Garuda - Garba Rujukan Digital

Pengembangan UI/UX pada aplikasi Go-Print Dengan menggunakan metode design thinking vannes wijaya; Muhammad Fadli; Yohanes Andika Dharma; Muhammad Rizky Pribadi
MDP Student Conference Vol 1 No 1 (2022): The 1st MDP Student Conference 2022
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.711 KB)

Abstract: Go-print is an application that has developed certain printing services that can be done online using delivery services. Time inefficiency is the main complaint of most people who want to do printing, besides that there are many other complaints such as quality is not guaranteed, prices exceed expectations. In this stage, it determines several ideas for problems in Go-Print application development, in this study using the Design Thinking Method, which is a method for designing software that relies on finding solutions to solve various problems. The method of solving the problem is not yet clear. Therefore, it is done by understanding the needs of users who will be involved in using the application by collecting many ideas in a brainstorming session, as well as taking a direct approach, through prototyping the Go-Print application, and direct testing. After going through various stages using the Design Thinking method, especially in determining several ideas starting from the problem, solution and benefits. So, it can be concluded that the Go-Print application has so far been the solution to meet the needs of users in the printing sector. Abstrak: Go-print merupakan aplikasi yang telah mengembangkan percetakan tertentu yang dapat dilakukan secara online dengan menggunakan layanan jasa antar. Ketidakefisienan waktu menjadi keluhan utama sebagian besar masyarakat yang ingin melakukan percetakan, selain itu masih banyak lagi keluhan lainnya seperti kualitas tidak terjamin, harga melebihi ekspektasi. Dalam tahapan ini menentukan beberapa ide untuk permasalahan pada pengembangan aplikasi Go-Print, pada penelitian ini menggunakan Metode Design Thinking, yang merupakan sebuah metode untuk mendesain sebuah software yang bertumpu pada pencarian solusi demi menyelesaikan berbagai permasalahan. Metode menyelesaikan masalah belum jelas. Oleh karena itu, dilakukan dengan cara memahami kebutuhan pengguna yang akan terlibat dalam penggunaan aplikasi dengan cara mengumpulkan banyak ide dalam sesi brainstorming, serta melakukan pendekatan langsung, melalui pembuatan prototype aplikasi Go-Print, dan pengujian secara langsung. Setelah melalui berbagai tahapan dengan menggunakan metode Design Thinking, khususnya dalam menentukan beberapa ideate dimulai dari masalah, solusi dan manfaatnya. Maka, dapat disimpulkan bahwa aplikasi Go-Print sejauh ini telah menjadi solusi untuk memenuhi kebutuhan pengguna dalam bidang percetakan.

Perancangan UI/UX Website Pengontrol Kelembapan Tanah Berbasis IoT dan Sensor Kynta, Diva Putri; Laksono, Ivan Luthfi; Wijaya, Vannes; Fadli, Muhammad; Hermanto, Dedy
MDP Student Conference Vol 3 No 1 (2024): The 3rd MDP Student Conference 2024
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v3i1.7278

Soil moisture is an important factor in agriculture, affecting plant health and crop productivity. Indonesia has experienced droughts that resulted in crop failure of 1,177 (31.95%) hectares out of a total of 3,685 hectares of agricultural land. With the arrival of the industrial era 5.0, there is an opportunity to improve soil moisture management. This research aims to design the UI/UX of an IoT and sensor-based soil moisture control website application. This system is designed to make it easier for farmers to monitor and manage soil moisture, so as to improve irrigation efficiency and crop quality. The method used in the design process of this application is the Design Thinking method. The final result obtained from this research is a User Interface and User Experience that can be a solution to existing problems. 61% of 12 respondents stated that the UI design of KeTan was very good, and 42% of 12 respondents gave very good scores for the UX of KeTan.

Comparison of SVM, Random Forest, and Logistic Regression Performance n Student Mental Health Screening Vannes Wijaya; Nur Rachmat
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.9

Mental health is an essential aspect for university students, as undetected mental health disorders can have a significant impact on students' academic performance and well-being. This study contributes by evaluating Synthetic Minority Oversampling Technique (SMOTE)'s role in improving classification models' performance. Despite the increasing use of machine learning in mental health detection, limited research has addressed the challenges posed by imbalanced datasets, particularly in smaller student populations. This research aims to develop a mental health early detection system based on student data from Multi Data University Palembang using the Mental Health Scale (SKM)-12 mental health measurement. The system aims to remind students' awareness of the importance of mental health. To improve accuracy, this research compares the performance of three models, namely Support Vector Machine, Random Forest, and Logistic Regression, both with and without using SMOTE. The dataset obtained is 78 students, and SKM-12 consists of several groups, namely optimal mental health profile with symbol (+-), maximum mental illness profile with symbol (++), minimum mental illness profile with symbol (--), and minimal mental health profile with symbol (-+). The results of this study using the Logistic Regression method using SMOTE obtained better model performance compared to other methods, with an accuracy of 89.28%, an average class precision of 89.5%, an average class recall of 89.75%, and an average F1 - class score of 88.5%. This research shows that overcoming class imbalance using SMOTE can significantly improve the performance of mental health classification models.

Pemantauan Kelembaban tanah Berbasis IoT Menggunakan Sensor Soil Moisture Laksono, Ivan Luthfi; Kynta, Diva Putri; Fadli, Muhammad; Wijaya, Vannes; Hermanto, Dedy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.8961

Soil is commonly utilized as a substrate for the cultivation of plants. The level of soil moisture has a significant impact on the growth and survival of nearby plants. Flourishing vegetation assimilates water from the soil, thereby impacting soil moisture. In addition, solar radiation induces water evaporation in the soil, leading to its desiccation. Excessively arid soil leads to plant wilting, whereas excessively saturated soil hinders the optimal growth of neighboring plants. This study focuses on the real-time detection of soil moisture using a Soil Moisture sensor. The approach employed in this research is Research and Development. The research process consists of three stages: planning, design, and assessment. The Soil Moisture sensor will collect data on the moisture content of the soil. This data will then be retrieved and documented by the ESP32 device, which will transmit and store it in a Firebase database. Once the data is recorded, it will be showcased on a website developed with the PHP programming language and the Laravel framework. This will allow users to monitor the exhibited information directly. The investigation yielded variations within each category for dry soil 28.5%, moist 58.4%, and wet 68%.

Pelatihan Pengembangan Desain Game Dengan HTML dan CSS Pada SMA Negeri 3 Palembang Wijaya, Vannes; Kynta, Diva Putri; Laksono, Ivan Luthfi
FORDICATE Vol 4 No 1 (2024): November 2024
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i1.9686

Seiring dengan kemajuan teknologi informasi dan komunikasi, perkembangan di bidang ini terus mengalami peningkatan yang pesat, salah satunya teknologi dalam game. Pelatihan ini bertujuan untuk memberikan pemahaman dasar dan praktik pengembangan desain game menggunakan teknologi web, yaitu HTML, CSS, dan JavaScript. Pada pelatihan ini siswa SMA Negeri 3 Palembang menggunakan platform CodePen sebagai media pembelajaran dengan metode teori dan praktek, CodePen merupakan sebuah website untuk menampilkan game dari coding yang dibuat. Dengan CodePen, struktur dan tampilan dapat memungkinkan peserta untuk membuat, menguji, dan berbagi kode secara interaktif dalam satu lingkungan kerja yang terintegrasi. Pada proyek pengabdian masyarakat ini bertujuan untuk mengenal teknologi pengembangan game berbasis web kepada peserta, meningkatkan pemahaman peserta terhadap dasar-dasar desain dan logika dalam pembuatan game, dan mengasah kreativitas. Pada pelatihan ini rata-rata hasil yang didapatkan dari Pre Test sebesar 11,4 dan rata-rata hasil yang didapatkan Post Test sebesar 45,34.

Penerapan SMOTE dan Regresi Logistik Pada Website Skrining Awal Kesehatan Mental Mahasiswa Wijaya, Vannes; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9046

Mental health is a very important aspect in realizing overall health. Students are an age group that experiences a transition from adolescence to adulthood, students tend to experience stress, especially those originating from the academic process. In this study, a website-based questionnaire system was developed to predict mental health profiles consisting of optimal mental health profiles (+-), maximum mental health profiles (++), minimal mental health profiles (--), and minimal mental health profiles (-+). The questionnaire questions and grouping of mental health profiles used the SKM-12 mental health measurement tool. The dataset used was obtained from 78 students at Multi Data University Palembang. The method used in this research is Logistic Regression using the data imbalance method, namely SMOTE with parameter solver newton-cg with data division, 70% training data and 30% test data. The results obtained in this study using confusion matrix model evaluation obtained an accuracy of 89.28% and model evaluation using K-fold cross validation obtained an accuracy of 87.43% for training data and 82.66% for test data.

Comparison of SVM, Random Forest, and Logistic Regression Performance n Student Mental Health Screening Vannes Wijaya; Nur Rachmat
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.9

Mental health is an essential aspect for university students, as undetected mental health disorders can have a significant impact on students' academic performance and well-being. This study contributes by evaluating Synthetic Minority Oversampling Technique (SMOTE)'s role in improving classification models' performance. Despite the increasing use of machine learning in mental health detection, limited research has addressed the challenges posed by imbalanced datasets, particularly in smaller student populations. This research aims to develop a mental health early detection system based on student data from Multi Data University Palembang using the Mental Health Scale (SKM)-12 mental health measurement. The system aims to remind students' awareness of the importance of mental health. To improve accuracy, this research compares the performance of three models, namely Support Vector Machine, Random Forest, and Logistic Regression, both with and without using SMOTE. The dataset obtained is 78 students, and SKM-12 consists of several groups, namely optimal mental health profile with symbol (+-), maximum mental illness profile with symbol (++), minimum mental illness profile with symbol (--), and minimal mental health profile with symbol (-+). The results of this study using the Logistic Regression method using SMOTE obtained better model performance compared to other methods, with an accuracy of 89.28%, an average class precision of 89.5%, an average class recall of 89.75%, and an average F1 - class score of 88.5%. This research shows that overcoming class imbalance using SMOTE can significantly improve the performance of mental health classification models.

Title

Found 7 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 7 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 7 Documents
Search