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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah KOMPUTASI Jurnal Nasional Teknik Elektro dan Teknologi Informasi
LM Rasdi Rere LM Rasdi Rere
STMIK Jakarta STI&K
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Harmony Search berbasis Chaos dan Random Adjusment untuk Perbaikan Kontras Citra LM Rasdi Rere; Bheta Agus Wardijono
Jurnal Ilmiah KOMPUTASI Vol 16, No 1 (2017): Juni
Publisher : STMIK JAKARTA STI&K

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Abstract

Harmony Search (HS) adalah salah satu metode optimasi Metaheurstik yang terbukti handal dalam beberapa tahun terakhir. Performa dari metode ini, seperti umumnya algoritma Metaheruistik lainnya, sangat bergantung dari pengaturan nilai parameternya, yang biasanya konstan selama proses perhitungan. Akan tetapi untuk menentukan nilai parameter suatu algoritma Metaheuristik tidaklah mudah, karena sangat bergantung dari karakteristik masalah yang dihadapi. Untuk mengatasi hal ini, sejumlah cara telah diusulkan untuk dapat mengatur parameternya secara otomatis, seperti menggunakan logika Fuzzy, metode Chaos ataupun teknik Random Adjustment. Selama beberapa tahun terakhir, metode-metode yang diusulkan ini telah dikembangkan secara independen, dan integrasi dari dua atau lebih metode tersebut belum banyak dilakukan. Karena itu dalam penelitian ini, sebuah metode baru yang menggabungkan Chaos dan teknik Random Adjusment pada algoritma HS diusulkan. Sebagai studi kasus penelitian, metode yang diusulkan diaplikasikan untuk perbaikan kontras pada citra Lena, Rice dan Cameraman. Hasil eksperimen yang diperoleh menunjukkan bahwa metode yang diusulkan lebih baik dari HS asli, HS dengan Chaos, maupun HS berbasis teknik Random Adjustment.
Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement Vina Ayumi; L.M. Rasdi Rere; Mohamad Ivan Fanany; Aniati Murni Arymurthy
Jurnal Ilmu Komputer dan Informasi Vol 10, No 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.372 KB) | DOI: 10.21609/jiki.v10i2.375

Abstract

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.
Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management Widi Hastomo; Nur Aini; Adhitio Satyo Bayangkari Karno; L.M. Rasdi Rere
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1778.055 KB) | DOI: 10.22146/jnteti.v11i2.2586

Abstract

Indonesia is committed to reducing greenhouse gas (GHG) emissions through a nationally determined contribution (NDC) scheme. The target to reduce GHG emissions is 29% through the business as usual (BAU) scheme or 41% with international aid. These ambitious targets require transformations in energy, food, and land-use systems, which need to cope with the potential trade-offs among many targets, such as food security, energy security, avoided deforestation, biodiversity conservation, land use competition, and freshwater use. Mitigation and adaptation have complementary roles in responding to climate change at both temporal and spatial scales. This paper aims to perform simulations and predictions on manure management emissions producing CO2eq using machine learning methods of long short-term memory (LSTM) and gated recurrent unit (GRU). The hidden layer architecture used was six combinations, while the dataset was obtained from the fao.org repository. The optimizer used in this paper was RMSprop, with a graphical user interface using the Streamlit dashboard. The results of this study are (a) cattle with fifteen epochs using hidden layer four combinations (LSTM, GRU, LSTM, GRU) yielded RMSE 450,601; (b) non-dairy cattle with fifteen epochs and one hidden layer (GRU, GRU, GRU, GRU) yielding RMSE 361.421; (c) poultry birds with twelve epoch values and three hidden layers (GRU, GRU, LSTM, LSTM) resulted in an RMSE value of 341.429. The challenges faced were the determination of epochs, the combination of hidden layers, and the characteristics of the relatively small number of datasets. The results of this study are expected to provide added value for developing better decision support tools and models to assess emission trends in the livestock sector and develop CO2eq emission mitigation strategies that lead to sustainable fertilizer management practices.
Co-Authors Adhitio Satyo Bayangkari Karno Aniati Murni Arymurthy Bheta Agus Wardijono Mohamad Ivan Fanany Nur Aini Vina Ayumi Widi Hastomo