Article Per Year (5 Year)
p-Index From 2021 - 2026
1.849
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Computer Science and Research Journal Of Artificial Intelligence And Software Engineering
Khaidar, Al
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Public Sentiment Analysis on the November 2025 Flood Disaster in Aceh Using Natural Language Processing and Lexicon-Based Approach Erwanda, Ade Putra; Khaidar, Al; Asrianda, Asrianda; Fikry, Muhammad; Khaldun, Ibnu
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8481

Abstract

Bencana banjir yang melanda Provinsi Aceh pada November 2025 merupakan salah satu bencana hidrometeorologi besar yang berdampak luas terhadap kehidupan masyarakat. Banjir terjadi di 16 kabupaten/kota dan mengakibatkan hampir 120 ribu jiwa terdampak, puluhan ribu warga mengungsi, serta kerusakan signifikan pada permukiman dan infrastruktur. Peristiwa ini memicu respons publik yang masif di media sosial, khususnya Instagram. Penelitian ini bertujuan untuk menganalisis sentimen respons masyarakat terhadap bencana tersebut menggunakan pendekatan Natural Language Processing (NLP) berbasis lexicon. Data diperoleh melalui proses data crawling terhadap 2.790 komentar Instagram, yang selanjutnya diproses melalui tahapan text cleaning, case folding, tokenization, stopword removal, dan stemming. Hasil analisis menunjukkan dominasi sentimen positif sebesar 62,51%, diikuti sentimen netral 24,98% dan negatif 12,51%. Temuan ini menunjukkan adanya apresiasi, harapan, serta kritik masyarakat terhadap penanganan bencana, dan dapat menjadi bahan evaluasi bagi pemangku kebijakan dalam meningkatkan strategi penanganan dan komunikasi bencana berbasis data.
Single Tuition Fee Classification Using Light Gradient Boosting Machine with Confusion Matrix Analysis Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8479

Abstract

Uang Kuliah Tunggal merupakan sistem pembiayaan pendidikan tinggi yang ditetapkan berdasarkan kemampuan ekonomi mahasiswa. Penetapan UKT yang masih dilakukan secara manual berpotensi menimbulkan subjektivitas dan ketidaktepatan klasifikasi. Penelitian ini bertujuan untuk mengembangkan model klasifikasi UKT berbasis data menggunakan metode Light Gradient Boosting Machine (LightGBM). Dataset yang digunakan terdiri dari 10.000 data mahasiswa Politeknik Negeri Lhokseumawe yang telah melalui tahap prapemrosesan dan transformasi fitur. Model dilatih menggunakan pembagian data latih dan data uji sebesar 80:20, serta dievaluasi menggunakan metrik akurasi, classification report, confusion matrix, dan 10-Fold Cross Validation. Hasil pengujian menunjukkan bahwa model LightGBM mencapai akurasi sebesar 98% pada data uji. Pengujian 10-Fold Cross Validation menghasilkan rata-rata akurasi sebesar 99,21% dengan standar deviasi 0,29%, yang menunjukkan stabilitas dan kemampuan generalisasi yang sangat baik. Hasil ini membuktikan bahwa LightGBM efektif dan andal untuk mendukung penetapan UKT yang lebih objektif dan berbasis data.
Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Co-Authors Alqhifari, Azka Amalia, Iklasni Arnawan Hasibuan Asrianda Asrianda Azzanna, Maghriza Diqti, Fadillah Fauziah Erwanda, Ade Putra Fajriana, Fajriana Hamdhana, Defry Hermansyah Hermansyah Ibnu Khaldun Jessika, Jessika Kurnia, Sri M. Ali, Rahmadi Muhammad Daud Muhammad Fikry Muhammad Fikry Muliana, Syarifah Muqarrabin, Khalis Al Nurdin Nurdin Nurdin Nurdin Rahmad Rahmad Rusadi, Athirah Taufiq Taufiq