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All Journal Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Teknoinfo Jurnal Cendikia CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Masyarakat Formosa (JPMF) Jurnal Publika Pengabdian Masyarakat
handoyo widi nugroho
Institut Informatika Dan Bisnis Darmajaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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PENERAPAN DATA MINING UNTUK PREDIKSI PENDAFTARAN PDB DI SMKN3 METRO MENGGUNAKAN MACHINE LEARNING Effendi, Mukhammad Khoirul; Sriyanto, Sriyanto; Goesderilidar, Goesderilidar; Nugroho, Handoyo Widi; Triloka, Joko
JSR : Jaringan Sistem Informasi Robotik Vol 9, No 1 (2025): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v9i1.482

Abstract

Penelitian ini bertujuan untuk menerapkan teknik data mining dalam memprediksi jumlah pendaftar Penerimaan Peserta Didik Baru (PPDB) di SMKN3 Metro menggunakan algoritma machine learning, khususnya Decision Tree (C4.5). Masalah utama yang dihadapi adalah tantangan pengelolaan data historis dan keterbatasan kapasitas sekolah dalam merencanakan penerimaan siswa secara efektif. Metode penelitian meliputi pengumpulan data historis pendaftaran, pra-pemrosesan data, penerapan algoritma machine learning, serta evaluasi kinerja model menggunakan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan koefisien determinasi (R²).Hasil penelitian menunjukkan bahwa model Decision Tree (C4.5) memiliki performa terbaik dibandingkan algoritma lain, dengan nilai MSE sebesar 290,948, RMSE 17,057, MAE 11,096, dan R² sebesar 0,893. Akurasi prediksi yang tinggi ini menunjukkan potensi besar algoritma tersebut dalam mendukung pengelolaan PPDB secara lebih efisien. Penelitian ini diharapkan dapat menjadi solusi inovatif bagi SMKN3 Metro dalam merencanakan penerimaan siswa baru dan optimalisasi sumber daya sekolah. Selain itu, model ini dapat menjadi referensi bagi institusi pendidikan lain dalam mengadopsi teknologi serupa.Kata Kunci: Data Mining, Prediksi Pendaftar, PPDB, Decision Tree, SMKN3 MetroAbstractThis research focuses on implementing data mining to predict the number of registrants for new student admissions (PPDB) at SMKN3 Metro using the C4.5 machine learning algorithm. The study aims to address annual challenges in data management and school capacity limitations. By leveraging historical registration data, an accurate predictive model is developed to assist the school in planning student admissions more effectively. The methodology includes data collection and preprocessing, application of the C4.5 algorithm, and model performance evaluation based on prediction accuracy. Preliminary results indicate that the C4.5 algorithm outperforms other models, achieving a Mean Squared Error (MSE) of 290.948, Root Mean Squared Error (RMSE) of 17.057, and a coefficient of determination (R²) of 0.893. These findings demonstrate the model's reliability in estimating the number of registrants for key competencies such as Software Engineering and Computer Network Engineering. This implementation is expected to improve the efficiency of the PPDB process and resource planning at SMKN3 Metro, while providing a practical application of data mining and machine learning in educational management.Keywords: Data Mining, PPDB Prediction, Machine Learning, C4.5 Algorithm, SMKN3 Metro
METODE KLASIFIKASI SPESIES IKAN BERDASARKAN KARAKTERISTIK MORFOMETRIK (Studi Kasus: Ikan Betok dan Ikan Kurisi) Kristyanto, Andi; Sriyanto, Sriyanto; Nugroho, Handoyo Widi
JSR : Jaringan Sistem Informasi Robotik Vol 9, No 1 (2025): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v9i1.473

Abstract

Produksi ikan betok dan ikan kurisi saat ini masih sangat bergantung pada hasil tangkapan dari alam, dan bila tidak dilakukan pembatasan, hal ini akan menyebabkan peningkatan dalam penangkapan ikan yang berdampak negatif pada jumlah populasi ikan di masa mendatang. Ikan betok merupakan salah satu spesies yang terancam punah, selain menghadapi penurunan populasi yang terus berlanjut, polusi, dan metode budidaya yang belum sepenuhnya maju pada saat ini. Dengan mengklasifikasikan ikan berdasarkan morfometrik dapat memberikan kontribusi dalam memonitor dan mengelola sumber daya perikanan. Penelitian ini bertujuan untuk menguji sejauh mana model klasifikasi ini efektif dan akurat dalam membedakan antara data set ikan betok dan ikan kurisi berdasarkan karakteristik morfometrik. Untuk mencapai tujuan ini, digunakan tiga metode data mining, yaitu Naive Bayes, K-Nearest Neighbor, dan Decision Tree. Penelitian ini merupakan penelitian eksperimen untuk menemukan algoritma terbaik dalam mengklasifikasi spesies ikan menggunakan metode Naive Bayes, K-Nearest Neighbor, dan Decision Tree. untuk mengklasifikasikan spesies ikan metode Decision Tree lebih unggul daripada metode Naive Bayes dan metode    K-Nearest Neighbor.  Dengan hasil pengujian yang dilakukan dengan mendapatkan nilai akurasi sebesar 94,74%.
Customer Loyalty Classification Using KNN and Decision Tree for Sales Strategy Development Mukhlisin, Mukhlisin; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15110

Abstract

Customer loyalty is a crucial element in maintaining business continuity in today’s competitive digital era. This study aims to classify customer loyalty levels based on sales and transaction behavior data using two supervised machine learning algorithms: K-Nearest Neighbor (KNN) and Decision Tree. The models were developed and evaluated using Python in the Google Colaboratory environment, utilizing a dataset of 250 customer records. The research process included data preprocessing, feature selection, normalization, data splitting, model building, and evaluation using accuracy, precision, recall, and F1-score metrics. Evaluation results showed that the Decision Tree algorithm delivered the best performance with 99.20% accuracy, 99.50% precision, 99.50% recall, and a 99.50% F1-score. Meanwhile, the KNN algorithm achieved 91.60% accuracy, 91.63% precision, 98.50% recall, and a 94.91% F1-score. These findings indicate that the Decision Tree model is more effective for classifying customer loyalty and can be implemented as a decision support tool for data-driven Customer Relationship Management (CRM) strategies.
Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy Iskandar, Muhammad Yashlan; Nugroho, Handoyo Widi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4877

Abstract

Early detection of lung cancer is essential for improving treatment outcomes and patient survival rates. This paper presents a comparative evaluation of two classification algorithms: Decision Tree and Random Forest, focusing on both predictive performance and computational efficiency. The models were tested using 10-fold cross-validation to ensure robustness. Both algorithms achieved the same accuracy of 93.3%. However, Random Forest slightly outperformed Decision Tree in recall (88.8% vs. 87.9%), F1-score (92.2% vs. 92.1%), and AUC (0.94 vs. 0.91), while Decision Tree obtained higher precision (97% vs. 95.9%). In terms of computational efficiency, Decision Tree demonstrated faster training and testing times, lower memory usage, and reduced energy consumption compared to Random Forest. The results reveal a clear trade-off between prediction quality and resource usage, highlighting the importance of selecting algorithms not only for their accuracy but also for their practicality in real-world healthcare scenarios. This comprehensive evaluation provides valuable insights for developing intelligent decision support systems that are both effective and resource-efficient, especially in environments with limited computing capacity. These findings contribute to the advancement of resource-aware intelligent systems in the field of medical informatics.
Improving the Performance of Higher Education Academic Information Systems Using Cloud Computing Technology Nugroho, Handoyo Widi; nurjoko, Nurjoko; Andani, Bethania
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1066

Abstract

Improving the performance of academic information systems using cloud computing technology is very relevant and important. The application of cloud computing technology to higher education academic information systems can also help improve data security and information risk management.  The research results show that cloud computing technology significantly improves the performance of academic information systems in higher education. This technology enables faster and easier access to academic information, and strengthens data management and data analysis capabilities.  
Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction Andani, Mia; Triloka, Joko; Irianto, Suhendro Yusuf; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14478

Abstract

Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
ARCHITECTURAL DESIGN OF THE SCHOOL ACADEMIC SYSTEM (E_MENGAJAR) USING THE OPEN GROUP ARCHITECTURE FRAMEWORK (TOGAF) AT SMPN 1 PAGELARAN PRINGSEWU DISTRICT Yulian, Hengky; Nugroho, Handoyo Widi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2130

Abstract

This research aims to design the architecture of a school academic system called E-Teaching at SMPN 1 Pagelaran, Pringsewu Regency, using the Open Group Architecture Framework (TOGAF) framework. This approach was chosen to ensure that the resulting system has a solid structure, is reliable, and is well integrated with the school's business processes. The research method used includes the main stages of TOGAF, namely Preliminary Phase, Architecture Vision, Business Architecture, Information Systems Architecture, Technology Architecture, and Opportunities and Solutions. The results of this research are in the form of an architectural model that includes main components such as student data management, learning administration, academic evaluation, and integration with the school management information system. The implementation of E-Teaching is expected to increase operational efficiency, simplify administrative processes, and support the overall learning process at SMPN 1 Pagelaran. The findings from this research provide a significant contribution to the development of academic information systems in secondary education environments, and show that the application of TOGAF can provide effective results in system architecture design.
Business Legality Training in the Context of Strengthening the Higher Education Entrepreneurship Ecosystem Anwar, Mashuril; Nugroho, Handoyo Widi; Zatika, Dinda Anna; Lilyana, Besti; Yuniwati, Yuniwati; Omega, Jessica Anatasya
Jurnal Pengabdian Masyarakat Formosa Vol. 2 No. 6 (2023): December 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/jpmf.v2i6.6979

Abstract

This service activity aims to provide education to IIB Darmajaya students who receive the P2MW program regarding business legality. Apart from that, this activity also aims to assist in registering the legal entity PT. Individuals and NIB for IIB Darmajaya student businesses receiving the P2MW program. This goal is achieved by conducting outreach, discussions, and the practice of registering PT legal entities. Individuals and NIB. The results of the activity show that business legality plays a strategic role in strengthening the university's entrepreneurial ecosystem. This activity implies that the efforts of IIB Darmajaya students who receive the P2MW program obtain a statement letter and certificate of registration for the establishment of a PT Legal Entity. Individual and NIB certificate.
Analisis Kepuasan Pengguna Pijar Sekolah SMK Kesuma Bangsa Dengan EUCS Dan TAM Ismail, Rendy; Nugroho, Handoyo Widi
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6830

Abstract

Dengan bantuan Pijar Sekolah, institusi pendidikan dapat menciptakan kurikulum digital yang menarik dan menyenangkan. Buku Digital Interaktif merupakan salah satu dari ribuan sumber daya digital yang tersedia di Pijar Sekolah, Buku Digital, Video Pembelajaran, hingga Laboratorium Virtual yang dapat digunakan oleh seluruh siswa untuk menunjang pembelajaran di sekolah. PLS merupakan klasifikasi metode pemodelan persamaan struktural SEM, dan analisis SEM merupakan kombinasi dari analisis regresi, analisis faktor, dan analisis jalur. Margin of error penelitian ini sebesar 95%. Untuk mengetahui kepuasan terhadap Aplikasi Pijar Sekolah menggunakan Technology Acceptance Model TAM dan End User Computing SatisfactionUCS. Populasi penelitian ini adalah pengguna Pijar Sekolah, pengambilan sampel menggunakan Rumus Slovin, analisis data menggunakan SmartPLS versi 3.2.9 dengan PLS-SEM. Hasilnya, dari tujuh hipotesis yang diajukan, dua hipotesis diterima dan lima lainnya ditolak. Jadi faktor yang mempengaruhi kepuasan pengguna adalah kemudahan penggunaan dan format. Hasil penelitian ini menunjukkan gambaran kepuasan pengguna terhadap sistem Pijar Sekolah. Bagi peneliti selanjutnya agar dapat melakukan pengembangan model dengan menambahkan variabel terhadap kepuasan pengguna, yaitu variabel perceived enjoyment yaitu untuk mengetahui bagaimana kenyamanan pengguna saat menggunakan sistem.
Co-Authors - Nurjoko Abdi Darmawan Abdul Lathif Sampurna, Abdul Lathif Agung Purwanto Alexander, Ganesis Andani, Bethania Andani, Mia Andrean Danofic Anwar, Mashuril Besti Lilyana, Besti Destanto, Tri Effendi, Mukhammad Khoirul Febri Suganda Goesderilidar Hary Sabita Idris Asmuni Iskandar, Muhammad Yashlan ismail, Rendy Kristyanto, Andi Kurniawan, Putra M Said Hasibuan Muhamad Muslihudin Muhammad Nur Ikhsanto, Muhammad Nur Mukhlisin Mukhlisin Omega, Jessica Anatasya Riko Herwanto Rini Nurlistiani Sadimin Sadimin Sefriyanto, Eka Siti Mukodimah Sriyanto Sriyanto Susanti, Yus Sutedi Sutedi Triloka, Joko Wasilah Wasilah Windari, Ratih Y. Suhendro Yan Aditiya Pratama Yulian, Hengky Yuniwati Yuniwati, Yuniwati Yusuf, Suhendro Zatika, Dinda Anna