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All Journal Media Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Statistika dan Matematika (Statmat)

Tarno Tarno

Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang

Author-ID : 3131521

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal) Reza Dwi Fitriani; Hasbi Yasin; Tarno Tarno
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.v10i1.30243

The Family Planning Program (KB) launched by the Government of Indonesia to address the problem of population control does not always produce the desired program results. In 2017, there were 7 users of the IUD contraceptive type of contraceptive who failed from 1,102 new IUD users in Kendal Regency so that the ratio of success and failure to the IUD KB program when compared to users of the new IUD KB is 0.64%: 99.36% . The ratio of success and failure of family planning programs which tend to be unbalanced makes it difficult to predict. One of the handling imbalanced data is oversampling, for example using Random Oversampling (ROS). Naive Bayes is used for classification because it’s easy and efficient learning model. The data in this study used 14 independent variables and 1 dependent variable. The results of this study indicate that the G-mean of Naive Bayes is less than 60%. The G-mean of ROS-Naive Bayes is 96.6%. It can be concluded that in this research, the ROS-Naive Bayes method is better than the Naive Bayes method for detecting the success status of IUD family planning in Kendal Regency. Keywords: Naive Bayes, Random Oversampling, G-mean

GENERALIZED PARETO DISTRIBUTION UNTUK PENGUKURAN VALUE AT RISK PADA PORTOFOLIO SAHAM SYARIAH DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Desi Nur Rahma; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market is one of long-term investment alternative. One of the traded products is stock, including sharia stock. The risk measurement is an important thing for investor in other that can decrease investment loss. One of the popular methods now is Value at Risk (VaR). There are many financial data that have heavy tailed, because of extreme values, so Value at Risk Generalized Pareto Distribution is used for this case. This research also result a Matlab GUI programming application that can help users to measure the VaR. The purpose of this research is to analyze VaR with GPD approach with GUI Matlab for helping the computation in sharia stock. The data that is used in this case are PT XL Axiata Tbk, PT Waskita Karya (Persero) Tbk, dan PT Charoen Pokphand Indonesia Tbk on January, 2nd 2017 until May, 31st 2017. The results of VaRGPD are: EXCL single stock VaR 8,76% of investment, WSKT single stock VaR 4% of investment, CPIN single stock VaR 5,86% of investment, 2 assets portfolio (EXCL and WSKT) 4,09% of investment, 2 assets portfolio (EXCL and CPIN) 5,28% of investment, 2 assets portfolio (WSKT and CPIN) 3,68% of investment, and 3 assets portfolio (EXCL, WSKT, and CPIN) 3,75% of investment. It can be concluded that the portfolios more and more, the risk is smaller. It is because the possibility of all stocks of the company dropped together is small. Keywords: Generalized Pareto Distribution, Value at Risk, Graphical User Interface, sharia stock

PENGUKURAN KINERJA PORTOFOLIO OPTIMAL SAHAM LQ45 MENGGUNAKAN METODE CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) Kristika Safitri; Tarno Tarno; Abdul Hoyyi
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.v10i2.29414

Investment is planting some funds to get profit and the stock is one of the type of investment in fincancial that the most interested for investors. To avoid the risk of investing, investors try to diversify their invesments by using portfolio. Stock portfolio is investment which comprised of various stocks from different companies, with the expect when the price of one stock decreases, while the other increases, then the investments do not suffer losses. Models that can be used to make a portfolio, one of them is Capital Asset Pricing Model (CAPM) and Liquidity Adjusted Capital Asset Pricing Model (LCAPM). CAPM is a model that connects expected return with the risk of an asset under market equilibrium condition. LCAPM is a method of new development of the CAPM model which is influenced by liquidity risk. To analyze whether the formed portfolio have a good performance or not, so portfolio perfomance assessment will be done by using The Sharpe Index. This research uses data from closing prices, transaction volume and volume total of LQ45 Index stock on period March 2016-February 2020 and then data of JCI and interest rate of central bank of the Republic of Indonesia. Based on The Sharpe Index, optimal portfolio is LCAPM model portfolio with 3 stock composition and the proportion investment are 32,39% for LPPF, 49,86% for SRIL and 17,75% for TLKM. Keywords: LQ45 Index, Portfolio, Capital Asset Pricing Model (CAPM), Liquidity Adjusted Capital Asset Pricing Model (LCAPM), The Sharpe Index.

PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT Annisa Sugesti; Moch. Abdul Mukid; Tarno Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

PERAMALAN HARGA CABAI MERAH MENGGUNAKAN MODEL VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (STUDI KASUS: HARGA CABAI MERAH PERIODE JANUARI 2012 SAMPAI DENGAN DESEMBER 2019 DI PROVINSI JAWA BARAT) Aulia Rahmatun Nisa; Tarno Tarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Chili is one of the vegetable commodities that has high economic value, because of it’s role is large enough to supply domestic needs as an export commodity in the food industry. The price of red chilliesalways increase in the month of Eid al-Fitr. This is due to the large number of people who use Red Chili as food they consume. Shifting the moon during the Eid al-Fitr will form a seasonal system with different periods, which became known as the Moving Holiday Effect. One of the calendar variation models used to eliminate the Moving Holiday Effect and has a simple processing flow is the RegARIMA model. The RegARIMA model is a combination of linear regression with ARIMA. In the regression model the weighting matrix is used as an independent variable and the price of red chili as the dependent variable. The weight value is obtained based on the number of days that affect Eid, which is 14 days. Based on the analysis the red chili price data in West Java Province with the period of January 2012 to December 2018, the RegARIMA model (1.0,0)(0,1,1) 12 is the best model because it has the smallest AIC. Forecasting results in 2020 showed an increase in the price of red chili in West Java occurred in May to coincide with the Eid al-Fitr holiday which fell on May 24, 2020, the sMAPE value obtained by 24.96%. It means, the forecast still in the level of reasonableness.

PEMILIHAN INPUT MODEL ANFIS UNTUK DATA RUNTUN WAKTU MENGGUNAKAN METODE FORWARD SELECTION DILENGKAPI GUI MATLAB (Studi Kasus: Jumlah Penumpang Kereta Api di Wilayah Jawa Non Jabodetabek) Tiara Sukma Valentina; Tarno Tarno; Alan Prahutama
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the methods that is commonly used to identify a time series model and input ANFIS (Adaptive Neuro Fuzzy Inference System) model is PACF plot. The PACF plot shows the correlation between current observations and previous observations visually. Formally there are several methods that are known to effectively identify ANFIS inputs, one of which is the Forward Selection regression method. With the same concept as PACF, the process of selecting ANFIS inputs using the Forward Selection method is based on the order of the correlatiom between the predictors of the response which is indicated by the magnitude of the correlation coefficient. This study discusses the Forward Selection method in simulation data that has stationary characteristics, stationary with outliers, non stationary, non stationary with outliers and implements data on the number of train passengers in the Non Jabodetabek Java region. ANFIS modeling on data of the number of train passengers in the Non Jabodetabek Java region produces AIC of 15,5617, MAPE of 8,5093% and RMSE of 571,33691. The result of this study is equipped with a GUI which is useful as a tool to facilitate users in processing data.Keywords : PACF Plot, Forward Selection, ANFIS, non stasionary, outlier

PENYUSUNAN DAN PENERAPAN METODE REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK ANALISIS DATA KURS IDR/USD Lamik Nabil Mu'affa; Tarno Tarno; Suparti Suparti
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The exchange rate of rupiah is one of the important prices in an open economy because the exchange rate can be used as a tool to measure the economic condition of a country. The movement of the rupiah exchange rate affected the Indonesian economy, maintaining the stability of the rupiah exchange rate became an important thing to do. In an effort to maintain the stability of the rupiah exchange rate, the factors that influence it must first be identified. Several factors affect the IDR / USD exchange rate, namely the large trade price index, foreign exchange reserves, money supply and interest rates. In this study, the Regression Adaptive Neuro Fuzzy Inference System (RANFIS) method was used to analyze the effect of predictor variables on IDR / USD exchange rates. The optimal RANFIS model is strongly influenced by three things, namely the determination of input predictor variable, membership functions, and number of clusters. Determination of the optimal RANFIS model is measured based on the smallest MAPE in-sample. Based on empirical studies applied to predictor variables on IDR / USD exchange rates, it was found that the RANFIS model was optimal, namely with 3 predictor variable inputs consisting of large trade price index variables, money supply and interest rates; with the gauss membership function; 2 clusters and rules produce an MAPE in-sample of 1.93% and an MAPE out-sample of 2.68%, so the performance of the RANFIS model has a very good level of accuracy.

PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH Endah Fauziyah; Dwi Ispriyanti; Tarno Tarno
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.v10i4.33102

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.

EXPECTED SHORTFALL DENGAN EKSPANSI CORNISH-FISHER UNTUK ANALISIS RISIKO INVESTASI SEBELUM DAN SESUDAH PANDEMI COVID-19 DILENGKAPI GUI R Reyuli Andespa; Di Asih I Maruddani; Tarno Tarno
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.v11i2.35457

In financial analysis, risk measurement is critical. Stocks are a sort of financial asset investment that is in high demand by investors. Expected Shortfall is one of the strategies used to assess stock investing risk (ES). ES is a risk metric that considers losses in excess of the Value at Risk (VaR). Cornish-Fisher Expansion (ECF) is used to calculate ES with data that deviates from normality and takes into account skewness and kurtosis values. This study used data from the closing price of Sri Rejeki Isman Tbk (SRIL) shares before and during the Covid-19 Pandemic (14 January 2019 to 18 May 2021), with non-normally distributed returns. According to the calculations, the risk that investors will bear using the ES ECF value for the next day before the Covid-19 Pandemic is 1.1752 and after the Covid-19 Pandemic is 3.3177% at a 95% confidence level. The risk that investors will bear for the next day before the Covid-19 Pandemic is 5.8928%, and after the Covid-19 Pandemic is 10.3703%, based on a 99% confidence level. The findings of the study reveal that the higher the amount of trust, the higher the risk.

KLASIFIKASI MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK DETEKSI AWAL RISIKO DIABETES MELITUS Chea Zahrah Vaganza Junus; Tarno Tarno; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 3 (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.11.3.386-396

Diabetes Mellitus is one of the four leading causes of death and therefore possible treatments are of crucial importance to the world leaders. Prevention and control of Diabetes Mellitus are often done by implementing a healthy lifestyle. Thus, both people with risk factors and people diagnosed with Diabetes Mellitus can control their disease in order to prevent complications or premature death.. For a proper education and standardized disease management the early detection of Diabetes Mellitus is necessary, which led to this conducted study about the classification of early detection of Diabetes Mellitus risk by utilizing the use of Machine Learning. The classification algorithms used are the Support Vector Machine and Random Forest where the performance analysis of the two methods will be seen in classifying Diabetes Mellitus data. The type of data used in this study is secondary data obtained from the official website of the UCI Machine Learning Repository consisting of 520 diabetes patient data taken from Sylhet Diabetic Hospital in Bangladesh with 16 independent variables and 1 dependent variable. The dependent variable categorizes the test result into positive and negative Diabetes Mellitus classes. The results of this study indicate that the Random Forest classification algorithm produces a better classification performance on Accuracy (98.08%), Recall (97.87%), Precision (98.92), and F1_Score (88.40%).

Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Adinda Putri Muzdhalifah Agus Rusgiyono Agustinus Salomo Parsaulian Alan Prahutama Anes Desduana Selasakmida Annisa Sugesti Arya Despa Ihsanuddin Aulia Rahmatun Nisa Budi Warsito Chea Zahrah Vaganza Junus Dede Andrianto Desi Nur Rahma Di Asih I Maruddani Dwi Ispriyanti Endah Fauziyah Fredy Yoseph Marianto Hasbi Yasin Hidayatul Musyarofah Inarotul Amani Rizki Ananda Inas Husna Diarsih Khusnul Umi Fatimah Kristika Safitri Lamik Nabil Mu'affa Lina Tanasya Moch. Abdul Mukid Mustafid Mustafid Pandu Anggara Pualam Wahyu Ratiasasadara Puspita Kartikasari Putriaji Hendikawati Reyuli Andespa Reza Dwi Fitriani Sri Sumiyati Subanar . Sudarno Sudarno Sugito Sugito Suparti Suparti Tiara Sukma Valentina Triastuti Wuryandari Yayan Setiawan

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