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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Industri EKSAKTA: Journal of Sciences and Data Analysis Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Information Systems Engineering and Business Intelligence JITEK (Jurnal Ilmiah Teknosains) Jurnal Matematika: MANTIK JMPM: Jurnal Matematika dan Pendidikan Matematika JATI UNIK : Jurnal Ilmiah Teknik dan Manajemen Industri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Ilmiah Soulmath : Jurnal Edukasi Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) Zeta - Math Journal Multitek Indonesia : Jurnal Ilmiah Mathvision : Jurnal Matematika Systemic: Information System and Informatics Journal Applied Technology and Computing Science Journal Specta Journal of Technology Indexia International Journal of Data Science, Engineering, and Analytics (IJDASEA) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat)
Dinita Rahmalia, Dinita
Universitas Islam Darul 'Ulum Lamongan
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Veterinary

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Application Kohonen Network and Fuzzy C Means for Clustering Airports Based on Frequency of Flight Rahmalia, Dinita; Herlambang, Teguh
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 3, August 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.442 KB) | DOI: 10.22219/kinetik.v3i3.608

Abstract

In Indonesia, the demands of air tranportation for reaching destination increase rapidly. Based on the flight schedule in airports spreading in Indonesia, the airports have different flight demand rate so that it requires clustering. This research will use two methods for clustering : kohonen network and Fuzzy C Means (FCM).Kohonen network is the type neural network which uses unsupervised training.Kohonen network uses weight vectors for training while FCM uses degree of membership. Both kohonen network and FCM, inputs are represented by the number of departure and arrival of airline in one day. For kohonen network, we update weight matrices so that minimizing the sum of optimum euclidean distance. For FCM, we update degrees of membership so that minimizing the objective function value.From the simulations, we can cluster the airports based on the number of departure and arrival of airline.
Teknik Penalti pada Optimisasi Berkendala Menggunakan Particle Swarm Optimization Rahmalia, Dinita
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 3, No 1: Maret - Agustus 2018
Publisher : Universitas Pesantren Tinggi Darul 'Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.48 KB) | DOI: 10.26594/jmpm.v3i1.1071

Abstract

Optimisasi berkendala adalah proses pencarian nilai optimum dengan memenuhi berbagai kendala. Optimisasi berkendala dapat diselesaikan dengan metode eksak maupun heurisitik. Dalam penelitian ini, akan digunakan metode heuristik seperti Particle Swarm Optimization (PSO). PSO adalah metode optimisasi yang terinspirasi dari perilaku populasi ikan atau unggas dalam mencari sumber makanan.PSO dapat digunakan pada optimisasi dengan kendala. Namun dalam update posisi partikel, supaya optimisasi memenuhi kendala, partikel akan dikenakan nilai penalti jika tidak memenuhi kendala. Simulasi diberikan pada dua model optimisasi.Hasil simulasi menunjukkan teknik penalti dapat menemukan pendekatan solusi optimal pada optimisasi berkendala.
Pengaruh Korelasi Data pada Peramalan Kelembaban Udara Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS) Rahmalia, Dinita; Rohmatullah, Arif
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 2 No 1 (2019): June
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v2i1.817

Abstract

Peramalan cuaca merupakan kegiatan yang penting untuk beberapa aktivitas. Dalam peramalan cuaca, terdapat beberapa komponen yang diamati seperti suhu udara, kelembaban udara, intensitas matahari, dan sebagainya. Berdasarkan penelitian sebelumnya, intensitas matahari dipengaruhi oleh salah satu komponen yaitu kelembaban udara sehingga pada penelitian ini akan dilakukan peramalan pada data kelembaban udara. Pada penelitian ini akan dilakukan peramalan pada data kelembaban udara meggunakan Adaptive Neuro Fuzzy Inference System (ANFIS). Dalam ANFIS terdapat beberapa parameter yang akan dioptimisasi yaitu premise parameter dan consequent parameter. ANFIS menggunakan metode hybrid dalam perhitungan. Terdapat dua bagian dari metode hybrid : alur maju dan alur mundur. Simulasi dilakukan pada tiga dataset dengan struktur yang berbeda. Berdasarkan hasil simulasi, hasil peramalan tergantung pada nilai korelasi. Semakin kuat nilai korelasi (korelasi mendekati 1) menghasilkan hasil peramalan yang lebih baik yang ditandai dengan nilai Mean of Square Error (MSE) yang kecil.
Optimizing the Membership Degree of Fuzzy Inference System (FIS) and Fuzzy Clustering Means (FCM) in Weather Data Using Firefly Algorithm Rahmalia, Dinita
CAUCHY Vol 6, No 4 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v6i4.8933

Abstract

In weather clustering, there are many variables which can be observed such as air temperature, humidity, sunlight intensity, and so on. In this research, Takagi-Sugeno Fuzzy Inference System (FIS) will be used for forecasting the sunlight intensity based on temperature and humidity and Fuzzy Clustering Means (FCM) will be used for clustering them based on fuzzy set. From the data consisting of temperature, humidity, and sunlight intensity, we will forecast sunlight intensity and cluster them into two clusters, three clusters, and four clusters by FCM method. In FIS method, the membership degree are often generated by trial and error. Also, the optimization of the initial of membership degree are required in FCM. Because the initial of membership degree are often generated by trial and error, in this research, we use heuristic method like Firefly Algorithm to optimize the membership degree. From the simulations, Firefly Algorithm can optimize the membership degree of FIS for forecasting the data with minimum Mean Square Error (MSE) and the initial of membership degree of FCM with two clusters, three clusters, and four clusters with minimum objective value.
Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system Mohamad Yusak Anshori; Teay Shawyun; Dennis V. Madrigal; Dinita Rahmalia; Fajar Annas Susanto; Teguh Herlambang; Dieky Adzkiya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp462-472

Abstract

Corona virus disease (COVID-19) have become a world health problem because they have attacked many people worldwide. Because this virus has spread massively in almost all countries, including Indonesia, the Indonesian government made some policies and rules to close down the hotels and restaurants to avoid the spread of COVID-19. Because of that, estimation of the number of closed down restaurants and hotels in Jakarta is vital for avoiding COVID-19 spreads further to other people, either domestic or foreign. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is chosen as the estimation method. In estimating the number of closed restaurants and hotels using ANFIS, supporting variables such as the amount of casualties in Jakarta, the amount of casualties in Indonesia, and the amount of casualties in the world is required. As a result, ANFIS can estimate the amount of closed down restaurants and hotels approaching the target. The simulations are organized by partitioning the dataset into two parts: data of (80%) and data of testing (20%). According to ANFIS simulations, ANFIS can estimate the number of closed down restaurants and hotels in training data with optimal RMSE equals 0.5324 and testing data with optimal RMSE equals 5.3198.
Estimasi Parameter Super Pairwise Alignment pada Kombinasi Virus Dengue Menggunakan Particle Swarm Optimization Dinita Rahmalia; Arif Rohmatullah; Mohammad Syaiful Pradana
Techno.Com Vol 18, No 3 (2019): Agustus 2019
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.28 KB) | DOI: 10.33633/tc.v18i3.2528

Abstract

Di Indonesia terdapat empat jenis virus dengue atau demam berdarah. Untuk melihat tingkat kesamaan (similarity) antara dua sekuens virus, dibutuhkan proses pensejajaran pada sekuens virus. Metode yang digunakan untuk pensejajaran pada dua sekuens virus adalah Super Pairwise Alignment (SPA). Nilai fungsi objective pada SPA adalah nilai penalty antara dua sekuens virus. Karena nilai fungsi objective tergantung pada parameter SPA, maka pada penelitian ini nilai parameter SPA akan diestimasi menggunakan metode heuristik seperti Particle Swarm Optimization (PSO). Simulasi diterapkan pada enam kombinasi virus dengue untuk proses estimasi parameter SPA. Berdasarkan hasil simulasi pada enam kombinasi virus dengue, PSO dapat menemukan parameter SPA yang optimal secara pendekatan. Parameter SPA yang optimal juga dapat mengetahui posisi dan panjang dari unit sekuens yang mengalami penambahan atau penghapusan.
Pengaruh Korelasi Data pada Peramalan Kelembaban Udara Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS) Dinita Rahmalia; Arif Rohmatullah
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 2 No 1 (2019): June
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v2i1.817

Abstract

Peramalan cuaca merupakan kegiatan yang penting untuk beberapa aktivitas. Dalam peramalan cuaca, terdapat beberapa komponen yang diamati seperti suhu udara, kelembaban udara, intensitas matahari, dan sebagainya. Berdasarkan penelitian sebelumnya, intensitas matahari dipengaruhi oleh salah satu komponen yaitu kelembaban udara sehingga pada penelitian ini akan dilakukan peramalan pada data kelembaban udara. Pada penelitian ini akan dilakukan peramalan pada data kelembaban udara meggunakan Adaptive Neuro Fuzzy Inference System (ANFIS). Dalam ANFIS terdapat beberapa parameter yang akan dioptimisasi yaitu premise parameter dan consequent parameter. ANFIS menggunakan metode hybrid dalam perhitungan. Terdapat dua bagian dari metode hybrid : alur maju dan alur mundur. Simulasi dilakukan pada tiga dataset dengan struktur yang berbeda. Berdasarkan hasil simulasi, hasil peramalan tergantung pada nilai korelasi. Semakin kuat nilai korelasi (korelasi mendekati 1) menghasilkan hasil peramalan yang lebih baik yang ditandai dengan nilai Mean of Square Error (MSE) yang kecil.
Optimisasi Perencanaan Produksi Pupuk Menggunakan Firefly Algorithm Dinita Rahmalia; Awawin Mustana Rohmah
Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): 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 (210.723 KB) | DOI: 10.15642/mantik.2018.4.1.1-6

Abstract

In Indonesia, there are many farmers as a livelihood because of fertile soil for agriculture and the demand for food. Production planning is the important part of managing cost spent by the company. In production planning, there are many constraints which have to be satisfied such as the number of productions, the number of workers, and the number of inventory. In previous research, constrained optimizations have been solved by exact method or heuristic method. In this research, production planning optimization will be solved by Firefly Algorithm (FA). FA works as a behavior of Firefly. One of firefly behavior used is less bright firefly will move toward brighter firefly. The simulation results show that FA method can find an approaching optimal solution of production planning like production cost, worker cost, and inventory holding cost satisfying the constraints of the number of productions, workers, and inventory.
Teknik Penalti pada Optimisasi Berkendala Menggunakan Particle Swarm Optimization Dinita Rahmalia
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 3, No 1: Maret - Agustus 2018
Publisher : Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v3i1.1071

Abstract

Optimisasi berkendala adalah proses pencarian nilai optimum dengan memenuhi berbagai kendala. Optimisasi berkendala dapat diselesaikan dengan metode eksak maupun heurisitik. Dalam penelitian ini, akan digunakan metode heuristik seperti Particle Swarm Optimization (PSO). PSO adalah metode optimisasi yang terinspirasi dari perilaku populasi ikan atau unggas dalam mencari sumber makanan.PSO dapat digunakan pada optimisasi dengan kendala. Namun dalam update posisi partikel, supaya optimisasi memenuhi kendala, partikel akan dikenakan nilai penalti jika tidak memenuhi kendala. Simulasi diberikan pada dua model optimisasi.Hasil simulasi menunjukkan teknik penalti dapat menemukan pendekatan solusi optimal pada optimisasi berkendala.
Analytic Hierarchy Process (AHP) dan Fuzzy TOPSIS pada Pemilihan Himpunan Pairing Terpilih dari Jadwal Penerbangan Dinita Rahmalia; Awawin Mustana Rohmah; Nuril Lutvi Azizah
JATI UNIK : Jurnal Ilmiah Teknik dan Manajemen Industri Vol 4, No 1 (2020): October
Publisher : Industrial Engineering, Engineering of Faculty, Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/jatiunik.v4i1.922

Abstract

In the flight industry, there are two highest costs such as fuel cost and crew cost. The crew cost is affected by pairings selected from flight schedule. This research will explain about selecting the set of selected pairings using Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (Fuzzy TOPSIS). Before using either Fuzzy AHP or Fuzzy TOPSIS, it will be formed collection of the set of selected pairings using greedy algorithm. After collection of the set of selected pairings is formed, then we determine goal and criteria. The goal is selecting the set of selected pairings from some alternatives. For each the set of selected pairings, there are some criterions such as the number of deadhead, the number of pairing A2, the number of pairing A3, the number of pairing A4, the number of pairing A5, and the number of pairing A6. Based on simulation results, both AHP and Fuzzy TOPSIS can select and give the rank of priority in entire the set of selected pairings.  Pada industri maskapai penerbangan, terdapat dua biaya yang sangat besar yaitu biaya bahan bakar dan biaya kru. Biaya kru dipengaruhi oleh pairing yang terpilih dari jadwal penerbangan. Pada penelitian ini akan dilakukan pemilihan himpunan pairing terpilih menggunakan metode Analytic Hierarchy Process (AHP) dan Fuzzy Technique for Order Performance by Similarity to Ideal Solution (Fuzzy TOPSIS). Sebelum menggunakan AHP atau Fuzzy TOPSIS, akan dibentuk kumpulan dari himpunan pairing terpilih menggunakan greedy algorithm. Setelah kumpulan himpunan pairing terpilih terbentuk, maka dibentuk goal dan kriteria. Goal adalah memilih himpunan pairing terpilih dari beberapa alternative. Pada setiap himpunan pairing yang terpilih, terdapat beberapa kriteria seperti jumlah deadhead, jumlah pairing A2, jumlah pairing A3, jumlah pairing A4, jumlah pairing A5, dan jumlah pairing A6. Berdasarkan hasil perhitungan, metode AHP dan Fuzzy TOPSIS dapat memilih dan memberi peringkat prioritas pada pemilihan himpunan pairing terpilih.
Co-Authors Abdul Muhith Adrianto, Dian Andy Suryowinoto Arif Rohmatullah Arif Rohmatullah, Arif Awawin Mustana Rohmah Dennis V. Madrigal Dian Adrianto Dieky Adzkiya Elin Rias Tuti Ericha Dwi Ayu Prahastini Fajar Annas Susanto Fathul Fuad Attamini Hendro Nurhadi Husenti, Nadya Karya, Denis Fidita Khozin Mu'tamar Kresna Oktafianto Kresna Oktafianto Luluk Faridah M. Faris Naufal Hamdani M. Yushak Anshori Mohamad Yusak Anshori Mohammad Naufal Abdi Mohammad Syaiful Pradana, Mohammad Syaiful Muhammad Syaiful Pradana Muhsin Nasrul Fawa’idl Nur Aini S, Nur Nuril Lutvi Azizah Sholikhah, Minhatin Nisaatus Sigit Pancahayani Siti Mutmainnah sutimin sutimin Teay Shawyun Teguh Herlambang Thomy Eko Saputro Tomasouw, Berny Pebo