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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 Teknik Informatika (JUTIF) 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 Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB 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 Proceeding Mercu Buana Conference on Industrial Engineering Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika 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 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i1.76

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.
PENGGUNAAN AHP DALAM SISTEM PENGAMBILAN KEPUTUSAN PEMILIHAN MARKETPLACE: STUDI KASUS : MARKETPLACE E-COMMERCE DI ERA DIGITAL Reza Maulana, Muhammad; Ariza, Rini; Hidayat, Chaerul; Halim Anshor, Abdul; Fatchan, Muhamad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12447

Abstract

Marketplace merupakan salah satu platform e-commerce terpopuler di Indonesia karena kemudahan transaksi dan aksesibilitas yang ditawarkannya. Namun, banyaknya pilihan di marketplace dapat membuat konsumen bingung saat menentukan platform mana yang memenuhi kebutuhan mereka. Penelitian ini bertujuan untuk menerapkan metode Analytical Hierarchy Process (AHP) dalam sistem pengambilan keputusan untuk membantu konsumen memilih marketplace terbaik berdasarkan banyak kriteria seperti harga, minat dan diskon. Data dikumpulkan melalui kuesioner kepada 10 responden yang aktif berbelanja online. Hasil analisis menunjukkan bahwa Shopee menempati posisi terdepan sebagai marketplace yang dipilih konsumen, disusul oleh Lazada dan Tokopedia. Kriteria diskon memiliki pengaruh paling besar terhadap keputusan konsumen. Kami berharap penelitian ini dapat menjadi referensi bagi konsumen untuk memilih marketplace yang tepat serta membantu pengusaha meningkatkan strategi pemasarannya
Implementasi Algoritma K-Nearest Neighbor dalam Klasifikasi Penyakit Kanker Paru Paru Hadiansyah, Zikri; Rozikin, Zaenur; Fatchan, Muhamad
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6195

Abstract

Lung cancer is one type of cancer with the highest death rate in the world. Smoking is the main risk factor that causes 20% of cancer deaths and 70% of lung cancer deaths in the world. However, people who do not smoke can also suffer from lung cancer, especially if they are frequently exposed to air pollution, live in an environment contaminated with dangerous substances, or have a family member who suffers from lung cancer. Early detection in the classification of lung cancer is an important factor in increasing the patient's chances of survival. Therefore, this study aims to classify lung cancer using the K-Nearest Neighbor algorithm. The K-Nearest Neighbor algorithm was chosen because in various studies it has a better level of accuracy compared to other supervised learning algorithms. To overcome data imbalance, the Random oversampling technique is used. Based on tests carried out using the Confusion Matrix, the results of measuring the performance values ​​of Accuracy, Precision, Recall and f1-score using the K-Nearest Neighbor algorithm with Random oversampling technique, it can be concluded that the K-Nearest Neighbor algorithm received an Accuracy value of 0.99, Precision 0.99, Recall 0.99 and f1-score 0.99.
Perbandingan Efektivitas Metode SAW dan AHP dalam Seleksi Penerima Beasiswa di LIPIA Jakarta Clarita, Anggita Risqi Nur; Fadhillah, Faizah Via; Nurhaliza, Zahra; Fatchan, Muhamad; Anshor, Abdul Halim
Jurnal Ilmiah SINUS Vol 23, No 1 (2025): Vol. 23 No. 1, Januari 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i1.877

Abstract

This research discusses the application of SAW and AHP methods in the selection process of scholarship recipients at LIPIA Jakarta. Given the complexity of assessing potential recipients, both methods are evaluated to determine which one provides the most accurate and efficient results. SAW and AHP methods are used to process the assessment of several criteria, such as Arabic Written Test, Arabic Oral Test, Diploma Score, Memorization and Good Behavior which have been given weights and scales according to their importance. The main objective of this research is to compare the effectiveness of the two methods in determining scholarship recipients who meet the criteria at LIPIA. The results of this comparison are expected to provide recommendations regarding the most suitable method to improve objectivity, accuracy, and efficiency in scholarship selection.
Co-Authors . Ermanto . Suratman Abdul Halim Anshor Abdul Hasyim Abizar Ar Rifa’i Rifa’i Afriantoro, Irfan Agus Suwarno, Agus Aguswin, Ahmad Ahmad Turmudi Zy al fiyan Alfarizi, Muhammad Farhan Andri Firmansyah Andrian Andrian Andriani Andriani Anisa Rahmawati Annisa Maulana Majid Aprila Hardi, Resty Apriyandi M Ardimansyah Ardimansyah 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 Darmawan, Steven Ryan Dendy K. Pramudito Doni, Muhamad Edora Edora Edora Edy Widodo Edy Widodo Edy Widodo Edy, Sarwo Edy Elkin Rilvani Endah Yaodah Kodratilah Fadhillah, Faizah Via Faris Muzaki, Moch. Nauval Fauziah , Sifa Firmansyah Putra, Dandy Fitriani Hadiansyah, Zikri Halim Anshor, Abdul Hari Sugeng Hendra Lesmana Herdyanto, Febro Hidayat, Chaerul Indra Permana, Indra Indradewa, Rhian Irfan Afriantoro Irsyad Syhruddin Jamroni, A. Reza Baehaqa Jamroni Linda Marlinda Listanto, Firgiawan Marayasa, I Gde Bayu Priyambada MAULANA, AFFAN Maulana, Donny Muhamad Ekhsan Muhamad Sudharsono 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 Pakpahan, Wyjentiadi Pengestu, Rayendra Pipin Angela Pratama, Galang Rintang Widya Purwanto Purwanto Putri Nabila Amir Qori yumansyah Qori Ramadhan, Reza Rizky Retno Purwani Setyaningrum Reza Maulana, Muhammad Rika Anugrahaini, Savariana Rindiani Tri Lestari Riyanto, Kuwat Riyanto Rozikin, Zaenur Siti Rahayu Sri Indriyani Sugiarto, Jumat Azzam SUPRAPTO suratman 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 Wiyanto - Yumansyah, Qori Yupita Fitria Riyanti