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All Journal International Conference on Engineering and Technology Development (ICETD) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Ilmiah Sinus bit-Tech Jurnal Informatika Ekonomi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal Automation Computer Information System (JACIS) Bulletin of Information Technology (BIT) International Journal Software Engineering and Computer Science (IJSECS) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Journal of Practical Computer Science (JPCS) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri Universal Raharja Community (URNITY Journal) Jurnal Lentera Pengabdian Jurnal Informatika Ekonomi Bisnis Riwayat: Educational Journal of History and Humanities International Journal of Applied Research and Sustainable Sciences (IJARSS) International Journal of Sustainable Applied Sciences (IJSAS) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK International Journal of Integrated Science and Technology EduBase: Journal of Basic Education
Muhamad Fatchan
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Sistem Informasi Inventori Gudang untuk Mengontrol Persediaan Barang pada Gudang Studi Kasus: PT. LG Indonesia Doni, Muhamad; Fatchan, Muhamad; Hadikristanto, Wahyu
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1809

Abstract

A system is defined as a combination of various components that are interrelated and work together, forming a unity with the aim of achieving certain targets of the system. The use of Model-Driven Web Engineering was chosen because this method is an effective basis for system development. Model-Driven Web Engineering provides developers and users with a clear view of how the system works and the developments that will be carried out. A stock system can be designed and implemented in the administration of PT LG Indonesia. Data storage in the system has been integrated with a database, enabling the inventory system to help PT LG Indonesia to reduce the potential for data loss and damage. Apart from that, fast and accurate access to data reports is also one of the advantages of this system.
Sistem Informasi Penyewaan Dump Truck Berbasis Website pada PT Media Mitra Teknik Engineering Nuraeniah, Iin; Fatchan, Muhamad; Suwarno, Agus
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 8 No. 1 (2024): Call for Paper: Volume 8 Nomor 1 Januari 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v8i1.13355

Abstract

PT Media Mitra Teknik Engineering, perusahaan konstruksi yang sedang menghadapi tantangan dalam proses penyewaan truk yang masih manual dan tidak efisien. Penelitian ini merespon dengan mengembangkan sistem informasi rental truk berbasis website menggunakan metode prototype. Metode prototype memungkinkan evaluasi langsung oleh perusahaan, dengan pengujian White Box dan blackbox testing. Hasilnya menunjukkan sistem berfungsi sesuai harapan, meningkatkan efisiensi dalam proses penyewaan dan mempermudah akses informasi bagi calon pelanggan. Sistem telah dipresentasikan dan diterima positif, menandakan penerimaan inovasi ini. Meskipun berhasil, penelitian ini menyadari potensi perbaikan lebih lanjut untuk memperluas fungsionalitas sistem dan mengoptimalkan proses penyewaan truk secara menyeluruh. Penelitian ini berkontribusi pada pengembangan teknologi informasi untuk mendukung pertumbuhan bisnis di sektor konstruksi, khususnya dalam efisiensi operasional melalui penyewaan truk berbasis web.
Perancangan dan Pengembangan Sistem Informasi Key Performance Indicator Marayasa, I Gde Bayu Priyambada; Fatchan, Muhamad; Tedi, Nanang
Jurnal Teknik Informatika UMUS Vol 5 No 2 (2023): November
Publisher : Universitas Muhadi Setiabudi

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Abstract

Penelitian ini mengeksplorasi perancangan dan pengembangan Sistem Informasi Key Performance Indicator (KPI) dengan pendekatan Metode Waterfall. KPI atau Key Performance Indicator telah menjadi elemen kunci dalam mengukur dan mengevaluasi kinerja organisasi. Penggunaan sistem informasi dalam pengelolaan KPI sangat penting khususnya di sektor publik untuk menjamin akuntabilitas dan transparansi yang optimal [1]. Studi ini menyoroti peran penting Metode Air Terjun dalam menciptakan kerangka terstruktur untuk mengelola data kinerja organisasi. Sistem informasi KPI yang dibangun tidak hanya mengacu pada teknologi, namun juga analisis mendalam terhadap indikator-indikator utama yang relevan. Sistem ini dirancang untuk menyediakan data real-time yang terukur dan andal, yang mendukung pengambilan keputusan berbasis bukti. Hasil penelitian ini memberikan wawasan berharga dan panduan praktis bagi organisasi khususnya di sektor publik untuk menerapkan sistem informasi KPI berdasarkan Metode Waterfall sesuai dengan kebutuhannya. Dengan pendekatan ini, organisasi diharapkan dapat meningkatkan kinerja dan transparansi.
Prediksi Defect Produk Casting Dengan Algoritma SVM Berbasis RBF dan Linier Listanto, Firgiawan; Fatchan, Muhamad; Hadikristanto, Wahyu
Jurnal Teknik Informatika UMUS Vol 5 No 2 (2023): November
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46772/intech.v5i2.1376

Abstract

Produksi barang casting (coran) merupakan proses manufaktur yang penting dalam berbagai industri, termasuk otomotif, konstruksi, dan banyak lainnya. Dalam proses produksi casting hal yang paling krusial adalah mengenai kualitas produk. Maka, dalam mengindentifikasi defect atau cacat pada produk adalah kunci untuk menghindari kerugian besar pada perusahaan, serta hal yang paling utama adalah menjaga kepuasan pelanggan. Karena pada era industri saat ini persaingan antar perusahaan industri semakin ketat, maka perusahaan harus mampu menghasilakan produk dengan kualitas terbaik agar tidak tertinggal dalam persaingan industri saat ini. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan metode prediksi defect produk casting menggunakan algoritma Support Vector Machine (SVM) dengan dua jenis kernel, yaitu Radial Basis Function (RBF) dan Linear. Pada penelitian ini mengumpulkan data kualitas produk casting yang sebelumnya berbentuk gambar diubah menjadi numerik agar dapat diklasifikasi dengan akurat menggunakan metode algoritma SVM. Data tersebut kemudian dibagi menjadi dua kelompok, yaitu data pelatihan (training data) dan data pengujian (testing data). Algoritma SVM dengan kernel RBF dan kernel Linier diterapkan pada data pelatihan untuk menghasilkan model prediksi. Hasil penelitian menunjukkan bahwa algoritma SVM dengan kernel RBF dan kernel Linier dapat digunakan untuk memprediksi defect produk casting. Namun, penggunaan kernel RBF cenderung memberikan kinerja yang lebih baik dalam memodelkan pola cacat dalam produk casting. Model prediksi yang dihasilkan mampu mengidentifikasi kemungkinan cacat dalam produk casting dengan tingkat akurasi yang memuaskan. Secara keseluruhan penelitian ini memberikan kontribusi penting dalam meningkatkan kualitas produksi dalam industri casting dengan mengimplementasikan algoritma SVM untuk prediksi defect. Dengan demikian, industri dapat mengurangi risiko cacat produk, kerugian yang signifikan, serta mampu bertahan di era persaingan industri saat ini.
Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification Tiani Ayu Lestari; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Sustainable Applied Sciences Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijsas.v2i5.1863

Abstract

This study investigates the integration of image histograms with Convolutional Neural Networks (CNNs) using TensorFlow for gender classification. The research focuses on preprocessing techniques that significantly reduce the dimensionality of image data, enhancing computational efficiency model performance. Data augmentation methods, including rotation, shifting, and flipping, were applied to diversify the training dataset. The CNN model achieved high accuracy and validation accuracy, demonstrating its robustness. The findings reveal that the preprocessing steps effectively condensed the pixel to be 151,321 while retaining critical features for classification. The study underscores the potential applications of this methodology in security, marketing, and healthcare, where accurate gender classification is essential. Future research should explore more diverse datasets, advanced model architectures, and enhanced feature extraction methods to further improve performance. This research contributes to the field by offering a comprehensive approach to efficient and accurate gender classification, supported by robust data augmentation and preprocessing techniques.
Valuation K-Nearest Neighbors and Naïve Bayes for Dringking Water Potability Classification Anisa Rahmawati; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Sustainable Applied Sciences Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijsas.v2i5.1864

Abstract

The availability of drinking water that is safe and suitable for consumption is important to support health and development. This research emphasises the importance of handling the clean water crisis through the evaluation of drinking water quality using data mining algorithms.  The dringking water quality evaluation method was selected using the K-Nearest Neighbors and Naive Bayes algorithms, replacing the manual method which is less responsive in predicting. The experimental process was conducted by utilising Kaggle website data by applying data processing and oversampling techniques to handle class imbalance in the dataset used. Bases on the research results, the accurancy of the K-Nearest Neighbors Algorithm reaches 65%, which is higher than the accuracy od the Naive Bayes Algorithm which is 64%. So it can be concluded that the K-Nearest Neighbors Algorithm is more effective in predicting the quality of water suitable for consumption. This research provides an in-depth insight into the use of technology and data analysis in dealing with the crisis in the availability of water suitable for consumption and offers suggestions for further research using more diverse methods and the use of more datasets to improve accuracy in evaluating the quality of potable water.
Comparative Analysis of Support Vector Machine and Random Forest Algorithms in Indonesian Batik Classification Oktavianto, Rainal Zulian; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Sustainable Applied Sciences Vol. 2 No. 6 (2024): June 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijsas.v2i6.2072

Abstract

This study compares the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in Indonesian batik image classification. Data collected from four batik categories: Pattern Batik Insang, Pattern Batik, Patterns Batik Dump, and Pattern Megamendung. Image feature extracted using Histogram of Oriented Gradients (HOG). SVM models with linear and RF kernels with 100 decision trees are trained and tested on this dataset. The evaluation results showed that the SVM has an accuracy of 88%, with precision and recall balanced between classes, while RF has an accuracy of 86%, with some classes showing excellent performance. SVM is superior in overall accuracy, but RF offers better interpretability and ease of setting parameters. The conclusions of this study suggest that both algorithms are able to effectively classify bacterial images, but the selection of the algority depends on the specific needs of the application. Further adjustment of parameters and additional preprocessing techniques are recommended to improve model performance. This research provides a strong foundation for further development in the classification of batic images using machine learning.
Pendampingan Inovasi Teknologi Dalam Sistem Pembayaran di PT. Gecok Halal Indonesia Naya, Candra; Fatchan, Muhamad; Permana, Indra; Fitriani
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024
Publisher : VINICHO MEDIA PUBLISINDO

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Abstract

Assistance with technological innovation in payment systems at PT. Gecok Halal Indonesia" will discuss the approach and methodology used in developing technological innovation capabilities in the company. This training aims to increase employee understanding and skills in adopting and implementing the latest innovations in payment systems that comply with halal standards in Indonesia. The main focus of the training includes an in-depth understanding of modern payment technology, innovation implementation strategies, and compliance with applicable halal regulations. The methodology used includes interactive training sessions, case studies and group discussions to ensure comprehensive understanding as well as practical application in the workplace. It is hoped that this training will provide a significant boost in accelerating the adoption of technological innovation at PT. Gecok Halal Indonesia, so that companies can continue to compete in an increasingly complex market and meet consumer demands for security and reliability in technology-based payment systems.
Improving Employee Retention Through Prediction and Risk Management Using Machine Learning Galang Rintang Widya Pratama; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Applied Research and Sustainable Sciences Vol. 2 No. 6 (2024): June 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijarss.v2i6.1960

Abstract

This research investigates the effectiveness of two machine learning models (Logistic Regression and Random Forest) in predicting employee turnover. This research uses IBM HR Analytics employee attrition and performance dataset and performance dataset from Kaggle and implements nested ensemble models in Google Colab. After data pre-processing steps such as feature merging, generation, engineering, cleaning, coding, and normalisation, the data is divided into training and testing sets. The models were trained and evaluated based on their accuracy. The results of averaging the three departments showed that the Random Forest model achieved the highest accuracy (97.7%) compared to Logistic Regression (94.6%). Therefore, this study shows that Logistic Regression is the most suitable model to predict employee turnover in the given dataset.
Comparison of Defective Casting Product Classification Results Using the K-Nearest Neighbors Algorithm Muhammad Farhan Alfarizi; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Applied Research and Sustainable Sciences Vol. 2 No. 6 (2024): June 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijarss.v2i6.1968

Abstract

This study compares the accuracy of K-Nearest Neighbors (KNN) and Naive Bayes algorithms in detecting defects in impeller products. Using a dataset of impeller images, we applied preprocessing, feature extraction, and selection techniques. The models were assessed using metrics such as precision, accuracy, F1-score, recall. and with KNN achieving 98.11% accuracy and Naive Bayes 85.38%. The t-SNE visualization confirmed distinct clustering of defective and non-defective products. Our findings suggest that KNN is more reliable for defect detection in industrial applications. These results provide valuable insights for implementing effective machine learning models in manufacturing quality control.
Co-Authors . Ermanto . Suratman Abdul Halim Anshor Abdul Hasyim Abizar Ar Rifa’i Rifa’i Agus Suwarno, Agus Aguswin, Ahmad Ahmad Turmudi Zy al fiyan Andri Firmansyah Andrian Andrian Anisa Rahmawati Annisa Maulana Majid Aprila Hardi, Resty Apriyandi M Ariza, Rini Asep Hidayat Asep Suprianto Ayu Fitriyani Aziz, Faruq B.M.A.S. Anaconda Bangkara Bagoes Ramadhan Bagus Dwi Saputro Butsianto, Sufajar Clarita, Anggita Risqi Nur Dahyoung Yenuargo Dendy K. Pramudito Doni, Muhamad Edora Edora Edora Edy Widodo Edy Widodo Edy Widodo Elkin Rilvani Endah Yaodah Kodratilah Fadhillah, Faizah Via Febro Herdyanto Fitriani Galang Rintang Widya Pratama Hadiansyah, Zikri Halim Anshor, Abdul Hari Sugeng Hendra Lesmana Hidayat, Chaerul Indra Permana, Indra Irfan Afriantoro Irsyad Syhruddin Jamroni, A. Reza Baehaqa Jamroni Linda Marlinda Listanto, Firgiawan Marayasa, I Gde Bayu Priyambada Moch. Nauval Faris Muzaki Muhamad Ekhsan Muhamad Sudharsono Muhammad Farhan Alfarizi Muhtajuddin Danny Najwa Sabilla, Nurul Nanang Tedi Kurniadi Nasution, Annio Indah Lestari Naufal Muyassar Naya, Candra Ngudi Wiyatno, Tri Nuraeniah, Iin Nurhadi Surojudin Nurhaliza, Zahra Nur’aeni Nur’aeni Oktavianto, Rainal Zulian Pengestu, Rayendra Pipin Angela Purwanto Purwanto Putri Nabila Amir Qori yumansyah Qori Retno Purwani Setyaningrum Reza Maulana, Muhammad Rika Anugrahaini, Savariana Rindiani Tri Lestari Rozikin, Zaenur Sifa Fauziah Sri Indriyani Sugiarto, Jumat Azzam SUPRAPTO suratman Surya Bintarti Surya Bintarti Suryadi Tedi, Nanang Tiani Ayu Lestari TITIN SUNARYATI Tri Ngudi Wiyatno Turmudi Zy, Ahmad Valentin*, M Ryan Bagus Wahyu Hadi Kristanto Wahyu Hadikristanto Wahyu Indrarti Widi Winjani Widiyawati , Widiyawati Yumansyah, Qori Yupita Fitria Riyanti