Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Indonesian Journal of Data and Science Journal Of Artificial Intelligence And Software Engineering Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Yagus Cahyadi
Prodi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Teknologi Digital Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Data Mining Untuk Memprediksi Status Kelulusan Mahasiswa Sumiyatun; Yagus Cahyadi; Edi Iskandar
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 21 No 3 (2023): September 2023
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v21i3.3

Abstract

Semakin lama operasional sebuah perguruan tinggi, semakin banyak lulusan (alumni) yang dihasilkan. Data alumni serta mahasiswa aktif, merupakan aset yang dapat digunakan untuk keperluan tertentu, salah satunnya adalah digunakan untuk memprediksi status kelulusan. Pendekatan yang umum digunakan adalah teknik penambangan data (data mining). Penelitian ini akan menerapakan teknik data mining untuk memprediksi status kelulusan mahasiwa. Pengolahan data akan memanfaatkan aturan asosiatif (association rule mining) dan menggunakan algoritma apriori. Algoritma apriori adalah algoritma dasar untuk penentuan frequent itemsets aturan asosiasi boolean. Algoritma ini mengontrol berkembangnya kandidat itemset dari hasil frequent itemsets dengan support-based pruning untuk menghilangkan itemset yang tidak menarik dengan menetapkan minsup. Berdasarkan hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa aplikasi data mining dengan menggunakan pendekatan aturan asosiasi dan algoritma apriori dapat digunakan untuk menampilkan informasi aturan status kelulusan. Informasi yang ditampilkan berupa nilai support dan confidence. Semakin tinggi nilai confidence dan support maka semakin kuat nilai hubungan antar atribut/item. Data alumni dan data mahasiswa yang diproses meliputi IPS2, IPS4, ORG, PRG dan SL
Implementation of Support Vector Machine Algorithm for Classification of Study Period and Graduation Predicate of Students Sumiyatun; Cahyadi, Yagus; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.214

Abstract

Introduction: Accurately predicting the duration of study and graduation predicates in higher education is essential for improving academic outcomes and decision-making. This study aims to classify students’ study period and graduation predicates in the Information Systems program at UTDI using the Support Vector Machine (SVM) algorithm. Methods: A dataset of 500 student records containing academic and demographic variables—including GPA, age, semesters, and graduation predicates—was processed through data cleaning, normalization, and feature selection. Study duration was categorized into three classes: short (≤4 years), medium (4–6 years), and long (>6 years). An SVM with a linear kernel was applied, and the model was evaluated using accuracy, precision, recall, and F1-score. Results: The SVM model achieved perfect classification for study duration, with 100% accuracy, precision, recall, and F1-score across all categories. For graduation predicate classification, the model attained 95.18% accuracy. While it performed well overall, it faced some difficulty distinguishing between "Cum Laude" and "Very Satisfactory" due to overlapping GPA ranges. The analysis identified GPA as the most influential feature in both classification tasks, while age and the number of semesters played supporting roles. Conclusions: The SVM model demonstrates strong capability in classifying study duration and graduation predicates, offering valuable insights for academic management. Although performance was high, especially for study period prediction, further refinement is suggested to enhance classification in overlapping categories. Future work may benefit from larger, more balanced datasets and exploration of advanced models to increase prediction reliability.
Bidirectional Long Short-Term Memory Model for Intent Classification in Customer Service Chatbot Cahyadi, Yagus; Redjeki, Sri; Almagrib, Ahmad; Satriani, Bayu; Naufal, Nabil
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6520

Abstract

The demand for responsive and efficient customer service is a crucial aspect of enhancing customer satisfaction, particularly in Indonesian government offices abroad. To address this challenge is implementing a chatbot system based on Bidirectional Long Short-Term Memory. This model can understand conversational contexts more comprehensively, enabling it to generate relevant and timely responses. This study aims to optimize chatbot performance in enhancing customer experience by implementing the Bi LSTM algorithm to handle intent classification of customer input data. Experimental results demonstrate that this model successfully improves evaluation metrics, achieving an accuracy of 84.64%, precision of 85%, recall of 85%, and an F1-score of 85%.
Co-Authors Edi Faizal Edi Iskandar Naufal, Nabil Satriani, Bayu Sri Redjeki Sri Redjeki Sumiyatun Sumiyatun