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All Journal Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information System, Applied, Management, Accounting and Research Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JISA (Jurnal Informatika dan Sains) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Ilmiah Wahana Pendidikan 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 Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Formosa Journal of Computer and Information Science Jurnal Lentera Pengabdian International Journal of Applied Research and Sustainable Sciences (IJARSS) International Journal of Sustainable Applied Sciences (IJSAS) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian JPM MOCCI : Jurnal Pengabdian Masyarakat Ekonomi, Sosial Sains dan Sosial Humaniora, Koperasi, dan Kewirausahaan SAINTEK International Journal of Integrated Science and Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi Welfare: Jurnal Pengabdian Masyarakat
Wahyu Hadikristanto
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 Library & Information Science Mathematics Social Sciences Other

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Grouping Production Goods Requirements Using the K-Means Clustering Method Setiawan, Dani Yuda Dwi; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2863

Abstract

The inventory management of production goods presents several challenges, including difficulties in distinguishing between necessary and unnecessary items, leading to overstocking and manual data processing issues. Additionally, the risk of data loss can impede the data processing workflow. Data testing is conducted to evaluate the accuracy of calculations and the functionality of the applied methods. The objective is to optimize production results and inventory levels in warehouses. The K-means algorithm, known for its simplicity and effectiveness, is utilized to identify clusters within the data. The first cluster (C0) has centroids at (60.33, 70.33) and includes stock data categorized as having no potential. This cluster comprises 35 records. The second cluster (C1) has centroids at (10.94, 7.11) and includes stock data categorized as available, consisting of 15 records. Testing with the RapidMiner Studio application confirms similar insights, with each cluster containing members that are divided into two clusters, each having optimal centroid values of (60.33, 70.33) for Cluster 1 (C0) and (10.94, 7.14) for Cluster 2 (C1), and a Davies-Bouldin Index evaluation score of 0.666.
Estimating Distributor Demand for Fishing Gear Products Using Linear Regression Algorithm Keswanto; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2864

Abstract

Fishing equipment plays a critical role in both recreational and commercial fishing activities across various aquatic environments. The challenge of managing inventory effectively is heightened by the fluctuating demand and the need to avoid overstocking, which can result in increased operational costs. To address this, a linear regression algorithm is utilized to predict demand for fishing products, using relevant independent variables to model the relationship with dependent variables such as monthly sales figures. This predictive model aims to provide actionable insights that can assist businesses in making informed decisions regarding inventory management and distribution strategies. The study employs the RapidMiner Studio application to develop and evaluate the model's performance, with the analysis yielding a Root Mean Square Error (RMSE) of 140.200. This relatively low RMSE value demonstrates the model's accuracy and effectiveness in forecasting demand, suggesting that the algorithm can be a valuable tool for optimizing inventory levels and ensuring product availability while minimizing excess stock.
Predicting Consumer Demand Based on Retail Stock Using the K-Nearest Neighbors Algorithm Putri N.A, Anindya; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2865

Abstract

Inefficient stock management, such as improper stock management, will result in excess or shortage of goods. Excess stock can cause high storage costs and the risk of unsold goods. Predict consumer needs based on stock. Analyze inefficient stock to improve shortages. One effective method for making this prediction is using the K-Nearest Neighbors (K-NN) algorithm. The K-NN algorithm is a simple but powerful machine-learning technique that can be used for classification and regression. The model scenario results show 24 objects in the Low-needs group and 14 in the High-needs group. Evaluation and performance testing using the Rapid Miner tool can also produce a relevant picture of the modelled scenario. The model implemented using the K-NN algorithm has an Accuracy value of 97.50% with a Standard Deviation of +/- 750%, then a Precision value of 100%, and a Recall value of 950%. By measuring model performance with cross-validation, the resulting accuracy has a standard deviation value, which aims to see the distance between the average accuracy and the accuracy of each experiment (iteration)
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.
Penerapan Aplikasi Web Untuk Inventori Gudang Di Zakir Konveksi Indramayu romanuddin, ahmad; pranoto, gatot tri; hadikristanto, wahyu
Jurnal Ilmiah Wahana Pendidikan Vol 9 No 23 (2023): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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

Abstract

This study aims to implement the availability of goods in warehouse inventory. This application was created to simplify the process of monitoring and managing inventory in warehouses, so as to increase efficiency and prevent shortages or excess stocks. The method used in this research is a case study method conducted at a retail company in Indramayu. Data were obtained through interviews and direct observation of inventory conditions at that time. The results of the study indicate that the implementation of the goods availability application is very helpful in monitoring and controlling the stock of goods in the warehouse. By using this application, management can more easily track the incoming and outgoing goods, generate inventory reports automatically, and provide notifications when a product has reached the minimum stock limit. It is hoped that by implementing the application of the availability of goods in warehouse inventory, it can maintain inventory stability and improve work efficiency in this retail business.
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.
Implementasi Pengembangan Aplikasi Sistem Manajemen Aset Berbasis Web Menggunakan Metode Waterfall Untuk Mengoptimalkan Penggunaan Aset Pada PT. Hutama Karya (Persero) ., Sapardi; Hadikristanto, Wahyu; Kurniadi, Nanang Tedi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 4 (2023): Oktober 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i4.948

Abstract

The utilization of communication and information technology is a suitable step for PT. Hutama Karya (Persero) in managing its assets to ensure optimal asset utilization. Previously, the asset management process relied on manual systems such as handwritten notes and Microsoft Excel as tools for data processing. The solution to effective asset management lies in the implementation of a web-based asset management system application, as the system is computerized, enabling faster and more accurate processes. PT. Hutama Karya (Persero) adopted the waterfall method in developing the asset management system application to ensure a systematic and well-planned development process. The asset management system application was developed using PHP programming language and CodeIgniter 3 framework, resulting in a responsive application interface. The application underwent successful testing, including black box testing to assess its functionality. The results of the testing indicate that the application performs well. With the implementation of this asset management system application, PT. Hutama Karya (Persero) can effectively manage its assets, optimize their utilization, and ensure faster and more accurate asset management processes.
CNN Algorithm Approach for Classification of Tomato Fruit Maturity Levels Taufik Hidayat; Muhammad 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.v2i5.1862

Abstract

The categorization of tomato maturity is covered in this study, which has important ramifications for the food sector and agriculture. For training efficiency, the approach uses augmentation with adjustments to rescale picture pixel values and shrink image sizes. According to the experiment's findings, accuracy increased by 93% throughout five training epochs. The training and validation graph indicates steady progress, despite the lack of significance in the improvement. Misclassifications that require correction are found during evaluation utilizing the confusion matrix. The study emphasizes that to enhance agricultural production management, flaws in the model must be filled and accuracy must be increased. The amount and diversity of photos in the dataset should be increased, as should the shooting angles and lighting conditions, and hyperparameters should be adjusted for future model performance optimization.
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.
Co-Authors ., Sapardi Abdul Halim Anshor Abdul Hasyim Abimanyu, Aldo Anggito Aceng Badruzzaman Achmad Firmansyah Putra Ade Muslim Agung Nugroho Ahmad Fauzi Ahmad Zy Ahmad, Asyari Ali Nurdiansyah Ananto Tri Sasongko Andika, Sophian Andri Firmansyah Anggara, Bastian Anisa Anisa Anisa Rahmawati Anshor , Abdul Halim Ariandi, Sheva Rizky Arvita Emarilis Intani Aswan S Sunge Atma, Dodit Ardi Ayu Fitriyani Badruzzaman, Aceng Dahyoung Yenuargo Dichi Setiawan Diki Febriani Dodit Ardiatma Doni, Muhamad Edi Junianto Edi Widodo Edora Edora Edora Edy Widodo Eko Budiarto Ermanto Fajar Arief Rachman Fatchan, Muhammad Fauzi Ahmad Muda Febro Herdyanto Galang Rintang Widya Pratama Gatot Tri Pranoto Gunawan, Ahmad Herol Herol Holwati Ikmal Riyan Firmansyah Imam Nasai2 Intan Sari Rahayu Irfan Afriantoro Irfan Afriantoro Ismasari Ismasari Jamroni, A. Reza Baehaqa Jamroni Karsito Keswanto Kurniadi, Nanang Tedi Laki, Abraham Leo Contantinus Meze Listanto, Firgiawan Maulida Ramadhan Mico Giovanni Dermawan Muhamad Fatchan Muhammad Farhan Alfarizi Muhammad Fatchan Muhammad Makmun Effendi Muhammad Najamuddin Dwi Miharja Muhammad Suprayogi2 Naufal Muyassar Nawangsih, Ismasari Nita Paramita Njai Njai Nur Azizah Nurhadi Surojudin Nurul Ariffaeni Islami Oktavianto, Rainal Zulian Permana , Indra Pradini, Purnama Sakhrial Prasetyo Prayoga, Dimas Preatmi Nurastuti Purdianto Purdianto Purnama Sakhrial Pradini Purwanto Purwanto Purwanto Putri N.A, Anindya Putri Nabila Adinda Adriansyah Rahmawati, Shinta Melliana Rahmawati Rasmiati Nur Aeni Retno Purwani Setyaningrum REZEKI, FITRI Risky Bambang Sutrisna romanuddin, ahmad Rosyati Adelia S Suprapto Sandi Salvan N N Sanudin Satria Permana, Muhammad Safri Sa’ad Khairudin Hanif Setiawan, Dani Yuda Dwi Siska Wulandari, Siska Sophian Andika Suderajat, Agung Sufajar, Sufajar Sufajar, Suprapto Suhardian Suhardian Suherman Suherman Suherman Sunaryati , Titin Sunita Dasman Sya syah Apriliyani Syach, Ridwan Syariefur Rakhmat, Adrianna Taofik Safrudin Taufik Hidayat Tiani Ayu Lestari Tiara Deswara Pungkas Tri Ngudi Wiyatno Turmudi Zy, Ahmad Vidya Anis Fitri Yahya, Adiba Yahya, Adibah Yoga Religia Yusup, Diana