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All Journal Jurnal Ilmiah Smart International Journal of Engineering, Science and Information Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Smart Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD)
Pieter Octaviandy
STMIK TIME
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ANALISIS MOTIVASI TERHADAP KINERJA KARYAWAN PADA STMIK TIME MEDAN Pieter Octaviandy; Mervin Angeline
Jurnal Manajemen Akuntansi Dan Administrasi Bisnis Vol 3 No 1 (2019): Jurnal Ilmiah SMART
Publisher : Sekolah Tinggi Manajemen Bisnis (STMB) MultiSmart

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.982 KB)

Abstract

Penelitian ini bertujuan untuk menganalisis dan mengetahui motivasi terhadap kinerja karyawan pada STMIK TIME Medan tahun 2019. Penelitian ini menggunakan metode deskriptif kuantitatif. Pengumpulan data menggunakan teknik studi pustaka, wawancara, observasi dan dokumentasi. Hasil dari penelitian ini menunjukan motivasi karyawan bekerja sangat tinggi yang dapat dilihat dari kinerja semester 1 (satu) 2019 yang cukup baik.
ANALISIS MOTIVASI TERHADAP KINERJA KARYAWAN PADA STMIK TIME MEDAN Pieter Octaviandy; Mervin Angeline
Jurnal Manajemen Akuntansi Dan Administrasi Bisnis Vol 3 No 1 (2019): Jurnal Ilmiah SMART
Publisher : Sekolah Tinggi Manajemen Bisnis (STMB) MultiSmart

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

Abstract

Penelitian ini bertujuan untuk menganalisis dan mengetahui motivasi terhadap kinerja karyawan pada STMIK TIME Medan tahun 2019. Penelitian ini menggunakan metode deskriptif kuantitatif. Pengumpulan data menggunakan teknik studi pustaka, wawancara, observasi dan dokumentasi. Hasil dari penelitian ini menunjukan motivasi karyawan bekerja sangat tinggi yang dapat dilihat dari kinerja semester 1 (satu) 2019 yang cukup baik.
Analisis Kepuasan Pengguna Terhadap Aplikasi Maxim di Kota Medan Menggunakan Model E-Servqual Rezkyta Agil; Pieter Octaviandy; Feriani Astuti Tarigan
Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD) Vol 1 No 1 (2025): November 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jiibd.v1i1.1825

Abstract

Perkembangan teknologi informasi telah memfasilitasi berbagai kegiatan, termasuk layanan transportasi online. Maxim, sebagai salah satu penyedia layanan transportasi digital, telah menerima banyak keluhan pengguna, terutama mengenai fitur navigasi, desain antarmuka, dan sistem pembayaran elektroniknya. Penelitian ini bertujuan untuk menganalisis kepuasan pengguna terhadap aplikasi Maxim di Kota Medan menggunakan metode E-Servqual, yang mencakup lima dimensi: tangibles, reliability, responsiveness, assurance, dan empathy. Penelitian menggunakan pendekatan kuantitatif dengan mendistribusikan kuesioner online kepada 104 responden. Hasil penelitian menunjukkan bahwa semua dimensi memiliki nilai GAP negatif, dengan berwujud menjadi faktor paling dominan yang mempengaruhi kepuasan pengguna. Sebagai solusinya, prototipe dasbor real-time dirancang untuk membantu pengembang memantau evaluasi fitur secara langsung. Penelitian ini diharapkan dapat menjadi acuan untuk meningkatkan kualitas layanan Maxim di masa mendatang.
An Analysis of User Satisfaction Toward the Maxim Application in Medan City using the E-Servqual Model Rezkyta Agil; Pieter Octaviandy; Feriani Astuti Tarigan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1688

Abstract

The development of information technology has facilitated various activities, including online transportation services. Maxim, as one of the digital transportation service providers, has received numerous user complaints, particularly regarding its navigation features, interface design, and electronic payment system. This study aims to analyze user satisfaction with the Maxim application in Medan City using the E-Servqual method, which includes five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The research used a quantitative approach by distributing online questionnaires to 104 respondents. The results showed that all dimensions had negative GAP values, with tangibles being the most dominant factor affecting user satisfaction. As a solution, a real-time dashboard prototype was designed to help developers monitor feature evaluations directly. This research is expected to serve as a reference for improving the quality of Maxim’s services in the future.
Hybrid Deep Fixed K-Means (HDF-KMeans) Muhammad Khahfi Zuhanda; Kelvin Leonardi Kohsasih; Pieter Octaviandy; Hartono Hartono; Dian Kurnia; Nurliana Tarigan; Manan Ginting; Manahan Hutagalung
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
Co-Authors Dian Kurnia Feriani Astuti Tarigan Hartono Hartono Kelvin Leonardi Kohsasih Manahan Hutagalung Manan Ginting Mervin Angeline Muhammad Khahfi Zuhanda Nurliana Tarigan Rezkyta Agil