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All Journal Jurnal Edukasi Universitas Jember Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah Universitas Batanghari Jambi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Bisma: Jurnal Bisnis dan Manajemen Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Soft Computing Exploration Studi Ilmu Manajemen dan Organisasi Jurnal Abdimas Ekonomi dan Bisnis Transekonomika : Akuntansi, Bisnis dan Keuangan Jurnal Relevansi : Ekonomi, Manajemen dan Bisnis Perwira Journal of Science and Engineering (PJSE) Reviu Akuntansi, Manajemen, dan Bisnis PENA ABDIMAS : Jurnal Pengabdian Masyarakat Journal of Advances in Information Systems and Technology Indonesian Journal of Informatic Research and Software Engineering Jurnal Pemberdayaan Ekonomi eProceedings of Management Journal of Student Research Exploration Journal of Information System Exploration and Research Recursive Journal of Informatics IJEB JPM JER Jurnal Akuntansi dan Governance Andalas Media Penelitian dan Pengembangan Kesehatan Jurnal Ekonomi, Manajemen, Akuntansi INOVTEK Polbeng - Seri Informatika Jurnal Abdi Negeri Recursive Journal of Informatics
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Journal : Journal of Information System Exploration and Research

The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm Korina, Nanda Putri; Prasetiyo, Budi; Hakim, Ade Anggian; Septian, M Rivaldi Ali
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.344

Abstract

Diabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values ​​in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values ​​in improving the performance of diabetes classification models.
Classification of Student Grading Using Naïve Bayes Method with Under-sampling Approach to Handle Imbalance Aziz, Alif Abdul; Prasetiyo, Budi
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.537

Abstract

This study explores the application of the Naive Bayes classification method to predict student grades based on important attributes such as timeliness of assignment submission, attendance rate, and quality of work. This research uses a dataset that includes three attributes, namely timeliness of submission, attendance level in learning, and evaluation of the quality of assignments collected by students. The pre-processing is performed to clean the data, followed by an under-sampling stage to balance the class distribution. Then, the classification model is evaluated and tested using specific data samples to measure prediction accuracy. The results showed a significant improvement in model accuracy after applying under-sampling, highlighting the importance of handling data imbalance in predictive analysis. The implications of these findings are not only relevant in the context of higher education, but also offer opportunities for further development in data-driven decision-making in various fields.
Analysis of the Stacking Ensemble Learning Model of Categorical Boosting and Naïve Bayes Algorithms for Crop Selection Based on Soil Characteristics Maulana, Ilham; Prasetiyo, Budi
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.604

Abstract

This study aims to develop a machine learning model for selecting crop types based on soil characteristics, using the Categorical Boosting and Naïve Bayes algorithms as base learners. Next, an ensemble learning technique using a stacking approach was applied to improve the performance of the base model that was built. This was done to analyze and compare the performance results of each ensemble model that was carried out. Model performance was evaluated using evaluation metrics including precision, recall, f1-score, and accuracy. The results of this study indicate that the stacking ensemble model with Random Forest as the meta learner can provide better performance compared to other ensemble models. This model achieved a precision of 98.85337%, a recall of 99.84848%, an F1-score of 99.84844%, an accuracy of 99.84848%, and a model training time of 78.61110 seconds. Based on these results, this study is expected to provide tangible contributions and new knowledge in plant selection classification based on soil characteristics, thereby aiding in the precise and efficient determination of suitable plant types.
Co-Authors Aditya Wardhana Afrizal Rizqi Pranata, Afrizal Rizqi Ahmad Roziqin, Ahmad Aisy, Salsabila Rahadatul Aji Purwinarko, Aji Alamsyah - Amidi Amidi, Amidi Anggraini, Tasya Fitria Anggyi Trisnawan Putra Ardila Rahma, Rana Aziz, Alif Abdul Azura, Amberia Narfi Bachtiar, Muhammad Irgi Bambang Widjajanta, Bambang Bayuaji, Hibatullah Zamzam Tegar Beta Noranita Biyantoro, Arell Saverro D.W, Made Bagus Paramartha Deske W. Mandagi Didimus Tanah Boleng Dinova, Dony Benaya Endang Sugiharti, Endang Fachrezi, Farhan Rifa Fadhilah, Muhammad Syafiq Fadlil, Affan Fajriati, Nafa Fata, Muhamad Nasrul Fata, Muhamad Nasrul Ferninda, Varin Fikri Mohamad Rizaldi Fitria, Yunita Fitriana, Jevita Dwi Hakim, Ade Anggian Hakim, M Faris Al Hakim, M. Faris Al Hani Fitria Rahmani Ilham Maulana Jhonatan, Edward Julianto, Richy Jumanto Jumanto , Jumanto Jumanto Jumanto, Jumanto Jumanto Unjung KA, Cecep Bagus Suryadinata Korina, Nanda Putri Leo nardo Lestari , Apri Dwi Lestari, Apri Dwi Lestari, Fitri Duwi Lintang, Irendra M. Faris Al Hakim Makrina Tindangen Marshanda, Ghea Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Muhammad Sugiharto Mukhlisin, Ahmad Munahefi, Detalia Noriza Mustaqim, Amirul Muzayanah, Rini Naufal Zuhdi, Hamzah Ndruru, Toni Krisman Nelly, Fredy Kusuma Nendya, Bima Nicko, Robertus Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nisa, Intan Khairun Niswah Baroroh Partini, Emilia Paundra, Fajar Pertiwi, Dwika Ananda Agustina Pradana, Fadli Dony PRASETYO, ERWIN Pratama, Muhammad Hasbi Puspo Dewi Dirgantari Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahmat Gernowo Ramadhian, M. Arief Rahman Ratih Hurriyati Riesnandar, Edi Ristiawati, Monika Riza Arifudin Robianty, Nenden Sondari Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Sadid, Moh Naufal Salsabila, Malika Putri Saparina, Iska Ayu Saputra, Angga Riski Dwi Satriawan, Grace Yudha Satrio Ardiansyah, Adi Seivany, Ravenia Septian, M Rivaldi Ali Subhan Subhan Sulastri, Ai Syaharani, Reisya Triyadi, Indra Ulhaq, Moch Daffa Dhiya Vember, Hilda Wahyu, Aufa Azfa Walean, Ronny H. Yahya Nur Ifriza Yosza Dasril Yulia Nur Hasanah