ANALISIS PENERIMAAN PENGGUNA SISTEM INFORMASI AKADEMIK, STUDI KASUS STIKES HARAPAN BANGSA

ABSTRAK Sistem informasi dan Teknologi informasi berkembang dengan sangatpesat dan berdampak signifikan terhadap segala bidang. Pemanfaatan teknologiinformasi tidak hanya pada pemanfaatan sektor bisnis, tetapi juga sektor publikyang salah satunya adalah lembaga perguruan tinggi. Efisiensi dan efektifitasproses informasi dengan menggunakan sistem informasi hanya akan terjadiapabila teknologi tersebut diterima oleh individu dalam organisasi. Tesis inimerupakan hasil riset yang akan membahas mengetahui faktor – faktor apa yangsajakah yang mempengaruhi penerimaan dosen, mahasiswa dan staf akademikdalam menggunakan Sistem Informasi Akademik STIKes Harapan Bangsa.Sejumlah 200 responden ikut berpartisipasi dalam penelitian ini. Adapun modeladopsi teknologi yang digunakan adalah model Technolgy Acceptance Model.Selain menguji variabel, penelitian ini juga menguji apakah teori TAM dapatdigunakan untuk mengetahui penerimaan pengguna SIAK-SHB di Stikes HarapanBangsa. Analisis data dilakukan dengan pendekatan Structural Equation Modeling. Berdasarkan hasil dari analisis diperoleh kesimpulan bahwa ActualSystem Use dipengaruhi oleh Behavioral Intention to Use, Behavioral Intention toUse dipengaruhi oleh Perceived Usefulness dan Perceived Usefulness dipengaruhioleh Perceived Easy of Use.Kata Kunci :Technolgy Acceptance Model, Structural Equation Modeling,Penerimaan Pengguna Sistem Informasi Akademi

Evaluasi Pembayaran Keuangan Siswa berdasarkan Penghasilan Wali Siswa menggunakan Metode Clustering K-Means

One of the problems that often occurs in school administration is the late payment of tuition fees. Therefore, it is necessary to evaluate the education payment process so that in the future the payment process can run in an orderly and disciplined manner. This study aims to create a cluster model for grouping student administration payments. This type of research is quantitative using the K-Means Clustering method to classify payment data based on 2 variables, namely the time of payment and the income of students' guardians carried out in private elementary schools in Semarang. The data used in this study is payment data for the 2019/2020 academic year, which totals 1,933 records, covering transactions from 419 students. Determining the number of clusters is calculated using the elbow method, the best clusters obtained from the data used are 3 clusters, namely clusters 0, 1 and 2. Our findings show that cluster 2 has the largest percentage of early monthly administration payments, namely 52.5%, the percentage is on time the highest was in cluster 1, namely 74.8%, and the highest percentage of late payments was in cluster 0, namely 28.6%. The results of the analysis show that the main factor for late payments is not the guardian's income but other external factors, as evidenced by the highest percentage of late payments in cluster 0, where the average income of student guardians is = 10,000,000

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve

ANALISIS PENYEBAB PERMASALAHAN KINERJA KARYAWAN DENGAN INTERRELATIONSHIP DIAGRAM (STUDI KASUS DI STIKES HARAPAN BANGSA PURWOKERTO)

ABSTRAK Abstrak : Kinerja merupakan hasil kerja yang dilakukan oleh karyawan sesuai dengan jabatan masing-masing dalam periode waktu tertentu, yang memberikan dampak terhadap pencapaian tujuan organisasi yang telah ditetapkan. Tujuan penelitian ini adalah untuk menganalisis faktor-faktor penyebab kinerja karyawan yang kurang baik dan untuk menganalisis keterkaitan antara faktor-faktor penyebab yang berkaitan dengan permasalahan kinerja antara satu penyebab dangan penyebab lain. Hasil penelitian menunjukkan bahwa faktor penyebab satu masalah dengan masalah lain adalah faktor beban kerja karena banyaknya mata kuliah yang diampu oleh dosen luar yang mengakibatkan keterlambatan penyerahan soal ujian dan keterlambatan penginputan nilai. Faktor disiplin diri karena manajemen waktu yang mengakibatkan ketidaktepatan pelaporan pangkalan pendidikan tinggi. Faktor kepemimpinan karena pimpinan jarang di tempat mengakibatkan keterlambatan pendisposisian surat masuk dan pembayaran honor kegiatan, perjalanan dinas dan supervisi praktek dibayarkan terlalu lama. Faktor pengawasan dan manajemen waktu karena lupa dan pola kerja mengakibatkan dosen tidak mengisi jurnal. Faktor pengawasan dan koordinasi karena harus adanya konfirmasi kehadiran dosen ke dosen yang bersangkutan sehingga terjadi ketidaktepatan waktu perekapan presensi dosen. Faktor scheduling karena masih ada perkuliahan satu minggu sebelum ujian mengakibatkan terjadinya keterlambatan presensi mahasiswa.  Faktor manajemen waktu, scheduling dan beban kerja karena banyaknya kegiatan dosen mengakibatkan keterlambatan penyerahan rancangan pertemuan perkuliahan dan perubahan jadual perkuliahan. Beban dosen mengajar tinggi banyaknya libur mengakibatkan perubahan jadual perkuliahan.Kata Kunci : Monitoring, Komunikasi, Kepemimpinan, Motivasi dan Disipli

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve