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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika & Sains Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information Systems Engineering and Business Intelligence JURNAL PENELITIAN Jurnal Fourier Jurnal Sains Matematika dan Statistika Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi saintis Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Jurnal Matematika: MANTIK BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Tadbir Muwahhid Wahana : Tridarma Perguruan Tinggi AXIOM : Jurnal Pendidikan dan Matematika DAYAH: Journal of Islamic Education Mathvision : Jurnal Matematika TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Vygotsky: Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Penelitian Tadris: Jurnal Pendidikan Islam STATISTIKA Engagement: Jurnal Pengabdian Kepada Masyarakat Economics Development Analysis Journal Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa Proceeding Of International Conference On Education, Society And Humanity

Moh. Hafiyusholeh

Universitas Islam Negeri Sunan Ampel Surabaya

Author-ID : 321227

Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Physics Social Sciences Transportation Other

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VEKTOR PRIORITAS DALAM ANALYTICAL HIERARCHY PROCESS (AHP) DENGAN METODE NILAI EIGEN Moh. Hafiyusholeh; Ahmad Hanif Asyhar
Jurnal Matematika MANTIK Vol. 1 No. 2 (2016): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.221 KB)

Pada Penelitian ini dikaji metode nilai eigen yang digunakan untuk mengkonstruksivektor prioritas model pengambilan keputusan yang dikenal dengan Analytical Hierarchy Process (AHP). AHP merupakan suatu metode pengambilan keputusan yang berdasarkan pada keragaman kriteria. Melalui metode nilai eigen ini diperoleh λ_mak≥n, dengan λ_mak adalah nilai eigen maksimum dan n adalah ukuran matriks. Untuk membatasi apakah suatu keputusan yang telah diambil dengan AHP sudah valid atau belum, bisa diverifikasi dengan menggunakan indeks konsistensi.

PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS Fitria Febrianti; Moh. Hafiyusholeh; Ahmad Hanif Asyhar
Jurnal Matematika MANTIK Vol. 2 No. 1 (2016): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (150.672 KB)

Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data and 70 checking data with RMSE value 2,2122E-14. This indicates that the Fuzzy C-Means method has a higher level of accuracy than the K-Means method

Implementation of The Open Jackson Queuing Network to Reduce Waiting Time Monike Febriyani Faris; Yuniar Farida; Dian C. Rini Novitasari; Nurissaidah Ulinnuha; Moh. Hafiyusholeh
Jurnal Matematika MANTIK Vol. 6 No. 2 (2020): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15642/mantik.2020.6.2.83-92

Waiting for service is a common thing in-hospital services. The more patients are waiting, the service delay increases, so waiting time in the queue gets longer. In health care in a hospital, a patient will queue several times in more than one queue in a hospital outpatient installation. The case study in this research is the queue system in the hospital's outpatient treatment, implementing an open Jackson queueing network to minimize waiting time. The workstations examined in this study were the registration, pre-consultation, and cardiology poly consultation, and pharmacy. The data is carried out for six days, counting the number of arrivals and departures with each point at intervals of 5 minutes. Applying the Jackson open queue network model, a recommendation was obtained for the hospital to increase employees' numbers. The registration workstation must have four servers; a poly cardiology workstation had three nurses and four doctors, while for pharmacy, had seven employees. With this personnel's addition, patients' total waiting time in the queuing system is approximately 12 minutes/patient. So, it can reduce waiting times in the queueing system that was initially 108 minutes/patient.

Analisis Peramalan Nilai Tukar Rupiah Terhadap Dollar dan Yuan Menggunakan FTS-Markov Chain Safira Yasmin Amalutfia; Moh. Hafiyusholeh
Vygotsky : Jurnal Pendidikan Matematika dan Matematika Vol 2, No 2 (2020): Vygotsky: Jurnal Pendidikan Matematika dan Matematika
Publisher : Universitas Islam Lamongan

Nilai tukar rupiah terdepresiasi sebesar 70% pada tahun 1998 hingga terjadi krisis ekonomi yang disebabkan oleh menurunnya pertumbuhan ekonomi mencapai angka -13.1%. Nilai tukar akan sangat berpengaruh dalam kestabilan perekonomian suatu negara. Oleh karenanya, diperlukan suatu peramalan untuk mengetahui bagaimana keadaan nilai tukar untuk beberapa periode kedepan untuk meminimalisir terjadinya krisis ekonomi terulang kembali. Tujuan dari penelitian ini adalah melakukan peramalan nilai tukar rupiah terhadap dollar dan yuan menggunakan FTS- markov chain. Hasil MAPE untuk kurs jual beli dollar dan yuan masing-masing adalah 0.53%, 0.48%, 0.42%, dan 0.41% yang membuktikan bahwa model yang terbentuk berada pada kriteria peramalan sangat baik sehingga dapat dilakukan peramalan untuk periode selanjutnya. Peramalan menggunakan FTS-markov chain menghasilkan peramalan selama 24 minggu kedepan.

Perbandingan Metode Single Linkage, Complete Linkage, dan Average Linkage pada Kesejahteraan Masyarakat pad a Kabupaten dan Kota di Jawa Timur Yanuwar Reinaldi; Nurissaidah Ulinnuha; Moh. Hafiyusholeh
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 1 (2021): September 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i1.14228

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.

Prediction of Sea Surface Current Velocity and Direction Using LSTM Irkhana Indaka Zulfa; Dian Candra Rini Novitasari; Fajar Setiawan; Aris Fanani; Moh. Hafiyusholeh
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 1 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.63669

Labuan Bajo is considered to have an important role as a transportation route for traders and tourists. Therefore, it is necessary to have a further understanding of the condition of the waters in Labuan Bajo, one of them is sea currents. The purpose of this research is to predict sea surface flow velocity and direction using LSTM. There are many prediction methods, one of them is Long short-term memory (LSTM). The fundamental of LSTM is to process information from the previous memory by going through three gates, that is forget gate, input gate, and output gate so the output will be the input in the next process. Based on trials with several parameters namely Hidden Layer, Learning Rate, Batch Size, and Learning rate drop period, it achieved the smallest MAPE values of U and V components of 14.15% and 8.43% with 50 hidden layers, 32 Batch size and 150 Learn rate drop.

Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm Unix Izyah Arfianti; Dian Candra Rini Novitasari; Nanang Widodo; Moh. Hafiyusholeh; Wika Dianita Utami
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63676

Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop.

Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method Eka Alifia Kusnanti; Dian C. Rini Novitasari; Fajar Setiawan; Aris Fanani; Mohammad Hafiyusholeh; Ghaluh Indah Permata Sari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.21-30

Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction. Objective: This study aims to predict the velocity and direction of ocean surface currents. Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data. Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%. Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions. Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions

Forecasts Marine Weather on Java Sea Using Hybrid Methods: TS-ANFIS Deasy Alfiah Adyanti; Ahmad Hanif Asyhar; ian Candra Rini Novitasari; Ahmad Lubab; Moh. Hafiyusholeh
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Indonesia is an archipelago. Consequently, themajorities are working around the sea such as a fisherman.While the number of activities at sea are increasing more accident occurred are rising. This research presents marine weather prediction system using Hybrid Methods TS-ANFIS(Adaptive Neuro Fuzzy Inference System – Time Series) in orderto anticipate bad weather and reduce risk. This method use bothocean current and wave height at Java Sea particularly on Gresikin order to forecast ocean current velocity and wave height. Inputvariables used in this paper are data at (t), an hour before (t-1),and two hours before (t-2) and obtained next hour, next 6 hours,next 12 hours, and next day prediction as output. The resultsindicate that ocean current speed attain 16.97327 cm/s; 13.22302cm/s; 10.21107 cm/s; 14.09871 cm/s with mean error is about0.12993; 1.5758; 1.3182; 0.82613 while wave height reach 0.45554m; 0.48286 m; 0.46395 m; 0.54571 m with mean error is about0.0012247; 0.018619; 0.046584; 0.060206. Therefore, it was safe tosailing on 1st January 2016.

Application of Fuzzy K-Nearest Neighbor (FKNN) to Detect the Parkinson’s Disease L.N. Desinaini; Azizatul Mualimah; Dian C. R. Novitasari; Moh. Hafiyusholeh
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

AbstractParkinson’s disease is a neurological disorder in which there is a gradual loss of brain cells that make and store dopamine. Researchers estimate that four to six million people worldwide, are living with Parkinson’s. The average age of patients is 60 years old, but some are diagnosed at age 40 or even younger and the worst thing is some patients are late to find out that they have Parkinson's disease. In this paper, we present a diagnosis system based on Fuzzy K-Nearest Neighbor (FKNN) to detect Parkinson’s disease. We use Parkinson’s disease dataset taken from UCI Machine Learning Repository. The first step is normalize the Parkinson’s disease dataset and analyze using Principal Component Analysis (PCA). The result shows that there are four new factors that influence Parkinson’s disease with total variance is 85.719%. In classification step, we use several percentage of training data to classify (detect) the Parkinson's disease i.e. 50%, 60%, 70%, 75%, 80% and 90%. We also use k = 3, 5, 7, and 9. The classification result shows that the highest accuracy obtained for the percentage of training data is 90% and k = 5, where 19 are correctly classified i.e. 14 positive data and 5 negative data, while 1 positive data is classified incorrectly.Keywords: Parkinson's disease; Fuzzy K-Nearest Neighbor; Principal Component Analysis. AbstrakPenyakit Parkinson merupakan kelainan sel saraf pada otak yang menyebabkan hilangnya dopamin pada otak. Para peneliti mengestimasi bahwa, empat sampai enam juta orang di dunia, menderita Parkinson. Penyakit ini rata-rata diderita oleh pasien berusia 60 tahun, namun beberapa orang terdeteksi saat berusia 40 tahun atau lebih muda dan hal terburuk adalah seseorang terlambat untuk mendeteksinya. Di dalam artikel ini, kami menyajikan sistem diagnosa penyakit Parkinson menggunakan metode Fuzzy K-Nearest Neighbor (FKNN). Kami menggunakan Data uji yang diperoleh dari UCI Machine Learning Repository yang telah banyak diterapkan pada masalah klasifikasi. Tahapan pertama yang kami lakukan adalah menormalisasi data kemudian menganalisisnya menggunakan Analisis Komponen Utama (Principal Component Analysis). Hasil Analisis Komponen Utama menunjukkan bahwa terdapat empat factor baru yang mempengaruhi penyakit Parkinson dengan variansi total 87,719%. Pada tahap klasifikasi, kami menggunakan beberapa prosentase data latih untuk mendeteksi penyakit yaitu 50%, 60%, 70%, 75%, 80% and 90%. Selain itu, kami menggunakan beberapa nilai k yaitu 3, 5, 7, and 9. Hasil menunjukkan bahwa klasifikasi dengan akurasi tertinggi diperoleh untuk 90% data latih dengan k = 5, dimana 19 diklasifikasikan secara tepat yaitu 14 data positif dan 5 data negatif, sedangkan satu data positif tidak diklasifikasikan dengan tepat.Keywords: penyakit Parkinson; Fuzzy K-Nearest Neighbor; Analisis Komponen Utama.

Co-Authors Abd. Rachman Assegaf Abdulloh Hamid Abdulloh Hamid Abdulloh Hamid Adyanti, Deasy Alfiah Agus Arianto Ahmad Hanif Asyhar Ahmad Zaenal Arifin Akbar, Fadilah Al Watsiqoh, Mila Haibatu Alfinatuzzahro Alfinatuzzahro Alfirdausy, Roudlotul Jannah Ambadar, Panreshma Rizkha Aris Fanani Aris Fanani Azizatul Mualimah Binar Rahmawati Dwi Prihatni Aliek Deasy Alfiah Adyanti Dian C. R. Novitasari Dian C. Rini Novitasari Dian C. Rini Novitasari Dian Yuliati Dianita Utami, Wika Dwi Agustina Eka Alifia Kusnanti Emi Fatchurin Fadhila, Riska Nuril Fahriza Novianti Fajar Setiawan FAJAR SETIAWAN Fajar Setiawan Fajar Setiawan Fanani, Aris Farida, Yuniar Fery Firmansyah Fitria Febrianti Ghaluh Indah Permata Sari Gita Purnamasari R Hani Khaulasari Hanni Garminia I Ketut Budayasa ian Candra Rini Novitasari Iflakhah, Mila Iftitah Ardiwira Pramesti Irkhana Indaka Zulfa Izzatul Aliyyah Kuntari, Maharani L.N. Desinaini Laila, Siti Alfin Nur Lubab, Ahmad Mardiyah, Ilmiatul Mif'atul Mahmudah Moh. Hartono Moh. Lail Kurniawan Mohd Kamarulnizam bin Abdullah Monike Febriyani Faris Nafi'ah Darojat, Umi Sarah Nanang Widodo Novitasari, Dian C Rini Nur Faujiyah Nurissaidah Ulinnuha Prasetijo, Dono Pudji Astuti Putri Rahmawati Putroue Keumala Intan Rini Novitasari, ian Candra Ririn Komaria Safira Yasmin Amalutfia Sari, Dian Candra Rini Novita Sari, Firda Silvie Afifatuz Zulfah Siti Lailiyah Sulistiyawati, Dewi Tatag Yuli Eko Siswono Unix Izyah Arfianti Wahyudi, Sharenada Norisdita Widyastuti, Naumi Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Yanuwar Reinaldi Yati, Winda Yayan Luthfi Khoirina Yuniar Farida Yuniar Farida, Yuniar Yuyun Monita Zainullah Zuhri

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