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All Journal Bulletin of Information Technology (BIT) Prosiding Seminar Nasional Keguruan dan Pendidikan (SNKP)
Farta wijaya, Rian
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Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Other

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Implementation of E-Commerce System as SME Development Strategy in the Digital Era Saputra, Maulian; Susilawati; Nurhaliza Sofyan, Siti; Aulia, Ananda; Ernawati, Andi; Oftasari, Ayu; Farta wijaya, Rian
Bulletin of Information Technology (BIT) Vol 5 No 3: September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i3.1545

Abstract

The implementation of e-commerce systems has become one of the main strategies in the development of Small and Medium Enterprises (SMEs) in the digital era. E-commerce allows SMEs to expand market reach, improve operational efficiency, and strengthen relationships with consumers through better data access. In addition, this digital platform offers benefits such as distribution cost savings, business process automation, and improved customer service. However, challenges in e-commerce adoption for SMEs include limited digital literacy, uneven technology infrastructure, and cybersecurity issues. To achieve the full potential of e-commerce, support from the government and private sector in the form of adequate policies, infrastructure, and training is required. This research aims to identify the benefits, challenges and solutions in implementing e-commerce for SMEs, in order to improve their competitiveness in an increasingly competitive global market.
Analisis Sentimen Penerapan Deep Learning dan Analisis Sentimen terhadap Gap Kompetensi Lulusan Lembaga Pendidikan dan Pelatihan Vokasi terhadap Dunia Kerja dengan Metode Long Short-Term Memory (LSTM) Yahya, Susilawati; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2031

Abstract

The gap between vocational graduates’ competencies and labor market demands remains a pressing issue in Indonesia. This study aims to analyze alumni perceptions regarding the alignment between competencies acquired during their studies at LP3I Banda Aceh and real-world job requirements. A quantitative approach was adopted using a deep learning method based on Long Short-Term Memory (LSTM). Data were collected through an online survey containing open-ended responses from 934 alumni, followed by preprocessing, tokenization, lexicon-based sentiment labeling, and data splitting into training and testing sets. The models developed included pure LSTM, LSTM with class weights, and Bidirectional LSTM (BiLSTM). Results indicate that BiLSTM achieved the highest performance with 90% accuracy and a weighted F1-score of 0.91. Additionally, 44.5% of respondents expressed neutral or negative sentiments, highlighting a mismatch between acquired competencies and industry demands. These findings underscore the urgency of curriculum evaluation and stronger collaboration between vocational institutions and the labor market. This study demonstrates that deep learning offers an efficient and objective tool for competency mapping in vocational education.
Sentiment Analysis Classification of E-commerce User Reviews Using Natural Language Processing (NLP) and Support Vector Machine (SVM) Methods Iqbal Wiranata Siregar, Jimmy; Putera Utama Siahaan, Andysah; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2018

Abstract

In the swiftly changing digital age, e-commerce has become a vital component of everyday living. Individuals actively share product reviews, whether favorable or unfavorable, which companies can utilize to grasp users' views on their services. An efficient approach for evaluating and categorizing user sentiments is required to aid in analyzing these reviews. In this scenario, the Support Vector Machine (SVM) and Natural Language Processing (NLP) methods offer the appropriate answer. This research intends to develop a classification model capable of sorting e-commerce user feedback into positive, negative, or neutral sentiments. Utilizing NLP methods to analyze the review text and SVM as the classification approach, this model aims to achieve high accuracy in identifying user sentiment. Words that do not affect sentiment analysis, like "and," "that," "for," are eliminated, and SVM is utilized once the review data is converted into vectors via the TF-IDF method. The labeled sentiment training data will be used to train the SVM model.
Rancangan Sistem Interface Tim Penggerak Pemberdayaan Kesejahteraan Keluarga Pada Desa Kelambir V Kebun Eka Putra; khairul; Farta Wijaya, Rian; Putra Nauli Harahap, Sutan
PROSIDING SEMINAR NASIONAL KEGURUAN DAN PENDIDIKAN (SNKP) Vol. 2 (2024): Prosiding Seminar Keguruan dan Pendidikan (SNKP) 2024
Publisher : LPPM UNIVERSITAS MUHAMMADIYAH MUARA BUNGO

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

Abstract

Pada Perkembangan Teknologi saat ini semakin canggih yang mana di instansi sudah banyak yang mengunakan aplikasi berbasis web. Pada Desa Kelambir v Kebun saat ini proses pengolahan data PKK saat ini masih menggunakan microsoft office dalam proses penyimpanan data. Proses penyimpanan data seperti ini dapat menyebabkan data tidak aman. Maka dari itu peniliti akan membuat suatu rancangan untuk menyimpan data dengan komputerisasi yang cukup memadai dan aman. Agar laporan yang akan dibutuhkan nantinya dapat diproses secara tepat oleh Pihak Desa Kelambir v Kebun. Dari masalah yang sudah didapat sehingga didapatlah sebuah solusi dengan cara menerapkan Rancangan sistem PKK berbasis web. Dengan adanya penelitian ini dapat mempermudah penyimpanan data dan pada saaat data dibutuhkan  tidak memakan waktu yang lama.
Co-Authors Andi Ernawati Andysah Putera Utama Siahaan Aulia, Ananda Eka Putra Iqbal Wiranata Siregar, Jimmy Khairul Muhammad Iqbal Nasution, Darmeli Nurhaliza Sofyan, Siti Oftasari, Ayu Putra Nauli Harahap, Sutan Saputra, Maulian Sitorus, Zulham Susilawati Yahya, Susilawati