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All Journal Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL INFORMATIKA Journal of Information System, Applied, Management, Accounting and Research Jurnal Inti Talafa : Jurnal Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 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 JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) 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 Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB 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 Jurnal Ilmiah Multidisiplin Indonesia 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 SISFOTENIKA 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Detect the Activity of Benign and Malignant Breast Cancer Ayu Fitriyani; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1870

Abstract

Breast cancer detection is an important stage for early cancer diagnosis. In this study, a Convolutional Neural Network (CNN) algorithm is used to detect breast cancer. The dataset used consists of MRI scan images of benign and malignant breast cancer, which are processed through breast image cropping and data augmentation. The model was trained using CNN architecture with transfer learning method of VGG-16 model. The results of the model training showed good performance with an accuracy of 62%. These findings show the potential of using CNN and transfer learning in improving early detection of breast cancer.
Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification Dahyoung Yenuargo; Muhamad Fatchan; Wahyu Hadikristanto
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1873

Abstract

In an era of increasing concern for environmental sustainability, waste management remains an important global issue. Efficient waste classification, in particular distinguishing between organic and recyclable materials, is essential for reducing environmental impact. Traditional manual classification methods are often error-prone and inefficient. This research evaluates the performance of SVM models with RBF and Polynomial kernels for waste classification, using SqueezeNet for feature extraction. Datasets from Kaggle were preprocessed and augmented to improve model training. The experimental results show that the SVM model with RBF kernel outperforms the Polynomial kernel in classifying organic and recyclable waste, with an accuracy of 97.9% compared to 97.3% for the Polynomial kernel. This finding underscores the importance of kernel selection and parameter tuning in optimising SVM models for non-linear classification tasks. This research contributes to the development of more efficient and accurate waste classification technologies, promoting better waste management practices. Further research is recommended to explore advanced feature extraction methods and expand the scope of classification to cover a wider range of waste categories.
Industrial Safety Helmet Detection: Innovative CNN-Based Classification Approach Herdyanto, Febro; Fatchan, Muhamad; Hadikristanto, Wahyu
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1925

Abstract

This study presents the development and evaluation of a CNN-based model for detecting safety helmets in industrial settings. Utilizing a dataset from GitHub, which includes images of individuals wearing safety helmets in various industrial environments, the model was trained using the YOLOv8 architecture over 100 epochs. The comprehensive training process involved data augmentation techniques to enhance generalization capabilities. The evaluation results demonstrated high precision (0.92) and recall (0.856) for helmet detection, with an overall mAP50 of 0.766. Visual analysis through precision-confidence curves confirmed the model's high reliability in detecting helmets at higher confidence thresholds. These findings suggest that the implementation of this model in real-time monitoring systems could significantly enhance industrial safety by reducing manual inspection efforts and ensuring compliance with safety regulations
Pelatihan Peningkatan Kemampuan Guru SMP IT Insan Kamil Cikarang Dalam Melakukan Evaluasi Pembelajaran Menggunakan Computer Base Test (CBT) Miharja, Muhammad Najamuddin Dwi; Edora, Edora; hadikristanto, Wahyu; Andika, Sophian; Herol, Herol
Welfare : Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2023): Welfare : June 2023
Publisher : Fakultas Ekonomi dan Bisnis Islam, IAIN Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/welfare.v1i2.506

Abstract

This community service program aims to improve the ability of IT Insan Kamil Cikarang Middle School teachers in conducting learning evaluations using the Computer-Based Test (CBT). Teachers will be given training and an introduction to CBT and given practice to create questions and enter them into the CBT platform. The training method used is a competency-based training approach using demonstration methods and hands-on practice in learning. The results of this training indicate an increase in teacher competency in designing, developing, and implementing information technology-based learning evaluations. It is hoped that this program can increase effectiveness and efficiency in learning evaluation, as well as increase student motivation in learning. Challenges that need to be overcome include the lack of understanding and use of technology by teachers and students, and the need for the right approach in implementing CBT. This program is expected to provide greater benefits for teachers and students.
Improving Employee Retention Through Prediction and Risk Management Using Machine Learning Pratama, Galang Rintang Widya; Fatchan, Muhamad; Hadikristanto, Wahyu
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 Alfarizi, Muhammad Farhan; Fatchan, Muhamad; Hadikristanto, Wahyu
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.
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Pranoto, Gatot Tri; Hadikristanto, Wahyu; Religia, Yoga
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
Analisis Faktor dan Prediksi Atrisi untuk Optimalisasi Retensi Karyawan Menggunakan Machine Learning A. Reza Baehaqa Jamroni Jamroni; Wahyu Hadikristanto; Muhamad Fatchan
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2301

Abstract

Atrisi karyawan merupakan fenomena penurunan jumlah tenaga kerja dalam sebuah organisasi yang disebabkan oleh faktor-faktor seperti pengunduran diri, pensiun, atau alasan lainnya. Fenomena ini dapat berdampak negatif pada perusahaan, termasuk penurunan produktivitas, gangguan operasional, dan meningkatnya biaya rekrutmen serta pelatihan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi atrisi karyawan dan mengembangkan model prediksi menggunakan algoritma machine learning, yaitu Random Forest dan K-Nearest Neighbors (KNN). Adapun penelitian ini menggunakan dataset IBM HR Analytics Employee Attrition & Performance. Metode penelitian melibatkan tahap pengumpulan data, pemrosesan data, pelatihan model menggunakan algoritma Random Forest dan KNN, serta evaluasi kinerja model berdasarkan akurasi, precision, recall, F1-score, AUC, dan ROC curve. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki akurasi 93% dan nilai AUC sebesar 0.98, lebih tinggi dibandingkan dengan KNN yang hanya mencapai akurasi 88% dan AUC 0.96. Selain itu, Random Forest menunjukkan kinerja yang lebih seimbang pada precision, recall, dan F1-score, serta lebih rendah dalam kesalahan prediksi pada kelas "Atrisi" dan "Tidak Atrisi". Pada analisis feature importance mengidentifikasi faktor utama yang mempengaruhi atrisi karyawan, seperti RelationshipSatisfaction, Work-Life Balance, Age, StockOptionLevel, dan NumberofCompaniesWorked. Temuan ini memberikan kontribusi penting bagi perusahaan dalam merancang strategi retensi yang lebih efektif dengan memanfaatkan data yang ada. Penelitian ini juga merekomendasikan penggunaan dataset yang lebih besar, serta penerapan algoritma dan teknik lain seperti SMOTE untuk meningkatkan akurasi model dalam prediksi atrisi di masa depan.
Sistem Informasi Penjualan Seragam Sekolah Berbasis Website Menggunakan Metode Waterfall Pada UD Badru Collection Eko Budiarto; Wahyu Hadikristanto; Rizky Igel Herisaputra
Jurnal SIGMA Vol 16 No 2 (2025): September 2025
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v16i2.7425

Abstract

UD Badru Collection adalah perusahaan yang bergerak dalam bidang jasa konveksi pembuatan seragam sekolah sejak tahun 1997 hingga saat ini. Dalam melakukan penjualan pengusaha masih menggunakan metode transaksi pembeli datang langsung ke toko serta pemasaran UD Badru Collection masih belum terkomputerisasi. Penelitian yang dilakukan oleh penulis dengan tujuan untuk merancang dan membangun sistem informasi penjualan seragam sekolah berbasis website serta memberikan solusi permasalahan yang dihadapi seperti pemasaran toko seragam sekolah serta mengelola data penjualan secara efektif dan efisien. Metode yang digunakan dalam penelitian ini berupa metode waterfall, jadi hasil penelitian ini membangun sistem informasi penjualan seragam sekolah. Sistem informasi penjualan seragam sekolah berbasis website ini dibangun menggunakan bahasa pemrograman PHP, HTML, serta mengintegrasikan MySQL sebagai server database. Sistem yang di hasilkan adalah sistem informasi penjualan seragam sekolah untuk mempermudah dalam pemasaran serta mempermudah penjual dan pembeli yang ingin melakukan transaksi.
Sistem Pendukung Keputusan Evaluasi Kepuasan Peserta Pelatihan E-Commerce Berbasis TOPSIS Kumara Davin Valerian; Wahyu Hadikristanto; Nanang Tedi Kurniadi
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.474

Abstract

Abstrak: Perkembangan teknologi digital, khususnya e-commerce, menuntut peningkatan kompetensi sumber daya manusia melalui pelatihan yang terarah dan berkualitas. Selain aspek pelaksanaan, keberhasilan pelatihan perlu diukur melalui evaluasi kepuasan peserta secara objektif berdasarkan berbagai kriteria penilaian. Meskipun metode sistem pendukung keputusan telah banyak diterapkan pada berbagai bidang evaluasi, penerapannya pada evaluasi kepuasan peserta pelatihan e-commerce masih relatif terbatas. Penelitian ini bertujuan untuk mengevaluasi kepuasan peserta pelatihan e-commerce menggunakan metode TOPSIS. Kebaruan penelitian ini terletak pada penerapan evaluasi kepuasan berbasis multi-kriteria menggunakan skala likert dan metode TOPSIS pada konteks pelatihan e-commerce, yang belum banyak dibahas pada penelitian sebelumnya. Data diperoleh melalui observasi, studi literatur, dan penyebaran kuesioner kepada 56 responden. Hasil pengolahan menunjukkan nilai preferensi berada pada rentang 0,28 hingga 1,00. Sebanyak 13 alternatif memperoleh nilai preferensi tertinggi sebesar 1,00, sedangkan alternatif ke-51 memperoleh nilai terendah sebesar 0,28. Hasil penelitian menunjukkan bahwa model evaluasi berbasis TOPSIS mampu menghasilkan pemeringkatan kepuasan peserta secara objektif dan terukur, sehingga berkontribusi dalam meningkatkan akurasi pengambilan keputusan pada evaluasi pelatihan e-commerce.Kata kunci: Sistem Pendukung Keputusan, Kepuasan Peserta, Pelatihan E-Commerce, TOPSIS, KuesionerAbstract: The development of digital technology, particularly e-commerce, demands increased human resource competency through targeted and quality training. In addition to implementation aspects, training success needs to be measured through objective participant satisfaction evaluation based on various assessment criteria. Although decision support system methods have been widely applied in various evaluation fields, their application to e-commerce training participant satisfaction evaluation is still relatively limited. This study aims to evaluate e-commerce training participant satisfaction using the TOPSIS method. The novelty of this study lies in the application of multi-criteria-based satisfaction evaluation using a Likert scale and the TOPSIS method in the context of e-commerce training, which has not been widely discussed in previous studies. Data were obtained through observation, literature review, and questionnaire distribution to 56 respondents. The results showed that preference values ranged from 0.28 to 1.00. A total of 13 alternatives obtained the highest preference value of 1.00, while the 51st alternative obtained the lowest value of 0.28. The results show that the TOPSIS-based evaluation model is able to produce objective and measurable participant satisfaction rankings, thus contributing to improving the accuracy of decision-making in e-commerce training evaluation.Keywords: Decision Support System, Participant Satisfaction, E-Commerce Training, TOPSIS, Questionnaire
Co-Authors ., Sapardi A. Reza Baehaqa Jamroni Jamroni Aas Novitasari Abdul Halim Anshor Abdul Hasyim Abimanyu, Aldo Anggito Achmad Firmansyah Putra Adrianna Syariefur Rakhmat Adriansyah, Putri Nabila Adinda Agung Nugroho Ahmad Gunawan Ahmad Zy Ahmad, Asyari Alfarizi, Muhammad Farhan Ali Nurdiansyah Ananto Tri Sasongko Andika, Sophian Andri Firmansyah Anggara, Bastian Anisa Anisa Anisa Rahmawati Anshor , Abdul Halim Ansor, Abdul Halim Ariandi, Sheva Rizky Arvita Emarilis Intani Aswan S Sunge Atthoriq, Syaifullah Ayu Fitriyani Badruzzaman, Aceng Dahyoung Yenuargo Dichi Setiawan Diki Febriani Dodit Ardi Atma Dodit Ardiatma Doni, Muhamad Edi Junianto Edi Widodo Edora Edora Edora Edora, Edora Edy Widodo Eko Budiarto Eko Budiarto Ermanto Fadlurrohman, Muhammad Shiddiq Fajar Arief Rachman Fatchan Fatchan Fatchan, Muhammad Fauzi Ahmad Muda Ferdyana Eka Prasetya Fibi Eko Putra Fitri Rezeki Gatot Tri Pranoto Hanif, Sa’ad Khairudin Herdyanto, Febro Herol, Herol Holwati Ikmal Riyan Firmansyah Imam Nasai2 Indradewa, Rhian Intan Sari Rahayu Irfan Afriantoro Irfan Afriantoro Ismasari Ismasari Karsito, Karsito Keswanto Kumara Davin Valerian Kurniadi, Nanang Tedi Laela Nur Rohmah Laki, Abraham Leo Contantinus Meze Listanto, Firgiawan Maulida Ramadhan Mico Giovanni Dermawan Miharja, Muhammad Najamuddin Dwi Muhamad Baharudin Muhamad Fatchan Muhammad Fatchan Muhammad Makmun Effendi Muhammad Suprayogi2 Nanang Tedi Kurniadi Nanang Tedi Kurniadi Naufal Muyassar Nawangsih, Ismasari Nita Paramita Njai Njai Nur Azizah Nurhadi Surojudin Nurul Ariffaeni Islami Oktavianto, Rainal Zulian Permana , Indra Prasetyo Prasetyo Pratama, Galang Rintang Widya Prayoga, Dimas Purdianto Purdianto Purnama Sakhrial Purwanto Purwanto Purwanto Putri N.A, Anindya Rahmawati, Shinta Melliana Rahmawati Rasmiati Nur Aeni Retno Purwani Setyaningrum Riska Puspa Anggraeni Putri Risky Bambang Sutrisna Rizky Igel Herisaputra romanuddin, ahmad Rosyati Adelia S Suprapto Sandi Salvan N N Sanudin Sanudin Sanudin Satria Permana, Muhammad Safri Setiawan, Dani Yuda Dwi Siska Wulandari, Siska Suderajat, Agung Sufajar, Sufajar Sufajar, Suprapto Suhardian Suhardian Suherman Suherman Suherman Sunaryati , Titin Sunita Dasman Sya syah Apriliyani Syach, Ridwan Taofik Safrudin Taufik Hidayat Tiani Ayu Lestari Tiara Deswara Pungkas Tiara Putri Tri Ngudi Wiyatno Turmudi Zy, Ahmad Vidya Anis Fitri Yahya, Adiba Yahya, Adibah Yoga Religia Yusup, Diana