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Ali Khumaidi
Universitas Krisnadwipayana
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Development of The Application for Car Audio Parts Detection Damage Using Case Based Reasoning Method and Nearest Neighbor Algorithm Andika Saputra; Ali Khumaidi
Jurnal Teknik Informatika C.I.T Medicom Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol13.2021.45.pp42-50

Abstract

PT. Denso Ten often receives car audio spare parts that are damaged due to shocks during the trip or sender's error. Damaged parts are collected and repaired by maintenance who has special skills manually. The limited number of maintenance operators and the frequent transfer of experts resulted in work delays due to insufficient spare parts. Spare parts repair work cannot be done by all employees because it requires special skills. The Case-based Reasoning approach and Nearest Neighbor algorithm are used to be developed for expert systems to support the detection of audio part damage so that it will speed up work and can be done by employees without special knowledge. The system can run and be used by users properly as needed and the results have good accuracy. The Case Base Reasoning method and the nearest neighbor algorithm work according to the rules and the calculation results are according to the expert's results.
Dissolved Oxygen Prediction of the Ciliwung River using Artificial Neural Networks, Support Vector Machine, and Streeter-Phelps Yonas Prima Arga Rumbyarso; Nuke L Chusna; 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.p06

Abstract

Evaluation of Ciliwung river water quality can be done by analyzing the distribution of dissolved oxygen (DO). The purpose of this research is to analyze the environmental parameters that affect the distribution of DO, by carrying out predictive modeling to estimate the distribution of DO in the Ciliwung River. The research data used primary data and secondary data, some of which were obtained from previous studies. The water quality parameters used are DO, temperature, biochemical oxygen demand, chemical oxygen demand, power of hydrogen, and turbidity. The dataset used has a missing value of 28.8%. To optimize the model results, preprocessing is carried out using a machine learning approach, namely comparing support vector machine (SVM), artificial neural networks (ANN), and linear regression. The three models were compared to predict DO, the results of performance evaluation of the SVM, ANN and Streeter-Phelps models had RMSE values of 0.110, 0.771, and 0.114.
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%.
Konfigurasi Hyperparameter Long Short Term Memory untuk Optimalisasi Prediksi Penjualan Ali Khumaidi; Dhistianti Mei Rahmawan Tari; Nuke L. Chusna
Faktor Exacta Vol 15, No 4 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i4.15286

Abstract

To support business development and competition, forecasting capabilities with good accuracy are required. PT. Sumber Prima Inti Motor does not want the customer's spare part needs not to be available when ordered, therefore an appropriate procurement and sales forecasting strategy is needed. Long Short Term Memory (LSTM) is a fairly good algorithm for forecasting, in this study using LSTM to predict sales of spare parts for the next 60 days. The CRISP-DM method is used and to obtain optimal model performance, hyperparameter configuration is performed. The configurations used are number of hidden layers, data partition, epoch, batch size, and dropout scenario. The best results from the LSTM model hyperparameter configuration are 3 hidden layers, 3 dropouts, epoch 150, and batch size 30. The performance of the training and testing models with RMSE is 0.0855 and 0.0846.
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.
PERANCANGAN APLIKASI PENILAIAN RUMAH TIDAK LAYAK HUNI MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING PADA KEGIATAN KEMENTERIAN PUPR Faisal Ruswanto; Herry Wahyono; Ali Khumaidi
TEKNOKRIS Vol 26 No 1 (2023): Jurnal Teknokris Edisi Juni
Publisher : Fakultas Teknik Unkris Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61488/teknokris.v26i1.244

Abstract

Determination of recipients of uninhabitable housing assistance (RTLH) is one of the problems that is of concern to the Directorate of Self-Help Housing of the Ministry of Public Works and Public Housing of the Republic of Indonesia, because the large number of data on incoming aid proposals makes it difficult to determine which beneficiaries deserve due to the limited budget. One way to determine beneficiaries is to use a decision system using the Simple Additive Weighting (SAW) method. This method will provide the value of the selected alternative preference as an indicator to determine the recipient of assistance as it can help the data processing team, stakeholders and leaders in the Directorate of Self-Help as the government which has a policy in determining the prospective recipients of the house renovation assistance objectively not subjectively. The data used was taken from data collection conducted by the field team in the Serang City area of Banten Province where the data obtained were 935 residents
Optimizing Bitcoin Price Predictions Using Long Short-Term Memory Algorithm: A Deep Learning Approach Khumaidi, Ali; Kusmanto, Panji; Hikmah, Nur
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1831.38-45

Abstract

Currently bitcoin is considered an investment tools, the value of bitcoin itself is unstable so it is difficult to predict which can cause losses for bitcoin traders. Some previous research shows that Long Short-Term Memory (LSTM) which is a deep learning approach as an improvement of RNN has the best performance in predicting stocks and cryptocurrencies compared to Support Vector Machine (SVM), Exponential Moving Average (EMA), and Moving Average (MA), and Seasonal Autoregressive Integrated Moving Average (SARIMA). LSTM has the disadvantage that it is difficult to understand in determining the best parameters and to obtain good results it needs strict hyperparameter adjustment. This study aims to find the best parameters in LSTM by selecting the amount of data, training data composition, batch size, epoch and the amount of prediction time and analyzing prediction performance. In this study, data collection was carried out in real time and was able to provide predictions for the next few days. The test results of the LSTM algorithm have a performance with an average accuracy of 93.69% with the parameters of the amount of bitcoin price data used is 3 years, with a percentage of train data of 85%, using 10 batch sizes, with a number of epochs 125, and the highest average accuracy rate for 7 days of prediction.
Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union Khumaidi, Ali; Darmawan, Risanto; Reztrianti, Diajeng
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.21819

Abstract

KLASIFIKASI MOLTING KEPITING SOKA MENGGUNAKAN ALGORITMA CONVOLUSIONAL NEURAL NETWORK Khumaidi, Ali; Nurpadilah, Aini
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 13 No 2 (2024): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v13i2.13984

Abstract

Kepiting Soka memiliki nilai ekonomi tinggi karena seluruh bagian tubuhnya dapat dimakan selama fase molting, saat cangkangnya lunak. Proses ini berlangsung singkat, hanya sekitar 5 jam sebelum cangkang mengeras kembali. Deteksi molting secara otomatis sangat diperlukan untuk mengoptimalkan waktu panen dan mencegah kehilangan produksi. Teknologi deep learning menggunakan arsitektur MobileNetV2 telah menunjukkan efisiensi dalam klasifikasi gambar. Penelitian ini bertujuan untuk mengklasifikasi kepiting soka dalam kondisi molting menggunakan arsitektur MobileNetV2, berbeda dengan metode pembelajaran mesin sebelumnya. MobileNetV2 dipilih karena kemampuannya dalam klasifikasi citra dengan sumber daya komputasi terbatas. Dataset berisi 260 citra kepiting, dengan fokus pada kepiting soka molting dan tidak molting, diproses melalui augmentasi, resize, dan pembagian data (80% data training, 10% validasi, dan 10% testing). Model ini menghasilkan akurasi tinggi pada pelatihan dan validasi sebesar 100%, membuktikan kemampuannya untuk mendeteksi kepiting molting secara efisien dan model tidak mengalami overfitting. Arsitektur MobileNetV2 berpotensi untuk diaplikasikan dalam sistem klasifikasi kepiting soka berbasis perangkat seluler.
Comparison of Convolutional Neural Network Models for Feasibility of Selling Orchids Chusna, Nuke L; Khumaidi, Ali
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2006.296-304

Abstract

Orchid flowers are one of the most popular ornamental plants, widely appreciated for their unique features and aesthetic appeal, making them highly potential for sales in the global market. While numerous studies have explored Orchid flower characteristics and disease detection, research on the classification of Orchid salability remains unexplored. This study addresses this gap by classifying Orchid flowers into three categories: saleable, potential saleable, and not saleable. Convolutional Neural Networks (CNN), known for their effectiveness in image-based classification, were employed in this study with performance enhancement through the application of transfer learning. Two prominent transfer learning architectures, VGG-16 and ResNet-50, were implemented and compared to evaluate their suitability for Orchid salability classification. The results demonstrated that the VGG-16 model significantly outperformed ResNet-50 in all evaluation metrics. The VGG-16 model achieved an accuracy of 98%, precision of 99%, recall of 97%, and an F1 score of 98%. In contrast, the ResNet-50 model yielded lower performance, with an accuracy of 69%, precision of 68%, recall of 56%, and an F1 score of 56%. The study also observed that increasing the training epochs from 25 to 50 had no significant impact on the performance of either model. This research highlights the superior performance of VGG-16 in Orchid salability classification and underscores the potential of transfer learning in advancing ornamental plant research.
Co-Authors Achmad Pahrul Rodji Ajat Zatmika Alif Utomo Andika Saputra Andrian Muljadi Angga Mulya Lelana Aris Purwanto Avip Kurniawan Chusna, Nuke L Darmawan, Risanto Darmiyanti, Lydia Dhistianti Mei Rahmawantari Faisal Ruswanto Getah Ester Hayatulah Harjono Padmono Putro Harry Dwiyana Kartika Harry Dwiyana Kartika Herry Wahyono Heru Sukoco Herwanto Herwanto Herwanto Herwanto Hudzaifah Al jihad Indra Permana Solihin Kusmanto, Panji Lukman Aditya Lydia Darmiyanti Moch Fachri Mochammad Fachri Nuke L. Chusna Nuke L. Chusna Nur Hikmah Nur Hikmah Nurpadilah, Aini Rahayu, Tuntun Rahmat Hidayat Reztrianti, Diajeng Ridwan Raafi'udin Sony Hartono Wijaya Tuntun Rahayu Wargijono Utomo Wiga Ariani Yonas Prima Arga Rumbyarso Yusuf Aras Ronisah