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All Journal Edumaspul: Jurnal Pendidikan Jurnal Tecnoscienza International Journal Software Engineering and Computer Science (IJSECS)
Labolo, Abdul Yunus
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection Labolo, Abdul Yunus; Utiarahman, Siti Andini; Lasulika, Mohamad Efendi; Drajana, Ivo Colanus Rally; Bode, Andi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1709

Abstract

This study addresses chili price volatility, an important concern that impacts the national economy and societal welfare. Fluctuations in chili prices in the retail market greatly influence market demand, thereby influencing farming decisions, especially chili cultivation. To help make better decisions, Researchers use forecasting, which is defined as the projection of future trends based on the analysis of historical data, using statistical methods. The K-Nearest Neighbor (K-NN) algorithm is used because of its resistance to high noise on large training datasets. However, challenges arise in determining the optimal value of 'k' and selecting related attributes. To overcome this, Feature Selection is applied to refine the model by removing irrelevant features, resulting in a significant reduction in the model error rate. This improvement indicates an increase in the efficiency of the K-NN algorithm with the incorporation of Feature Selection. Our findings show that the model, with backward elimination in Feature Selection, achieves a Root Mean Square Error (RMSE) of 0.202, outperforming the model using forward selection. The prediction accuracy of this model reaches an average of 78.86%, which is much higher than the baseline data of 50%. This shows the success of the proposed method in predicting chili prices.
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%.
The Utilizing of Microsite through S.id Platform as a Digital School Administration Portal for SMPN 01 Duhiadaa Adam, Muh Wahyuddin S.; Labolo, Abdul Yunus
Edumaspul: Jurnal Pendidikan Vol 9 No 2 (2025): Edumaspul: Jurnal Pendidikan (In Press)
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v9i2.9115

Abstract

This study aims to identify the use of the S.id microsite platform as a digital portal for teachers and education personnel in school administration at SMPN 01 Duhiadaa. The method used is a mixed method with a sequential exploratory design, combining qualitative (observation, interview) and quantitative (satisfaction survey) approaches. The development of the training program refers to the ADDIE model which includes the Analysis, Design, Development, Implementation, and Evaluation stages. The results of the study show that before the training, most teachers were not familiar with S.id platform. After participating in the workshop, participants were able to create an account, shorten the URL, adjust the appearance of the microsite, and produce digital products such as curriculum vitae, learning tools, and administrative tools. The survey data showed that 100% of respondents rated the interface access and interface to be easy to use, more than 70% rated the speed and administrative features as needed, 73% were satisfied, and 27% were very satisfied, while 91% rated the technical support adequate. These findings confirm that the use of microsites S.id effectively support the transformation of school administration towards an efficient and user-friendly digital system. ADDIE-based training programs have proven to be relevant to improve educators' technology skills while encouraging the creation of modern and sustainable school information management.
Co-Authors Adam, Muh Wahyuddin S. Andi Bode Drajana, Ivo Colanus Rally Mohamad Efendi Lasulika Mooduto, Sarlis Utiarahman, Siti Andini