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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi CogITo Smart Journal InComTech: Jurnal Telekomunikasi dan Komputer Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Manajemen Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotek : Jurnal Informatika dan Teknologi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT
Ainul Yaqin
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Sistem Informasi Monitoring dan Evaluasi Bidikmisi Ainul Yaqin
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6, No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2797.254 KB) | DOI: 10.25126/jtiik.2019641108

Abstract

Beasiswa merupakan bantuan biaya untuk menunjang proses pendidikan, bidikmisi merupakan salah satu program beasiswa yang diberikan pemerintah melalui Direktorat Jendral Pendidikan Tinggi (DITJEN DIKTI) Kementrian Riset Teknologi dan Pendidikan Tinggi (Ristekdikti) mulai tahun 2010 kepada mahasiswa yang memiliki potensi akademik memadai dan kurang mampu secara ekonomi. Pengelolaan data bidikmisi diserahkan kepada perguruan tinggi masing-masing dengan bantuan pendanaan bersama antara perguruan tinggi dan Ristekdikti. Proses pendafartaran seleksi mahasiswa bidikmisi telah diimplementasikan menggunakan sistem informasi manajemen pada https://bidikmisi.belmawa.ristekdikti.go.id. tetapi sim tersebut masih berfokus pada proses pendaftaran saja. Permasalahan muncul dari sisi pengelola di perguruan tinggi dalam melakukan monitoring dan evaluasi perkembangan studi mahasiswa bidikmisi. Maka diharapkan adanya sistem informasi yang dapat memonitoring data mahasiswa bidikmisi untuk pengelolaan data bidimisi. Sistem informasi monitoring dan evaluasi bidikmisi yang dirancang oleh peneliti diharapkan menjadi inovasi baru dalam pengelolaan data mahasiswa bidikmisi. Penerapan sistem informasi monitoring dan evaluasi bidikmisi mampu melakukan pelaporan perkembangan IPK setiap mahasiswa penerima bidikmisi serta menunjukkan hasil rata-rata loadtime 4.13s dan rata-rata pagesize 555KB pada kecepatan internet 0.31Mbps serta persentase session, pages dan hits dengan nilai 100%. AbstractScholarship is financial assistance to support the education process, bidikmisi is one of the scholarship programs provided by the government through the Directorate General of Higher Education (DITJEN DIKTI) Ministry of Research Technology and Higher Education (Ristekdikti) starting in 2010 to students who have adequate academic potential and are less able economy. Bidikmisi data management is handed over to their respective universities with the help of joint funding between universities and RISTEKDIKTI. The registration process for Bidikmisi student selection has been implemented using a management information system at https://bidikmisi.belmawa.ristekdikti.go.id. but the MIS is still focused on the registration process only. Problems arise from the side of managers in universities in monitoring and evaluating the development of the study of Bidikmisi students. Then it is expected that there is an information system that can monitor Bidikmisi student data for bidimisi data management. The monitoring and evaluation information system of Bidikmisi designed by researchers is expected to be a new innovation in bidikmisi student data management. The application of Bidikmisi monitoring and evaluation information system is able to report the development of GPA for each student receiving bidikmisi and shows the results of average load time 4.13s and pagesize average 555KB at 0.31Mbps internet speed and percentage sessions, pages and hits with a value of 100%. 
Sistem Informasi Rekam Medis Berbasis Web Pada Klinik Risa Rafana Menggunakan Metodologi Extreme Programming Jihadul Akbar; Ainul Yaqin
Infotek: Jurnal Informatika dan Teknologi Vol 4, No 2 (2021): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (763.776 KB) | DOI: 10.29408/jit.v4i2.3680

Abstract

Risa Rafana is a clinic that located at Wage Batujai West Praya, Central Lombok, West Nusa Tenggara Province. Risa Rafana clinic has problems in medical records processing because the existing system could not systematically assess patient, so the clinicians had trouble for finding the data of patients when had been in manual system. The is development method that was used  is Extreme Programming methodology. The method  prioritizing  of clients as a resource person who understand the system that will be created, from the client itself  acquired user story. The Stages that will be created such as planning, design, coding and testing. The information system is designed by concept of UML (Unified Modeling Language) and developed using Framework PHP (Hypertext Preprocessor), which is a web-based CodeIgniter. The  processing of database by MySQL. The information system could process data of patients, doctors, specialists, labs, rontgen, medications, so it could make medical records information and made report of patient’s surgery, reporting of laboratory check, reporting of medicine prescription, reporting of drug selling that has been given to patients, so it made easier to diagnose the disease
SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN PEMBERIAN BIDIKMISI DENGAN FUZZY LOGIC (STUDI KASUS STMIK AMIKOM YOGYAKARTA) Ainul Yaqin
CogITo Smart Journal Vol 2, No 1 (2016): CogITo Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (634.211 KB) | DOI: 10.31154/cogito.v2i1.13.42-53

Abstract

Sebagaimana isi Undang-undang Pasal 76 No. 12 Tahun 2012 tentang Pendidikan Tinggi, Amikom merupakan salah satu perguruan tinggi yang diberikan amanah oleh pemerintah melalui direktorat jenderal pendidikan tinggi kementrian pendidikan nasional RI, untuk mengelola program bidikmisi. Bidikmisi merupakan bantuan biaya pendidikan untuk calon mahasiswa baru tidak mampu secara ekonomi namun yang berpotensi akademik baik.Berdasarkan data yang ada di STMIK Amikom, ada banyak calon mahasiswa penerima bidikmisi dengan berbagai macam latar belakang. Dalam implementasinya di amikom, pemberian bidikmisi masih menggunakan sistem manual dengan mencocokan profil penerima berdasarkan persayaratan bidikmisi, namun belum menggunakan sistem peringkat kelayakan penerima bidikmisi. Dalam proses pemilihan calon mahasiswa penerima bidikmisi di amikom masih memungkinkan pemilihan atau penilaian bersifat subyektif.Penerapan sistem pendukung keputusan berbasis logika fuzzy ke dalam sistem penentuan kelayakan penerima bidikmisi mampu memberikan solusi dan inovasi baru di bagian kemahasiswaan STMIK Amikom Yogyakarta. Target luaran dari dari penelitian ini adalah meningkatkan tingkat akurasi penentuan kelayakan bidikimisi.
Perbandingan Algoritma Naïve Bayes, K-Nearest Neighbors dan Random Forest untuk Klasifikasi Sentimen Terhadap BPJS Kesehatan pada Media Twitter Tamrizal A.M; Ainul Yaqin
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 1 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i1.13642

Abstract

Dari sejak didirikan, BPJS terus berusaha meningkatkan kualitas pelayanan termasuk menyediakan berbagai layanan pengaduan. Selain fasilitas pengaduan yang telah disediakan oleh BPJS, media sosial seperti twitter sebenarnya dapat dijadikan sebagai tempat untuk mengumpulkan informasi yang berkaitan dengan BPJS. Berbagai keluhan maupun apresiasi terhadap pelayanan BPJS sering disuarakan melalui media twitter. Pada penelitian ini, dilakukan pengujian tiga algoritma machine learning yaitu Naïve Bayes, K-Nearest Neighbors dan Random Forest, untuk mengetahui dan membandingkan tingkat akurasi dari masing-masing algoritma tersebut dalam melakukan klasifikasi terhadap sentimen masyarakat terhadap BPJS Kesehatan melalui media twitter. Pada penelitian ini dataset diperoleh dengan melakukan scrapping menggunakan twitter API. Data yang diperoleh kemudian diseleksi dan dilakukan labeling. Dari hasil seleksi dan labeling didapatkan dataset sebanyak 150 tweet yang terdiri atas 50 tweet positif, 50 tweet negative dan 50 tweet netral yang akan digunakan dalam percobaan. Pada percobaan dengan menggunakan 90% data untuk training dan 10% data untuk testing, didapatkan tingkat akurasi sebesar 80% Naive Bayes, 67% K-Nearest Neighbors dan 87% Random Forest.
Analisis Sentimen Ketidakstabilan Harga Gabah Berbasis Data Twitter Dedy Sugiarto; Reyhan Dwi Putra; Wahyu Hidayat; Ema Utami; Ainul Yaqin
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 13, No 1 (2022): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v13i1.2197

Abstract

Pendapat masyarakat baik bersifat positif, negatif maupun netral terkait sebuah kebijakan tertentu atau fenomena di masyarakat ini menjadi hal yang berharga untuk dianalisis melalui sebuah metode yang disebut sebagai analisis sentimen. Kasus dalam penelitian ini adalah  turunnya harga gabah pada awal sampai dengan pertengahan tahun 2021. Penelitian ini bertujuan untuk mengetahui persentase polaritas sentiment yang muncul bila dikaitkan dengan kata kunci harga gabah serta menentukan tingkat akurasi prediksi kelas sentiment menggunakan metode Naïve Bayes.  Hasil penelitian menunjukkan persentase sentiment terbesar adalah bersifat negative sebanyak 46.30 persen,  netral 32,70 persen dan positif sebanyak 20,99 persen. Hasil wordcloud juga menunjukkan pengguna twitter mengkaitkan persoalan harga gabah ini dikaitkan dengan impor beras, peran pemerintah serta pupuk. Hasil klasifikasi menunjukkan nilai akurasi yang cukup baik yaitu sebesar 67,32 persen.
Pemanfaatan Office 365 Dan Teknologi Cloud Bagi Guru TK ABA Kadipolo di Masa Pandemi Covid-19 Ainul Yaqin; Alfriadi Dwi Atmoko
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 3 (2022): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v6i3.5394

Abstract

This community service activity was carried out to provide assistance to TK ABA Kadipolo teachers, this is regarding changes in academic activity models due to conditions were caused by the Covid-19 pandemic. The implementation of community service activities is carried out in 3 stages, starting from pre-activities, then continued with activities carried out in the form of online and offline training related to the use of office 365 and Cloud Computing Technology. As well as the evaluation stage which is carried out by obtaining feedback testimonials from participants. The material in training activities using office 365 online by accessing the office.com page and cloud computing technology as a storage used in training is One Drive. In general, community service activity funded by LPM Universitas AMIKOM Yogyakarta in the 2020 on TDPT program was carried out well and provided new insights for participants about the application of cloud computing technology.
KLASIFIKASI DETEKSI PENGGUNAAN MASKER MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Nalda Kresimo Negoro; Ema Utami; Ainul Yaqin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 2 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i2.3748

Abstract

Berhubungan dengan era revolusi industri 5.0 kita perlu bersyukur semua pekerjaan menjadi dimudahkan dengan terdigitalisasi. Berbagai pekerjaan dapat diselesaikan jauh lebih mudah, cepat dan secara otomatis. Konsep era industri 5.0 memiliki fokus pendayagunaan aspek dari manusia, data dan teknologi berbasis modern. Manusia dan sistem saling terhubung dan mendapatkan hasil maksimal dengan bantuan AI. Konsep ini memberikan dampak positif untuk menghadapi perubahan besar pada transformasi digital. Perkembangan pesat dari transformasi digital saat ini ada pada pendeteksian objek menggunakan machine learning. Deteksi objek adalah teknik dari computer vision dalam pembacaan pengenalan objek pada gambar ataupun video. Pada penelitian ini diterapkan klasifikasi deteksi objek dengan algoritma Convolutional Neural Network (CNN) menggunakan arsitektur VGG16Net dengan mengklasifikasikan wajah bermasker dan tidak bermasker. Dataset yang digunakan untuk proses training diperoleh dari kaggle berjumlah 3.725 menggunakan masker, 3.828 tidak menggunakan masker dan dataset untuk proses testing menggunakan dataset personal berjumlah 16 dataset. Evaluasi jaringan pelatihan model menggunakan confusion matrix sedangkan tahap pengujian menggunakan SSD ResNet10. Hasil evaluasi dari rancangan implementasi pelatihan model didapatkan nilai akurasi 0,992%, presisi 1.000, dan recall 0,984. Kemudian hasil pengujian testing mendapatkan nilai tertinggi dengan akurasi 100%.
Comparison of Modified Nazief&Adriani and Modified Enhanced Confix Stripping algorithms for Madurese Language Stemming Enni Lindrawati; Ema Utami; Ainul Yaqin
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.20103

Abstract

The Madurese language has a unique morphology. The morphological uniqueness can be used to find basic words. The basic word process is called stemming. Stemming can be developed into an application for translating Madurese into Indonesian and even other languages. It can support the development of a Madurese language text plagiarism system. Stemming research on the Madurese language is still rare. Therefore, this study aims to find the basic words of the Madurese language using modifications to the Nazief & Adriani algorithm and Enhanced Confix Stripping (ECS) modifications. The study used 1000 Madurese words, consisting of 630 prefix words, 74 ending words, and 296 confix words. The results showed that the modification of the Nazief & Adriani algorithm was better, shown by the accuracy obtained of 88.8% with overstemming of 0.7% and understemming of 10.5%. As for ECS, an accuracy of 74.0% was obtained, 0.4% overstemming, and 25.6% understemming. In the same process, Nazief&Adriani's modification is faster than the ECS modification. For the Nazief&Adriani modification, it takes 13.31 seconds while for the ECS modification, it takes 210.88.
Prediksi Harga pada Trading Forex Pair USDCHF Menggunakan Regresi Linear Mohammad Edi; Ema Utami; Ainul Yaqin
Jurnal Manajemen Informatika JAMIKA Vol 13 No 2 (2023): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v13i2.9826

Abstract

In the era of globalization, free trade grows rapidly and technology develops, affecting economic competition. Forex, foreign exchange trading, is one of the investments used to face this challenge. Technical and fundamental analysis is used to predict price movements in forex trading. Previous studies have used linear regression algorithms and other techniques for price prediction in forex. In this study, the linear regression algorithm is used to predict closing prices in forex trading because the linear regression algorithm is an algorithm that has been widely used in predictions, its strengths are in estimating simple model parameters and data based on time series. In addition, the linear regression algorithm can perform analysis using several independent variables so that the prediction results can be more accurate. The purpose of this study is to create a forex price prediction model, to make it easier for traders to make price predictions. A dataset of 2066 data was obtained through the metatrader software and processed through the preprocessing stage. The linear regression model was created using 5 scenarios, and the evaluation was carried out using the Mean Squared Error (MSE) and Root Mean Square Error (RMSE) values to select the best model. The results show that linear regression is able to predict the closing price of the USDCHF pair. The best linear regression model is obtained using the independent variable in scenario 1, namely the Open variable, with a linear regression equation of y=0.0145+0.9849x, the best MSE is 0.0000328509 and the best RMSE is 0.0057315705.
Pengolahan Data Sensor Gerak Ponsel untuk Klasifikasi Karakteristik Mengemudi Lisa Dinda Yunita; Ema Utami; Ainul Yaqin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i3.6050

Abstract

Driving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The data collection method involved recording the driver’s smartphone motion sensor during trips. The data were then exported from the system for further processing. The main objective of this study is to process the data by creating a classification model with the best performance in handling smartphone motion sensor data. The results of this research are expected to be implementable models to address road safety issues in the future. Additionally, by utilizing driver characteristic detection technology, awareness of safe driving practices can be enhanced. The research methodology used data mining with machine learning classification modeling using random forest (RF), support vector machine (SVM), and decision tree (DT) methods. The test results indicate that the RF model performed the best with an accuracy of 91.22%. Furthermore, this study found that speed was the most influential factor in identifying safe or unsafe driving behavior. The developed classification model shows the potential to improve traffic management efficiency and contribute to safer transportation. By leveraging driver characteristic detection technology, it is hoped that awareness of safe driving practices will increase, leading to a safer road environment.
Co-Authors Alfriadi Dwi Atmoko Atin Hasanah Dedy Sugiarto Ema Utami Enni Lindrawati Jihadul Akbar Lisa Dinda Yunita Mohammad Edi Nalda Kresimo Negoro Reyhan Dwi Putra Saiful Bahri Samuel Adhi Bagaskoro Tamrizal A.M Wahyu Hidayat