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Analysis of the Utilization of Information Technology, Competency and Organizational Culture with Work Motivation as Intervening Variables on the Performance of Lecturers at Sekolah Tinggi Ilmu Ekonomi Mandala Difari Afreyna Fauziah; Purnamie Titisari; Nurhayati Nurhayati
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 5, No 2 (2022): Budapest International Research and Critics Institute May
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i2.5684

Abstract

This study aims to examine and analyze the direct and indirect effects of the use of information technology, competence and organizational culture through work motivation on the performance of lecturers at STIE Mandala Jember. This research was conducted on permanent lecturers at STIE Mandala Jember. The number of respondents in this study were 39 people. The sampling technique used is a saturated sample in which the entire population in the study is sampled. Data analysis was carried out usingPartial Least Square - Structural Equation Model (PLS-SEM). HasThe research shows that the use of information technology, competence and work motivation has a significant positive effect on lecturer performance, while organizational culture has no significant effect on lecturer performance.
Meningkatkan Literasi Teknologi melalui Webinar Pintu Gerbang Menuju Digital Hermansyah, Masud; Andita Prasetyo , Nur; Wahid, Abdul; Afreyna Fauziah, Difari; Muliawan, Agung
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 3 No 2 (2023)
Publisher : Institut Teknologi dan Sains Mandala

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Abstract

In the ever-evolving digital era, technology has become a major driving force in social, economic and educational change. Information and communication technologies (ICTs) have changed the way people work, communicate, and learn. As technology advances, it is important for individuals to have sufficient technological literacy to be able to participate actively in a digital society. The aim of this webinar is to provide an in-depth understanding of digital technology and teach practical skills in using it wisely. This webinar presents a series of topics related to digital technology, including digital transformation of Internet of Things (IoT) Technology in the Industrial World, Information Security Culture, and Computer and Network Security. By using the Zoom Video Communications application, webinar participants can easily participate from their respective locations, thus enabling broad participation and more flexibility for students to learn about technology. This webinar succeeded in increasing high school and vocational students' interest in the field of technology, as well as opening their insights about various career opportunities in the digital era. In addition, students also become more aware of the importance of ethics and responsibility in using technology, and are aware of its impact on society.
Inovasi Pemanfaatan Limbah Kulit Kopi Menjadi Produk Yang Bernilai Tinggi Sebagai Pengembangan Produk Pada Gapoktan Desa Mulyorejo Oktavinda, Diana; Dwi Natasya, Amelia; Syaifuddin, Ageng; Daffa Duta Perdana, Mohammad; Narisa, Defani; Angga Pranadaning, Fadila; Zahro Alifia Rohma, Sofiatus; Edwar Yudhistira, Gedion; Aini, Arifatul; Nurfaizeh, Nurfaizeh; laili, Nufilatul; Shofyan, Firdaus; Hermawan, Muhammad; Afreyna Fauziah, Difari
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 3 No 2 (2023)
Publisher : Institut Teknologi dan Sains Mandala

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Abstract

Coffee is one of the plantation commodities that has quite high economic value among other plantation crops such as cocoa and tea. One of the villages as a smallholder coffee producer is the village of Mulyorejo. Mulyorejo Village is the largest coffee producing village out of nine villages in Silo District, Jember Regency. So far, coffee pod waste has caused many problems in its handling and is left to rot, stacked and burned, all of which have a negative impact on the environment, so we need to think about countermeasures. Coffee skin waste has the potential to be processed back into a product because it has a fairly high economic value. The activities carried out were in the form of counseling and demonstrations on the manufacture and introduction of Coffee Skin Tea products. The purpose of holding counseling activities is to increase target knowledge about the potential of coffee skin waste, the processing system of coffee skin waste, the benefits of coffee skin waste for health, and the nutritional content of coffee skin waste.
Pemberdayaan Masyarakat Melalui Penyuluhan Terhadap Digitalisasi UMKM Sabilirrasyad, Iqbal; Wiranto, Ferry; Afreyna Fauziah, Difari; Andita Prasetyo, Nur; Azim, Fauzan
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 3 No 2 (2023)
Publisher : Institut Teknologi dan Sains Mandala

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Abstract

Kegiatan digitalisasi merupakan kegaiatan wajib disegala sektor. Dengan berkembangnya teknologi digital dan perubahan era yang mengarah pada era 5.0 Society, tuntutan untuk menggunakan digital dalam segala kesempatan menjadi hal yang krusial. Dalam bidang ekonomi electronic data interchange (EDI) telah terjadi dalam seluruh sektor ekonomi. Termausk untuk pemasaran dalam ekonomi digital sekarang telah berfokus pada penggunaan sosial media dan penguatan kepada relasi konsumen dan penjual di dunia maya. Untuk meningkatkan kebutuhan sumber daya infomrasi dan teknologi dilakukan penyuluhan untuk digitalisasi UMKM. Dimana UMKM merupakan tombak sirkuliasi dari perekonomian Indonesia, dan dirasa kemampuan UMKM Indonesia untuk memenuhi sumber daya manusia dalam bidang informasi dan teknologi masih kurang. Hasil dari penyuluhan untuk UMKM dan masyarakat umum adanya peningkatan pemahaman terhadap test yang diberikan antara sebelum penyuluhan berjalan dengan setelah berjalan sebesar 3%.
Klasifikasi Berita Politik Menggunakan Algoritma K-nearst Neighbor Fauziah, Difari Afreyna; Maududie, Achmad; Nuritha, Ifrina
BERKALA SAINSTEK Vol 6 No 2 (2018)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v6i2.9256

Abstract

Klasifikasi konten berita politik menggunakan algoritma K-Nearest Neighbor merupakan suatu proses untuk mengklasifikasikan berita politik ke dalam tiga subkategori yang lebih spesifik yaitu pilkada, UU ORMAS dan reshuffle kabinet. Algoritma yang digunakan dalam penelitian ini adalah algoritma K-Nearest Neighbor. Algoritma K-Nearest Neighbor merupakan suatu pendekatan klasifikasi yang mencari semua data training yang paling relatif mirip atau memiliki jarak yang paling dekat dengan data testing. Algoritma ini dipilih karena K-Nearest Neighbor merupakan algoritma yang sederhana dengan mencari kategori mayoritas sebanyak nilai K yang telah ditentukan sebelumnya. nilai K yang digunakan pada penelitian ini adalah K=3, K=5, K=7 dan K=9. Mekanisme dari sistem klasifikasi konten berita ini dimulai dengan tahap preprocessing. Berita politik yang dimasukkan kedalam sistem akan melewati empat tahap preprocessing yaitu case folding, tokenizing, stopword dan stemming. Tahap selanjutnya yaitu tahap pembobotan term. Pembobotan atau term weighting merupakan proses mendapatkan nilai term yang berhasil diekstrak dari proses sebelumnya yaitu proses preprocessing. Algoritma yang digunakan untuk tahap pembobotan pada penelitian ini adalah algoritma TFIDF. Setelah didapatkan nilai dari bobot term, kemudian dicari nilai jarak antar dokumen menggunakan algoritma cosine similarity. Langkah berikutnya adalah melakukan pengurutan data dalam data training berdasarkan hasil perhitungan nilai jarak. Selanjutnya, dari hasil pengurutan tersebut diambil sejumlah K data yang memiliki nilai kedekatan. Tujuan dari penelitian ini adalah sistem mampu mengimplementasikan algoritma KNN pada dokumen yang memiliki similarity yang tinggi. Pada penelitian ini dilakukan 3 pengujian dengan tiga variasi dataset yang berbeda dengan empat nilai K. Hasil akurasi yang terbaik didapatkan ketika sistem menggunakan nilai K=9 yang menunjukkan nilai precision sebesar 100%, recall sebesar 100% dan nilai f-measure sebesar 100%. Kata Kunci: klasifikasi, algoritma K-Nearest Neighbor, TFIDF, cosine similarity, confusion matrix.
Penerapan Metode Analytical Hierarchy Process (AHP) Pada Penilaian Pegawai Teladan Muliawan, Agung; Sabilirrasyad, Iqbal; Fauziah, Difari Afreyna
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.76

Abstract

One of the factors supporting the success of a business place is productive employees who have maintained and improved qualification standards. The company's appreciation for exemplary employees can be given by giving gifts or awards. Employee performance assessment can be done to determine employees who are qualified and highly dedicated to the company. However, many companies experience difficulties in evaluating employee performance because the calculations are still manual so that they are less effective and objective, one of which is SMK Visi Global Jember. The research will apply the Analytical Hierarchy Process (AHP) method in determining the best employees at SMK Visi Global Jember so that the selection process is right on target with the needs of the criteria given. The required criteria include honesty, loyalty, commitment, discipline and cooperation which will be processed to produce the highest rank for determining recommendations for exemplary employees. The results of this study produce a Consistency Ratio (CR) value of 0.083 so that the value of giving preferences is consistent and can be used in determining exemplary employees at SMK Visi Global Jember
Handling Stunting as a Management Community Service Agung Muliawan; Difari Afreyna Fauziah; Ahmad Nurdianyah; Arini Shufia Dwi Sukmawati; Muhammad Rijalus Sholihin
RECORD: Journal of Loyality and Community Development Vol. 1 No. 1 (2024): January - April 2024
Publisher : Medikun Publisher

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Abstract

Stunting is a condition of chronic malnutrition caused by insufficient nutritional intake over a long period of time due to inadequate food supply to meet nutritional needs. Stunting is a problem that is difficult to solve if the factors causing stunting in each region cannot be controlled. Basically, the layer that interacts most intensively with patients diagnosed with stunting directly is the posyandu cadres who are the first counselors for mothers and children at the lowest level. The method used in this Collaborative Real Work Lecture (KKN) student service activity is counseling and training to improve the skills and role of the targets, namely posyandu and RDS cadres in the prevention and early detection program of stunting in children and toddlers. This activity aims to directly increase the role of posyandu cadres who are very close to the community in resolving stunting problems and indirectly to motivate the community to participate in paying attention to the growth and development of their children so that their growth and development can be optimal. It is hoped that the knowledge of Lampeji Village cadres and RDS members regarding stunting prevention will increase and the knowledge gained can be applied to the Lampeji Village community so that they can contribute to parenting and assisting children's growth and development
Detection of Diabetes in Pregnant Women Using Machine Learning as an Effort Towards Golden Indonesia 2045 Muliawan, Agung; Rohim, Muhamat Abdul; Fauziah, Difari Afreyna; Yusuf, Hamzah Fansuri
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1418

Abstract

One of the goals of the Golden Indonesia 2045 program is to utilize health technology to enhance public health, with diabetes being a prominent concern. This research aims to employ ensemble classifier optimization techniques in machine learning for the early detection of diabetes among pregnant women. The study uses physiological data, including variables such as glucose levels, number of pregnancies, skin thickness, blood pressure, insulin levels, body weight, family history, and age. By combining multiple models, ensemble classifiers can enhance prediction accuracy, stability, and overall model performance. This research utilizes an open Kaggle dataset on pregnant women to train and test machine learning models, specifically Support Vector Machine (SVM) and Deep Learning, incorporating ensemble techniques such as bagging and boosting. Experimental results indicate that the ensemble classifier approach significantly enhances diabetes classification, with SVM using bagging achieving the highest accuracy at 76.95%. These findings suggest that ensemble classifier methods could be a valuable tool for early diabetes detection, providing timely intervention and improved risk management during pregnancy, which supports the objectives of improving public health under the Golden Indonesia 2045 initiative.
IMPLEMENTATION OF MACHINE LEARNING ON EMPLOYEE ATTRITION BASED ON PERFORMANCE PARAMETERS USING PARTICLE SWARM OPTIMIZATION AND ENSEMBLE CLASSIFER METHODS Fauziah, Difari Afreyna; Muliawan, Agung; Dimyati, Muhaimin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3442

Abstract

This research aims to apply machine learning to predict the start of employee attrition by considering performance parameters and other related factors in the company environment. Employee attrition refers to employee turnover in an organization for various reasons such as resignation, moving, retirement, and so on. This research uses a dataset originating from the IBM HR Analytics Employee Attrition dataset available on Kaggle (https://www.kaggle.com/) which consists of 35 attributes. Particle Swarm Optimization (PSO) method is a dimension reduction method to improve the efficiency and performance of machine learning models by reducing unnecessary data. The machine learning approaches used in the early prediction of employee attrition in this research are Support Vector Machine, Deep Learning and Neural Network methods. This research will combine the dimensionality reduction process with machine learning to obtain employee attrition prediction results that are optimized using the Ensemble method, namely Bagging and Boosting to increase the accuracy value of the prediction results. The results of this research show that applying dimensionality reduction using the PSO method can improve the accuracy of results on the IBM HR Analytics Employee Attrition dataset. The best accuracy in attrition prediction was obtained by the Deep Learning method with an accuracy value of 86.94%, a precision value of 88.90%, and a recall value of 96.40% after combining it with PSO and optimizing with Bagging.
Optimasi Pembuatan Jadwal Perkuliahan Menggunakan Algoritma Genetika Berbasis Pendekatan Multivariat Rohim, Muhamat Abdul; Wiranto, Ferry; Fauziah, Difari Afreyna
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 6 No 1 (2025): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v6i1.160

Abstract

Penjadwalan perkuliahan merupakan salah satu aspek penting dalam manajemen akademik di perguruan tinggi. Proses penjadwalan yang dilakukan secara manual sering kali menghadapi berbagai kendala, seperti keterbatasan ruang, preferensi dosen, serta distribusi jadwal yang tidak merata. Penelitian ini bertujuan untuk mengoptimalkan proses penjadwalan perkuliahan menggunakan Algoritma Genetika (AG) agar lebih efisien dan mengurangi konflik jadwal. Data yang digunakan dalam penelitian ini meliputi 20 ruang kelas, 50 dosen, serta rata-rata 120 jadwal kuliah per semester. Implementasi sistem dilakukan menggunakan bahasa pemrograman PHP, dengan tahapan penelitian meliputi pengumpulan data, analisis kendala, perancangan algoritma, implementasi, dan evaluasi hasil. Hasil penelitian menunjukkan bahwa sistem berbasis AG mampu menghasilkan jadwal perkuliahan yang lebih merata, dengan waktu pemrosesan sekitar 1 hingga 5 menit dan tanpa adanya konflik jadwal. Dengan demikian, pendekatan ini terbukti lebih efektif dibandingkan metode manual yang sebelumnya digunakan.