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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa ILKOM Jurnal Ilmiah Jurnal Teknologi Sistem Informasi dan Aplikasi
Silfianti, Widya
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Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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PENGUKURAN MUTU LAYANAN WEBSITE PENDAFTARAN PENERIMAAN MAHASISWA BARU PADA UNIVERSITAS GUNADARMA MENGGUNAKAN METODE WEBQUAL Nugraha, Adam Huda; Silfianti, Widya
Jurnal Ilmiah Informatika Komputer Vol 21, No 2 (2016)
Publisher : Universitas Gunadarma

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

Abstract

Perkembangan website yang demikian pesat memberikan dampak pada perubahan layanan organisasi ataupun perusahaan. Website suatu perusahaan harus mempresentasikan kehadiran perusahaan tersebut di mata pelanggan secara virtual, sehingga konsumen menjadi percaya dan melakukan transaksi secara online melalui website perusahaan. Oleh karena itu, perlu dilakukan analisis kualitas website untuk mengukur keberhasilan suatu website. Kualitas suatu website bisa dianalisis dengan menggunakan metode Webqual. Saat ini, penggunaan website tidak hanya sebagai media promosi atau iklan tapi telah berkembang menjadi media penyedia informasi di bidang pendidikan. Misalnya, Universitas Gunadarma yang membuka Pendaftaran Penerimaan Mahasiswa Baru (PPMB) melalui jalur website PPMB dengan mengisi formulir pendaftaran secara online. Akan tetapi, pada website PPMB online Universitas Gunadarma masih terdapat masalah atau kendala dari sisi user yang berpengaruh terhadap admin. Oleh karena itu, pada penelitian ini dilakukan pengukuran mutu layanan website pendaftaran penerimaan mahasiswa baru Universitas Gunadarma yang bertujuan untuk mengukur mutu layanan website tersebut. Metode yang digunakan dalam penelitian ini adalah Webqual 4.0 dengan sampel sebanyak 97 responden. Hasil penelitian menunjukkan kualitas pelayanan ditinjau dari tangibles sebesar 53,6% responden menyatakan pelayanan sudah baik, ditinjau dari reliability sebesar 56,7% responden menyatakan pelayanan sudah baik, ditinjau dari responsiveenes sebesar 60,8% responden menyatakan pelayanan baik, ditinjau dari assurance sebesar 54,6% responden menyatakan pelayanan baik, dan ditinjau dari empaty sebesar 72,2% responden menyatakan pelayanan sudah baik. Kata kunci: Kualitas Website, Pengukuran Mutu, Website PPMB, Webqual 4.0
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN FRAMEWORK TENSORFLOW DENGAN METODE FASTER REGIONAL CONVOLUTIONAL NEURAL NETWORK UNTUK PENDETEKSIAN JERAWAT Hasma, Yunita Aulia; Silfianti, Widya
Jurnal Ilmiah Teknologi dan Rekayasa Vol 23, No 2 (2018)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2018.v23i2.2459

Abstract

Jerawat sering dialami oleh kaum wanita maupun pria dari usia remaja hingga dewasa. Banyak rumah sakit dan klinik kecantikan yang dapat di datangi oleh para penderita untuk memeriksakan jerawat tersebut. Penelitian ini merupakan implementasi dari pendeteksian jerawat menggunakan image processing dan secara realtime, lalu sistem akan mengklasifikasikan jerawat yang ada pada wajah. Jerawat yang dapat dikenali oleh sistem ini yaitu jerawat, bekas, dan pus. Sistem deteksi dan klasifikasi ini dibuat dengan metode deep learning dengan menggunakan bahasa pemrograman Python, yang dibantu dengan menggunakan framework TensorFlow dengan model Faster R-CNN. Sistem ini hanya dapat berjalan di laptop dengan memiliki Python versi 3.6 di dalamnya dan telah memliki library Numpy, TkInter, Matplotlib, dan OpenCV dan juga memiliki kamera pada laptop yang digunakan agar dapat menjalankan sistem secara realtime yang didukung dengan GPU yang memadai. Perancangan alur aplikasi menggunakan flowchart diagram. Hasil uji terhadap sistem menggunakan perbandingan objek yang terdeteksi dengan yang seharusnya lalu dibagi dan dikalikan dengan seratus persen. Hasil yang didapat dari pengujian cukup baik menggunakan metode deep learning.
Evaluation of Machine Learning Models for Predicting Cardiovascular Disease Based on Framingham Heart Study Data Suhatril, Ruddy J; Syah, Rama Dian; Hermita, Matrissya; Gunawan, Bhakti; Silfianti, Widya
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1952.68-75

Abstract

The Framingham Heart Study Community is a research community that produces data related to Cardiovascular Disease (CVD). This research applies technology to predict CVD using machine learning based on data from the Framingham Heart Study. The eight machine learning algorithms are deployed in this study, they are decision tree, naïve bayes, k-nearest neighbors, support vector machine, random forest, logistic regression, neural network, and gradient boosting.This research uses several stages of research such as load dataset, preprocessing data, data modeling, evaluation of various data modelling, and input new data.  The best performance was produced by the random forest model with an accuracy value of 0.84, a precision value of 0.84, a recall value of 0.85, an f1-score value of 0.79 and an AUC value of 0.72. The prediction generated by the proposed machine learning model is high risk or low risk of CVD.
A Deep Learning Approach for Tourism Destination Recommendation Using IndoBERT and TF-IDF Silfianti, Widya; Syah, Rama Dian; Suhendra, Adang; Isra, Ali; Darmayantie, Astie; Ohorella, Noviawan Rasyid
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3069.241-251

Abstract

The rapid development of information technology has transformed various sectors, including tourism, where recommendation systems play a vital role in providing personalized services. Tourists are often faced with a wide range of destination choices, making decision-making increasingly complex. To address this, Artificial Intelligence (AI) and Natural Language Processing (NLP) can be leveraged to enhance recommendation accuracy through deeper analysis of destination descriptions. This study proposes a tourism destination recommendation system combining IndoBERT, SimCSE, and TF-IDF methods. IndoBERT was applied to capture semantic and contextual meaning in the Indonesian language, SimCSE improved sentence-level embeddings, and TF-IDF extracted essential keywords from descriptions. The system was implemented on a website to generate personalized recommendations based on user input. Evaluation results demonstrated that the composition of IndoBERT and TF-IDF achieved strong performance, with precision, recall, and F1-score values of 1.0 at a similarity threshold of 0.20. However, higher thresholds reduced recall and F1-score, indicating that a lower threshold provided a better balance between accuracy and coverage. The recommendation outputs matched user preferences, and functional testing showed that all website features performed successfully. These findings highlight the effectiveness of combining semantic and keyword-based methods for tourism recommendation. Future work could expand the dataset, integrate user feedback, and benchmark against other state-of-the-art models to further enhance system performance.
Rancang Bangun Aplikasi Absensi Pegawai dengan Face Recognition Berbasis Android di PT. Nutech Integrasi Prakoso, Galih; Silfianti, Widya
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i2.38812

Abstract

The adaptation of new habits after the COVID-19 pandemic has created a new habit, namely work activities that can be done from home. PT Nutech Intgerasi has new rules as a form of adaptation to new habits, called Flexible Working Arrangements, which is work from home, work from office and onsite work systems. The current attendance system cannot cover these three work systems. Therefore, a new system is needed so that the head unit and the Human Capital & Organization team can continue to record and monitor each employee. The system is built using System development Life Cycle method and face recognition technology, then added Geolocation to record the location, the system was built with the JavaScript programming language. In the initial stage, the application has been implemented in the Business and Product Development department. The application has successfully recorded employee attendance and recorded the location when employees make attendance. For the face recognition feature of the five test data taken, four of them show data accuracy below the euclidean distance threshold value, where the euclidean distance threshold value is 0.40, even one of the test data managed to have an euclidean distance of 0.18 which means that the level of similarity is very high because the smaller the euclidean distance, the higher the level of similarity. In addition, by giving a lower limit value at the face detection stage of 0.90, only faces that are clearly visible and with good lighting are captured.
Co-Authors Adang Suhendra Bhakti Gunawan Darmayantie, Astie Galih Prakoso, Galih Hasma, Yunita Aulia Isra, Ali Matrissya Hermita Nugraha, Adam Huda Ohorella, Noviawan Rasyid Rama Dian Syah Suhatril, Ruddy J