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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Proceeding Biology Education Conference Media Pustakawan STATISTIKA Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics

Budi Warsito

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 413102

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) Sang Nur Cahya Widiutama; Budi Warsito; Sudarno Sudarno
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.v11i1.34000

Every company must have a way to solve the problems faced by its customers, PT. PLN Persero, the Indonesian national energy utility, must have a method to handle consumer complaints. PT. PLN Persero has a recovery time strategy for resolving consumer concerns, but it is not always effective in doing so. The QUEST algorithm (Quick, Unbiased, and Efficient Statistical Tree) approach is used to classify the problem of the recovery time policy failing on specific complaints. Classification of complaint data in order to obtain characteristics and factors as the main influence on the complaints and be able to provide new opinions for PT. PLN to address customer complaints. The QUEST method is a classification tree technique with two nodes per split that yields an unbiased variable. The QUEST method may be used with both category and numerical data. QUEST uses three stages to create a classification tree: picking the splitting variable, identifying the split point, and pausing the split. The classification tree generated has a tree depth of four layers and obtained three essential factors in the classification, namely weather, the number of customers experiencing the same event, and distance from the site. The classification tree accuracy level is 0.851 (or 85.1%), with a prediction error rate of 0.149 (or 14.9%).Keywords: binary classification tree, recovery time, QUEST algorithm.

KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN ALGORITMA C5.0 DI KABUPATEN PEMALANG Fatiya Nur Umma; Budi Warsito; Di Asih I Maruddani
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.v10i2.29934

Pemalang regency is a district which has amount of poverty around 16.04%. One of the effort that must be improved in tackling poverty is increasing the accuracy of the government program’s target. The improvement of target accuracy is expected to give the better impact on the welfare of the population. This study classified the poverty status of households in Pemalang regency using C5.0 Algorithm. The poverty status of households is divided into two classes, namely poor and non-poor. There was an imbalance of data in both classes. Data imbalances were handled by using Synthetic Minority Oversampling Technique (SMOTE). From the research that has been done, SMOTE application in classification of household poverty status affected the evaluation value of the model. Previously the model could not classify the minority class and after using SMOTE the model produced an average value of sensitivity 25.80%. SMOTE application increased the average value of specificity from 91.16% to 94.91%. However, SMOTE application decreased the average value of accuracy which originally 91.16% down to 82.2%.Keywords : C5.0, Household poverty, Classification, SMOTE

PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS Tri Yani Elisabeth Nababan; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.

PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) Gayuh Kresnawati; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA Ratna Kencana Putri; Budi Warsito; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

PENERAPAN GRADIENT BOOSTING DENGAN HYPEROPT UNTUK MEMPREDIKSI KEBERHASILAN TELEMARKETING BANK Silvia Elsa Suryana; Budi Warsito; Suparti Suparti
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.v10i4.31335

Telemarketing is another form of marketing which is conducted via telephone. Bank can use telemarketing to offer its products such as term deposit. One of the most important strategy to the success of telemarketing is opting the potential customer to create effective telemarketing. Predicting the success of telemarketing can use machine learning. Gradient boosting is machine learning method with advanced decision tree. Gardient boosting involves many classification trees which are continually upgraded from previous tree. The optimal classification result cannot be separated from the role of the optimal hyperparameter. Hyperopt is Python library that can be used to tune hyperparameter effectively because it uses Bayesian optimization. Hyperopt uses hyperparameter prior distribution to find optimal hyperparameter. Data in this study including 20 independent variables and binary dependent variable which has ‘yes’ and ‘no’ classes. The study showed that gradient boosting reached classification accuracy up to 90,39%, precision 94,91%, and AUC 0,939. These values describe gradient boosting method is able to predict both classes ‘yes’ and ‘no’ relatively accurate.

APLIKASI NAÏVE BAYES CLASSIFIER (NBC) PADA KLASIFIKASI STATUS GIZI BALITA STUNTING DENGAN PENGUJIAN K-FOLD CROSS VALIDATION Riza Rizqi Robbi Arisandi; Budi Warsito; Arief Rachman Hakim
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.v11i1.33991

The case of stunting in Indonesia is a problem that has been discussed for a long time. One of many efforts to overcome this problem is through an accelerated stunting reduction program to improve the nutritional status of the community and also to reduce the prevalence of stunting or stunted toddlers. Generally, the index used to determine the nutritional status of stunting toddlers height compared to age. This study aims to identify the classification results, evaluate the model, and predict the nutritional status of stunting toddlers using the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data processing system used is the GUI-R (Graphical User Interface) in order to facilitate the analysis process by implementing the Shiny Package in the Rstudio program. The results of accuracy using Naïve Bayes Classifier with 10-Fold Cross Validation test obtained the highest accuracy on the 6th iteration with an accuracy 94.39%, while the lowest accuracy on the 8th iteration with an accuracy 82.08%. Overall, the average accuracy in each iteration is 88.46%, so it can be concluded that Naïve Bayes Classifier model considered good enough to classified data on the nutritional status of stunting toddlers.Keywords: Stunting, Data Mining, Naïve Bayes Classifier, K-Fold Cross Validation, Shiny Package

PENGENDALIAN KUALITAS PRODUK MINO DI HOME INDUSTRY “SARANG SARI” BANYUMAS Winahyu Handayani; Tatik Widiharih; 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.v6i4.30386

Mino is Banyumas’s signature souvenir that is fancied by the public. High competitiveness makes mino manufacturers are prosecuted to improve the quality of their products. One of the ways to ascertain whether a product has a good quality is by looking at the number of defective products, the less the number of defective products the better the quality. The objective of the study is to minimize broken and burnt products and also size faultiness of the mino. Control Charts and R are used to view defectiveness data from mino’s diameter and mino’s weight respectively, where as Control Chart p is used to see the data of burnt and broken mino. Furthermore, the value of process capability (Cpk) used to review whether the process is considered capable or not capable. The result and analysis at “Sarang Sari” Nopia and Mino’s Home Industry Banyumas show attribute data in the form of broken and burned defects is restrained after eliminating seven observations data. Thereupon, the variable data in the form of mino’s weight data is restrained after omitting the three observations data with Cpk value is 1.1180, and for mino’s diameter data process has been restrained with Cpk value of 0.9559. Factors that are affecting mino’s defectiveness are equipment, method and measurement. Meanwhile, the profit value of this mino home industry business is Rp 9.276.110 per month. Keywords: Mino, Chart Control, Process Capability, Economic Analysis

PEMODELAN REGRESI POISSON BIVARIAT PADA JUMLAH KEMATIAN IBU HAMIL DAN NIFAS DI JAWA TENGAH TAHUN 2017 Arbella Maharani Putri; Alan Prahutama; Budi Warsito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The maternal mortality rate is one of the indicators that determine the prosperity level of society in a country. Most of the maternal mortality caused by pregnancy maternal mortality and postpartum maternal mortality. Central Java is one of the provinces with the biggest number of pregnancy maternal mortality and postpartum maternal mortality in Indonesia. The number of pregnancy maternal mortality and postpartum maternal mortality follow Poisson Distribution and it has a significant correlation. Therefore, the writer analyzed factor that influences the number of pregnancy maternal mortality and postpartum maternal mortality using Univariate and Bivariate Poisson Regression method. Results from this study obtained that in the Univariate Poisson Regression variables that significantly influence pregnancy maternal mortality and postpartum maternal mortality are the percentage of pregnant women implementing K1 (X1), percentage of childbirth women that has puerperal health service (X6) and percentage of household with clean and healthy behavior (X7). In the Bivariate Poisson, the best model is the second model which assuming that covariance is an equation.Keywords: Pregnancy of Maternal Mortality, Postpartum Maternal Mortality, Bivariate Poisson Regression.

PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH Di Mokhammad Hakim Ilmawan; Budi Warsito; Sugito Sugito
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Faisal Fikri Utama Faliha Muthmainah Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Yanuar Yoga Prasetyawan Yundari, Yundari

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