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All Journal International Journal of Electrical and Computer Engineering Journal of Information Technology and Computer Science ALINIER: Journal of Artificial Intelligence & Applications JEECS (Journal of Electrical Engineering and Computer Sciences) Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Andreas Nugroho Sihananto
Universitas Pembangunan Nasional Veteran Jawa Timur
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Chicken feed optimization using evolution strategies and firefly algorithm Andreas Nugroho Sihananto; M. Shochibul Burhan; Wayan Firdaus Mahmudy
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.99 KB) | DOI: 10.11591/ijece.v9i1.pp585-592

Abstract

Mixing broiler chicken and layer hens feed using various feed ingredients is a difficult task. The feed must fulfill the minimum nutrient requirement and must break the constraint. Some classic approach like Pearson’s Square has been already introduced to solve this problem. However, the approaches cannot guarantee to fulfill nutrient requirements and desirable price. The two metaheuristic algorithms Evolution Strategies (ES) and Firefly Algorithms (FA) are being proposed in this paper to know how well they performed this problems. Result show that ES is perform much better compared to classic Pearson’s Square, but ES itself is outperform by FA on both cases.
Rainfall Forecasting Using Backpropagation Neural Network Andreas Nugroho Sihananto; Wayan Firdaus Mahmudy
Journal of Information Technology and Computer Science Vol. 2 No. 2: November 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.659 KB) | DOI: 10.25126/jitecs.2017229

Abstract

Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815;  0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.
Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization Tirana Noor Fatyanosa; Andreas Nugroho Sihananto; Gusti Ahmad Fanshuri Alfarisy; M Shochibul Burhan; Wayan Firdaus Mahmudy
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.719 KB) | DOI: 10.25126/jitecs.20161215

Abstract

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result
Implementasi Metode K-NN dalam Klasterisasi Kasus Kesehatan Jantung Anggraini PS; Andreas Nugroho Sihananto; Dwi Arman Prasetya
ALINIER: Journal of Artificial Intelligence & Applications Vol. 3 No. 2 (2022): ALINIER Journal of Artificial Intelligence & Applications
Publisher : Program Studi Teknik Elektro S1 ITN Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/alinier.v3i2.5761

Abstract

Penyakit jantung penyebab kematian nomer satu berdasarkan data yang diperoleh dari WHO (world health organization). Penyakit jantung terjadi ketika darah yang mengalir ke otot jantung berhenti sehingga menyebabkan gangguan jantung. Hal ini menyebabkan adanya kebutuhan mendefinisikan sistem pendukung keputusan yang membantu dokter dalam mengambil keputusan untuk mengambil tindakan pencegahan terhadap penderita penyakit jantung. K-NN (K-Nearest Neighbor) merupakan metode yang sangat sederhana, paling populer, sangat efisien dan efektif untuk pengenalan pola. K-NN merupakan pengklasifikasi lurus ke depan dengan sampel diklasifikasikan berdasarkan kelas tetangga terdekatnya. Basis data medis memiliki volume tinggi. Jika kumpulan data berisi atribut yang berlebihan dan tidak relevan, maka klasifikasi dapat menghasilkan hasil yang kurang akurat. Penelitian ini menerapkan metode klasifikasi K-NN diharapkan dapat mengatasi permasalahan untuk efektifitas dan akurasi dalam mendeteksi kesehatan jantung. Dalam penelitian ini mencakup pengukuran performa, yaitu: presisi, recall, f-measure, dan akurasi menggunakan metode K-NN dengan nilai K = 3. Dataset yang digunakan dari UCI Machine Learning Repository pada 303 pasien penyakit jantung. Hasil yang didapatkan ialah presisi 0.70, recall 0.94, dan f-measure 0.81, dan akurasi 70% yang termasuk dalam klasifikasi baik dari nilai K terdekat sehingga metode K-NN dapat digunakan dalam mendeteksi kesehatan jantung.
Implementation of Least Square Algorithm to Predict Monthly Revenue (Case Study: Djuju’s Grocery Store) Aditya Rizqi Ardhana; Chrystia Aji Putra; Andreas Nugroho Sihananto
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 1 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i1.3

Abstract

Business owners need to estimate their revenue, which is crucial for the sustainability of their operations. Thus, entrepreneurs such as micro, small, and medium-sized business owners, as well as owners of grocery stores, leverage technological advancements to maximize their sales operations. However, manual sales activities can pose challenges for managing sales data, such as disorganized sales record keeping, failure to record sales of high-volume customers, and time-consuming manual reporting for revenue predictions. To address these issues, researchers have developed a revenue prediction information system. In this study, revenue and profit predictions for the following period were calculated using the Least Square algorithm with the Mean Absolute Percentage Error (MAPE). An example calculation for a 12-month period resulted in a revenue forecast of Rp. 2,837,687.76 for the month of June 2023 with a MAPE of 12.71%.
Indonesian Sign Language Image Detection Using Convolutional Neural Network (CNN) Method Andreas Nugroho Sihananto; Erista Maya Safitri; Yoga Maulana; Fikri Fakhruddin; Mochammad Ervinda Yudistira
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.37

Abstract

In Indonesia, there are two sign languages utilized by the deaf community, SIBI and BISINDO. Unfortunately, the majority of non-deaf individuals and deaf companions are not proficient in sign language. To address this communication gap, information systems can play a pivotal role in recognizing sign language speech. Recently, researchers conducted a study using the Convolutional Neural Network (CNN) algorithm to predict sign language for both SIBI and BISINDO datasets. The aim was to develop a model that could accurately translate sign language into written or spoken language, thus bridging the gap between deaf and non-deaf individuals. The research found that the CNN algorithm performed optimally on epoch 50 for SIBI with a testing accuracy of 93.29%, while for BISINDO, it achieved the best result on epoch 40 with a testing accuracy of 82.32%. These results suggest that the CNN algorithm has the potential to accurately recognize and translate sign language, thus improving communication between deaf and non-deaf individuals in Indonesia.
Co-Authors Aditya Rizqi Ardhana Anggraini PS Chrystia Aji Putra Dwi Arman Prasetya Erista Maya Safitri Fikri Fakhruddin Gusti Ahmad Fanshuri Alfarisy M Shochibul Burhan M. Shochibul Burhan Mochammad Ervinda Yudistira Tirana Noor Fatyanosa Wayan Firdaus Mahmudy Yoga Maulana