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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal Computer Science (ijcs)
Kurniawan, Muhamad Dicky
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Implementasi Algoritma K-Means Untuk Klasterisasi Data Obat Puskesmas Kotabaru Kurniawan, Muhamad Dicky; Priyatna, Bayu; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.693

Abstract

Drug management is one of the things needed to manage drug supplies. Proper planning of drug needs makes drug procurement efficient and effective so that drugs are available in sufficient types and quantities as needed and easily obtained when needed. The purpose of this study was to classify drug data at the Kotabaru Health Center which can be used as a reference in making decisions in planning and controlling drug needs at the Health Center. The data used in this study are the Kotabaru Health Center annual report data from 2019 to 2021. Data processing in this study uses the K-means clustering method with rapidminer tools which is a data grouping technique by dividing the existing data into one or two forms. more clusters. The results of this study divide the drug data into 4 clusters, namely the first cluster (C0) with very low usage consisting of 27 drugs, the second cluster (C2) with low usage consisting of 6 drugs, the third cluster (C3) with high usage consisting of 1 drug, and the fourth (C2) with the highest usage consisting of 1 drug.
Implementasi Algoritma K-Means Untuk Klasterisasi Data Obat Puskesmas Kotabaru Kurniawan, Muhamad Dicky; Priyatna, Bayu; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.693

Abstract

Drug management is one of the things needed to manage drug supplies. Proper planning of drug needs makes drug procurement efficient and effective so that drugs are available in sufficient types and quantities as needed and easily obtained when needed. The purpose of this study was to classify drug data at the Kotabaru Health Center which can be used as a reference in making decisions in planning and controlling drug needs at the Health Center. The data used in this study are the Kotabaru Health Center annual report data from 2019 to 2021. Data processing in this study uses the K-means clustering method with rapidminer tools which is a data grouping technique by dividing the existing data into one or two forms. more clusters. The results of this study divide the drug data into 4 clusters, namely the first cluster (C0) with very low usage consisting of 27 drugs, the second cluster (C2) with low usage consisting of 6 drugs, the third cluster (C3) with high usage consisting of 1 drug, and the fourth (C2) with the highest usage consisting of 1 drug.
Implementasi Naive Bayes Untuk Klasifikasi Gangguan Tidur Amelia, Mutiara Mega; Fazrin, Bintang Maulana; Panjaitan, Yogi Yosua; Kurniawan, Muhamad Dicky; Khasanah, Nurul
Indonesian Journal Computer Science Vol. 4 No. 1 (2025): April 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/t18ryp42

Abstract

Kualitas tidur yang baik merupakan aspek penting dari gaya hidup sehat, dengan dampak signifikan terhadap kesejahteraan fisik dan mental individu. Tidur yang cukup dan berkualitas telah terbukti meningkatkan fungsi kognitif, memperkuat sistem kekebalan tubuh, dan mengurangi risiko terkena berbagai penyakit kronis. Namun, gangguan tidur seperti insomnia dan sleep apnea dapat mengganggu pola tidur dan berpotensi menyebabkan dampak negatif pada kesehatan seseorang. Dalam konteks ini, penelitian ini bertujuan untuk menyelidiki dan mengklasifikasikan gangguan tidur berdasarkan sejumlah atribut yang berkaitan dengan gaya hidup dan kesehatan tidur. Dataset yang digunakan terdiri dari 374 entri, yang mencakup beragam atribut tentang gaya hidup dan kesehatan tidur. Metode Naive Bayes digunakan untuk melakukan klasifikasi, dengan melakukan empat proporsi pembagian data training dan testing: 60:40, 70:30, 80:20, dan 90:10. Tahapan penelitian meliputi pengumpulan data, preprocessing, pemodelan menggunakan algoritma Naive Bayes, evaluasi model, dan analisis hasil. Hasil penelitian menunjukkan adanya peningkatan akurasi seiring dengan peningkatan proporsi data training, dengan akurasi tertinggi mencapai 92.11% pada pembagian data 90:10. hasil tersebut termasuk dalam kategori excellent classification, menunjukkan keberhasilan model dalam mengidentifikasi dan mengklasifikasikan gangguan tidur berdasarkan atribut yang diberikan.
Co-Authors Amelia, Mutiara Mega Fazrin, Bintang Maulana Nurapriani, Fitria Nurul Khasanah, Nurul Panjaitan, Yogi Yosua Priyatna, Bayu