Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat
Nurdin, Arliyanti
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Socialization of Buginese Language E-learning Application with Lontara Script Translation Feature at SMPN 2 Pangkajene Nurtanio, Ingrid; Yohannes, Christoforus; Bustamin, Anugrayani; Mokobombang, Novy Nur R A; Areni, Intan Sari; Tahir, Zulkifli; Adnan, Adnan; Marindah, Tyanita Puti; Paundu, Ady Wahyudi; Nurdin, Arliyanti; Musyfirah, Kamtina; Hikmah, Nur; Mahdaniar, Mahdaniar
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 7 No 2 (2024): Kolaborasi yang Kuat untuk Kekuatan Kemasyarakatan
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v7i2.528

Abstract

Preservation of indigenous languages, especially Bugis with Lontara script, is an important challenge in the digital era. In school activities, mastery and learning of local languages ​​are associated with one subject, namely Muatan Lokal. However, the main problem that is often encountered is the use of indigenous languages ​​that are increasingly minimally socialized, thus reducing students' interest and motivation in learning this local language, especially in SMP Negeri 2 Pangkajene Class VII. This community service aims to be a forum for socializing research results in the Department of Informatics and Electrical Engineering, Hasanuddin University in the form of an E-learning application that facilitates interactive learning of Bugis with Lontara script. In addition, this activity is expected to contribute to the advancement of knowledge and technology by providing E-learning tools, in the form of mobile applications, to support learning both at school and at home. The process of introducing this application involves quantitative analysis in the form of an initial survey (pre-test) which includes the user experience (in this case students) when learning Bugis and then ends with a final survey (post-test) and System Usability Scale (SUS) testing to determine the student's experience when using this Bugis language application. The results obtained indicate the formation of Bugis language learning motivation after participants are familiar with this E-learning application with an increase of 52% from 32% less motivated to 84% very motivated. In addition, this Bugis language E-learning application also reached an acceptable level based on SUS with a value of 74.
Analisis Kredit Pembayaran Biaya Kuliah Dengan Pendekatan Pembelajaran Mesin Nurdin, Arliyanti; Amelia Zunaidi, Rizqa; Arkan Fauzan Wicaksono, Muhammad; Lobita Japtara Martadinata, Agi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 2: April 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20236301

Abstract

Salah satu tantangan dalam institusi keuangan adalah manajemen risiko kredit. Hal ini juga terjadi pada institusi pendidikan swasta dimana pengelolaan keuangan dilakukan secara mandiri serta sumber dana mayoritas berasal dari mahasiswa. Setiap institusi harus menjamin kesehatan finansial melalui monitoring cashflow. Adanya penundaan atau kredit pembayaran biaya kuliah mahasiswa akan mempengaruhi cashflow institusi. Oleh karena itu dibutuhkan analisis kredit sebagai tindakan preventif guna mencegah terjadinya kredit yang bermasalah dan meminimalkan risiko kredit lainnya yang timbul di kemudian hari. Pada penelitian ini, algoritma machine learning digunakan untuk analisis kredit pembayaran biaya kuliah pada perguruan tinggi. Dataset yang digunakan adalah data riwayat tagihan, transaksi pembayaran, dan data pengajuan kredit/ angsuran. Tahap perancangan sistem terdiri dari preprocessing, pemilihan fitur, pemodelan, pengujian dan evaluasi hasil. Berdasarkan hasil pengujian algoritma dengan kinerja terbaik adalah KNN dengan recall untuk prediksi “gagal bayar” sebesar 0,8 dan prediksi “berhasil” sebesar 0,76.  Model machine learning ini kemudian ditanamkan dalam sebuah sistem informasi analisis kredit biaya kuliah. Selain itu juga sistem akan memberikan skor setiap pengajuan berdasarkan metode scorecard. Semakin tinggi skor kredit semakin kecil risiko gagal bayarnya. Skor kredit ini berkisar antara 250 – 600. Jika kredit yang diajukan diprediksi “gagal bayar” dengan skor kredit rendah atau berpotensi menjadi piutang macet, sistem akan merekomendasikan untuk menilik ulang skema pengajuan kredit dari mahasiswa tersebut agar mahasiswa tetap dapat melanjutkan pendidikan dan cash collection ratio tetap baik. AbstractOne of the challenges in financial institutions is credit risk management. This also occurs in private educational institutions where financial management is carried out independently and most of funding sources come from students. Each institution must ensure financial health through cashflow monitoring. Any delay or credit in paying student tuition fees will affect the institution's cashflow. Therefore, credit analysis is needed as a preventive measure to prevent non-performing loans and minimize other credit risks that arise in the future. In this study, machine learning algorithms are used for credit analysis for paying tuition fees activity at universities. The datasets used are billing history data, payment transactions, and credit/installment application data. The system design stage consists of preprocessing, feature selection, modeling, uji and evaluation of results. Based on the results of uji the algorithm with the best performance is KNN with a recall for the prediction of "failure to pay" of 0,8 and prediction of "success" of 0,76. This machine learning model is then embedded in a tuition credit analysis information system. In addition, the system will provide a score for each submission based on the scorecard method. The higher the credit score, the lower the risk of default. This credit score ranges from 250 – 600. If the proposed credit is predicted to be "in default" with a low credit score or has the potential to become bad debts, the system will recommend reviewing the student's credit application scheme so that students can continue their education and cash collection ratio remains good.
Co-Authors Adnan Adnan Areni, Intan Sari Arkan Fauzan Wicaksono, Muhammad Bustamin, Anugrayani Ingrid Nurtanio Lobita Japtara Martadinata, Agi Mahdaniar, Mahdaniar Marindah, Tyanita Puti Mokobombang, Novy Nur R A Musyfirah, Kamtina Nur Hikmah Paundu, Ady Wahyudi Yohannes, Christoforus Zulkifli Tahir Zunaidi, Rizqa Amelia