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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Teknika Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengabdian UntukMu NegeRI INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI TESLA: Jurnal Teknik Elektro Indonesian Review of Physics (IRiP) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Unri Conference Series: Community Engagement Jurnal PkM (Pengabdian kepada Masyarakat) Journal of Community Engagement Research for Sustainability Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Informatics and Enginering Dedication Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Makara Journal of Technology Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Informatika: Jurnal Pengembangan IT BATOBO: Jurnal Pengabdian Kepada Masyarakat PADIMAS: Jurnal Pengabdian Masyarakat CSRID
Noveri Lysbetti M
Universitas Riau
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Journal : Jurnal Informatika: Jurnal Pengembangan IT

 1 
Rancang Bangun Aplikasi Bon Permintaan Dan Pengeluaran Barang Menggunakan Metode Prototype Berbasis Website Aielsa Naomi Athaya; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5220

Abstract

Goods purchase requisitions and goods issue documents are receipts for purchase requisitions and goods issues for distribution goods from the unit of work to the warehouse. pt. Perkebunan Nusantara V still uses the manual method of registering and approving the Goods Request Form using a form filled out by a factory assistant and signed by multiple parties. Therefore, it takes 5-30 business days to collect all signatures. If all parties are present, the product request notification can be signed and approved immediately. However, if this is not the case, the bill of goods approval process will be delayed. For this reason, urgent needs often result in goods being released from the warehouse before the invoice has been fully approved. Therefore, there is a need for an application that helps companies manage good purchase requisitions from warehouses. The application is implemented as a website that allows users to approve notes step-by-step online. The prototyping method allows developers to design and build systems more efficiently because discussions take place between users and developers during the system development process. PHP Laravel is used as programming language and MySQL as database. The tests for this application are based on the ISO 9126 test standard and give the following results: According to the USE survey, functionality scored 100%, reliability scored A, usability scored 90.07 across the four factors, efficiency scored B, performance score 88%, The structural score was 87%. Maintainability was evaluated as A grade with a debt ratio of 2.6%, and portability was evaluated as 100%. This application reduced the approval time to less than 5 hours and test results showed that the application works well and is suitable for enterprise use
Pengenalan Alfabet SIBI Menggunakan Convolutional Neural Network sebagai Media Pembelajaran Bagi Masyarakat Umum Zahrah Fadhilah; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5221

Abstract

SIBI is one of the Sign Languages used in Indonesia and has been widely used in the community, especially the school (SLB). Communication limitations of the deaf and speech community cause limited communication with the general public, especially many general public who do not know Sign Language or SIBI. For this reason, this research was conducted in order to become a learning media for the general public in recognizing the SIBI alphabet so that it can support communication with the deaf and speech community. This research was conducted to become a medium that can be used as a learning medium in the introduction of the SIBI alphabet. The method used in this research is CNN. CNN is used because it is a deep learning method that has the most significant results in image recognition. The data used is 2,600 images which are divided into 80% training data and 20% validation data. Training was done ten times by comparing the parameters that produce the best accuracy. The parameters used are batch size and epoch. From ten trials, the best accuracy is obtained using batch size 8 and epoch 50. The best accuracy produced is 85% training accuracy and 87% validation accuracy.
Deep Learning untuk Identifikasi Daun Tanaman Obat Menggunakan Transfer Learning MobileNetV2 Rio Juan Hendri Butar-Butar; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5217

Abstract

Medicinal plants are plants used as alternative medicines for healing or preventing various diseases due to their active substances. The utilization of medicinal plants in Indonesia has been widespread among the community since ancient times and is a heritage passed down from ancestors. Medicinal plants have leaf structures that are almost similar between one plant and another, which can lead to confusion for some people and require precision in identifying the leaves of medicinal plants. Incorrect identification can have negative consequences for the users. In recent years, deep learning has been used to identify objects because of its ability to interpret images. This study used a transfer learning method to identify medicinal plants. Transfer learning utilizes a pre-trained model to learn and perform new tasks, making it suitable for smaller datasets. The pre-trained model used in this study is MobileNetV2. MobileNetV2 has a lightweight architecture and high accuracy. Fine-tuning techniques were applied in this study to improve the model's performance. Several experiments were conducted with parameters such as epochs and fine-tuning layers to obtain the best results. The research yielded a training accuracy of 97%, validation accuracy of 96%, and testing accuracy of 93%.
Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN Ummi Sri Rahmadhani; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5229

Abstract

Mushrooms are plants that do not have true roots and leaves. There are many types of mushrooms that have been identified worldwide, with various shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy, health, and others. One of the benefits of mushrooms is as a food source in Indonesia, but not all types can be consumed. To identify mushroom species, the concepts of Genus and species can be used. The concept of Genus is considered easier because it groups mushroom types based on similar morphological characteristics. Therefore, a model is needed to classify mushrooms based on consumable and toxic genera. The method used in this research is Convolution Neural Network (CNN) due to its good predictive results in image recognition. The model in the research utilizes three convolution layers, three MaxPooling layers, and two dropout layers. The use of dropout aims to reduce overfitting in the model. The research uses a dataset of 1200 images with a training and testing data ratio of 70:30, resulting in 840 training data and 360 testing data. The best accuracy achieved by this model is 89% for training and 82% for validation. Therefore, it can be concluded that the model is able to classify mushrooms based on Genus using the CNN method
Co-Authors Abu Yazid Raisal, Abu Yazid Afrianti, Dedeh Kurnia Aielsa Naomi Athaya Akbar Hanafi Siregar Albert Timbul Siregar Ali, Nurhalim Dani Alwi, Rangga Aminuyati Amirul Latief Azzmi Amzah, Ridho Al Andhi, Rahmat Rizal Anhar Anhar Anhar Anisa Lutfia Antonius Rajagukguk Antonius Rajagukguk Antonius Rajagukguk, Antonius Ayunda Widia Kusuma Azwir Rezari Celfin Chandro Nainggolan Siahaan Dani Ali, Nurhalim Daniati, Septania Dedy Nurahmadin Demiza, Khosfikra Desty, Hygiana Prima Dewi Fitri Novita Pasaribu Dian Yayan Sukma, Dian Yayan Dwi Nur Indah Sari, Dwi Nur Indah Edy Ervianto Eka Novvala Dewi Eko Marjan Elfrida Nova Sartika Elirza Halena Elizabeth, Ivena Era Yohana Oktaviani Silalahi Esther Joan Ruthmika Sianturi Fadli Julianto Erga Fahmi, Ziddan Fakhriyah, Salsabila Febrizal Ujang Feranita Feranita Ferdian, Fhinta Syahila Feri Candra Fiki Sanora Firdaus Firdaus Fitria Sari Guspi Candra Hadiwandra, T Yudi Hassan, Rohana Henti Nuraini Napitupulu Hygiana Prima Desty Ibrahim, Sajid Illahi, Hayatul Izzi, Mambaul Jelita Mianarta Rajagukguk Jesslin Halim Kelvin Rainey Salim Mahindra, Syaputra Dwi Mar Qosim Marbun, Andreas Marlina Octa Venita Gultom Marsaulina Marpaung Maulana, Farhan Muhammad Ilham Muhammad, Satyo Mulia Sakti Rambe, Mulia Sakti Nofri Afandi Noviana, Eka Rani Nuraini, Dwi Nur Indah Sari Nurhalim Dani Ali Nurhalim Nurhalim Nurhalim Nurhalim, Nurhalim Nursaldila Nursaldila Nurul Whusto Octavia, Bunga Okpi Pranata Reskha Okta Rivaldi Padang, Junelka Lisendra Pratama, Muhammad Yogi Purnomo, Karin Dwi Putri, Katya Blinda R., Antonius Rahyul Amri Raja Hizkia Hutabalian Ramadhan, Ashry Ramadhan, Riyan Putra Ramadhan, Roza Syahputra Rani Fitri Arya Ningsi Raudah, Aulia Reski Lasari Ridho Al Amzah Rio Juan Hendri Butar Butar Rio Juan Hendri Butar-Butar Rizka Dwi Saputri Rosiki, Muhammad Habib Rosma, Iswadi Hasyim Sakti Hutabarat Sakti Hutabarat Sakti Hutabarat Salhazan Nasution Saputra, Muhammad Hakim Saputri, Rizka Dwi Septiyandi Kurniawan Settian Dwi Cahaya Siagian , Ruben Cornelius Siregar, Vivi Devina Siti Zubaidah Sofinanda Sari Solly Aryza Surya Sahri Ramadhan Suwitno Taruli Devi Sihombing Ummi Sri Rahmadhani Vadri, Yasminne R.A.S Valendino, Muhammad Wakhidah Rohayati Wijaya, Puri Yasminne R.A.S Vadri Yoga Pratama Yohanes, Edwin Yudha Hadi Martha Zahrah Fadhilah