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Andi Bode
Universitas Ichsan Gorontalo
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Algoritma Backpropagation Menggunakan PSO Prediksi Penerimaan Retribusi Peminjaman Rumah Adat Dulohupa Sarlis Mooduto; Abdul Yunus Labolo; Andi Bode; Ivo Colanus Rally Drajana
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v6i2.711

Abstract

Regional retribution as payment for services or granting certain permits specifically granted and/or issued by local governments for personal or business interests. Gorontalo City Government has several public facilities that are used as a source of regional income in the form of taxes or levies. The Dulohupa traditional house levy carried out by the Gorontalo City Youth and Sports Tourism Office often experiences ups and downs because it is caused by uncertainty about rentals or competition. The purpose of this research is to overcome the existing problems by predicting retribution receipts using the backpropagation method, the use of particle swarm optimization (PSO) to increase the accurate value in predicting. The data collected is daily quantitative univariate time series data. This type of data is the Dulohupa Traditional House Retribution Receipt Data. The dataset taken from the levy receipt variable has 211 records. The best model is generated on the backpropagation algorithm using the particle swarm optimization (PSO) selection feature, which can be seen from the smallest error rate of 0.122. Thus the addition of a selection feature can improve the performance of an algorithm. The results of the predictions for the next four months from January to April which have been denormalized with an average number of predictions of Rp. 1,806,789 with an error value of 0.112.
Penerapan Algoritma Spport Vector Machine dan K-Nearest Neighbor Menggunkan Feature Selection Backward Elimination Untuk Prediksi Status Penderita Stunting Pada Balita Abdul Yunus Labolo; Sarlis Mooduto; Andi Bode; Ivo Colanus Rally Drajana
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v6i2.713

Abstract

Stunting adalah malnutrisi yang ditandai dengan tinggi badan, diukur dengan standar deviasi dari WHO. Dinas Kesehatan Provinsi Gorontalo khususnya dibidang Gizi mengenai stunting, selama ini melakukan kegiatan pemantauan tiap-tiap puskesmas dan posyandu. Pemantauan dan pendataan terkait stunting di berbagai puskesmas di wilayah Gorontalo merupakan faktor penting dalam menentukan faktor tumbuh kembang baik dalam kandungan maupun bayi yang dilahirkan. Masalah yang sering muncul adalah data yang dikumpulkan untuk underestimasi selalu tidak akurat setiap bulannya, karena hanya perkiraan yang dihitung berdasarkan kasus Puskesmas. Prediksi yang akurat diperlukan untuk mengatasi permasalahan yang ada. Data mining didefinisikan sebagai ekstraksi informasi berharga atau berguna dari industri pertambangan atau database yang sangat besar. Penelitian ini menggunakan algoritma K-Nearest Neighbor (K-NN) dan Support Vector Machine (SVM) menggunakan feature selection backward elimination. Berdasarkan hasil eksperimen, diprediksi jumlah penderita stunting menggunakan algoritma Support Vector Machine (SVM), dan k-Nearest Neighbor (K-NN) menggunakan Backward Elimination (BE). Tingkat error terkecil hasil RMSE 2,476 pada algoritma k-nearest neighbor. Adapun perbandingan antara hasil prediksi jumlah penderita stunting dibulan januari yaitu 23 orang dengan data aktual jumlah penderita stunting yakni 26 orang. Hasil prediksi menghasilkan nilai keakuratan 88,46%.
SUPPORT VECTOR MACHINE BERBASIS CHI SQUARE UNTUK PREDIKSI HARGA BERAS ECER KABUPATEN POHUWATO Sunarto Taliki; Ivo Colanus Rally Drajana; Andi Bode
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 5 No. 2 (2022): June 2022
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v5i2.899

Abstract

One of the staple foods for most Indonesians is rice. Rice is one of the staple foods most consumed by the people of Indonesia, the need for rice is also increasing, considering the very large and scattered population of Indonesia. The ups and downs of rice prices also have an impact on farmers because of their large production. The solution to dealing with uncertain changes in the retail price of rice is to predict prices. One way to find out the estimated retail price of rice is to make predictions using the Support Vector Machine algorithm using Chi Square. The results of the experiments that have been carried out, the prediction of rice prices has been successfully carried out. The smallest error rate in the Support Vector Machine algorithm model is RMSE 733,061. Then the proposed model approaches the value of perfection, because the comparison of the experimental results of rice price predictions produces an average accuracy value of 95.82%. Thus, the proposed method is declared successful.
Co-Authors Abd Rahmat karim Haba Abdul Yunus Labolo Ahmad Asdiranto A Marisang Azminuddin I. S. Azis Balok, Admin Budy Santoso Dedi Setiawan Djailani, Moh. Adrian Drajana, Ivo Colanus Rally Fatma YR Dunggio Idris, Irma Surya Kumala Ikbal Iskandar Irma Surya Kumala Idris Irvan A. Salihi Ivo Colanus Rally Drajana Jorry Karim Labolo, Abdul Yunus Merin Nurlaisa Abbas Mochammad Sakir Moh. Adrian Djailani Moh. Yasir Mohamad Efendi Lasulika Muhamad, Azhar Muis Nanja Nur, Muh Jabal Posumah, Risky Randa Ramadhani, Syarifah Fitrah Risky Randa Posumah Rofiq Harun Sarlis Mooduto Serwin Siska Udilawaty Sunarto Taliki Swastiani Dunggio Syahrir Abdussamad Syarifah Fitrah Ramadhani Talodo, Moh. Arif N Tangahu, Rizqi Adiputera Utiarahman, Siti Andini Warid Yunus Yasin Aril Mustofa Yulianti Lasena Yurike Musa Zohrahayaty Zulfrianto Yusrin Lamasigi