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All Journal Jurnal Tecnoscienza Journal of Law, Society, and Islamic Civilization
Sarlis Mooduto
Universitas Ichsan Gorontalo
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences 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): 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): 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%.
Restorative Justice Berbasis Kearifan Lokal dalam Penyelesaian Tindak Pidana Ringan di Era Digital Yeti S. Hasan; Sarlis Mooduto; Nur Lazimatul Hilma Sholehah
Journal of Law, Society, and Islamic Civilization Vol 13, No 2: Oktober 2025
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jolsic.v13i2.108339

Abstract

Restorative justice (RJ) has emerged as an alternative approach within the criminal justice system, particularly in resolving minor offenses. In Indonesia, RJ practices are closely intertwined with local wisdom that emphasizes consensus, reconciliation, and the restoration of social harmony. However, the digital era presents new challenges, as information technology not only accelerates case resolution but also raises issues of unequal access to justice. This study aims to analyze the practice of restorative justice, grounded in local wisdom, for handling minor criminal offenses and to examine its relevance within the context of legal digitalization in Indonesia. The research employed a qualitative method with a juridical-empirical approach, involving literature review, analysis of statutory regulations, and interviews with law enforcement officials and traditional leaders in Gorontalo. The findings reveal that local wisdom provides social legitimacy for RJ practices, thereby strengthening public acceptance of out-of-court settlements. Furthermore, legal digitalization, through e-justice and e-documentation systems, offers opportunities to expand access and transparency, although it still faces challenges related to infrastructure and digital literacy. This study underscores that integrating local wisdom with digital innovation is a strategic step to optimize the implementation of restorative justice in Indonesia. Therefore, it is crucial to formulate a digital legal framework that is adaptive to local values to promote substantive justice.
Co-Authors Abdul Yunus Labolo Andi Bode Ivo Colanus Rally Drajana Nur Lazimatul Hilma Sholehah Yeti S. Hasan