Article Per Year (5 Year)
p-Index From 2020 - 2025
0.882
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Wahana Pendidikan JEMSI (Jurnal Ekonomi, Manajemen, dan Akuntansi) EduMatika: Jurnal MIPA Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam AKSIOMA : Jurnal Sains Ekonomi dan Edukasi
Simanjuntak, Ferdyanto Abangan
Unknown Affiliation
Religion Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
ANALISIS FAKTOR FAKTOR YANG MEMPENGARUHI PELUANG PENERIMAAN PELAMARAN KERJA MENGGUNAKAN METODE REGRESI LOGISTIK BINER Silaban, Dewi Fortuna; Simanjuntak, Ferdyanto Abangan; Lumbantobing, Imelda Octavia; Napitupulu, Rusmawanty; Arnita
Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 5 No. 1 (2024): Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3483/trigonometri.v5i1.7724

Abstract

Penelitian ini menerapkan analisis regresi logistik biner untuk menilai faktor-faktor yang mempengaruhi kemungkinan diterimanya pelamar kerja. Variabel independen dalam model ini meliputi durasi pendidikan terakhir (X1) dalam tahun, lama pengalaman kerja (X2) dalam tahun, dan jenis kelamin pelamar (X3), di mana 1 menunjukkan laki-laki dan 0 menunjukkan perempuan. Variabel dependen adalah status penerimaan (Y), di mana 1 berarti diterima dan 0 berarti tidak diterima. Analisis ini bertujuan untuk mengukur dampak setiap variabel independen terhadap kemungkinan penerimaan selama proses seleksi, menggunakan SPSS untuk pengujian data. Uji kecocokan model menunjukkan nilai signifikansi sebesar 0,857, yang mengindikasikan tidak ada perbedaan signifikan antara klasifikasi yang diprediksi dan yang diamati, sehingga memvalidasi kesesuaian model untuk analisis lebih lanjut. Nilai Nagelkerke R Square sebesar 0,405 menunjukkan bahwa variabel independen menjelaskan 40% varians dalam status penerimaan, sementara sisanya dijelaskan oleh faktor-faktor yang belum diuji. Uji pengaruh simultan mengungkapkan nilai signifikansi 0,002, mengonfirmasi dampak kolektif variabel independen terhadap variabel dependen. Uji pengaruh parsial menunjukkan bahwa durasi pendidikan dan pengalaman kerja secara signifikan mempengaruhi penerimaan, dengan nilai signifikansi masing-masing 0,021 dan 0,011. Model ini secara akurat memprediksi karyawan yang diterima (akurasi 100%) tetapi gagal memprediksi mereka yang tidak diterima (akurasi 0%), menghasilkan akurasi klasifikasi keseluruhan yang moderat sebesar 50%. Model regresi logistik biner ini cocok untuk analisis lebih lanjut dalam mengevaluasi faktor-faktor yang mempengaruhi penerimaan karyawan, dengan potensi peningkatan kemampuan prediktif melalui penggabungan variabel relevan tambahan.
Analisis Faktor Tingkat Kebahagiaan Negara Menggunakan Data World Happiness Report dengan Metode Newton Raphson Lumbantobing, Imelda Octavia; Br Surbakti, Arnis Wulan Andari; Maulana, Bintang; Simanjuntak, Ferdyanto Abangan
Jurnal Ilmiah Wahana Pendidikan Vol 11 No 2.C (2025): Jurnal Ilmiah Wahana Pendidikan 
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini menganalisis faktor-faktor yang memengaruhi tingkat kebahagiaan negara berdasarkan data World Happiness Report tahun 2022. Dengan menggunakan metode Newton-Raphson, penelitian ini memodelkan hubungan non-linear antara berbagai variabel independen, seperti GDP per kapita, dukungan sosial, harapan hidup sehat, kebebasan membuat pilihan hidup, dan kemurahan hati, dengan variabel dependen berupa skor kebahagiaan. Hasil penelitian menunjukkan hubungan positif antara GDP per kapita dan tingkat kebahagiaan, meskipun tidak linier untuk semua negara. Metode Newton-Raphson terbukti efektif dalam menghasilkan estimasi parameter yang akurat, membantu pemahaman lebih dalam tentang determinan kebahagiaan. Temuan ini memberikan wawasan penting untuk strategi kebijakan yang lebih baik dalam meningkatkan kesejahteraan masyarakat
Pengaruh Tingkat Bunga Diskrit dan Jangka Waktu Pinjaman terhadap Skema Amortisasi dan Total Biaya Pelunasan Utang Ndruru, Albert Servant; Simangusong, Enjelita; Simanjuntak, Ferdyanto Abangan; Harefa, Alfon Brian
JEMSI (Jurnal Ekonomi, Manajemen, dan Akuntansi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Sekretariat Pusat Lembaga Komunitas Informasi Teknologi Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jemsi.v11i3.4066

Abstract

This study analyzes the effect of discrete interest rates and loan terms on the amortization scheme and total debt repayment costs. Debt amortization is a method of paying off loans periodically consisting of principal and interest. Variations in interest rates and loan terms can affect the amount of installments, the proportion of interest payments to principal, and the total costs that must be paid by the borrower. Using a quantitative approach and numerical simulation, this study evaluates the relationship between these variables to understand their impact on the debt repayment structure. The results show that higher interest rates increase total interest expenses, while longer loan terms can reduce monthly installments but increase total repayment costs due to interest accumulation. These findings are expected to help financial institutions in formulating optimal credit policies and provide guidance for borrowers in choosing the most efficient loan scheme.
Development of Deep Learning Model Based on Convolutional Neural Network (CNN) for Brain Tumor Classification Using MRI Images Sinaga, May Rani Tabitha; Tampubolon, Bungaria; Daulay, Nurfitri Humayro; Triana, Dinie; Hani, Aulia; Simanjuntak, Ferdyanto Abangan; Arnita, Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1105

Abstract

Brain tumor classification using MRI images presents a critical challenge in medical radiology. This study develops a deep learning model based on Convolutional Neural Network (CNN) to classify brain MRI images into four categories: Normal, Glioma, Meningioma, and Pituitary. A publicly available dataset from Kaggle consisting of 20,672 images was used, with preprocessing and data augmentation applied. The model architecture includes convolutional, pooling, flatten, dense, and dropout layers, optimized using the Adam optimizer and categorical crossentropy loss function. The evaluation results show that the model achieved an overall accuracy of 96% with high f1-scores across all classes, particularly for the Pituitary class (0.98). The main contribution of this study lies in the integration of diverse data augmentation techniques and Explainable AI (XAI) methods, enabling the visualization of key areas in MRI images that support classification decisions. The proposed model is not only accurate but also demonstrates strong generalization and interpretability, making it a promising tool for clinical decision support systems in brain tumor diagnosis.
Analisis Menggunakan Peta Kendali I-MR dan Diagram Pareto pada Produksi Kayu Lapis di PT. SLJ Global Tbk, Samarinda Lubis, Mery Christyn; Simanjuntak, Ferdyanto Abangan; Payana, Sandi Dwi
AKSIOMA : Jurnal Sains Ekonomi dan Edukasi Vol. 2 No. 6 (2025): AKSIOMA : Jurnal Sains, Ekonomi dan Edukasi
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/aksioma.v2i6.1340

Abstract

This study aims to evaluate the quality of plywood production at PT. SLJ Global Tbk using the Statistical Quality Control (SQC) approach, specifically through the application of the I-MR control chart. The data used were collected from the production output during September 2021. The analysis was conducted using both the Individuals Chart (I-Chart) and the Moving Range Chart (MR-Chart), complemented by a Pareto analysis to identify the most dominant types of defects. The results indicate that the overall production process is within statistical control limits; however, there is a violation of Western Electric Rule 4, suggesting a potential shift in the production process. The most dominant types of defects identified are Press Mark, Delamination, and Overlapped, which together account for over 75% of all detected defects. These findings highlight the need for targeted improvements in these areas to enhance product quality. The study is limited by a short observation period and lacks a thorough investigation into the root causes of the defects. Future research is recommended over a longer period and should incorporate root cause analysis to support comprehensive quality improvement efforts.
Co-Authors Arnita Arnita Arnita Br Surbakti, Arnis Wulan Andari Daulay, Nurfitri Humayro Hani, Aulia Harefa, Alfon Brian Lubis, Mery Christyn Lumbantobing, Imelda Octavia Maulana, Bintang Napitupulu, Rusmawanty Ndruru, Albert Servant Payana, Sandi Dwi Silaban, Dewi Fortuna Simangusong, Enjelita Sinaga, May Rani Tabitha Tampubolon, Bungaria Triana, Dinie