Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Faktor Exacta
Harry Dwiyana Kartika
Universitas Krisnadwipayana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of Kernel Support Vector Machine in Predicting Judges' Decisions at the Bekasi District Court Harry Dwiyana Kartika; Getah Ester Hayatulah; Ali Khumaidi
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p03

Abstract

Proses persidangan suatu perkara pidana di Pengadilan Negeri Bekasi pada tahun 2019-2021 dengan rata-rata lama proses yang diperlukan untuk memutuskan perkara oleh hakim adalah 65-an hari. Pada penelitian ini mengusulkan penggunaan machine learning sebagai alat bantu untuk mempercepat keputusan hakim. Kasus tindak pidana berdasarkan jenis acara pidana dibagi menjadi 3 jenis yaitu pidana biasa, pidana singkat, dan pidana cepat. Data penelitian yang digunakan adalah jenis acara pidana biasa dengan status perkara minutasi yang dipublikasikan sebanyak 1.642 kasus. Proses pengolahan data mengunakan python dengan preprocessing data case folding, remove punctuation, tokenization dan removal stopword kemudian untuk pembobotan kata menggunakan TF-IDF. Untuk memprediksi putusan lama pemidanaan menggunakan pendekatan klasifikasi Support Vector Machine. Sebelum pemodelan dilakukan splitting data dengan perbandingan 80:20 dan hasil perbandingan pemodelan klasifikasi menggunakan SVM dengan 4 kernel yaitu linear (89,4%), RBF (88,4%), sigmoid (88,4%), dan polynomial (89,1%). Kernel SVM terbaik adalah kernel linear dengan nilai akurasi sebesar 89,4% dan nilai error sebesar 10,6%.
Development of a Production Machine Maintenance Predictive Model Using the Elman Recurrent Neural Network Algorithm Ajat Zatmika; Harry Dwiyana Kartika; Ali Khumaidi
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15450

Abstract

PT Simba Indosnack Makmur is a factory that produces snacks. In the production process the machine has worked very optimally, the problem that is often faced by the Quality Control department is often finding non-standard product weights. This problem is caused by a machine that already requires maintenance. So far, the maintenance process has to get approval from the manager, which sometimes takes quite a long time to be inspected so that the maintenance process is delayed, which results in reduced production targets. By implementing a predictive maintenance model that utilizes time series data in the production process, applying the Elman Recurrent Neural Network will be able to provide notifications for machine maintenance before the machine is inaccurate in snack production. The Elman structure was chosen because it can make iterations much faster, thus facilitating the convergence process. The input vector used uses windows size. The results of the study using a target error of 0.001 show the smallest MSE value of 0.002833 with windows size 11. Then by using 13 neurons in the hidden layer a minimum error value of 0.003725 is obtained.
Co-Authors Ajat Zatmika Ali Khumaidi Getah Ester Hayatulah