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All Journal Jurnal Ilmiah Kursor Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Informatika Kaputama (JIK) Jurnal Teknik Informatika (JUTIF) Jurnal Manajemen Informatika Jayakarta Archive: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Deny Jollyta
Institut Bisnis Dan Teknologi Pelita Indonesia
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Public Health Social Sciences

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Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases Priyanto, Dadang; Iman, Ahmad Robbiul; Jollyta, Deny
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1544.262-270

Abstract

Indonesia, with its very large population, is a potential market for drugs trafficking. Hence, seriousness is needed in cracking down or preventing drug trafficking. Narcotics are substances or drugs that can cause dependence or addicted and other negative impacts on users. The problem is that drug users do not realize and even ignore diseases caused by drug addiction. The diseases can be life-threatening for users, such as inflammation of the liver, heart disease, hypertension, stroke, and others. The prevalence rate of drug abuse in West Nusa Tenggara (NTB) is included in the high category, reaching 292 cases or around 37.24% cases. This study aimed to create an application that can classify various diseases of drug users using the naïve bayes and KNN methods. The results of this study indicated that there was a very close relationship between drug users and various deadly diseases. The prediction results showed that the naive bayes method provided a prediction accuracy of 94.5% while the KNN showed a prediction accuracy of 92.5%. This shows that the naive bayes method provides better predictive performance than the KNN in the data set of drug addicts in NTB.
Machine Learning Decision Support System for Heart Disease Prediction with Optuna and Threshold Optimization Ramdhan, William; Hutahaean, Jeperson; Jollyta, Deny; Karim, Abdul
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5684

Abstract

Cardiovascular disease remains a major global health challenge, necessitating accurate and reliable decision support systems for early detection. This study proposes a machine learning–based decision support system that integrates ensemble learning, automated hyperparameter optimization using Optuna, and decision threshold tuning. The system was evaluated using several baseline machine learning models, including Logistic Regression, SVM, KNN, Decision Tree, and Random Forest, with the Random Forest model selected for optimization. Hyperparameter tuning with Optuna and decision threshold optimization led to a significant improvement in accuracy (95.0%) and ROC–AUC (0.977), with the optimized model outperforming all baseline models. This approach demonstrates improved sensitivity, reduced false negatives, and enhanced predictive performance, offering a clinically reliable tool for early heart disease detection. The results emphasize the importance of model optimization and decision threshold calibration in clinical decision support systems.
Cluster Analysis Based on McKinsey 7s Framework in Improving University Services Deny Jollyta; Dwi Oktarina; Gusrianty; Renita Astri; Lina Arliana Nur Kadim; Ni Gusti Ayu Dasriani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2005.51 KB) | DOI: 10.59934/jaiea.v1i1.45

Abstract

The epidemic of Covid-19 has impacted all aspects of human life, including education. Academic and administrative services for academic community are suffering, as a result of the fact that not all universities are able to provide online services to help break the chain of Covid-19 distribution. This is due to a lack of human competencies to use technology and a lack of information technology resources, necessitating the development of new strategies by universities to address these flaws. The goal of this study is to develop a university service strategy based on McKinsey 7s cluster results on the part that is having issues based on questionnaire data. The questionnaire is organized on seven McKinsey elements. The Manhattan distance calculation and the K-Medoids algorithm results demonstrated that the structure, system, skill and staff are all part of elements that clustered in k=2 and has to be addressed in aiding services during the Covid-19 pandemic. The McKinsey 7s showed that universities service enhancements may be achieved by combining clustering techniques and McKinsey framework.
Co-Authors Abdul Karim Adiya, M. Hasmil ahmad kamal, ahmad Akwilla, Hiskia Alyauma Hajjah Andreas Halim Tandiano Ayu Dasriani, Ni Gusti Buaton, Relita Dadang Priyanto Dafwen Toresa Darmanta Sukrianto Darmanta Sukrianto Des Suryani Des Suryani Dewi Nasien Dixsen Dixsen Dwi Oktarina Dwi Oktarina Dwi Oktarina Dwi Oktarina Eri Yanto Erlin Erlin Fauzi, Achmad Fenly, Fenly Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty, Gusrianty Gustientiedina Hadi Dwi Putra Hairani Hairani Herman Mawengkang Hutri Rizkiyah Alda Iman, Ahmad Robbiul Irvan Firnando Jeffry Kody Jeperson Hutahaean Johan Johan Johan Johan Kadim, Lina Arliana Nur Kody, Jeffry Lasman Toni Loneli Costaner Marlim, Yulvia Nora Muhammad Siddik Muhammad Siddik Muhammad Zarlis Muhammad Zarlis, Muhammad Mukhsin Mukhsin N Novriyenni Oktariana, Dwi Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Renita Astri Ricky Wijaya Rivensin Rivensin Ryan Christian Sabna, Eka Saputra, Fandri Siddik, Muhammad Sukrianto, Darmanta Surianto Surianto Syahril Effendi Tandiano, Andreas Halim Taslim Taslim Teddy Pratama Tony Tony Toresa, Dafwen Triwijoyo, Bambang Krismono Vincent Wijaya Wahyu Joni Kurniawan Wilda Susanti William Ramdhan Willy Fernando