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All Journal Media Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Tatik Widiharih

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 2977112

Decision Sciences, Operations Research & Management Other

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PENGENDAIAN MULTIVARIATE DENGAN DIGRAM KONTROL MEWMA ENGGUNAKAN METODE SIX SIGMA (STUDI KASUS PT FUMIRA SEMARANG TAHUN 2019) Puspita Ayu Utami; Mustafid Mustafid; Tatik Widiharih
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

As one of the biggest corrugation producing industries, PT Fumira Semarang is always required to fulfill customer needs by continuously improving their quality. Galvanized Steel is the raw material for the production of corrugation at PT Fumira Semarang. There are three important quality characteristics to be controlled in order that the results of galvanized steel production fit the standards to be manufactured as corrugation are waves, rust, and scratches. Six Sigma is a method for controlling quality. Six Sigma has focus on reducing defects, by standard 3,4 defects per one million opportunties. This research aims to identify the galvanized steel production process using Six Sigma method with MEWMA control chart and the capability of the process to fit the standards. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is a tool used to control multivariate process averages. The result of this research are MEWMA control chart with lambda 0.7 shows that the process is controlled statistically and The Sigma value for waves is 2,33, for rust 2,05, and for scratches 2,64. And the research reveals the galvanized steel production process has not fit to the standard because the process capabilty index is 0,2805. Keywords: Galvanized Steel, Quality Control, Six Sigma, Multivariate Exponentially Weighted Moving Average, Process Capability Analysis

PENERAPAN DIAGRAM KENDALI MAXIMUM MULTIVARIATE CUMULATIVE SUM (MAX-MCUSUM) PADA PENGENDALIAN KUALITAS PRODUK KACANG (Studi Kasus: Produk Kacang Garing di PT XY) Sintia Rizki Aprilianti; Tatik Widiharih; Sudarno Sudarno
Jurnal Gaussian Vol 10, No 4 (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.v10i4.30139

Now, Statistical quality control be a particular concern to large companies.PT XY is one of the largest nut company in Indonesia that has implemented the quality standards of a product. Max-MCUSUM control chart becomes a tool that is graphically used to monitor and evaluate whether the process is under control or nut. Based on Cheng and Thaga (2005), Max-MCUSUM control chart takes precedence over detecting small shift based on average and variability in industry data. The quality characteristic of Kacang Garing will be variables, namely broken nut skin, bean seed 1, and foam nut skin. Max-MCUSUM control chart is controlled with the control limit (h) from ARL (Average Run Length) simulation of 370 is 429,69. ARL is an average of samples that need to be decribed before it goes out of control. The research continued with multivariate capability process with MCp worth 0,905 and MCpk worth 1,355. Those value indicates that Kacang Garing has met the quality specification stipulated by PT XY. Broken nut skin becomes the most dominant cause based on pareto chart and carried out analysis by using fishbone chart so that is known the main factor causing broken nut skin are machine, material, and method.

KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X) Chalimatus Sa'diah; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 2 (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.v10i2.29923

One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.

ANALISIS VARIANSI PADA RANCANGAN BUJUR SANGKAR YOUDEN DENGAN DUA DATA HILANG Amalina Sari Dewi; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Youden Square Design (YSD) is an incomplete latin square design with at least one row/column which can’t run in an experiment. In this research we took 5x4 YSD (one column is not runned in an experiment). This design has a balance characteristic from a balanced incomplete block design where all treatments appears with the same number in each row. Missing data can occur in YSD. In this discussion, YSD with two missing data was used. Missing data is estimated by an iterative method then we arrange analysis of variance and LSD test. Analysis of variance with two missing data in YSD is calculated by adjusting the treatment sum of squares with it’s bias value and the total degrees of freedom and error degrees of freedom are substracted by two. LSD test is carried out if the treatment has a significant effect to the response. To clarify the discussion in YSD, example of application in the field of industry is given by observing the effect of the assembly method to the length of assembly time of X component. The assembly method has an effect to the length of assembly time of X component and if the missing data are and so the suggested assembly method is E method because it has the fastest average assembly time. Keywords: YSD, Missing Data, Analysis of Variance, LSD Test

KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Isna Wulandari; Hasbi Yasin; Tatik Widiharih
Jurnal Gaussian Vol 9, No 3 (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.v9i3.27416

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN.

PENGGUNAAN WEIGHTED PRODUCT (WP) DAN ELIMINATION ET CHOIX TRANDUSIANT LA REALITÉ (ELECTRE) DALAM MENENTUKAN TEMPAT BERBELANJA KEBUTUHAN RUMAH TANGGA TERFAVORIT BERBASIS GUI MATLAB (Studi Kasus : Ritel Modern di Kota Surakarta) Syavhana Yusricha Zuhri Putri; Sudarno Sudarno; Tatik Widiharih
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.v6i4.30384

Surakarta is one of the fastest growing cities. One of them is marked by many shopping places to fulfill household needs. This causes competition between shopping places. Based on these conditions, a method is needed to assess the customer's favorite shopping place to create a shopping place that matches the customer's expectations. Methods that can be applied to choose the most favorite shopping place are WP and ELECTRE. These two methods can make a decision to get a favorite alternative based on certain criteria in solving Multi Attribute Decision Making (MADM) problems. There are eight alternatives and thirteen criterias. The alternatives are Indomaret Point, Alfamidi, Superindo, Lotte Mart, Hypermart, Carrefour, Luwes Group and Goro Assalam. While the criterias are price of goods, service, stock of goods, arrangement of goods, hygiene, location, ease of transaction, facility, employee appearance, place comfort, employee friendliness, security, and courtesy of employee. The result of this study shows that the favorite type of shopping place for household needs according to WP and ELECTRE method is Carrefour. This study also produces a GUI Matlab programming application that can help users in performing data processing.Keyword : MADM, WP, ELECTRE, Shopping place, GUI Matlab

PERBANDINGAN DIAGRAM KONTROL MEWMA DAN DIAGRAM KONTROL T2 HOTELLING UNTUK PENGENDALIAN KUALITAS PRODUK KAIN POLYESTER (Studi Kasus : PT Daya Manunggal Kota Salatiga) Abdiyasti Nurul Arifa; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fabrics is one of the most important needs of human life, so demand for clothing is greatly increased. Polyester fabric is a superior product at PT Daya Manunggal Salatiga because it has good quality. The quality of the fabric is very important because it is very influential in the competition to attract consumer interest. To maintain the consistency of the quality of the products produced in accordance with specifications, it is necessary to control the quality of the production process. The quality characteristics used in the production process of polyester fabric are thick layers, thin layers, two weft threads partially and two weft threads one more interconnected with one another, so multivariate control diagrams are used. Multivariate Exponentially Weighted Moving Average (MEWMA) and T2 Hotelling are control diagrams for monitoring mean process. The results showed that the MEWMA control diagram with lambda 0.7 yielded controlled results with a BKA value of 14.56021. Whereas in the Hotelling T2 control diagram a data reduction of four revisions was made to achieve controlled results with a final BKA value of 10.10928. The controlled production process obtained multivariate process capability values of 0.9672105 <1 which means the process is not capable. Comparison of results from the two methods shows that the MEWMA control diagram is more sensitive than the T2 Hotelling control diagram.Keywords: Fabric, Multivariate Exponentially Weighted Moving Average (MEWMA), Hotelling T2, Process Capability Analysis

IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA Ghina Nabila Saputro Putri; Dwi Ispriyanti; Tatik Widiharih
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.v11i1.33996

Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed the accuracy comparison of both methods used the Modified Partition Coefficient (MPC) cluster validity. The clustering process was carried out five times by the number of clusters ranging from 3 to 7 clusters. The results of the study showed the Fuzzy C-Means method is a better method compared to the Fuzzy Possibilistic C-Means method in clustering data tweets, with the number of clusters formed is 4. The content type formed is related to promo, discount, cashback, prize quizzes, and event promotions organized by Tokopedia. Content with the highest average number of retweets and likes is about automotive deals, sports tools, and merchandise offerings. So, that PT Tokopedia can use this content type as a tool for advertising on Twitter because it gets more likes by followers of @tokopedia.Keywords: Data Tweets, Clustering, Fuzzy C-Means, Fuzzy Possibilistics C-Means, Modified Partition Coefficient.

VALUE AT RISK PADA PORTOFOLIO SAHAM DENGAN COPULA ALI-MIKHAIL-HAQ Delsy Nurutsaniyah; Tatik Widiharih; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is one alternative to increase assets in the future. Investors can invest in a portfolio to reduce the level of risk. Value at Risk (VaR) is a measuring tool that can calculate the worst loss over a given time period at a given confidence level. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is used to model data with high volatility. The teory of copula is a powerful tool for modeling joint distribution for any marginal distributions. Ali-Mikhail-Haq copula from Archimedean copula family can be applied to data with dependencies τ between -0.1817 to 0.3333. This research uses Ali-Mikhail-Haq copula with a Monte Carlo simulation to calculate a bivariate portfolio VaR from a combination stocks of PT Pembangunan Perumahan Tbk. (PTPP), PT Bank Tabungan Negara Tbk. (BBTN), and PT Jasa Marga Tbk. (JSMR) in the period of March 3, 2014 - March 1, 2019. The results of VaR calculation on bivariate portfolio for next 1 day period obtained the lowest VaR is owned by bivariate portfolio between PTPP and JSMR with a weight of 30% and 70% at confidence level of 99%, 95%, and 90% respectively are 4.014%, 2.545%, and 1.876%.Keywords: Value at Risk, GARCH, Ali-Mikhail-Haq Copula, Monte Carlo

PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017) Eka Lestari; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords: time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

Co-Authors Abdiyasti Nurul Arifa Abdul Hoyyi Ade Irma Pramudita Agus Rusgiyono Amalina Sari Dewi Anestasya Nur Azizah Arief Rachman Hakim Aulia Resti Bramesa Winanda Nugraha Budi Warsito Chalimatus Sa'diah Cylvia Evasari Margaretha Delsy Nurutsaniyah Denny Jales Manalu Dheanisa Widyanti Di Asih I Maruddani Diah Aliyatus Saidah Diah Safitri Dilla Retno Deswita Dinda Virrliana Ramadhanti Dwi Ispriyanti Eka Lestari Farisiyah Fitriani Fath Ezzati Kavabilla Fithroh Oktavi Awalullaili Genisia Pramestiloka Aulia Ghina Nabila Saputro Putri Gunardi . Hasbi Yasin Immawati Ainun Habibah Irma Dwi Tyana Isna Wulandari Iut Tri Utami Moch. Abdul Mukid Muhamad Syukron Mustafid Mustafid Nesari Nesari Nisa Afida Izati Puspita Ayu Utami Puspita Kartikasari Qorina Rara Sartika Rita Rahmawati Rizki Pradipto Widyantomo Rukun Santoso Ryan Anugrah Salma Farah Aliyah Salma Innassuraiya Sensiani Sensiani Setya Adi Rahmawan Silvy ‘Aina Salsabila Sintia Rizki Aprilianti Sisca Indah Pratiwi Sri Haryatmi Sudarno Sudarno sudarno sudarno Sugito Sugito Suparti Suparti Syavhana Yusricha Zuhri Putri Tita Aulia Edi Putri Winahyu Handayani

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