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All Journal International Conference on Engineering and Technology Development (ICETD) CommIT (Communication & Information Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL 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 Teknik Industri Terintegrasi (JUTIN) 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 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 International Journal of Educational and Life Sciences (IJELS) Jurnal Pelita Pengabdian SAINTEK International Journal of Integrated Science and Technology Dedikasi : Jurnal Pengabdian Lentera 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning Danny, Muhtajuddin; Fatchan, Muhamad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1259

Abstract

The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven.
Ethno-Edutainment Electronic Module (EMEE) to Strengthen Local Cultural Character in Elementary School Students Titin Sunaryati; Muhamad Fatchan; Muhamad Sudharsono; Pipin Angela
EduBase : Journal of Basic Education Vol. 6 No. 1 (2025): EduBase : Journal of Basic Education
Publisher : LJPI UI Bunga Bangsa Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47453/edubase.v6i1.3186

Abstract

The erosion of local cultural values due to globalization poses a significant challenge to the development of character in Indonesian students, who are expected to uphold tolerance, ethics, and mutual respect. Objective: This study aims to develop an Android-based ethno-edutainment electronic module to strengthen local cultural character among elementary school students. Novelty: The module integrates ethno-edutainment and local cultural values into a digital learning platform, offering an innovative approach to character education. Methods: The module was developed using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) within a Research and Development (R&D) framework. The product was validated by media, content, and language experts, and tested on 55 students at Pondok Bambu 06 Duren Sawit State Elementary School, East Jakarta. Results: The validation results showed that the module was categorized as "very good" across all aspects. The practicality test revealed positive student responses, and the effectiveness test demonstrated a significant improvement in learning outcomes, with an average pre-test score of 70.54 and a post-test score of 87.45. Conclusion: The module is effective in supporting character education, with interactive features and local cultural content that enhance student engagement and strengthen their national identity.
PREDICTION OF 2024 PRESIDENTIAL ELECTION USING K-NN WITH METRIC APPROACHES CHEBYSHEV AND EUCLIDEAN BASED ON TWITTER DATA INVESTIGATION Darmawan, Steven Ryan; Fatchan, Muhamad; Maulana, Donny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1720

Abstract

The potential difference between the popularity of presidential candidates on social media and in the general public poses a serious challenge in predicting the outcome of the 2024 presidential election. Technical constraints in collecting, cleaning and analyzing dynamic and large-scale social media data can threaten the accuracy and validity of predictions. To overcome this problem, careful steps and in-depth understanding are needed. Therefore, this study aims to predict the winner of the 2024 presidential election from the popularity of presidential candidates Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto on Twitter. The K-Nearest Neighbor (K-NN) method with the Both Metric approach (Euclidean and Chebyshev) was used to analyze 51,192 tweet data through the Knowledge Discovery in Database (KDD) stage using Orange software. The evaluation results show almost the same performance, with AUC values of 0.725 for Euclidean and 0.720 for Chebyshev. The CA result was 55.6% for Euclidean and 55.4% for Chebyshev. Although F1, precision, and recall were almost the same, overall, the Euclidean metric was better. The prediction shows Prabowo Subianto as the most popular candidate on Twitter. Nonetheless, these results need to be interpreted with caution and strengthened with further analysis and additional data to get a more comprehensive conclusion. This research shows that K-NN with both metrics can provide predictions above 50%, reliable enough to be able to predict the most popular candidates on Twitter.
Optimasi Pencatatan Dan Perhitungan Barang NG Di CV Anugrah Ikhasn Keluarga menggunakan Metode Agile Ramadhan, Reza Rizky; Fatchan, Muhamad; Wiyanto, Wiyanto; Edy, Sarwo Edy; Riyanto, Kuwat Riyanto
Jurnal SIGMA Vol 16 No 1 (2025): Juni 2025
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v16i1.6051

Abstract

Dalam industri manufaktur, kualitas produk menjadi faktor krusial yang menentukan keberhasilan perusahaan. CV Anugrah Ikhsan Keluarga, yang bergerak dalam produksi komponen logam, menghadapi tantangan dalam pencatatan barang Not Good (NG) secara manual yang rentan terhadap kesalahan dan manipulasi data. Hal ini menyebabkan ketidaksesuaian antara laporan produksi dan data gudang, serta kurangnya monitoring real-time yang menghambat pengambilan keputusan. Penelitian ini bertujuan mengembangkan sistem pencatatan dan perhitungan barang NG secara otomatis menggunakan metode Agile. Sistem dibangun dengan JavaScript dan framework Next.js yang mendukung server-side rendering (SSR) dan static site generation (SSG) untuk performa optimal. Metode Agile dipilih guna memfasilitasi iterasi cepat dan umpan balik berkelanjutan. Hasil penelitian diharapkan meningkatkan efisiensi pencatatan, mencegah manipulasi data, dan memungkinkan monitoring real-time, serta memberikan kontribusi dalam pengembangan sistem serupa dan referensi akademik di bidang teknik informatika.
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
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.
Transforming Supply Chain Forecasting Using Transformer Models and K-NN Analysis Faris Muzaki, Moch. Nauval; Fatchan, Muhamad; Afriantoro, Irfan
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.v2i6.2026

Abstract

The study optimizes supply chain logistics in Asia using the K-Nearest Neighbors (K-NN) algorithm to enhance delivery efficiency and profitability. It suggests that future research should explore ensemble methods and deep learning models for better accuracy and robustness. Comparative analyses with traditional models provide valuable insights. Investigating the impact of real-time data analytics and IoT can improve visibility and control. Big data analytics for predictive models in risk management and resilience against disruptions like natural disasters and geopolitical instability is crucial. Exploring collaborative networks where stakeholders share data and resources can significantly advance logistics efficiency. These directions will help develop efficient, resilient, and sustainable supply chain systems, offering practical solutions for businesses in Asia's complex market.
Co-Authors . Ermanto . Suratman A. Reza Baehaqa Jamroni Jamroni Abdul Halim Anshor Abdul Hasyim Abdul Rahman Abizar Ar Rifa’i Rifa’i Afriantoro, Irfan Agus Suwarno, Agus Aguswin, Ahmad Ahmad Turmudi Zy al fiyan Alfarizi, Muhammad Farhan Amali Amali Anaconda Bangkara Andri Firmansyah Andrian Andrian Andriani Andriani Anggi Muhammad Rifai Anggita Risqi Nur Clarita Anisa Rahmawati Annisa Maulana Majid Aprila Hardi, Resty Apriyandi M Ardimansyah Ardimansyah Asep Hidayat Asep Suprianto Asep Suprianto Ayu Fitriyani Aziz, Faruq Bagoes Ramadhan Bagus Dwi Saputro Butsianto, Sufajar Dahyoung Yenuargo Darmawan, Steven Ryan Dendy K. Pramudito Dhea Tara Monika Doni, Muhamad Edora Edora Edora Edy Widodo Edy Widodo Edy Widodo Edy, Sarwo Edy Elkin Rilvani Ema Utami Endah Yaodah Kodratilah Faizah Via Fadhillah Faris Muzaki, Moch. Nauval Fauziah , Sifa Fitriani Hadiansyah, Zikri Hari Sugeng Hendra Lesmana Herdyanto, Febro Indra Permana, Indra Indradewa, Rhian Irfan Afriantoro Irsyad Syhruddin Karina Imelda Linda Marlinda Listanto, Firgiawan M Ryan Bagus Valentin* Marayasa, I Gde Bayu Priyambada MAULANA, AFFAN Maulana, Donny Muhamad Ekhsan Muhamad Sudharsono Muhammad Alif Ridwan Hanafi 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 Putri, Hana Silvia Dwi Qori yumansyah Qori Ramadhan, Reza Rizky Retno Purwani Setyaningrum Rika Anugrahaini, Savariana Rindiani Tri Lestari Rini Ariza 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 Ubaeddillah, Saeful Wahyu Hadi Kristanto Wahyu Hadikristanto Wahyu Indrarti Widi Winjani Widiyawati , Widiyawati Wiyanto Wiyanto - Yumansyah, Qori Yupita Fitria Riyanti