Article Per Year (5 Year)
p-Index From 2021 - 2026
4.467
P-Index
This Author published in this journals
All Journal PROSIDING SEMINAR NASIONAL Jurnal Statistika Universitas Muhammadiyah Semarang Value Added : Majalah Ekonomi dan Bisnis Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Variance : Journal of Statistics and Its Applications Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Litbang Edusaintech Parameter: Journal of Statistics Journal of Data Insights Prosiding Seminar Nasional Unimus Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal Meteorologi dan Geofisika LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Emerging Statistics and Data Science Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Fauzi, Fatkhurokhman
Program Studi Statistika, Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 36 Documents
Search

 1   2   3   4 
STATISTICAL DOWNSCALING USING REGRESSION NONPARAMETRIC OF FOURIER SERIES-POLYNOMIAL LOCAL OF CLIMATE CHANGE Utami, Tiani Wahyu; Fauzi, Fatkhurokhman; Yuliyanto, Eko
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1411-1418

Abstract

Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE Fauzi, Fatkhurokhman; Setiayani, Wiwik; Utami, Tiani Wahyu; Yuliyanto, Eko; Harmoko, Iis Widya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1439-1448

Abstract

The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.
CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Nur, Indah Manfaati; Nugrahanto, Rifqi; Fauzi, Fatkhurokhman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1575-1584

Abstract

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.
Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters Fauzi, Fatkhurokhman; Erlinda, Relly; Arum, Prizka Rismawati
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20385

Abstract

Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster. 
Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result Haris, M. Al; Setyaningsih, Laras Indah; Fauzi, Fatkhurokhman; Amri, Saeful
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20267

Abstract

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.
KLASIFIKASI STATUS KESEJAHTERAAN MASYARAKAT KABUPATEN KEPULAUAN MENTAWAI DENGAN METODE REGRESI LOGISTIK BINER DAN CLASSIFICATION AND REGRESSION TREE (CART) Sanur, Lulu Anata; Haris, M Al; Fauzi, Fatkhurokhman
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 1 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv3i01pp71-84

Abstract

Kabupaten Kepulauan Mentawai merupakan salah satu daerah yang saat ini masih berstatus sebagai daerah tertinggal, ditandai dengan minimnya ketersediaan fasilitas sarana prasarana. Pembangunan infrastruktur adalah kunci utama untuk memajukan daerah Kabupaten Kepulauan Mentawai seperti adanya trans daerah sebagai penghubung antar pulau sehingga ekonomi kemasyarakatan akan turut tumbuh. Tujuan penelitian ini untuk mengetahui karakteristik atau variabel yang memiliki pengaruh pada pengkategorian status kesejahteraan rumah tangga ke dalam klasifikasi miskin dan tidak miskin. Klasifikasi adalah suatu teknik statistik yang digunakan untuk mengelompokkan data yang telah tersusun secara sistematis. Ada dua pendekatan berbeda untuk mengklasifikasi objek, yaitu metode parametrik dan metode nonparametrik. Penelitian ini memakai metode regresi logistik biner dan Classification and Regression Tree (CART) karena memiliki performasi yang baik, sehingga dalam penelitian ini akan mencoba memperoleh perbandingan nilai akurasi terbaik diantara kedua metode tersebut. Lalu hasilnya akan di evaluasi dengan nilai APER dan nilai akurasi klasifikasi. Data yang digunakan adalah hasil Susenas tahun 2022 sebanyak 326 sampel dengan data testing dan data training adalah 20% dan 80%. Dari hasil penelitian kedua metode, variabel umur, tingkat pendidikan terakhir, dan kesehatan kepala rumah tangga memiliki pengaruh signifikan terhadap model klasifikasi. Akurasi klasifikasi model regresi logistik biner mencapai 93,94% yang lebih tinggi dibandingkan dengan model klasifikasi CART yang bernilai 89,40%. Oleh karena itu, bisa ditarik kesimpulan bahwa model regresi logistik biner ialah pemilihan terbaik untuk memprediksi faktor kesejahteraan rumah tangga di Kabupaten Kepulauan Mentawai.
Perbandingan Klasifikasi Random Forest, Support Vector Machines, dan LGBM Pada Klasifikasi Kualitas Udara di Jakarta Anisa Ma'u Luthfi; Fatkhurokhman Fauzi
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 9 No. 2 (2024): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v9i2.1912

Abstract

Udara bersih merupakan kebutuhan semua makhluk di bumi guna menunjang keberlangsungan hidup. Kualitas udara di Jakarta merupakan isu serius yang perlu mendapat perhatian serius dari pemerintah dan publik. Ada beberapa faktor utama yang menyebabkan polusi udara di Jakarta. Pertama, industri yang semakin berkembang dengan cepat di wilayah metropolitan ini berkontribusi signifikan terhadap emisi gas buang yang mencemari udara. Selain itu, pertambahan jumlah kendaraan bermotor dan mobilitas tinggi juga menyebabkan peningkatan emisi gas buang yang mencemari udara. Kegiatan pembakaran sampah yang tidak teratur dan hujan asam juga turut memperburuk kualitas udara di Jakarta. Penelitian sebelumnya menunjukkan bahwa klasifikasi kualitas udara berdasarkan data Indeks Standar Pencemaran Udara (ISPU) di DKI Jakarta dengan dataset menunjukkan hasil penelitian dari kedua algoritma yang digunakan yakni metode Support Vector Machine memiliki akurasi yang lebih baik dalam melakukan klasifikasi dibandingkan dengan K-Nearest Neighbor dengan nilai akurasi pada SVM sebesar 98% sedangkan KNN sebesar 96%.Pada penelitian  dilakukan Nugroho dan Kawan-kawan didapatkan hasil penelitian dengan dataset data Indeks Standar Pencemaran Udara (ISPU) di DKI Jakarta dengan algoritma Random Forest didapatkan hasil akurasi sebesar 90%. Sehingga sebagai bahan perbandingan serta untuk memilih model terbaik dan meningkatkan akurasi model untuk penelitian-penelitian sebelumnya maka di penelitian ini dilakukan perbandingan algoritma antara SVM, Random Forest, dan LGBM. Hasil percobaan menunjukkan bahwa tingkat akurasi model yang menggunakan random forest memiliki akurasi tertinggi sebesar 98%.
A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis Achmad Fauzan; Fatkhurokhman Fauzi; Rhendy K P Widiyanto; Khairil Anwar Notodiputro; Bagus Sartono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6751

Abstract

Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linear regression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression Tree (MERT), in predicting dairy productivity based on the 2024 National Dairy Productivity Survey data. Predictive performance was evaluated using MSEP, RMSEP, MAD, and MAPE, with MERT consistently outperforming LMM in accuracy and robustness. Permutational Multivariate Analysis of Variance (PERMANOVA) test results reinforced this finding, yielding a pseudo-F value of 224.7 and a p-value of 0.001, indicating statistically significant differences in model performance. Key predictors of MERT model included farm altitude, the previous week’s milk production, and the amounts of concentrate feed given, which are part of significant predictor variables in LMM. This finding underscores MERT’s superiority in modeling complex agricultural datasets while providing interpretable insights through data-driven segmentation. The study advocates policy focus in sustainable milk production as well as the availability of high quality of feed and altitude-based dairy farms location to improve milk productivity. Should these focuses implemented by the industry, combined with the MBG Program, Indonesia would be progressing better towards achievement of SDGs Goal 2 and 3.
Penerapan K-Means Clustering dalam Mengelompokkan Kabupaten/Kota Berdasarkan Indikator IPM di Jawa Tengah: Penerapan K-Means Clustering dalam Mengelompokkan Kabupaten/Kota Berdasarkan Indikator IPM di Jawa Tengah Fadillah, Muhammad Reza; Ardiansyah, Muhammad Rifqy; Junaidi, Muhammad Rifki; Fauzi, Fatkhurokhman
Emerging Statistics and Data Science Journal Vol. 3 No. 3 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.3.art20

Abstract

IPM merupakan ukuran keberhasilan pembangunan manusia yang didasarkan pada tiga dimensi utama, yaitu kesehatan, pendidikan, dan standar hidup layak. Jawa Tengah dipilih sebagai objek penelitian karena menunjukkan tren peningkatan IPM yang konsisten, namun masih terdapat disparitas antar daerah. Penelitian ini bertujuan untuk mengelompokkan Kabupaten/Kota di Jawa Tengah berdasarkan indikator pembentuk IPM tahun 2023 dengan menggunakan metode analisis cluster non-hirarki K-Means. Metode ini dipilih karena efisien, sederhana, dan mampu menghasilkan pengelompokan yang jelas. Cluster pertama terdapat 16 Kabupaten/kota dengan indikator dari faktor-faktor yang berpengaruh pada IPM tingkatan rendah, cluster kedua terdapat 4 Kabupaten/kota dengan indikator dari faktor-faktor yang berpengaruh pada IPM tingkatan tinggi, cluster ketiga terdapat 15 Kabupaten/kota  dengan  indikator  dari  faktor-faktor yang berpengaruh pada IPM tingkatan sedang.
PREDIKSI RESIKO GEMPA MENGGUNAKAN MODEL SPATIAL POINT PROCESS Rahman, Budiono; Utami, Tiani Wahyu; Fauzi, Fatkhurokhman
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page39-38

Abstract

Pertemuan lempeng Eurasia, lempeng Indo-Australia, dan lempeng Pasifik terjadi di sekitar Pulau Sulawesi dan Maluku. Pertemuan ketiga lempeng ini menyebabkan sering terjadi gempa bumi. Faktor-faktor yang mempengaruhi gempa bumi secara geologis adalah jarak terhadap zona subduksi, gunung berapi, dan sesar aktif. Untuk keperluan mitigasi risiko bencana gempa bumi, perlu dilakukan prediksi risiko terjadinya gempa bumi. Pada penelitian ini menggunakan model inhomogeneous thomas cluster process yang digunakan untuk memprediksi risiko kejadian gempa bumi. Variabel yang digunakan dalam penelitian ini adalah koordinat zona subduksi, sesar aktif, dan gunung api di pulau Sulawesi dan Maluku. Hasil yang diperoleh berdasarkan fungsi K gempa bumi di pulau Sulawesi dan Maluku membentuk cluster. Uji chi-square menyimpulkan kejadian gempa bumi di Pulau Sulawesi dan Maluku bersifat inhomogen (tidak homogen). Berdasarkan model estimasi inhomogeneous thomas cluster process, variabel zona subduksi, sesar aktif, dan gunung berapi mempunyai pengaruh yang signifikan terhadap terjadinya gempa bumi. Dari ketiga variabel yang mempengaruhi terjadinya gempa terbesar (1,8 kali) adalah variabel letak gunung api. Berdasarkan hasil prediksi, Provinsi Gorontalo, Sulawesi Utara, Sulawesi Tengah, Maluku, dan Maluku Utara memiliki risiko gempa bumi yang tinggi.
Co-Authors - Tarsan Achmad Fauzan Agung Subakti Nuzulullail Ahmad Amrullah B Alfidha Rahmah Alifia Puspita Sari Alwan Fadlurohman Amri, Saeful Amrullah, Setiawan Anawai Basman, Aprilla Anggun Erya Santika Anisa Ma'u Luthfi Anita Retno Indriani Arafat, Amaliah Sholeha Ardelita Ika Fadhlillah Ardiansyah, Muhammad Rifqy Aulia, Syifa Azizah, Apipah Nur Bagus Sartono Barlian, Seftia Amelia Rizki Burhanuddin Izzul Salam Dannu Purwanto Dewi Ratnasari Wijaya Dwi Agustina Eko Yuliyanto, Eko Eny Winaryati Erika Siva Aulia Erlinda, Relly Fabiola, Gwenda Fadillah, Muhammad Reza Fatikha Adha Fahreza Fauziah Rahma Gabriella Hilary Wenur Hanif Nur Ibrahim Haris, M Al Haris, M. Al Hasbi Assidiqi Iis Widya Harmoko Iis Widya Harmoko Iis Widya Harmoko, Iis Widya Indah Manfaati Nur Indah Manfaati Nur Indra Firmansyah Iqbal Kharisudin Izzah, Nasyiatul Junaidi, Muhammad Rifki Khikman, Muhammad Alvaro Laila Khoirun Nisa Lestari, Febi Anggun Lia Aryanti Sholekhah Litasya Shofwatillah M. Al Haris Moh Yamin Darsyah Moh. Yamin Darsyah Multiyaningrum, Riska Nida Faoziatun Khusna Ninu, Maria Febronia Nugrahanto, Rifqi Nur, Rachmat Kahfiwan Oktavia Sri Banowati Pandiriyan, Muhammad Tegar Permatasari, Shella Heidy Prizka Rismawati Arum Putra, Septian Malik Putri Ayu Firnanda Putri, Agata Dwi Putri Putri, Melfia Verahma Qonita Syalsabilla Handayani Rahma Nurmalita Rahmah, Alfidha Rahman , Budiono Rahman, Budiono Rahmawati, Gita Ramadhan, Abimanyu Arya Rhendy K P Widiyanto Rizma Novinda Puteri Rochdi Wasono RR. Ella Evrita Hestiandari Safril Ahmadi Sanmas Salmaa Fauziah Sam'an, Muhammad Sanmas, Safril Ahmadi Sarah, Albertus Dion Septi Winda Utami Setiayani, Wiwik Shinta Amaria Soffi Amalia Nur Kholifah Syaharani, Nabbila Dyah Syahrani, Nabbila Dyah Syaifullah, Ahmad Reyhan Syifa Aulia Tari Fitri Ningsih Tiani Wahu Utami Tiani Wahyu Utami Watur, Annisa Cahyaningrum Widiyanti, Karin Dita Wiwit Putri Nur Izzaturrohmah Wulan Sari, Wulan Yan Nazala Bisoumi Yuliardi, Fahrul Raditiar Yuni Nurkuntari