulana Budi Warsito Budi Warsito Chiputra, Dhimas Wahyu Deden Ardiansyah Di Asih I Maruddani Dias Ayu Budi Utami, Dias Ayu Budi Djanggan Sargowo Dwi Agung Prasetyo, Dwi Agung Dwi Ispriyanti Dzikra, Fathiyyah Yolianda Endang Fatmawati Ermin Rachmawati Faadillah, Muhamad Nabil Fernandes Simangunsong, Fernandes Frengki, Muhammad Handirosiyanto, Ikhwan Hasbi Yasin Hasbi Yasin Herawati, Chania Putri Agustin Hermawan, Regita Cahyaningtyas Indratmoko, Daryll Alessandro Irma Damayanti Ismail, Mahrus Iut Tri Utami Jannah, Berliana Khomarudin Gilang Ramadhan Kosasih, Deny Poniman Leonardo Benito Maspaitella Lusi Agus Setiani Maulana, Syafiq Moch. Abdul Mukid Murdahayu Makmur Nanda Eka Prasetya Navydien, Miliarni Deida Novaria, Rachmawati Nurramadhan, Fadli Olandina Cahyani P Palupi, Aisyah Anudya Pinareswati, Shafira Tri Pinggala , Waode Prasetya , Syalaizha Febtria Putri Prastiawan, Andi Prastyadi Wibawa Rahayu Pratiwi, Yulita Dwi Puspitasari, Alvina Puspitasari, Rizki Dian Ridho, Wahyu Anwar riskiyah, Riskiyah - Riyan Hadithya Rizal Firmansyah Saputra, Indra Wahyu Sinambela, Nadiyah Hafidah Sisca Novalia Subarkah, Pungkas Sugiastari, Yuanita Putri Sugito Sugito sukristyanto, Agus Suparti Suparti Syahrir Syahrir Tarsadi, Tarsadi Triastuti Wuryandari Ul Haq, Hasna Faridah Dhiya Utomo, Khesya Khusnul Fadhilah Vella Septia Renanda Wardhani, Syanindita Puspa Yeremia, Dennis Yuciana Wilandari Yundari, Yundari Yunita Pipiet Sugandhi Zulkarnain, Steven Agilo