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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Tecnoscienza Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Sunarto Taliki
Universitas Ichsan Gorontalo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote Alhamad, Apriyanto; Azis, Azminuddin I. S.; Santoso, Budy; Taliki, Sunarto
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 5, No 3 (2019): Volume 5 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v5i3.37188

Abstract

Kematian yang disebabkan penyakit jantung masih sangat tinggi, sehingga perlu peningkatan upaya-upaya pencegahannya, misalnya dengan meningkatkan capaian model prediksinya. Penerapan metode-metode machine learning pada dataset publik (Cleveland, Hungary, Switzerland, VA Long Beach, & Statlog) yang umumnya digunakan oleh para peneliti untuk prediksi penyakit jantung, termasuk pengembangan alat bantunya, masih belum menangani missing value, noisy data, unbalanced class, dan bahkan data validation secara efisien. Oleh karena itu, pendekatan imputasi mean/mode diusulkan untuk menangani missing value replacement, Min-Max Normalization untuk menangani smoothing noisy data, K-Fold Cross Validation untuk menangani data validation, dan pendekatan ensemble menggunakan metode Weighted Vote (WV) yang dapat menyatukan kinerja tiap-tiap metode machine learning untuk mengambil keputusan klasifikasi sekaligus untuk mereduksi unbalanced class. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan tersebut memberikan akurasi sebesar 85,21%, sehingga mampu meningkatkan kinerja akurasi metode-metode machine learning, selisih 7,14% dengan Artificial Neural Network, 2,77% dengan Support Vector Machine, 0,34% dengan C4.5, 2,94% dengan Naïve Bayes, dan 3,95% dengan k-Nearest Neighbor.
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.
Aplikasi Diagnosa Penyakit Tanaman Cabai Merah menggunakan Algoritma K-Nearest Neighbor Sunarto Taliki; Serwin Serwin; Jabal Nur; 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.v6i2.712

Abstract

Tanaman cabai merah merupakan komoditas holtikultura yang begitu sangat penting bagi kebutuhan dan keperluan manusia, seperti, ramuan obat-obatan tradisional, sebagai bumbu untuk makanan, dimakan bersama makanan ringan dan lain-lain. Dilihat dari tingkat serangan dan kondisi pertanian cabai merah di lapangan saat ini masi terkendala dengan belum adanya rekomendasi metode pengendalian yang efektif sehingga petani cenderung menggunakan pastisida kimia yang berdampak negatif terhadap lingkugan. Untuk mendiagnosa berbagai jenis penyakit yang menyerang tanaman cabai merah diperlukan seorang pakar/ahli. Pada peniltian ini akan membangun sebuah aplikasi yang dapat mendiagnosa dan memberikan solusi kepada petani mengenai masalah penyakit tanaman cabai merah. Aplikasi sistem pakar diagnosa penyakit tanaman cabai dapat diimplementasikan dengan melihat hasil pengujian berdasarkan konsultasi diagnosis serta solusi yang diberikan. Hal ini dapat dilihat pada jenis penyakit Busuk Akar dengan gejala kasus G01, G02 nilai Bobot 3.1, Gejala Dipilih (Benar) dan Nilai Kedekatan K-NN (3/4) = 0.75.
K-NEAREST NEIGHBOR MENGGUNAKAN FEATURE SELECTION BACKWARD ELIMINATION UNTUK PREDIKSI JUMLAH PERMINTAAN DARAH PADA PMMI KOTA GORONTALO Yulianti Lasena; Sunarto Taliki; Mohamad Efendi Lasulika; Andi Bode
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.172

Abstract

The importance of the availability of blood at PMI, it is expected that PMI always maintains the amount of blood supply to meet the need for blood transfusions. Prediction of blood supply is needed to overcome problems related to bloodstock supply at PMI Gorontalo. The application of predicting the number of blood requests with the K-Nearest Neighbor Algorithm can be done to overcome the existing problems. K-NN is a non-parametric algorithm that can be used for classification and regression. The last few decades have been used in prediction cases, but the K-NN algorithm is better if feature selection is applied in selecting features that are not relevant to the model, the feature selection used in this study is Backward Selection. This study aims to determine the error value in predicting the number of requests for blood at the PMI in Gorontalo City. Meanwhile, the purpose of this research is to find the error value of the K-Nearest Neighbor Algorithm and Feature Selection which can be used as a reference for PMI in making policies to make various efforts to maintainbloodstockk in the future.
K-NEAREST NEIGHBOR MENGGUNAKAN FEATURE SELECTION BACKWARD ELIMINATION UNTUK PREDIKSI JUMLAH PERMINTAAN DARAH PADA PMMI KOTA GORONTALO Yulianti Lasena; Sunarto Taliki; Mohamad Efendi Lasulika; Andi Bode
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.172

Abstract

The importance of the availability of blood at PMI, it is expected that PMI always maintains the amount of blood supply to meet the need for blood transfusions. Prediction of blood supply is needed to overcome problems related to bloodstock supply at PMI Gorontalo. The application of predicting the number of blood requests with the K-Nearest Neighbor Algorithm can be done to overcome the existing problems. K-NN is a non-parametric algorithm that can be used for classification and regression. The last few decades have been used in prediction cases, but the K-NN algorithm is better if feature selection is applied in selecting features that are not relevant to the model, the feature selection used in this study is Backward Selection. This study aims to determine the error value in predicting the number of requests for blood at the PMI in Gorontalo City. Meanwhile, the purpose of this research is to find the error value of the K-Nearest Neighbor Algorithm and Feature Selection which can be used as a reference for PMI in making policies to make various efforts to maintainbloodstockk in the future.
Co-Authors Andi Bode Andi Bode Apriyanto Alhamad Azminuddin I. S. Azis Budy Santoso Ivo Colanus Rally Drajana Mohamad Efendi Lasulika Muh Jabal Nur Serwin Yulianti Lasena