Article Per Year (5 Year)
p-Index From 2020 - 2025
0.983
P-Index
This Author published in this journals
All Journal Jurnal Tecnoscienza International Journal Education and Computer Studies Innovative: Journal Of Social Science Research
Mooduto, Sarlis
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Penerapan Metode AHP pada Penerima Bantuan Sosial Keluarga Miskin di Dusun Bolongga Kabupaten Gorontalo Utara Mooduto, Sarlis; Sumarni, Sumarni
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16584

Abstract

Penelitian ini bertujuan untuk: 1) menerapkan metode AHP pada penerima bantuan sosial keluarga miskin dengan menggunakan metode Analytical Hierarchy Process (AHP), dan 2) mengetahui kriteria-kriteria dari data penerima bantuan sosial keluarga miskin didusun bolongga menggunakan metode AHP. Teknik pengumpulan data menggunakan data primer, dengan cara observasi dan wawancara. Objek penelitian penulis yaitu penerima bantuan sosial keluarga miskin, kemudian di olah dengan metode analytical hierarchy process dari hasil penelitian ini data yang di gunakan sebanyak 30 data penerima bantuan sosial keluarga miskin (PKH) yang di ambil dari kantor Desa Leboto. Dengan hasil akhir yaitu menghasilkan perangkingan dari penerima bantuan sosial keluarga miskin (PKH) di dapat 3 rangking yaitu Rita Piloman mendapatkan rangking 1, iwan ali mendapatkan rangking ke2 dan Jumira dusi meraih rangking 3 dari perhitungan excel pada penerima bantua sosial keluarga miskin (PKH).
Algoritma Backpropagation Menggunakan PSO Prediksi Penerimaan Retribusi Peminjaman Rumah Adat Dulohupa Mooduto, Sarlis; Labolo, Abdul Yunus; Bode, Andi; Drajana, Ivo Colanus Rally
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): 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 Labolo, Abdul Yunus; Mooduto, Sarlis; Bode, Andi; Drajana, Ivo Colanus Rally
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): 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%.
Pengembangan Sistem E-Administrasi Perizin Usaha Berbasis Web Untuk Efisiensi Layanan Desa Sumarni, Sumarni; Mooduto, Sarlis
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.20015

Abstract

perancangan dan implementasi sistem e-administrasi perizinan usaha berbasis web untuk keefisiensi layanan Desa Jembatan Merah, Kecamatan Tomilito. Latar belakang masalah yang diangkat adalah proses pengajuan izin usaha manual yang lambat, kurang transparan, dan rentan kesalahan data. Penelitian ini bertujuan untuk merancang dan menerapkan sistem digital guna mempermudah proses pengajuan dan pengelolaan izin usaha secara efektif dan efisien. Hasil penelitian menunjukkan bahwa sistem yang dibangun berhasil membantu aparatur desa dalam memberikan pelayanan izin usaha yang lebih cepat, transparan, dan terstruktur. Implementasi website e-administrasi ini secara signifikan meningkatkan kualitas layanan administrasi desa, khususnya dalam digitalisasi pengurusan izin usaha.
Classification of Skipjack Freshness Quality Based on Local Binary Pattern and Gray Level Co-Occurrence Matrix Using K-Nearest Neighbor Y Lamasigi, Zulfrianto; Efendi Lasulika, Mohamad; Mooduto, Sarlis
International Journal Education and Computer Studies (IJECS) Vol. 5 No. 3 (2025): NOVEMBER
Publisher : Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijecs.v5i3.5791

Abstract

Katsuwonus pelamis or skipjack tuna is one of the results of fishing commodities from Gorontalo Province. The quality of fresh fish can be degraded easily if not handled and stored properly. Thus, in this study an automatic system for classifying the freshness level of skipjack tuna based on digital image processing techniques was introduced. It uses Local Binary Pattern (LBP) to extract local texture features and Gray Level Co-occurrence Matrix (GLCM) for statistical texture analysis with classification done by K-Nearest Neighbor (K-NN) algorithm using Euclidean distance as a measurement between features. There were 819 training images and 140 test images used in four categories: Fresh, Not Fresh, Worth Consuming, and Rotten. Tests on several values of k showed that the highest accuracy was at k = 1 with an accuracy rate of 86.42% while the lowest was at k = 9 with a rate of 49.28%. This indicates that the combination LBP-GLCM applied in K-NN has potentiality to capture texture difference effect from various levels fish freshness. This method is non-destructive and could be onboard application for fish quality monitoring as well as automatic system for freshness evaluation.
Co-Authors Andi Bode Drajana, Ivo Colanus Rally Efendi Lasulika, Mohamad Labolo, Abdul Yunus Sumarni Sumarni Y Lamasigi, Zulfrianto