Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Rekayasa elektrika

Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Junta Zeniarja; Abu Salam; Farda Alan Ma'ruf
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.804 KB) | DOI: 10.17529/jre.v18i2.24047

Abstract

Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Junta Zeniarja; Abu Salam; Farda Alan Ma'ruf
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.24047

Abstract

Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
The Development of Javanese Glossary Website as a Form of Language Maintenance and Revitalization Muljono, Muljono; Zeniarja, Junta; Rokhman, Nur; Nugroho, Raden Arief; Suryaningtyas, Valentina Widya; Aryanto, Bayu
Jurnal Rekayasa Elektrika Vol 20, No 2 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i2.34638

Abstract

As a vital component of cultural identity, language is under pressure as a result of globalization. This article discusses the creation of a website that provides a dictionary of Javanese phrases to help preserve and revitalize the language. In this study, we collect, categorize, and display Javanese words on electronic resources. In addition, the system usability scale (SUS) was used to conduct usability tests on the investigated websites to determine how user-friendly they actually were. Gathering terms from multiple sources, categorizing them, and developing a user-friendly interface with a search bar are all steps in the process of making a website. Users from all walks of life fill out the SUS questionnaire as part of the usability testing process. The test results reveal how well the website satisfies its users' requirements. Creating a database of Javanese words online and putting it through the SUS test is a great example of how technology can be used to help preserve a language and its heritage. It is believed that by taking this step, more people will become familiar with the Javanese language and become invested in its continued existence in the modern world. The usability testing results demonstrate that the development strategy and interface design effectively fostered a positive user experience. High scores on the SUS questionnaire, with an average rating of 80.25, indicate that users find the website satisfactory and user-friendly.
Co-Authors Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adi Wibowo Afridiansyah, Rahmanda Agus Winarno Agus Winarno, Agus Ahmad Alaik Maulani Ailsa Nurina Cahyani Ainul Yaqin Alan Ma’ruf, Farda Alya Nurfaiza Azzahra Anisatawalanita Ukhifahdhina Anugrah, Muhammad Ikhsan Ardytha Luthfiarta Ardytha Luthfiarta Asih Rohmani Asih Rohmani Asih Rohmani Atika Rahmawati Bayu Aryanto Budi Warsito Cahyani, Ailsa Nurina Candra, Rejka Aditya Catur Supriyanto Catur Supriyanto Debrina Luna Arghata Mangkawa Deby Arida NiMatus Sa’adah Devi Ayu Rachmawati Dianti, Reza Nur Diyan Adiatma Dzaky, Azmi Abiyyu Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Esmi Nur Fitri Esmi Nur Fitri Esmi Nur Fitri Fajarudin Zakariya Farda Alan Ma'ruf Farda Alan Ma’ruf Ferry Bintang Nugroho Fikri Budiman Fikri Budiman Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Guruh Fajar Shidik Haresta, Alif Agsakli Harun Al Azies Ida Ayu Putu Sri Widnyani Ika Novita Dewi Jaya, Sava Irhab Atma Khoirunnisa, Emila Kiki Widia Kurniawan Ridwan Surohardjo Kurniawan, Defri L. Budi Handoko Luh Putu Ratna Sundari Lutfi Kharisma M Hafidz Ariansyah M. Hafidz Ariansyah Manurung, Ayub Michaelangelo Mas'ud, Ryan Ali Maulani, Ahmad Alaik Mufida Rahayu Muhammad Jamhari Muhammad Joyo Satrio Muljono Muljono Nabila, Qotrunnada Nitho Alif Ibadurrahman Novi Hendriyanto Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pratama, Rifky Ariya Pulung Nurtantio Andono Putra, Vander Mulya Putri, Rusyda Tsaniya Eka Raden Arief Nugroho Rama Eka Saputra Ramadhan Rakhmat Sani Ramadhan, Ahnaf Irfan Ramadhan, Muhammad Eky Restu Agung Pamuji Rezaroebojo, Rizal Riyan Ardiansyah Rohman, Adib Annur Savicevic, Anamarija Jurcev Setiawan, Dicky Setiawan Sindhu Rakasiwi Sri Winarno Sri Winarno Sri Winarno Syabilla, Mutiara Utomo, Danang Wahyu Valentina Widya Suryaningtyas, Valentina Widya Wibowo Wicaksono Wibowo Wicaksono