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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo Riau Journal of Empowerment Jurnal Informatika Kaputama (JIK) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Anugerah: Jurnal Pengabdian Kepada Masyarakat Bidang Keguruan dan Ilmu Pendidikan INCODING: Journal of Informatics and Computer Science Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Rizki Muliono
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Deteksi Pola Kunjungan Pasien Berdasarkan Status Kesehatan Menggunakan Algoritma DBSCAN Faisal Razaq; Rizki Muliono
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.979

Abstract

This study identified eight visit clusters grouped into four service profiles: Acute (Clusters 1 5; 1,186/3,000 ≈ 39.5%; mean age 22.8 years; peaks on Saturday at 19:00 and Thursday at 08:00; predominant diagnoses: dengue fever, typhoid, acute respiratory infection, influenza, and gastroenteritis), Chronic (Clusters 3 4; 924/3,000 ≈ 30.8%; mean age 66–67 years; peaks on Thursday at 08:00 and Friday at 13:00; predominantly COPD, type 2 diabetes mellitus, heart failure, hypertension, and kidney failure), Routine Follow-up (Clusters 2 7; 590/3,000 ≈ 19.7%; mean age 41–42 years; peaks on Thursday at 11:00 and Friday at 15:00; including post-operative follow-up, annual check-ups, adult vaccination, cholesterol screening, and nutrition counseling), and Emergency (Clusters 0 6; 300/3,000 = 10%; mean age 44–46 years; peaks at 22:00 on Thursdays and Sundays; predominantly ischemic stroke, myocardial infarction, road-traffic injuries, appendicitis, and asthma exacerbations). The age–time–diagnosis patterns indicate a distinct segmentation of service needs: acute cases are concentrated among younger patients and peak on weekends and weekday mornings; chronic cases cluster among older adults with morning–midday weekday peaks.
Analisis Persebaran Penyakit di Wilayah Menggunakan Algoritma K-Means Berbasis Data Kunjungan Fasilitas Kesehatan Zatin Suhaira; Rizki Muliono
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.983

Abstract

This study aims to analyze the distribution of diseases based on patient visit data to various healthcare facilities using the K-Means clustering method. The research data were obtained secondarily from the Kaggle platform, namely the ‘Healthcare Dataset’, which contains patient information, including healthcare facility attributes, medical conditions, and other related data. The determination of the optimal number of clusters was carried out using the Elbow Method, while the quality of clustering was evaluated with two internal metrics, namely the Silhouette Score and the Davies–Bouldin Index (DBI). The clustering results produced three main clusters with distinct characteristics. The first cluster was dominated by patients diagnosed with arthritis in the age group of 55–59 years with blood type O+. The second cluster showed a predominance of obesity in the age group of 35–39 years with blood type AB+, while the third cluster indicated cancer cases in the age group of 65–69 years with blood type O-. The evaluation resulted in a Silhouette Score of 0.5349 and a DBI of 0.5830, indicating that the clustering quality is fairly good, with compact and well-separated clusters. These findings not only highlight variations in disease distribution across healthcare facilities but also provide a foundation for mapping disease patterns and supporting strategic decision-making in public health..
Penggunaan Algoritma Fuzzy C-Means untuk Optimalisasi Pengelompokan Data Cuaca dalam Prediksi Curah Hujan di Indonesia Safrina Hidayah; Rizki Muliono
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.839

Abstract

This study develops an information system to optimize rainfall data clustering in Indonesia using the Fuzzy C-Means method. Rainfall clustering aims to provide accurate information about climatic conditions by categorizing regions into three rainfall levels: high, medium, and low. The data used in this study were obtained from observations by the Meteorology, Climatology, and Geophysics Agency (BMKG) from 2011 to 2015 across various provinces. The Fuzzy C-Means method was selected due to its ability to handle uncertainty by assigning membership degrees to each cluster. The resulting clustering information is expected to assist the community and relevant sectors such as agriculture, fisheries, and regional planning in predicting rainfall and making informed decisions. The developed system can also be extended to process other weather data, including air quality and wind speed.
Co-Authors -, Muhathir Alwi, Buchori Bunaya Arthavia Sitorus Darkani, M. Farhan Dedi Sahputra Effiati Juliana Hasibuan Esrayanti Simanjuntak Faisal Razaq Fikri, Ridho Ahmad Harahap , Ryanda Fadillah Hasudungan, Andre Ida Royani Irfansyah Iswandi Iswandi Juanda Hakim Lubis Juanda Hakim Lubis Laia, Aldi Irfan Marischa Elveny, Marischa Marpaung, Febriady Merri Hafni Muhammad Akbar Siregar Muhammad Farhan Darkani Muhammad Hamdani Santoso Muhathir Muhathir Muhathir Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Nurul Hidayani Nurul Khairina Prasetyo , Dewo Riza Dwi Putri Safrina Hidayah Samsul A Rahman Sidik Hasibuan Sandy, Boy Siahaan, Ricardo Fransdoli Siregar, Yusril Izza Haholongan Sitopu, Essay Puspita Sri Juwita Sri Wahyuni Supriatno, Supriatno Susilawati Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Tino Hermanto Y. Syah, Rahmad B. Yopan Rahmad Aldori Zatin Suhaira Zebua, Meniati Zulfikar Sembiring Zulfikar Sembirirng