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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 Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Proceeding Biology Education Conference Media Pustakawan STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
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 DATA RUNTUN WAKTU DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Saputra, Arsyil Hendra; Tarno, Tarno; Warsito, Budi
Jurnal Gaussian Vol 1, No 1 (2012): 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 (460.124 KB) | DOI: 10.14710/j.gauss.v1i1.570

Abstract

One popular method of time series analysis is ARIMA. The ARIMA method requires some assumptions; residual of model must be white noise, normal distribution and constant variance. The ARIMA model tends to be better for time series data which is linear. Whereas for the nonlinear time series data have been widely studied by nonlinear methods, one of that is Adaptive Neuro Fuzzy Inference System or ANFIS. The ANFIS method is a method that combines techniques Neural Network and Fuzzy Logic. In this thesis discussed the ANFIS method specifically for the analysis of time series data that have characteristics such as stationary, stationary with outlier, non stationary and non stationary with outlier, and the data of Indonesian palm oil prices is used as a case study. The ANFIS results which were obtained are compared with the results of ARIMA method by the value of RMSE. Based on the analysis and discussion, it is obtained that the results of ANFIS method are better than the results of ARIMA method.
PEMODELAN MARKOV SWITCHING AUTOREGRESSIVE Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 3 (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 (510.436 KB) | DOI: 10.14710/j.gauss.v3i3.6449

Abstract

Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, transition probabilities and duration of the regime can be informed. In this case conducted exchange rate of Rupiah to US Dollar modeling with MSAR. The best model is MS(2)-AR(1) with transition probabilities from depreciation to appreciation is 0,052494 and appreciation to depreciation is 0,746716. Duration of the depreciation state is 19,04986 days and appreciation state is 1,339198 days.
KOMPUTASI MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE – RADIAL BASIS FUNCTION NETWORK (GSTAR-RBFN) Warsito, Budi; Yasin, Hasbi; Hakim, Arief Rachman
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Generalized Space Time Autoregressive (GSTAR), merupakan salah satu model yang digunakan untuk memodelkan data time series yang diamati pada beberapa lokasi. Radial Basis Function Neural Network (RBFN) adalah salah satu model jaringan syaraf tiruan yang dapat digunakan untuk pemodelan data time series. Pada penelitian ini akan dibangun sebuah model spatio temporal yang menggabungkan antara model GSTAR dengan model RBFN. Model GSTAR berperan dalam penentuan lag input pada model RBFN. Model ini dinamakan dengan GSTAR-RBFN. Untuk memudahkan proses pengolahan data telah disusun sebuah software statistik yang berbasis antarmuka berupa Graphical User Interface (GUI). Dalam penelitian ini, model GSTAR-RBFN diaplikasikan pada data tinggi gelombang laut di perairan Semarang. Hasil penelitian menunjukkan bahwa dengan menggunakan GUI GSTAR-RBFN, pengolahan data spasio temporal dapat dilakukan dengan sangat mudah.  Kata kunci:  GUI, GSTAR, RBFN, Tinggi Gelombang
FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD Atmaja, Dinul Darma; Widowati, Widowati; Warsito, Budi
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.98-107

Abstract

Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.
Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan Mustaqim, Mustaqim; Warsito, Budi; Surarso, Bayu
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 2 (2019): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i2.1705

Abstract

Combination of Synthetic Minority Oversampling Technique (SMOTE) and Backpropagation Neural Network to handle imbalanced class in predicting the use of contraceptive implants  Kegagalan akibat pemakaian alat kontrasepsi implan merupakan terjadinya kehamilan pada wanita saat menggunakan alat kontrasepsi secara benar. Kegagalan pemakaian kontrasepsi implan tahun 2018 secara nasional sejumlah 1.852 pengguna atau 4% dari 41.947 pengguna. Rasio angka kegagalan dan keberhasilan pemakaian kontrasepsi implan yang cenderung tidak seimbang (imbalance class) membuatnya sulit diprediksi. Ketidakseimbangan data terjadi jika jumlah data suatu kelas lebih banyak dari data lain. Kelas mayor merupakan jumlah data yang lebih banyak, sedangkan kelas minor jumlahnya lebih sedikit. Algoritma klasifikasi akan mengalami penurunan performa jika menghadapi kelas yang tidak seimbang. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan data kegagalan pemakaian kontrasepsi implan. SMOTE menghasilkan akurasi yang baik dan efektif daripada metode oversampling lainnya dalam menangani imbalance class karena mengurangi overfitting. Data yang sudah seimbang kemudian diprediksi dengan Neural Network Backpropagation. Sistem prediksi ini digunakan untuk mendeteksi apakah seorang wanita mengalami kehamilan atau tidak jika menggunakan kontrasepsi implan. Penelitian ini menggunakan 300 data, terdiri dari 285 data mayor (tidak hamil) dan 15 data minor (hamil). Dari 300 data dibagi menjadi dua bagian, 270 data latih dan 30 data uji. Dari 270 data latih, terdapat 13 data latih minor dan 257 data latih mayor. Data latih minor pada data latih diduplikasi sebanyak data pada kelas mayor sehingga jumlah data latih menjadi 514, terdiri dari 257 data mayor, 13 data minor asli, dan 244 data minor buatan. Sistem prediksi menghasilkan nilai akurasi sebesar 96,1% pada epoch ke-500 dan 1.000. Implementasi kombinasi SMOTE dan Neural Network Backpropagation terbukti mampu memprediksi pada imbalance class dengan hasil prediksi yang baik.  The failed contraceptive implant is one of the sources of unintended pregnancy in women. The number of users experiencing contraceptive-implant failure in 2018 was 1,852 nationally or 4% out of 41,947 users. The ratio between failure and success rates of contraceptive implant, which tended to be unbalanced (imbalance class), made it difficult to predict. Imbalance class will occur if the amount of data in one class is bigger than that in other classes. Major classes represent a bigger amount of data, while minor classes are smaller ones. The imbalance class will decrease the performance of the classification algorithm. The Synthetic Minority Oversampling Technique (SMOTE) was used to balance the data of the contraceptive implant failures. SMOTE resulted in better and more effective accuracy than other oversampling methods in handling the imbalance class because it reduced overfitting. The balanced data were then predicted using backpropagation neural networks. The prediction system was used to detect if a woman using a contraceptive implant was pregnant or not. This study used 300 data, consisting of 285 major data (not pregnant) and 15 minor data (pregnant). Of 300 data, two groups of data were formed: 270 training data and 30 testing data. Of 270 training data, 13 were minor training data and 257 were major training data. The minor training data in the training data were duplicated as much as the number of data in major classes so that the total training data became 514, consisting of 257 major data, 13 original minor data, and 244 artificial minor data. The prediction system resulted in an accuracy of 96.1% on the 500th and 1,000th epochs. The combination of SMOTE and Backpropagation Neural Network was proven to be able to make a good prediction result in imbalance class.
ANALISIS KETAHANAN HIDUP PENDERITA DENGUE HEMORRHAGIC FEVER (DEMAM BERDARAH) DENGAN REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus : Rumah Sakit Islam Nahdlatul Ulama Demak) Ummayah, Putri Qodar; Sudarno, Sudarno; Warsito, Budi
Jurnal Gaussian Vol 10, No 3 (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.v10i3.32793

Abstract

Dengue hemorrhagic fever is an acute febrile disease caused by the dengue virus, which enters the human bloodstream through the bite of a mosquito of the genus Aedes Aegypti or Aedes Albopictus. Based on World Health Organization (WHO) records, it is estimated that 500,000 dengue hemorrhagic fever patients require hospital treatment every year and most of the sufferers are children. To analyze the relationship between recovery time in dengue fever patients and the factors that influence it using regression analysis, the dependent variable is the failure time and the function of the response variable tends to fail constant so to find out the relationship using Cox proportional hazard regression. Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is a method used to describe data analysis in terms of time from the time of origin defined until a certain event occurs. In this study, the recovery time of dengue fever patients as a function of failure is proportional. The observations used by the researchers for each patient were not the same. The population of this study were all patients with dengue fever. The data used was obtained from the medical record section for data on the length of hospitalization of patients regarding the recovery of patients with dengue fever. The conclusion of the research shows that the factors that affect the recovery time of dengue fever patients are hematocrit, platelets, immunoglobulin G, and immunoglobulin M. 
Metaheuristic optimization in neural network model for seasonal data Budi Warsito; Rukun Santoso; Hasbi Yasin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.20409

Abstract

The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data.
PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG Dwi Ispriyanti; Alan Prahutama; Tarno Tarno; Budi Warsito; Hasbi Yasin; Pandu Anggara
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.127-133

Abstract

Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process.
ANALISIS SENTIMEN PT TIKI JALUR NUGRAHA EKAKURIR (PT TIKI JNE) PADA MEDIA SOSIAL TWITTER MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK Salma Farah Aliyah; Hasbi Yasin; Suparti Suparti; Budi Warsito; Tatik Widiharih
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.103-113

Abstract

In the 2000s until now, e-commerce systems have continued to develop throughout the world and even in Indonesia. PT Tiki Jalur Nugraha Ekakurir (PT Tiki JNE) is a freight forwarding company that provides convenience for the public in carrying out online shopping activities, and shipping other goods. The large volume of shipments makes PT Tiki JNE have several problems in service that have led to several kinds of responses from users. Sentiment analysis on Twitter social media can be an option to see how PT Tiki JNE’s users respond to services that have been provided. These responses are classified into positive sentiments and negative sentiments. In this research data processing is performed using text mining as the initial source of numerical data from document data which will later be classified using the Artificial Neural Network model with the Resilient Backpropagation algorithm. Data labeling is done manually and sentiment scoring. The test results show that the best model obtained is FFNN 867-7-1 by using the evaluation model 10-Fold Cross Validation to get an overall accuracy performance of 80.27%, kappa accuracy of 39.13%, precision of 69.04%, recall of 70.56%, and f-measure of 69.8% which can be interpreted that the model used is quite good. Analysis of the results using wordcloud shows the tendency of opinion sentiment categories depending on the words used in the tweet.
PENGELOLAAN LIMBAH BATIK CAIR SECARA BIOLOGIS PADA UKM BATIK MUTIARA HASTA DAN KATUN UNGU SEMARANG Budi Warsito
WARTA LPM WARTA LPM, Vol. 21, No. 2, September 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/warta.v21i2.5602

Abstract

Batik merupakan salah satu peninggalan budaya nenek moyang bangsa Indonesia yang perlu terus dipelihara dan dilestarikan. Salah satu persoalan yang muncul dari usaha pembuatan batik adalah limbah yang dihasilkan. Limbah yang langsung dibuang tanpa proses penyaringan terlebih dahulu sehingga dapat membahayakan bagi lingkungan. Oleh karena itu perlu dilakukan kegiatan pendampingan bagi para pengusaha batik agar melakukan pengolahan lebih dahulu limbah yang dihasilkan sebelum dibuang ke lingkungan. Kegiatan pengabdian masyarakat ini dilakukan melalui tahapan penyuluhan bagi UKM batik Mutiara Hasta dan Katun Ungu di Kota Semarang serta pembuatan bak pengolah limbah secara biologis dengan media meliputi kerikil, kerakal, bioball dan batu zeolit. Kadar polutan pada limbah yang dihasilkan dari kedua UKM tersebut jauh melebihi ambang batas yang diijinkan. Melalui pengolahan limbah secara biologis diharapkan limbah yang dibuang tidak lagi membahayakan lingkungan dan UKM dapat melakukan pengolahan secara berkelanjutan karena metode ini tidak memerlukan biaya yang besar.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Pamuji 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 Aries Susanty 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 Winarno, Bowo Yanuar Yoga Prasetyawan Yundari, Yundari