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

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Diversifikasi Olahan Ikan Bandeng oleh UKM Primadona dalam Program Pengabdian IbPE 2016-2018 Sugito Sugito; Alan Prahutama; Tarno Tarno; Abdul Hoyyi
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 10, No 1 (2019): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v10i1.3556

Abstract

Ikan bandeng merupakan bahan makanan yang tinggi akan protein, vitamin dan mineral. Salah satu cara untuk meningkatkan pemasaran adalah mix marketing, salah satunya adalah mix marketing produk. Mix marketing produk yang dapat dilakukan adalah dengan diversifikasi produk. Olahan ikan bandeng yang terkenal adalah di kabupaten Pati. UKM Primadona merupakan UKM yang bergerak pada olahan ikan bandeng dan merupakan salah satu UKM binaan dari Universitas Diponegoro dalam program pengabdian Ipteks bagi Produk Ekspor (IbPE) 2016-2018. Dalam binaan tersebut, yang menjadi salah satu program adalah diversifikasi produk UKM Diversifikasi produk yang dilakukan oleh UKM Primadona atas binaan tim pengabdi adalah keripik kulit dan abon duri ikan bandeng. Kulit ikan bandeng merupakan hasil filet dari daging ikan bandeng. Kulit ikan bandeng dicampur dengan tepung beras, tepung tapiokan dan rempah-rempah lainnya untuk diolah menjadi keripik kulit yang renyah. Tekstur keripik kulit ikan bandeng adalah renyah, mempunyai pola sisik ikan. Kandungan protein, vitamin dan mineral keripik kulit ikan bandeng juga cukup tinggi. Untuk abon duri ikan bandeng sangat berkhasiat karena kandungan kalsiumnya cukup tinggi.
EFFECTS OF CALENDAR VARIATIONS ON THE INDONESIA STOCK EXCHANGE: AN EMPIRICAL STUDY OF POTENTIAL STOCKS Putriaji Hendikawati; Subanar Subanar; Abdurakhman Abdurakhman; Tarno Tarno
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 4, No 1 (2022)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v4i1.14921

Abstract

This study examines the effect of calendar variations on potential stocks on the Indonesia Stock Exchange. Calendar variations are observed in telecommunications, retail, food and cigarettes sub-sectors. The observed calendar variations are divided into two: the holiday effect, namely the effect of the month of Ramadan, the effect of the Eid al-Fitr holiday, and the effect of changes in the month of the Eid holidays; and the trading day effect, namely the effect of the day of the week and month of the year effects. ARIMA and ARIMAX model is used to see the effect of previous return data and the calendar variations on predicting stock returns. Descriptively, there is the effect of calendar variations due to Ramadan and Eid holidays and the influence of Monday and January effect. The existence of calendar variations does not apply equally to all types of stocks and to all observation time periods. The calendar variation tends to vary, does not form a clear pattern, does not consistently affect stock returns on the Indonesia Stock Exchange and is not statistically significant. Based on the analysis, it was found that the Monday effect and January effect are the most common phenomena in the Indonesian stock exchange.
KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART) Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid; Tarno Tarno
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.866 KB) | DOI: 10.14710/medstat.12.1.63-72

Abstract

Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.
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

Abstract

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

Abstract

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%).
PENERAPAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) UNTUK MERAMALKAN PENERBANGAN DOMESTIK PADA TIGA BANDAR UDARA DI PULAU JAWA Adinda Putri Muzdhalifah; 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.11.3.332-343

Abstract

The number of flights is a thing to measure the marketing performance of aviation services. Forecasting the number of flights is done so that airlines can make decisions in increasing the number of passengers and revenue. Forecasting the number of flights at various airports has relationship between time and location. The suitable method for forecasting the number of flights is Generalized Space Time Autoregressive (GSTAR) method. GSTAR is a method that used for forecasting time series data that has a relationship between time and location and has heterogeneous characteristics. This study applied the GSTAR method to model and forecast the number of domestic flights at three airports in Java, namely Husein Sastranegara Airport Bandung, Ahmad Yani Semarang, and Juanda Surabaya. The research chose those three airports because the impact of Covid-19 is very severe in that area. The weight used in this study is the distance inverse weight. The resulting model is a model with differencing 1, autoregressive order 1, and spatial order limited to 1 so that the model formed is the GSTAR model (11)-I(1). The GSTAR (11)-I(1) meets the assumptions of residual white noise and normal multivariate. The model also has sMAPE values for each airport: 2.60%, 4.18%, and 9.89%. Therefore, it can be concluded that the forecasting results of Husein Sastranegara Airport Bandung, Ahmad Yani Airport Semarang, and Surabaya Juanda Airport are very accurate.
ANALISIS SENTIMEN PENERAPAN PPKM PADA TWITTER MENGGUNAKAN NAÏVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI-SQUARE Pualam Wahyu Ratiasasadara; Sudarno Sudarno; Tarno Tarno
Jurnal Gaussian Vol 11, No 4 (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.11.4.580-590

Abstract

Dissemination of information related to the implementation of PPKM takes place very quickly, especially on social media networks. Positive and negative news certainly has an impact on public opinion or sentiment on the implementation of PPKM. Sentiment analysis is needed to determine behavior or opinions in the form of reviews, ratings, or tendencies of the author towards a particular topic. In this study, the data used is public opinion on Twitter social media with the keyword "PPKM" from November 2, 2021 to November 8, 2021 and obtained data as many as 12,616 tweets which then deleted duplicate data to become 6,465 data. Data classification was performed using Naïve Bayes with Chi-Square feature selection and the data were classified into positive and negative classes. The results of the classification performance using Nave Bayes with Chi-Square feature selection obtained an accuracy of 83% which means that the Nave Bayes classification model with Chi-Square feature selection is quite effective in classifying public opinion on the implementation of PPKM.
PENERAPAN METODE FUZZY TIME SERIES MENGGUNAKAN PARTICLE SWARM OPTIMIZATION ALGORITHM UNTUK PERAMALAN INDEKS SAHAM LQ45 Arya Despa Ihsanuddin; Dwi Ispriyanti; Tarno Tarno
Jurnal Gaussian Vol 12, No 1 (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.12.1.10-19

Abstract

Stocks have a volatile nature and it is difficult to predict the ups and downs. Therefore, stock data forecasting is done by investors to get a picture of future results. Fuzzy Time Series is a time series method that is suitable for forecasting fluctuating stock data because it does not require the fulfillment of assumptions such as normality and stationarity, but the Fuzzy Time Series method has weaknesses in determining intervals. So that in this study, interval optimization will be carried out on Fuzzy Time Series with Particle Swarm Optimization algorithm to predict LQ45 stock index data, Particle Swarm Optimization algorithm is used because it produces more optimal interval values compared to other optimization methods such as Genetic Algorithm. The data to be used is the closing price of the LQ45 stock index on January 5, 2020 to December 26, 2021. Forecasting using the Fuzzy Time Series method produces a SMAPE value of 1.53%, then after optimization using the Particle Swarm Optimization algorithm, the SMAPE value decreases to 1, 27%. Therefore, it can be concluded that optimization using Particle Swarm Optimization on Fuzzy Time Series produces a more optimal forecasting value. 
PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH Tarno Tarno; Agus Rusgiyono; Budi Warsito; Sudarno Sudarno; Dwi Ispriyanti
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (506.342 KB) | DOI: 10.14710/medstat.11.1.65-78

Abstract

The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test
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