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All Journal Informatics and Computer Engineering Journal Journal of Artificial Intelligence and Digital Business Jurnal Ilmu Komputer dan Informatika
Sipayung, Sardo Parningotan
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics

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Analisis Pengelompokan  kabupatan/kota di Provinsi Sumatera Utara berdasarkan Penyakit Menular Menggunakan Algoritma K-Means suriaty padang, suriaty; Setiani Hulu; Sipayung, Sardo Parningotan
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11779

Abstract

Infectious diseases remain a major public health problem in North Sumatra Province. The uneven distribution of infectious diseases across districts and cities has resulted in suboptimal disease control efforts that are often not well targeted. Several infectious diseases that still require special attention include tuberculosis, leprosy, malaria, and dengue fever. Variations in disease incidence among regions indicate the need for an analytical method capable of describing disease distribution patterns in a structured manner to support regional-based health policy prioritization. This study applies a data mining approach using clustering methods with the K-Means algorithm to group districts and cities in North Sumatra Province based on infectious disease characteristics. The data used include indicators of tuberculosis case detection, tuberculosis treatment success rates, the number of leprosy cases, malaria morbidity rates, and dengue fever morbidity rates. The study area covers Tapanuli Tengah, Toba Samosir, Labuhanbatu, Simalungun, Dairi, Karo, Deli Serdang, Langkat, Nias Selatan, Pakpak Bharat, Serdang Bedagai, Batu Bara, Padang Lawas, and Labuhanbatu Utara. The research stages consist of data preprocessing, determining the number of clusters, distance calculation using Euclidean Distance, and iterative processes until stable clustering results are obtained. The results show that districts and cities in North Sumatra Province can be grouped into three clusters, namely regions with high, medium, and low levels of infectious diseases. This clustering is expected to support decision-making in determining priority areas for infectious disease control by local governments
Analisis Pengaruh Kecanduan Bermedia Sosial Terhadap Kemampuan Belajar Menggunakan Algoritma Naive Bayes Saragih, Sonia Elvrida; Harefa, Lonia; Sipayung, Sardo Parningotan
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6258

Abstract

Perkembangan teknologi informasi telah meningkatkan intensitas penggunaan media sosial di kalangan pelajar dan mahasiswa. Meskipun media sosial memberikan kemudahan dalam komunikasi dan akses informasi, penggunaan yang berlebihan dapat menyebabkan kecanduan yang berdampak negatif terhadap aktivitas akademik. Kecanduan bermedia sosial diketahui dapat menurunkan konsentrasi, mengganggu pola tidur, serta memengaruhi kondisi psikologis, sehingga berpotensi menurunkan kemampuan belajar. Penelitian ini bertujuan untuk menganalisis pengaruh kecanduan bermedia sosial terhadap kemampuan belajar siswa menggunakan pendekatan data mining dengan algoritma Naive Bayes. Data penelitian diperoleh dari dataset Social Media Addiction Among Students yang tersedia pada platform Kaggle, yang mencakup atribut usia, jenis kelamin, tingkat pendidikan, negara, durasi penggunaan media sosial harian, platform yang paling sering digunakan, jam tidur, skor kesehatan mental, status hubungan, dan skor kecanduan. Atribut Affects Academic Performance digunakan sebagai kelas dalam proses klasifikasi. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan, penerapan algoritma Naive Bayes, serta evaluasi hasil klasifikasi. Hasil penelitian menunjukkan bahwa siswa dengan tingkat kecanduan media sosial yang tinggi memiliki kecenderungan lebih besar mengalami penurunan kemampuan belajar. Dengan demikian, penelitian ini menegaskan bahwa pengendalian penggunaan media sosial sangat penting untuk menjaga efektivitas dan kualitas belajar. Selain itu, algoritma Naive Bayes terbukti mampu memberikan kinerja yang baik dalam mengklasifikasikan pengaruh kecanduan media sosial terhadap kemampuan belajar.
Clustering Opini Publik Status Bencana Nasional di Sumatera Menggunakan Algoritma K-Means Simamora, Raymon; Nababan, Andre Septianus; Sipayung, Sardo Parningotan
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

This study aims to classify public opinion regarding the determination of national disaster status in Sumatra using data obtained from social media platform X (formerly Twitter). Public opinion data were collected from user-generated text posts related to flood and landslide events that occurred in November 2025 through a crawling process utilizing Node.js and the tweet-harvest library. The collected data underwent preprocessing stages, including noise removal, text normalization, and duplicate elimination. Subsequently, the textual data were transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting method through the Process Documents from Data operator in RapidMiner. Clustering analysis was conducted using the K-Means algorithm with five clusters. The results indicate that public opinion is classified into several dominant themes, namely government policy decisions, humanitarian assistance, disaster causation, housing needs for affected communities, as well as security and field monitoring activities. These findings demonstrate that the K-Means algorithm is effective in identifying patterns of public opinion from unstructured textual data and can support data-driven evaluation of disaster management policies.
Co-Authors Harefa, Lonia Nababan, Andre Septianus Saragih, Sonia Elvrida Setiani Hulu Simamora, Raymon suriaty padang, suriaty