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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Al'Adzkiya International of Computer Science and Information Technology Journal
WARDANI, SUMITA
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Penerapan Data Mining untuk Memprediksi Penjualan di Erigo Store dengan K-Nearest Neighbor Saragih, Rini Hartati; Pamungkas, William Aldo; Yumna, Farhan; Sitanggang, Delima; Wardani, Sumita
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.655

Abstract

Berdasarkan hasil penelitian ini, pemodelan yang dilakukan dengan menerapkan algoritma K-Nearest Neighbor. Hasil akurasi yang diperoleh dari prediksi penjualan untuk 5 kategori produk adalah shirt sebesar 100%, Jacket sebesar 95%, Pants sebesar 92,31%, Outwear sebesar 89,47% dan T-Shirt sebesar 60%. Dari total keseluruhan prediksi kategori penjualan diperoleh hasil akurasi klasifikasi penjualan keseluruhan produk sebesar 83,62% dengan pengujian menggunakan tools rapid miner untuk menentukan penjualan setiap produk per tahunnya.
ANALISIS PELAYANAN UNIT PEMBUATAN KARTU KUNING (AK-1) MENGGUNAKAN METODE SERVQUAL PADA DINAS KETENAGAKERJAAN KOTA MEDAN Aisyah, Siti; Shyntia, Dian; WARDANI, SUMITA; WIJAYA DEWANTORO, RICO; Purba, Windania; Nababan, Marlince NK; Dharshinni, N P
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2185

Abstract

Sesuai dengan pasal 25 ayat 3 kartu kuning (AK-1) adalah kartu tanda bukti pendaftaran pencari kerja. Istilah Kartu kuning (AK-1) ini berasal bentuk kartu tanda bukti pendaftaran pencari kerja yang berwarna kuning. Kartu kuning (AK-1) digunakan oleh para pencari kerja sebagai keterangan bahwa para pencari kerja belum dan sedang mencari kerja. Banyaknya pencari kerja yang tidak diimbangi dengan penempatan kerjanya, sehingga masih adanya pengagguran yang tersisa akibat tidak meratanya penyaluran tenaga kerja dengan banyak nya lowongan yang ada menunjukkan bahwa masih terjadi masalah lain terkait dengan pelayanan kartu kuning (AK-1) pada Dinas Ketenagakerjaan Kota Medan. Selain masalah tersebut, adanya keluhan pemohon tentang sarana dan prasarana yang kurang dalam pelayanan kartu kuning (AK-1) juga menjadi sorotan tersendiri menyangkut kualitas pelayanan kartu kuning (AK-1) yang diberikan oleh Dinas Ketenagakerjaan Kota Medan. Dalam model Servqual, kualitas jasa didefinisikan sebagai penilaian atau sikap global berkenaan dengan superioritas suatu jasa. Penillaian kualitas pelayanan perlu dilakukan untuk mengetahui bagimana kualitas pelayanan unit pembuatan kartu kuning (AK-1) pada Dinas Ketenagakerjaan Kota Medan. Peningkatan kualitas pelayanan yang dilakukan oleh Dinas Ketenagakerjaan Kota Medan dari Gap Servqual sudah baik.
OPTIMIZATION OF LUNG CANCER CLASSIFICATION METHOD USING EDA-BASED MACHINE LEARNING Purba, Windania; Wardani, Sumita; Lumbantoruan, Diana Febrina; Silalahi, Fransiska Celia Ivoi; Edison, Thomas Leo
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3413

Abstract

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.  
Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach Prabowo, Agung; Wardani, Sumita; Muis, Abdul; Gea, Radiman; Tarigan, Nathanael Atan Baskita
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3611

Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches
Internet of Things-Based Intelligent Health Monitoring System Wardani, Sumita
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 3, No 1 (2022)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v3i1.226

Abstract

IoT in health monitoring is the main key in providing better medical facilities to patients and facilitating doctors and hospitals as well. The system proposed here consists of various medical devices such as sensors and web-based or mobile-based applications that communicate via a network of connected devices and help monitor and record health data and medical information. The proposed result of this mini research is to build a system to provide a medical monitoring system to patients even in remote areas without a hospital in their area by connecting via the internet and retrieving information via their health status through the wearable device provided in the kit using raspberry pi microcontroller which can record patient's heart rate, blood pressure. The system will provide information to the patient's family members and doctors about the patient's current health status and this system has complete medical information in case of an emergency. Information retrieval can be used to analyze and predict chronic disorders or other diseases such as heart attack at its initial stage using data mining techniques which will also provide a profitable approach to decision making.
Penerapan Metode Teorema Bayes pada Sistem Pakar dalam Mendiagnosa Tingkat Kepastian Penyakit Batu Empedu Riandini, Meisarah; Wardani, Sumita; Handayani, Meli
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 2 (2025): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i2.11465

Abstract

Batu empedu atau yang secara ilmiah disebut Cholelithis merupakan penyakit yang dapat ditemukan di dalam kandungan empedu atau di dalam saluran empedu atau bahkan pada keduanya. Dalam kasus penanganannya dibutuhkan seorang spesialis yang mampu mendiaknosa kondisi pasien agar dapat diobati. Namun keterbatasan jumlah spesialis mengakibatkan penanganan sering kali terlambat. Oleh karena itu dalam perkembangannya seorang pakar mulai dapat dibantu menggunakan teknologi informasi yang sering disebut sebagai sistem pakar. Sistem pakar sendiri memiliki ketentuan-ketentuan yang sistematis sesuai dengan algoritma dan metode yang ada. Termasuk metode yang bernama Teorema Bayes, diterapkan pada sebuah sistem pakar dengan memahami gejala-gejala suatu penyakit sehingga mampu memberikan hasil analisis diagnosa layaknya seorang pakar. Dengan menerapkan algoritma ini maka proses penanganan penyakit, termasuk batu empedu juga dapat ditangani bagi masyarakat sebagai pertolongan pertama atau pencegahan sebelum benar-benar ditangani oleh seorang pakar, atau setidaknya sistem pakar yang disediakan meningkatkan efektifitas dan efesiensi pakar dalam menangani pasien.
Co-Authors Abdul Muis Agung Prabowo Delima Sitanggang, Delima Edison, Thomas Leo Gea, Radiman Handayani, Meli Lumbantoruan, Diana Febrina N P Dharshinni Nababan, Marlince NK Pamungkas, William Aldo Purba, Windania Riandini, Meisarah Saragih, Rini Hartati Shyntia, Dian Silalahi, Fransiska Celia Ivoi Siti Aisyah Tarigan, Nathanael Atan Baskita WIJAYA DEWANTORO, RICO Yumna, Farhan