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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JOIN (Jurnal Online Informatika) Jurnal Ilmu Kefarmasian Indonesia
Wahyono Wahyono
Universitas Gadjah Mada
Biochemistry, Genetics & Molecular Biology Computer Science & IT Health Professions Medicine & Pharmacology Public Health

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Sistem Rekomendasi Berbasis Genetic Algorithm: Studi Kasus Pembelian Komponen Komputer dan Aksesorisnya Abdul Kholik; Erwin Eko Wahyudi; Kristiawan Devianto; Nabila Sholihah; Yaqutina Marjani Santosa; Wahyono Wahyono
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2018
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Pembelian komponen komponen komputer dan aksesorisnya merupakan salah satu permasalahan optimasi, yaitu menentukan pembelian secara online atau offline agar pembelian lebih optimal dan efisien. Dalam penelitian ini, algoritma genetika akan digunakan untuk pemecahan masalah optimasi pembelian komponen komputer dan aksesorisnya. Tujuan dari penelitian ini adalah penggunaan algoritma genetika sebagai sistem rekomendasi untuk menentukan total harga minimal serta jalur yang paling optimal dalam pembelian komponen komputer di 12 toko, terdiri dari 6 toko offline dan 6 toko online. Representasi kromosom menggunakan gabungan antara pengkodean biner dan pengkodean bilangan bulat. Selanjutnya, proses seleksi parents menggunakan metode roulette wheel, proses crossover menggunakan metode uniform crossover, proses mutasi dilakukan secara random, dan proses seleksi survivor menggunakan metode best selection. Pengujian dilakukan menggunakan iterasi 10 sampai 100 dengan interval 10 kali, dan didapatkan nilai total biaya minimal adalah Rp. 18.811.000,-
Pengaruh Kubebin, Senyawa Lignan Pqner cubeba L.f. terhadap Pelepasan Histamin dari RPMCs AGUNG ENDRO NUGROHO; WAHYONO WAHYONO; SUBAGUS WAHYUONO; KAZUTAKA MAEYAMA
JURNAL ILMU KEFARMASIAN INDONESIA Vol 8 No 2 (2010): JIFI
Publisher : Fakultas Farmasi Universitas Pancasila

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Cubebin is a lignan isolated from Indonesian plant Piper cubeba L.f fruits. Cubebin has been found to strongly inhibit contraction of isolated-trachea of guinea pig induced by histamine or metacholine. ln the present study, the effcct of cubebin was investigated on histamine release from rat peritoneal mast cells (RPMCS), a connective tissue mast cell. Compound 48/80 (G Protein activator), thapsigargin (SERCA inhibitor), ionoinycin (calcium ionophore), and PMA (PKC activator) were used as inducers for histamine release from connective tissue mast cell. Histamine released in the medium was measured by HPLC-Huorometry. The results showed that cubebin at the concentration of 30 and 100 µM inhibited the histamine release from RPMCS induced by thapsigargin by 12,86 ± 1,84 % and 40.38 ± 1.93 %, respectively. In addition, there was a partial inhibition seen in respond to ionornycin, with no effects towards compound 48/80 or PMA, These data indicate that cubebin inhibited thc histamine release from connective mast cells, and might involve in the activation of sarco/ endoplasmic reticulum Ca2+ ATPase.
Prediction of Indonesian Inflation Rate Using Regression Model Based on Genetic Algorithms Faisal Dharma; Shabrina Shabrina; Astrid Noviana; Muhammad Tahir; Nirwana Hendrastuty; Wahyono Wahyono
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.532

Abstract

Inflation occurs where there is an increase in the price of goods or services in general and continuously in a country. Uncontrolled inflation will have an impact on the decline of the Indonesian economy. Therefore, the prediction of future inflation levels is necessary for the government to develop economic policies in the future. Prediction of inflation levels can be done by studying historical past Consumer Price Index (CPI) data. Regression methods are often used to solve prediction problems. The problem of finding the optimal prediction model can be seen as an optimization problem. Genetic algorithms are often used to deal with optimization problems. Thus, this work proposed to use a genetic algorithm-based regression model for predicting inflation levels. The model was trained and evaluated using real CPI data which obtained from the Indonesian Central Bank. Based on the experiment, it is proved that the proposed model is effective in predicting the inflation level as it gains MSE of 0.1099.
Co-Authors Abdul Kholik Agung Endro Nugroho Astrid Noviana Erwin Eko Wahyudi Faisal Dharma Indrawati Kurnia Setyani KAZUTAKA MAEYAMA Kristiawan Devianto Muhammad Tahir Nabila Sholihah Nirwana Hendrastuty Shabrina Shabrina Subagus Wahyuono Teuku Nanda Saifullah Sulaiman Yaqutina Marjani Santosa