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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Indonesian Journal on Software Engineering (IJSE) JURNAL MEDIA INFORMATIKA BUDIDARMA EVOLUSI : Jurnal Sains dan Manajemen Jurnal Mantik JAMI: Jurnal Ahli Muda Indonesia Jurnal Sistem Informasi Akuntansi Conten : Computer and Network Technology
Nadiyah Hidayati
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Penerapan Data Mining Untuk Memprediksi Kompetensi Siswa Menggunakan Metode Decission Tree ( Studi Kasus SMK Multicomp Depok ) Rizmayanti, Ade Irma; Hidayati, Nadiyah; Nugraha, Fitra Septia; Gata, Windu
Swabumi Vol 9, No 1 (2021): Volume 9 Nomor 1 Tahun 2021
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v9i1.8363

Abstract

Abstract- This study discusses the application of data mining to predict student competencies using the decision tree method. In this study applying data mining to predict student competency using the decision tree method. This research was conducted to predict student learning outcomes based on report card grades semester 1, semester 2 semester 3 and semester 4. Data were then managed using Rapid Miner to facilitate predicting student competencies. The study was conducted at Multicomp SMK which has 3 majors namely Hospitality Accommodation, Online Business and Marketing and Multimedia. Research using data from students in each department includes class X and class XI. The application of data mining is used to predict student competencies by using a decision tree and C 4.5 algorithm as a support as well as a comparison to determine the competency of students of Multicomp Depok Vocational School based on both methods. This method is able to measure the ability of students appropriately and be able to provide an understanding at a certain level according to the needs of Indonesian education has a pattern and learning strategy based on students' reasoning abilities. Students are expected to be able to analyze a problem well and find the right solution. students are not accompanied by an adequate education system or curriculum. Teacher competencies that are not evenly distributed in various schools and governments are felt to be very lacking in realizing reasoning based education systems.
Analisis Penerimaan dan Penggunaan Aplikasi Gojek Menggunakan Model UTAUT Nadiyah Hidayati; Yudi Ramdhani
JAMI: Jurnal Ahli Muda Indonesia Vol. 1 No. 1 (2020): Juni 2020
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v1i1.22

Abstract

Abstrak Objektif. Aplikasi Gojek merupakan aplikasi berbasis android yang menjadi pintu masuk bagi pelanggan untuk mendapatkan layanan yang disediakan PT Gojek Indonesia. Penelitian ini dilakukan untuk menganalisa faktor-faktor yang mempengaruhi penerimaan dan penggunaan aplikasi Gojek menggunakan model Unified Theory of Acceptance and Use of Technology (UTAUT) dengan 4 variabel bebas dan 1 variabel terikat yaitu ekspektasi kinerja, ekspektasi usaha, faktor sosial, kondisi-kondisi pemfasilitasi, dan niat perilaku. Penelitian ini dilakukan terhadap 100 responden pengguna aplikasi Gojek pada SMK MVP Ars Internasional. Material and Metode. Model UTAUT digunakan untuk mengetahui tingkat keberhasilan penerimaan aplikasi Gojek agar dapat diterima oleh masyarakat. Metode pengolahan data yang digunakan adalah regresi linear berganda yang menggunakan software SPSS 22. Hasil. Dari pengolahan data tersebut didapatkan hasil bahwa variabel ekspektasi kinerja, ekspektasi usaha, faktor sosial dan kondisi-kondisi pemfasilitasi memiliki nilai korelasi sebesar 0,867 terhadap niat perilaku, artinya antara variabel independen dan dependen dalam penelitian ini memiliki hubungan yang sangat kuat, nilai R Square (R2) sebesar 75,2% sedangkan sisanya dipengaruhi variabel lain. Kesimpulan. Dengan demikian dapat disimpulkan bahwa secara simultan, variabel ekspektasi kinerja, ekspektasi usaha, faktor sosial, dan kondisi-kondisi pemfasilitasi berpengaruh secara positif dan signifikan terhadap niat perilaku aplikasi Gojek. Sedangkan secara parsial, hanya variabel ekspektasi kinerja dan faktor sosial yang memiliki pengaruh positif dan signifikan terhadap niat perilaku aplikasi Gojek. Sedangkan variabel ekspektasi usaha dan kondisi-kondisi pemfasilitasi tidak memiliki pengaruh positif dan signifikan terhadap niat perilaku dalam menggunakan aplikasi Gojek. Abstrack Objective. Gojek application is an android-based application that is the entrance for customers to get the services provided PT Gojek Indonesia. This study was conducted to analyze the factors that influence the acceptance of Gojek applications using the UTAU) method with 4 independent variables and 1 dependent variable namely Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Behavioral Intention. This research was conducted on 100 respondents of the Gojek application SMK MVP Ars International. Materials and Methods. The UTAUT model is used to determine the level of success in accepting Gojek applications to be accepted by the community. The data processing method used is multiple linear regression using SPSS 22 software. Results From the data processing, the results show that the variables of performance expectancy, effort expectancy, social influence, and facilitating conditions have a correlation value of 0,867 to behavioral intention, meaning between independent and dependent variables in this study has a strong relationship, the value of R Square (R2) of 75,2% while the rest is influenced by other variables. Conclusion. Thus it can be concluded that simultaneously, the variable performance expectancy, effort expectancy, social influence, and facilitating conditions positively and significantly affect the behavioral intention of Gojek applications. While partially, only the performance expectancy and social influence variables that have a positive and significant influence on the behavioral intention of Gojek application. While the effort expectancy and facilitating conditions variable does not have a positive and significant influence on Behavioral Intentionin using the Gojek application.
Penerapan Algoritma Klasterisasi dan Klasifikasi pada Tingkat Kepentingan Sistem Pembelajaran di Universitas Terbuka Nadiyah Hidayati; Ade Irma Rizmayanti; Chintamia Bunga Sari Dewi; Rhini Fatmasari; Windu Gata
Swabumi Vol 8, No 2 (2020): Volume 8 Nomor 2 Tahun 2020
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v8i2.8385

Abstract

Universitas Terbuka merupakan Perguruan Tinggi Negeri (PTN) ke-45 di Indonesia yang menerapkan sistem belajar terbuka dan jarak jauh, keberhasilan pembelajaran lebih ditentukan oleh adanya jiwa kemandirian dan motivasi tinggi dari mahasiswa. Untuk mengetahui keberhasilan sistem pembelajaran yang diberikan, dilakukan survei menggunakan kuesioner yang dibagikan kepada mahasiswa untuk mengetahui penilaian dari masing-masing mahasiswa. Tujuan dari penelitian ini adalah untuk mengklaster dan mengklasifikasi data hasil kuesioner tingkat kepentingan sistem pembelajaran Universitas Terbuka dengan menggunakan software RapidMiner 9.0.0.3. Metode klasterisasi yang digunakan adalah algoritma k-medoids, sedangkan metode yang digunakan untuk klasifikasi adalah algoritma Naïve Bayes, k-NN, dan C4.5. Dari pengolahan data tersebut didapatkan hasil 2 klaster dengan pembagian data sebanyak 273 pada klaster 0 dan klaster 1 sebanyak 97 data. Pada proses klasifikasi, algoritma Naïve Bayes memperoleh nilai akurasi paling tinggi dibandingkan dengan algoritma yang lain dengan nilai akurasi sebesar 72,70% dengan nilai AUC sebesar 0,499. Sedangkan algoritma k-NN memperoleh nilai akurasi sebesar 71,62% dengan nilai AUC sebesar 0,438 dan algoritma C4.5 memperoleh nilai akurasi sebesar 68,92% dengan nilai AUC sebesar 0,450.
Ear Identification Using Convolution Neural Network Nadiyah Hidayati; Mawadatul Maulidah; Elin Panca Saputra
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i1.2263

Abstract

Today's identification system has become a necessity for system security. One method of identification system that has a high level of security and accuracy is biometrics. Biometrics uses parts of the human body that are considered unique and can differentiate between one individual and another. One of the new biometrics that has become a concern in the world of research on biometrics is the ear. Ears have several advantages that other biometrics do not have, one of which is that they are not affected by changes in age. The purpose of this study was to determine the accuracy of the Convolutional Neural Network (CNN) algorithm in identifying ear images. CNN is currently one of the most superior algorithms in the field of object classification and identification. In this study, the ears that will be identified are images taken from the Kaggle dataset of 780 ears from 13 individuals with 60 images for each individual. This study resulted in a training accuracy of 96,3% and a testing accuracy of 79,7%.
Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning Elin Panca Saputra; Mawadatul Maulidah; Nadiyah Hidayati; Andi Saryoko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4786

Abstract

In our current study, we are doing a comparison of several algorithms that we have tested, namely in searching for the accuracy level of learning performance in students, the problem of this research is how to get the results of excellent generalization abilities so that a higher accuracy value is obtained. Our goal is to get the best-performing accuracy level results and then to identify features that can affect student learning performance. From the results of the algorithm that we have tested, four of them are Naïve Bayes, Support Vectore Machine, Neural Network and KNN contained in machine learning. The results of the four algorithms for the Naïve Bayes algorithm have an accuracy value of 96.30%, the Support Vectore Machine algorithm has an accuracy of 98.70%, and the Naural Network algorithm has an accuracy of 99.50% and the last one with the KNN algorithm produces an accuracy of 94.80%. it can be concluded that using the Neural Network algorithm is an algorithm with the best performance than using other algorithms in evaluating student learning performance, besides that the Neural Network can be used as an excellent alternative to be used as predictions, especially in the field of education.
Analisis Quality of Service (QoS) Performa Jaringan Internet Wireless LAN PT. Bhineka Swadaya Pertama Elin Panca Saputra; Andi Saryoko; Mawadatul Maulidah; Nadiyah Hidayati; Sopiyan Dalis
Evolusi : Jurnal Sains dan Manajemen Vol 11, No 1 (2023): Jurnal Evolusi 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v11i1.14955

Abstract

In the current era of digitalization, the use of ISPs or Internet Service Providers is very commonly used in the business world because they have effective and flexible services because they can be used wirelessly or without cables to meet their needs. Currently, there are many connection service providers in Indonesia, First Media is one of several existing ISPs. First Media offers a stable and fast internet connection, unlimited internet without quota and unlimited download speed of up to 10 Mbps and 768 Kbps upload speed, and has several other features that can be used wirelessly or without cable as needed. The use of wireless-based networks in general must have a service standard known as Quality Of Service (QOS). QoS is used in measuring the performance of an internet network in order to provide even better service. In conducting internet network analysis, Quality Of Service (QOS) parameters are needed which include packet loss, delay/latency, Throughput, Jitter, it will produce information in the form of network analysis results and the results of this analysis can be used as recommendations so that in the future it can develop internet networks to be more good again and can support the addition of other services. The purpose of this research is to analyze the Quality Of Service (QOS) of the internet network at PT. Bhineka Swadaya Pertama and knowing the quality of the Wireless Lan internet network at "PT. The First Unity of Self-Help”. From the results of this study it is hoped that the quality level of the LAN network at PT. Bhineka Swadaya Pertama and can check the Quality of Service of the LAN network at PT. Bhinneka Swadaya First. Keywords: Network Performance, Quality of Service (QOS), Wireless Lan
EKSTRAKSI FITUR DENGAN COLOR HISTOGRAM DAN CLASSIFIER RANDOM FOREST PADA CITRA KUPU-KUPU Nadiyah Hidayati; Maulidah, Mawadatul
JAMI: Jurnal Ahli Muda Indonesia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v4i2.172

Abstract

Objektif. Penelitian dalam pengolahan citra banyak dikembangkan dalam berbagai bidang, misalnya kesehatan, pertanian, kesenian, aneka ragam hayati dll. Salah satu penelitian yang berkembang adalah pengklasifikasian jenis serangga yaitu kupu-kupu. Kupu-kupu merupakan salah satu serangga yang menguntungkan bagi manusia, namun populasi spesies kupu-kupu di Indonesia banyak yang menurun atau terancam punah. Dengan banyaknya jenis kupu-kupu dalam berbagai bentuk, corak yang berbeda, dan keunikan diperlukan suatu teknik yang memfasilitasi pembelajaran dengan lebih efisien. Kupu-kupu dijadikan dataset karena mempunyai pola tekstur yang unik dan warna serta bentuk yang beragam. Tujuan dari penelitian ini adalah mengklasifikasikan jenis kupu-kupu dengan menggabungkan hasil ekstraksi fitur dan metode classifier. Material and Metode. Pada penelitian ini diusulkan sebuah penggabungan tiga hasil ekstraksi fitur diantaranya color histogram, haralick, dan hu-moments. Ekstraksi dilakukan terhadap 2400 citra kupu-kupu yang dibagi menjadi 2 kelas. Penggabungan hasil ketiga ekstraksi fitur tersebut selanjutnya dilakukan proses klasifikasi menggunakan metode Random Forest (RF). Hasil. Pengujian yang telah dilakukan menunjukkan nilai akurasi sebesar 75% sedangkan nilai precision sebesar 78% dan recall sebesar 69%. Kesimpulan. Algoritma classification RF (Random Forest) mempunyai nilai akurasi tertinggi dibandingkan dengan algoritma classification yang lainnya. Sedangkan hasil ekstraksi fitur terbaik pada eksperimen ekstraksi fitur color histogram.
Sistem Informasi Evaluasi Produktivitas Karyawan Pada PT Cosmoprof Indikarya Pemalang Fatma Wati, Fanny; Hidayati , Nadiyah
JAMI: Jurnal Ahli Muda Indonesia Vol. 5 No. 1 (2024): Juni 2024
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v5i1.284

Abstract

Kemajuan di bidang teknologi informasi belakangan ini berkembang sangat pesat apalagi di iringi dengan makin maraknya internet di kalangan masyarakat. PT. Cosmoprof Indokarya merupakan sebuah perusahaan yang memproduksi dan mengekspor bulu mata palsu yang sudah berdiri selama 10 tahun. Sistem administrasi perusahaan PT. Cosmoprof Indokarya telah terkonsep sedemikianrupa dengan baik. Sistem administrasi perusahaan PT. Cosmoprof Indokarya telah terkonsep sedemikianrupa dengan baik. Dari sistem yang telah terkomputerisasi maupun sistem yang masih dengan konsep manual, semua berjalan dengan semestinya. Namun, dalam pelaksanaan evaluasi produktivitas karyawan, PT.Cosmoprof Indokarya Permalang, masih menggunakan konsep sistem manual yang kurang efektif dalam pelaksanaannya dikarenakan laporan masih menggunakan excel dan dikirim via email yang menghambat kecepatan laporan. Maka dari itu kami menciptakan sebuah sistem informasi yang bernama “SIEPOKAR (Sistem Informasi Evaluasi Produktivitas Karyawan)”. Sistem Informasi Evaluasi Produktivitas Karyawan merupakan merupakan sebuah sistem yang berfungsi untuk mengumpulkan data performa karyawan bagian produksi guna memudahkan tim HR dalam memantau dan mengevaluasi karyawan bagian produksi berdasarkan target yang diberikan perusahaan.
Implementasi Algoritma SVM dan Naive Bayes untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi OneDrive Widodo, Andrian Eko; Wati, Fanny Fatma; Hidayati, Nadiyah
Indonesian Journal on Software Engineering Vol 10, No 2 (2024): IJSE 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v10i2.23523

Abstract

Abstrak Teknologi saat ini menawarkan solusi penyimpanan data secara online yang dikenal sebagai cloud storage. Contohnya adalah OneDrive, yang memungkinkan pengguna untuk menyimpan, berbagi, dan mengakses berkas secara fleksibel dari berbagai platform. Dengan populernya OneDrive, peningkatan penggunaannya membawa implikasi besar terhadap pengalaman pengguna dan keberhasilan bisnis Microsoft. Persepsi dan pengalaman pengguna sangat penting dalam menilai kualitas aplikasi, karena ulasan di platform seperti Google Play Store memberikan gambaran nyata tentang kepuasan dan kebutuhan pengguna. Agar layanan suatu aplikasi dapat digunakan dan diterima oleh pengguna, maka harus memiliki pengalaman pengguna yang baik. Seiring meningkatnya jumlah pengguna di Google Play Store, ulasan yang diberikan juga terus bertambah. Ulasan ini sering menjadi sumber informasi penting tentang produk atau aplikasi perangkat lunak tertentu. Namun, jumlah ulasan yang banyak dan beragam dapat memberikan dampak positif maupun negatif bagi pengembang. Oleh karena itu, diperlukan sistem otomatis untuk mengolah data ulasan tersebut melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi OneDrive di Google Play Store, untuk melihat kecenderungan opini pengguna, apakah bersifat negatif atau positif. Penelitian ini menggunakan metode Naive Bayes dan Support Vector Machine (SVM) untuk mengklasifikasikan ulasan pengguna menjadi sentimen positif atau negatif. Proses penelitian meliputi perumusan masalah, perancangan penelitian, pengumpulan data ulasan, pengolahan data, penyajian data dalam bentuk tabel, dan analisis data. Peneliti berharap hasil penelitian ini dapat membantu pengembang aplikasi dalam menilai kekurangan dan kelebihan aplikasi mereka serta menjadi bahan evaluasi di masa mendatang.               Kata kunci: Algoritma SVM, Naïve Bayes, OneDrive Abstract Current technology offers online data storage solutions known as cloud storage. An example is OneDrive, which allows users to store, share, and access files flexibly from various platforms. With the popularity of OneDrive, its increasing use has major implications for the user experience and the success of Microsoft's business. User perception and experience are very important in assessing the quality of an application, because reviews on platforms such as the Google Play Store provide a real picture of user satisfaction and needs. In order for an application service to be used and accepted by users, it must have a good user experience. As the number of users on the Google Play Store increases, the reviews given also continue to increase. These reviews are often an important source of information about a particular software product or application. However, the large number and variety of reviews can have a positive or negative impact on developers. Therefore, an automated system is needed to process the review data through sentiment analysis. This study aims to analyze user sentiment towards the OneDrive application on the Google Play Store, to see the tendency of user opinion, whether it is negative or positive. This study uses the Naive Bayes method and Support Vector Machine (SVM) to classify user reviews into positive or negative sentiment. The research process includes problem formulation, research design, review data collection, data processing, data presentation in tabular form, and data analysis. The researcher hopes that the results of this study can help application developers in assessing the shortcomings and advantages of their applications and become evaluation material in the future.Keywords: SVM algorithm, Naïve Bayes, OneDrive
Sistem Informasi Penjualan Sparepart Motor pada Toko Ketapang Motor Margasari Wati, Fanny Fatma; Anggraini, Recha Abriana; Hidayati, Nadiyah; Maulidah, Mawadatul
Jurnal Sistem Informasi Akuntansi (JASIKA) Vol. 5 No. 1 (2025): Mei 2025
Publisher : LPPM UBSI Kampus Kota Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jasika.v5i1.9094

Abstract

Developments in the increasingly rapid technological era have resulted in business people encouraging their companies to run more effectively along with the times. This is done in order to maximize profits. One technology that is often looked at is information system technology in the form of websites. The Ketapang Motor Shop, which is a motorbike spare parts and accessories trading business, has several problems including the sales report section, stock reports that are not recorded and not filed every month so that existing data is often lost. In sales transactions, payments are still made using notes so that the calculation and inventory of goods is not yet effective. Therefore, a website-based sales information system was created using the waterfall system development method to overcome this problem. The website was created using several programming languages, namely HTML, PHP, CSS, Bootstrap, and Javascript with MySQL as the data storage database. The existence of this website will help companies, especially in the recording process at the cashier so that the quality of the information presented is more accurate.
Co-Authors Ade Irma Rizmayanti Andi Saryoko Andrian Eko Widodo Anggraini, Recha Abriana Chintamia Bunga Sari Dewi Fatma Wati, Fanny Gata, Windu Mawadatul Maulidah Nugraha, Fitra Septia Panca Saputra, Elin Rhini Fatmasari Rizmayanti, Ade Irma Siti Nurajizah, Siti Sopiyan Dalis Taufik Rahman Wati, Fanny Fatma Windu Gata Yudi Ramdhani