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All Journal TEKNIK INFORMATIKA Zonasi: Jurnal Sistem Informasi Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Journal Of Human And Education (JAHE)
Efendi, Akmar
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Aerospace Engineering Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Addressing Class Imbalance in Machine Learning for Predicting On-Time Student Graduation at The Islamic University of Riau Efendi, Akmar; Defit, Sarjon
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.45913

Abstract

Timely graduation is an important indicator of academic performance in higher education. However, many students still fail to graduate on time, prompting the need for predictive models to support academic decision-making. This study aims to analyze the impact of class imbalance on machine learning algorithm performance in predicting student graduation at the Islamic University of Riau. Data were obtained through questionnaires and labeled into “graduated on time” and “not on time” classes, which were initially imbalanced. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied during preprocessing to balance the dataset. Four machine learning algorithms were compared: Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The evaluation was conducted with and without SMOTE, using accuracy, precision, recall, F1-score, and confusion matrix. Results showed significant performance improvements after applying SMOTE, with all models achieving around 99% accuracy. SVM achieved the most stable results across both conditions. The study highlights the effectiveness of SMOTE in improving classification fairness and reliability, especially in datasets with class imbalance. This work may assist universities in early intervention for students at risk of late graduation.
Meningkatkan Literasi dan Numerasi Melalui Program Pojok Baca Pada Siswa SD IT Diniah Pekanbaru Oktadela, Resy; Elida, Yusti; Efendi, Akmar; Ismail, Syofianis; Shalawati, Shalawati
Journal Of Human And Education (JAHE) Vol. 4 No. 6 (2024): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i6.2106

Abstract

Literasi numerasi adalah kemampuan memahami dan menggunakan simbol serta angka untuk menyelesaikan masalah dalam kehidupan sehari-hari. Berdasarkan hasil survei, tingkat literasi numerasi di Indonesia masih tergolong rendah. Hal ini dipengaruhi oleh berbagai faktor, termasuk kemampuan menyelesaikan masalah matematika dan tingkat literasi siswa. Selain itu, banyak siswa sekolah dasar yang kurang termotivasi untuk membaca dan berhitung. Kegiatan pengabdian ini bertujuan meningkatkan literasi dan numerasi siswa melalui program "Pojok Baca," khususnya dalam membaca teks berbahasa Inggris. Sasaran kegiatan adalah siswa SD IT Diniah Pekanbaru. Metode pelaksanaan mencakup pendampingan melalui survei, sosialisasi, pelatihan, pembelajaran, dan evaluasi. Hasil program menunjukkan peningkatan kemandirian siswa sebesar 65% dan kemampuan literasi numerasi sebesar 55%. Program "Pojok Baca" ini berpotensi dikembangkan lebih lanjut menjadi "Pojok Digital" untuk mendukung literasi digital.
PREDIKSI PERUBAHAN JUMLAH PENDUDUK DI KOTA PEKANBARU MENGGUNAKAN ALGORITMA NAIVE BAYES Efendi, Akmar; Siswanto, Apri; Andika, Rio Fit
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 3 (2024): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i3.22346

Abstract

Penelitian ini mengaplikasikan Algoritma Naive Bayes untuk memprediksi perubahan jumlah penduduk di Kota Pekanbaru berdasarkan data historis dari tahun 1995 hingga 2022. Tujuan dari penelitian ini adalah untuk memahami tren demografis dan memberikan prediksi yang dapat digunakan sebagai dasar dalam pengambilan keputusan terkait perencanaan pembangunan kota. Data jumlah penduduk diolah untuk mengkategorikan perubahan tahunan sebagai peningkatan ("Increase") atau penurunan ("Decrease"). Algoritma Naive Bayes dilatih dan diuji untuk memprediksi kategori perubahan tersebut. Hasil penelitian menunjukkan bahwa Algoritma Naive Bayes mencapai akurasi sebesar 89.29%. Algoritma ini cukup efektif dalam memprediksi peningkatan jumlah penduduk, namun memiliki keterbatasan dalam memprediksi penurunan jumlah penduduk, yang diidentifikasi sebagai akibat dari ketidakseimbangan data. Meskipun demikian, hasil prediksi ini memberikan wawasan yang penting bagi pengambil kebijakan dalam merencanakan pembangunan dan penyediaan layanan publik di Kota Pekanbaru. Untuk penelitian lebih lanjut, disarankan untuk mengeksplorasi metode penyeimbangan data dan model pembelajaran mesin alternatif untuk meningkatkan akurasi prediksi.
Identification of Batik Motif Based Deep Learning-Convolutional Neural Network Approach Oktarino, Ade; Tasri, Yanti Desnita; Efendi, Akmar
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 68 No 3 (2024): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v68i3.381

Abstract

Batik, a rich Indonesian cultural heritage, boasts a diverse array of motifs, each reflecting the unique philosophy of different regions. However, this diversity can make it challenging to distinguish between various batik patterns. This study aims to identify batik motifs using the Convolutional Neural Network (CNN) method. This research dataset comprises 521 digital batik images, encompassing five distinct motifs: Betawi, Cendrawasih, Kawung, Megamendung, and Parang. The data underwent a rigorous processing pipeline, including pre-processing, image segmentation, and feature extraction using Gray Level Co-occurrence Matrix (GLCM). Subsequently, a CNN model was employed for classification. The experimental results yielded an impressive average accuracy of 99.2% in identifying batik motifs. This outcome underscores the efficacy of deep learning, particularly CNNs, in recognizing and categorizing intricate batik patterns. This study may expect to serve a foundational step towards the development of advanced, automated batik recognition systems.
Co-Authors Andika, Rio Fit Elida, Yusti Ismail, Syofianis Oktadela, Resy Oktarino, Ade Sarjon Defit Shalawati, Shalawati Tasri, Yanti Desnita