Article Per Year (5 Year)
p-Index From 2020 - 2025
3.049
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS JUITA : Jurnal Informatika Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Sistem Teknik Industri Jurnal Mnemonic Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Irwan Budiman
Fakultas MIPA, Universitas Lambung Mangkurat Banjarbaru, Kalimantan Selatan
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation Hidayah, Noor; -, Muliadi; Budiman, Irwan; Nugrahadi, Dodon Turianto; Herteno, Rudy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14424

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS) merupakan permasalahan pada kelompok masyarakat yang memiliki kesulitan dalam menjalankan fungsi sosial. Penelitian dilakukan untuk mengetahui karakteristik permasalahan di wilayah Kalimantan Selatan dengan menggunakan klasterisasi. Metode klasterisasi yang digunakan adalah SOM dan pengisian data kosong menggunakan NBI yang dibandingkan dengan Metode Statistik (Mean, Median, dan Modus). Proses dimulai dari mengisian data kosong dengan NBI dan Metode Statistik, dilanjutkan dengan klaster SOM dan hasil klaster dievaluasi menggunakan DBI. Hasil yang didapatkan adalah perbaikan NBI menempati hasil klasterisasi terbaik dengan nilai 0,032 pada pembagian 2 klaster. Klaster pertama berjumlah 8 wilayah yaitu Tanah Laut, Kota Baru, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara, Tabalong, dan Tanah Bumbu. Klaster kedua berjumlah 5 wilayah yaitu Banjar, Barito Kuala, Balangan, Banjarmasin, dan Banjarbaru. Tingkat prioritas yang diperoleh dari rata-rata klaster didapatkan bahwa klaster kedua sebagai prioritas pertama.
Decreasing Total Inventory Cost by Controlling Inventory in Motorcycle Dealer David; Engmir; Budiman, Irwan; Tampubolon, Jusra
Jurnal Sistem Teknik Industri Vol. 22 No. 2 (2020): JSTI Volume 22 Number 2 July 2020
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v22i2.3930

Abstract

This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.
PERANCANGAN RENCANA PENGADAAN BAHAN BAKU PADA PERUSAHAAN JASA KONTRUKSI wijaya, chandra; Santonius, celvin; Tampubolon, Jusra; Budiman, Irwan
Jurnal Sistem Teknik Industri Vol. 22 No. 2 (2020): JSTI Volume 22 Number 2 July 2020
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v22i2.3975

Abstract

Abstract. Persediaan bahan baku merupakan bahan terpenting dalam produksi yang dilakukan oleh perusahaan. Pengendalian persediaan terhadap kuantitas bahan baku yang dilakukan gudang juga merujuk kepada proses produksi. Bahan baku dapat diperoleh dari supplier atau perusahaan yang menghasilkan bahan baku.Tujuan penelitian ini adalah untuk mengetahui total biaya persediaan semen dan mengetahui batas minimal dan maksimal untuk pemesanan semen yang harus dilakukan oleh perusahaan Cv.XYZ. Metode analisis data yang digunakan adalah Metode Period Order Quantity POQdansizing Lot For Lot didapatkan dengan Metode sizing Lot For Lot adalah metode yang memiliki biaya yang paling rendah yaitu sebesar Rp 712,136 bila dibandingkan dengan metode Period Order Quantity (POQ) dengan biaya sebesar Rp 909,904 dan batas minimal dan maksimal pembelian semen adalah 90 sak. Abstrak. Inventory of raw materials is the most important material in production carried out by the company. Inventory control of the quantity of raw materials carried out by the warehouse also refers to the production process. Raw materials can be obtained from suppliers or companies that produce raw materials. The purpose of this study was to determine the total cost of cement supply and determine the minimum and maximum limits for ordering cement that must be carried out by a Cv Company. XYZ. The data analysis method used is the Period Order Quantity (POQ) Method and the Map Control Method is obtained by the Sizing Lot For Lot method which has the lowest cost of IDR 712,136 when compared to the Period Order Quantity (POQ) method with a fee of IDR 909,904 and the minimum and maximum limit for buying cement is 90 cigarettes.
Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction Rizky, Muhammad Hevny; Faisal, Mohammad Reza; Budiman, Irwan; Kartini, Dwi; Abadi, Friska
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90437

Abstract

The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms
IMPLEMENTASI METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN MODIFIED K-NEAREST NEIGHBOR PADA KLASIFIKASI CITRA DAUN TANAMAN HERBAL Nurdiansyah, Nurdiansyah; Muliadi, Muliadi; Herteno, Rudy; Kartini, Dwi; Budiman, Irwan
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.6664

Abstract

Klasifikasi citra tanaman herbal dapat dilakukan berdasarkan bentuk daun yang dikenal juga sebagai pengenalan citra tanaman herbal. Pada pengenalan citra dilakukan dengan mengidentifikasi ciri bentuk daun tanaman herbal dan dilakukan klasifikasi citra daun tersebut. Jumlah data citra yang digunakan sebagai 200 data yang terbagi kedalam 5 kelas. Sehingga masing-masing kelas terdiri dari 40 data citra. Masyarakat umumnya akan kesulitan untuk mengenal jenis tanaman herbal berdasarkan melihat secara sekilas pada daun. Pada metode PCA digunakan untuk mengurangi dimensi citra daun herbal dan metode Modified KNN digunakan untuk mengklasifikasikan citra daun herbal berdasarkan fitur. Pada pembagian data 6:4 nilai akurasi tertingginya sebesar 89 % pada K=2 dan akurasi terendahnya pada 78 % pada K=9. Pembagian data 7:3 nilai akurasi tertingginya sebesar 87 % pada K=4 dan akurasi terendahnya pada 82 % pada K=9. Dan Pada pembagian data 8:2 nilai akurasi tertingginya sebesar 93 % pada K=3 dan akurasi terendah sebesar 84 % pada K=4.
Uji Sensitivitas Metode Aras Dengan Pendekatan Metode Pembobotan Kriteria Sahnnon Entropy Dan Swara Pada Penyeleksian Calon Karyawan Halimah, Halimah; Kartini, Dwi; Abadi, Friska; Budiman, Irwan; Muliadi, Muliadi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 4 No. 2 (2020)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v4i2.194

Abstract

Penelitian ini melakukan uji sensitivitas metode Additive Ratio Assessment (ARAS) pada penyeleksian calon karyawan dengan pendekatan pembobotan kriteria menggunakan metode Shannon Entropy dan metode Stepwise Weight Assessment Ratio Analysis (SWARA) yang bertujuan untuk mengukur seberapa sensitif metode ini jika diterapkan pada sebuah kasus pengambilan keputusan. Data yang digunakan ialah data penyeleksian calon karyawan. Uji Sentitivitas pada penelitian ini digunakan untuk mengetahui metode yang lebih sensitif saat diterapkan pada suatu kasus. Metode perangkingan menggunakan ARAS karena metode perangkingan ini memiliki fungsi utilitas dan nilai optimalisasi. Metode Shannon Entropy bobot kriteria diperoleh berdasarkan perhitungan data alternatif penyeleksian karyawan, sedangkan metode SWARA bobot kriteria diperoleh dari pakar atau si pengambil keputusan. Hasil penelitian ini menunjukkan bahwa metode yang paling sensitif dengan kasus penyeleksian calon karyawan adalah metode SWARA-ARAS yang pemberian bobotnya berdasarkan pakar atau si pengambil keputusan dengan hasil sebesar 91,24203% lebih tinggi dibandingkan metode Shannon Entropy-ARAS yang hasil sebesar 74,75263%.
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, Mohammad Reza; Herteno, Rudy
Computer Engineering and Applications Journal Vol 13 No 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i2.469

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media Yunida, Rahmi; Faisal, Mohammad Reza; Muliadi; Indriani, Fatma; Abadi, Friska; Budiman, Irwan; Prastya, Septyan Eka
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.319

Abstract

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.
Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning Al Habesyah, Noor Zalekha; Herteno, Rudy; Indriani, Fatma; Budiman, Irwan; Kartini, Dwi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.381

Abstract

TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.
Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Co-Authors Adi Mu'Ammar, Rifqi Ahdyani, Annisa Salsabila Ahmad Tajali Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Andi Farmadi Antoh, Soterio Azizah, Azkiya Nur Chandra Wijaya david Dodon Turianto Nugrahadi Dwi Kartini, Dwi Engmir Faisal, M. Reza Fathmah, Siti Fatma Indriani Friska Abadi Halimah Halimah Herteno, Rudy Jumadi Mabe Parenreng Kartika, Tengku Heny Mahmud Mahmud Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi MULIADI -, MULIADI Noor Hidayah Nurdiansyah Nurdiansyah Prastya, Septyan Eka Radityo Adi Nugroho Rizky, Muhammad Hevny Rozaq, Hasri Akbar Awal Said, Muhammad Al Ichsan Nur Rizqi Santonius, celvin Saragih, Triando Hamonangan Tampubolon, Jusra Tuti Sarma Sinaga Wahyu Caesarendra Winda Agustina YILDIZ, Oktay Yunida, Rahmi