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ANALISIS DAN PERANCANGAN SISTEM INFORMASI PELAYANAN ADMINISTRASI PUBLIK MENGGUNAKAN UNIFIED MODELING LANGUAGE (UML) Vitriani Vitriani; Finanta Okmayura; Robby Satria
Simtika Vol. 5 No. 1 (2022): JURNAL SIMTIKA, JANUARI 2022
Publisher : Fakultas Ilmu Komputer Universitas Dharmas Indonesia

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Penelitian ini membahas tentang perancangan sistem informasi pelayanan administrasi publik menggunakan Unified Modelling Language (UML) Sistem ini dirancang atas dasar banyaknya permasalahan yang ada pada pelayanan administrasi publik di kantor desa atau nagari seperti sekretaris desa atau nagari tidak berada di tempat pada saat masyarakat membutuhkan surat keterangan tidak mampu untuk mengajukan beasiswa anaknya. Sistem ini dirancang menggunakan model Unified Modelling Language (UML). Dengan adanya sistem ini diharapkan permasalahan pelayanan administrasi publik di kantor desa dan nagari dapat teratasi dan kinerja perangkat desa dan nagari bisa lebih maksimal.

PERANCANGAN E-TOURISM UNTUK MENINGKATKAN PARIWISATA DI KOTA SAWAHLUNTO Gunawan Ali; Dwi Winarti; Vitriani Vitriani; Finanta Okmayura; Robby Satria
Simtika Vol. 5 No. 2 (2022): JURNAL SIMTIKA, MEI 2022
Publisher : Fakultas Ilmu Komputer Universitas Dharmas Indonesia

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Sistem Informasi Pariwisata Berbasis Web Sebagai Media Promosi Kota Sawahlunto ini digunakan sebagai alat bantu penyampaian informasi kawasan wisata, penginapan dan tempat-tempat kuliner yang ada di Kota Sawahlunto. Sistem Informasi ini merupakan salah satu bentuk promosi pariwisata yang ada di Kota Sawahlunto, agar menarik para wisatawan yang ingin dikunjung ke Kota Sawahlunto. Penelitian ini diharapkan dapat bermanfaat sebagai media informasi dan promosi wisata yang ada di Kota Sawahlunto, sehingga masyarakat luas akan semakin mengenal tempat-tempat wisata yang ada di Kota Sawahlunto. Sistem informasi pariwisata berbasis web sebagai media promosi Kota Sawahlunto dibuat dengan menggunakan bahasa pemrograman PHP, dengan database MySQL

LSTM Network Hyperparameter Optimization for Stock Price Prediction Using the Optuna Framework Edi Ismanto; Vitriani Vitriani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.24944

In recent years, the application of deep learning-based financial modeling tools has grown in popularity. Research on stock forecasting is crucial to understanding how a nation's economy is doing. The study of intrinsic value and stock market forecasting has significant theoretical implications and a broad range of potential applications. One of the trickiest challenges in projects involving deep learning and machine learning is hyperparameter search. In this paper, we evaluate and analyze the optimal hyperparameter search in the long short-term memory (LSTM) model developed to forecast stock prices using the Optuna framework. We examined a number of hyperparameters with several LSTM architectures, including optimizers (SGD, Adagrad, RMSprop, Nadam, Adamax, dan Adam), LSTM hidden units, dropout rates, epochs, batch size, and learning rate. The results of the experiment indicated that of the four LSTM models tested—model 1 single LSTM, model 2 single LSTM, model 1 LSTM stacked, and model 2 LSTM stacked—model 1 single LSTM was the most effective. Single LSTM version 1 offers the lowest losses when compared to other models and had the lowest root mean square error (RMSE) score of 7.21. When compared to manual hyperparameter tuning, automatic hyperparameter tuning has lower losses and is better.

Teaching Factory Model Using Flipped Classroom and Knowledge Management System Based in Improving 21st Century Competencies in Vocational High Schools Vitriani Vitriani; Finanta Okmayura; Gunawan Ali; Robby Satria
AL-ISHLAH: Jurnal Pendidikan Vol 15, No 2 (2023): AL-ISHLAH: Jurnal Pendidikan
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v15i2.3785

The students in Vocational High Schools are demanded to have skills and competencies to face the world industry, but sadly many Vocational School Graduates have the incompatibility of skills and competencies, which seriously causes high unemployment rates. Therefore, an effective learning model needs to be implemented to overcome this problem. This research aims to produce a Teaching Factory Model Using Flipped Classroom and Knowledge Management System (KMS) Based. Providing a learning model that can be a solution to increase the competence of vocational students in meeting the needs of the industrial world and the business world in facing challenges in the Revolution 4.0 era. The research methodology used is Research and Development with ADDIE stages. This research produces a Model Teaching Factory based Flipped Classroom and Knowledge Management System. Based on the study results, the learning model teaching factory based Flipped Classroom and Knowledge Management System valid, practical and effective in Enhancing 21st Century Competencies in Vocational High Schools. The renewal of the model is designed to calibrate the Teaching Factory model based on Flipped Classroom and Knowledge Management System, a 21st century competency needed by the industrial world.

Peningkatan Omzet Penjualan Melalui Diversifikasi Produk dan Toko Online pada UMKM Al Baik Food Saat Pandemi Covid-19 Finanta Okmayura; Vitriani; Pratama Benny Herlandy; Regiolina Hayami; Risnal Diansyah
ABDIMAS EKODIKSOSIORA: Jurnal Pengabdian Kepada Masyarakat Ekonomi, Pendidikan, dan Sosial Humaniora (e-ISSN: 2809-3917) Vol 2 No 1 (2022): Juni 2022
Publisher : Fakultas Ekonomi dan Bisnis Universitas Muhammadiyah Riau

Tujuan dari kegiatan pengabdian ini adalah untuk membantu UMKM Al Baik Food dalam meningkatkan omset penjualan melalui diversifikasi produk dan promosi. Al Baik Food sendiri merupakan UMKM yang menjual berbagai macam cemilan asinan khas Pekanbaru. Pemilik Usaha Al Baik Food ini mengatakan dalam mengembangkan usahanya ini masih bersifat konvensional karena penjualannya hanya melalui whatsapp saja. Selain itu, kemasan produk cemilan yang dihasilkan Al Baik Food masih bersifat sederhana. Sementara itu selama pandemi covid 19 omset penjualan pun mengalami penurunan. Oleh karena itu diperlukan strategi promosi dan pemasaran produk yang menarik untuk untuk meningkatkan strategi penjualan UMKM ini. Promosi nantinya akan dilakukan melalui media sosial dan juga toko online yang berbasis android. Kemudian UMKM Al Baik Food ini akan dilakukan pembinaan, pemberdayaan dan pendampingan melalui pemberian pelatihan pembuatan toko online berbasis android. Selain itu, diiversifikasi produk juga perlu dilakukan dengan memberikan pelatihan pembuatan kemasan yang menarik konsumen.

APLIKASI E-TRAINING BERBASIS KNOWLEDGE MANAGEMENT SYSTEM PADA MASA PANDEMI Vitriani Vitri; Gunawan Ali Gunawan; Finanta Okmayura Fina; Robby Satria Robby
JURNAL FASILKOM Vol 13 No 01 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i01.4798

Penelitian ini dilakukan untuk mengembangkan Aplikasi E-Training berbasis Knowledge Management System pada masa pandemi. Penelitian ini dilaksanakan sebagai upaya memberikan kontribusi dalam mendukung kebijakan pemerintah terkait dengan pembatasan physical dan social distancing pada masa pandemi Covid-19 ini. Penelitian ini akan diimplementasikan oleh guru Sekolah Menengah Kejuruan Muhamaddiyah 2 Pekanbaru dalam rangka meningkatkan kompetensi profesionalnya. Mengingat kompetensi profesional merupakan kompetensi yang sangat dibutuhkan oleh seorang guru dalam menunjang proses pembelajaran untuk menghasilkan lulusan yang berkompeten dan harus dilakukan secara berkelanjutan. Semua guru dapat saling berbagi pengetahuan, pengalaman, baik dalam bidang pembelajaran, karir ataupun kepakaran. Sehingga dengan demikian akan terwujud kesatuan guru yang memiliki kompetensi profesional unggul, menjadi fasilitator dan pathner bagi siswa untuk mencapai tujuan pembelajaran dalam menghantarkan siswa meraih cita-citanya. Tujuan dari penelitian ini untuk menghasilkan aplikasi E-Training berbasis Knowledge Management System pada masa pandemi sehingga dapat meningkatkan kompetensi peserta sesuai dengan bidang keahliannya dan mengungkapkan validitas, praktikalitas dan efektivitas aplikasi E-Training berbasis Knowledge Management System. Penelitian ini menggunakan metode penelitian Research and Development, dengan 10 langkah atau tahapan penelitian. Penelitian ini dilakukan di SMK Muhammadiyah 2 Pekanbaru, Kota Pekanbaru, Provinsi Riau dengan mengambil sampel guru-guru SMK Muhammadiyah 2 Pekanbaru. Penelitian direncanakan akan menghasilkan Aplikasi E-Training berbasis Knowledge Management System, Buku panduan pelatihan, Buku petunjuk penggunaan aplikasi sebagai administrator dan user. Untuk luaran penelitian dengan target publish artikel ilmiah pada jurnal Jurnal Ilmiah Pendidikan dan Pembelajaran (JIPP), jurnal nasional terakreditasi Sinta 2, HAKI dan aplikasi tepat guna. Penelitian ini diharapkan dapat memberikan manfaat dan kemudahan bagi guru-guru untuk melaksanakan pelatihan dalam rangka meningkatkan kompetensinya secara berkelanjutan.

Perancangan Sistem Informasi Absensi Siswa Menggunakan QR Code Berbasis Web Vitriani; Gunawan Ali; Wahyu Nur Rohman; Melly Novalia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 5 (2023): April 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i5.752

The attendance system in education field is generally done manually, including school attendance, where this is very inefficient because there are often errors during the recapitulation process of student attendance data. The aim of this study is to design a Student Attendance Information System Using a Web Based QR Code to provide convenience in managing student attendance data. The type of research was Research and Development (R&D) with a Waterfall development model consisting of several stages, which are: Needs Analysis, System Design, Implementation, Testing, and Maintenance. The resulting system tested by using Black box test and validated by a system expert with the result is "Excellent" category which is validated by using usability aspect analysis through a questionnaire and the result of calculating the converted value is "Very Good". It shows that the Student Attendance Information System by using this Web-Based QR Code can provide convenience in managing data efficiently and make the attendance process more practical

A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection Edi Ismanto; Januar Al Amien; Vitriani Vitriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3885

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.

A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection Edi Ismanto; Januar Al Amien; Vitriani Vitriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3885

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.

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