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All Journal Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer Networks, Architecture and High Performance Computing
SUMITA WARDANI
universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media

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ANALISIS PELAYANAN UNIT PEMBUATAN KARTU KUNING (AK-1) MENGGUNAKAN METODE SERVQUAL PADA DINAS KETENAGAKERJAAN KOTA MEDAN Siti Aisyah; Dian Shyntia; SUMITA WARDANI; RICO WIJAYA DEWANTORO
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 5 No. 2 (2022): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
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.
Smart Prediction Model For Unplanned Icu Transfer Based On Deep Learning Optimization : An Article Review Sumita Wardani; Muhammad Uwais Akbar; A. Henpra Yogi Sitanggang; Joshua Baen Tupa; Johanes Pardede; Abdi Dharma
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Problem on units ICU already is problem which critical and already happened since long ago, for the ICU is one of the highest costs unit in hospitals, which made a system to predict activity on ICU is very demanding. COVID-19 shows the need for excellent time management in dealing with the abnormal flow of patients. Prediction of ICU transfer can be useful for patients and medical personnel to reduce medical cost and giving the time required by the nurses to prepare themselves for a huge patients flow. Reviews of related articles are carried out through the Google Scholar database. Screening then conducted based on identified article based on criteria eligibility. There are 7 final articles that assessed on a large scale data samples, method algorithm, and performance from the model which used on the article. Results obtained from this study which follow PRISM flow show a number of variable indicators that are commonly applied, namely: age, gender, liver function, blood pressure, pulse rate, temperature, respiratory rate, kgd and ECG data features. The best test results was achieved by research by Jonathan Rubin, et al due to the large number of varied data sets used, much more than other studies. This research also used adaptive boosting and gradient tree boosting approaches and evaluated with 4 main parameter that is accuracy, sensitivity, specificity, and AUC ROC. This study succeed in reaching performance evaluation model of 72.8% sensitivity, 76.3% specificity, 76.2% accuracy and 79.9% AUC ROC
Pengaruh Pembelajaran E-Learning terhadap Komunikasi Mahasiswa Selama Covid19 Menggunakan Metode Regresi Linear Sumita Wardani; Cindy Suci; Fitriani Bidaya; Festiana Telaubanua; Marlince NK Nababan
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 6 No. 3 (2022): Volume 6 Nomor 3 Agustus 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v6i3.11574

Abstract

Pandemi Covid-19 membuat pemerintah Indonesia mengeluarkan kebijakan baru untuk menghentikan penyebaran Covid-19, yaitu melaksanakan ajakan masyarakat untuk melakukan Physical Distancing. Penyebaran virus corona membuat sejumlah perguruan tinggi dan sekolah membatasi kegiatan tatap muka akibat virus covid-19, membuat sejumlah kampus di Indonesia salah satunya kampus Universitas Prima Indonesia, mulai menerapkan sistem perkuliahan E-learning. Berdasarkan survei, responden terbanyak berasal dari program studi sistem informasi. Dan pertanyaan yang paling banyak diajukan adalah pertanyaan apakah dosen memberikan materi perkuliahan sesuai dengan rencana pembelajaran dengan hasil 82% sedangkan pertanyaan yang paling banyak dijawab Tidak ada pertanyaan apakah ada kendala berkomunikasi dengan dosen happy college pemberani dengan hasil 48%. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pembelajaran e-learning terhadap komunikasi mahasiswa di Universitas Prima Indonesia. Beberapa penelitian telah dilakukan untuk menangani kasus covid dengan algoritma regresi linier. Pada penelitian ini peneliti mencoba memprediksi Pengaruh Pembelajaran E-Learning Terhadap Komunikasi Siswa Selama Misa Covid19 menggunakan algoritma Regresi Linier. Kemudian peneliti akan mengukur nilai akurasi skor dari algoritma ini. Hasil ini mencapai 53,48% untuk tahap pelatihan sedangkan untuk tahap pengujian mencapai 38,43%.
OPTIMIZATION OF LUNG CANCER CLASSIFICATION METHOD USING EDA-BASED MACHINE LEARNING Windania Purba; Sumita Wardani; Diana Febrina Lumbantoruan; Fransiska Celia Ivoi Silalahi; Thomas Leo Edison
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 6 No. 2 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
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 Agung Prabowo; Sumita Wardani; Abdul Muis; Radiman Gea; Nathanael Atan Baskita Tarigan
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
Co-Authors A. Henpra Yogi Sitanggang Abdi Dharma Abdul Muis Agung Prabowo Cindy Suci Dian Shyntia Diana Febrina Lumbantoruan Festiana Telaubanua Fitriani Bidaya Fransiska Celia Ivoi Silalahi Johanes Pardede Joshua Baen Tupa Marlince NK Nababan Muhammad Uwais Akbar Nathanael Atan Baskita Tarigan Radiman Gea RICO WIJAYA DEWANTORO Siti Aisyah Thomas Leo Edison Windania Purba