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INDONESIA
INOVTEK Polbeng - Seri Informatika
Published by Politeknik Negeri Bengkalis
ISSN : 25279886     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Inovasi dan Teknologi Seri Informatika (Jurnal INOVTEK Polbeng - Seri Informatika) Politeknik Negeri Bengkalis merupakan jurnal informatika berbasis penelitian ilmiah. Jurnal ini diharapkan dapat sebagai wadah akademisi, peneliti dan praktisi menyebarkan hasil penelitian. Jurnal INOVTEK Polbeng - Seri Informatika menerbitkan naskah berkaitan dengan Web and Mobile Computing, Image processing, System Cerdas, Sistem Informasi, Database, DSS, IT project management, Geographical Information System, Teknologi Informasi, Computer Network and Security, Wireless Sensor Network, dan lainya.
Arjuna Subject : -
Articles 35 Documents
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Search results for , issue "Vol 9, No 1 (2024)" : 35 Documents clear
DETEKSI PLAGIAT TESIS BERBAHASA INDONESIA MENGGUNAKAN METODE COSINE SIMILARITY Ansis, Syukry; Listyaningsih, Endang Palupi; Soetanto, Hari
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4003

Abstract

This research aims to test the performance of the Cosine Similarity method in comparison with the Jaccard Similarity method and to obtain the percentage of similarity. Sample data is obtained from students' data at Budi Luhur campus. The test model will be evaluated by comparing several original theses with documents containing plagiarism. The original documents are processed using Natural Language Processing (NLP) methods. One important NLP method is the Jaro Winkler method, which focuses on spelling correction. Subsequently, text mining algorithms are applied for text processing. The results showed that the Cosine Similarity method achieved high accuracy, at 96.63%, demonstrating its ability to classify documents well as plagiarism or not. The use of Jaccard Similarity shows low accuracy, around 50.5%, but provides an overview of potential improvements or updates to the model to improve performance.Keywords - Cosine Similarity, Jaccard Similarity Thesis Classification, Threshold, NLP
ANALISIS KUALITAS LAYANAN APLIKASI LINKAJA TERHADAP KEPUASAN PENGGUNA MENGGUNAKAN METODE E-SERVQUAL DAN KANO Ramadani, Ela; Hamzah, Muhammad Lutfhi; Syaifullah, Syaifullah; Saputra, Eki
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3814

Abstract

LinkAja merupakan bagian dari e-wallet yang merupakan layanan keuangan elektronik berbasis aplikasi yang bertujuan untuk memberikan kemudahan kepada pengguna memungkinkan pelanggan melakukan transaksi seperti pembelian, pembayaran, transfer,dan lainnya dengan cepat dan mudah. Hasil survey menunjukkan bahwa masalah pertama yang pengguna rasakan adalah sangat kecewa sistem error dan sering terjadi gangguan seperti saldo ada tapi tidak bisa mengirim kesesama pengguna ataupun ke rekening bank. Hasil dari perhitungan metode e-service quality pada setiap atribut masih memiliki rata-rata keseluruhan nilai gap negatif sebesar 0,800 yang berarti Q ≤ 1. Untuk hasil perhitungan kano, 11 atribut teridentifikasi sebagai kebutuhan pengguna yang jelas dan diharapkan dapat dipenuhi, diprioritaskan dan ditingkatkan dalam layanan, 10 atribut layanan perlu diimplementasikan, jika atribut tersebut terpenuhi maka pendapatan dari atribut tersebut dapat meningkatkan kepuasan pengguna yang tinggi walaupun kinerja atribut tersebut mengalami penurunan. Dan 6 atribut layanan yang dibutuhkan untuk mempengaruhi kepuasan pengguna dapat dilihat dari bagaimana layanan tersebut. Aplikasi LinkAja sudah cukup baik namun masih perlu dilakukan evaluasi dan perbaikan pada setiap layanannya agar dapat digunakan dengan lebih efisien. Tujuan penelitian ini adalah untuk menentukan tingkat kepuasan pengguna terhadap layanan yang ditawarkan oleh aplikasi LinkAja.
Penerapan Algoritma Cerdas Bidirectional Encoder Refresentations From Transformers Dalam Menganalisis Opini Publik Terhadap Produk Yang Mengalami Boikot Sulaeman, Asep Surahman; Sujjada, Alun; Kharisma, Ivana Lucia
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4252

Abstract

Social media, particularly Instagram, has become a primary platform for expressing opinions and participating in public discussions on various social, economic, and political issues. One of the prominent issues on social media is product boycotting. Boycotting a product can significantly impact the brand's image and sales. Famous brands such as McDonald’s, KFC, Starbucks, Burger King, and Pizza Hut are the main targets in boycott actions. This study uses a dataset of 1,750 comments from Instagram accounts on related products. The data is divided into two labels, positive and negative, based on automatic labeling from transformers and manual labeling. Sentiment analysis results show that McDonald’s has 41.43% positive sentiment and 58.57% negative sentiment, KFC has 85.14% positive and 14.86% negative, Starbucks has 97.71% positive and 2.29% negative, Burger King has 50% positive and negative, and Pizza Hut has 80.57% positive and 19.43% negative. Modeling results using the pre-trained Bidirectional Encoder Representation From Transformers (BERT) from Bert-Base-Uncased show accuracy results for McDonald’s products at 84.14%, KFC products at 95%, Starbucks products at 94.16%, Burger King products at 91.42%, and Pizza Hut products at 93.80%.
Rancang Bangun Sistem Realtime Notification Progress to Customer Berbasis Website Novtarina, Dita Aulia; Candra, Feri
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4199

Abstract

PT Agung Automall SM. Amin Pekanbaru is a branch of PT Agung Automall, a company operating in the automotive sector. PT Agung Automall SM. Amin, there are several divisions of work, one of which is the Body Paint (BP) division. Problems that arise in the Body Paint (BP) division is that the current system in Body Paint (BP) is less effective, it's because customers have difficulty knowing the progress of their cars. Customers are required to ask the service advisor to find out the progress of their car service. However, with a work system like this, human error often occurs, such as service advisors forgetting/being late in providing information on customer car service progress to customers. Therefore, a system is needed that can provide WhatsApp notifications of customer car service progress. This system uses the WhatsApp API to send notifications to customers. The design stages of this system use the prototype development method. This research produces a Website-based Realtime Notification Progress System that can be used by customers to see the progress of their car. Testing of this system was carried out using black box testing and showed that the system was running well.
PREDIKSI NILAI PENGADAAN BARANG DAN JASA PADA SEBUAH PERUSAHAAN PARIWISATA MENGGUNAKAN METODE ARIMA DAN FUZZY TIME SERIES Wati, Lisna; Solichin, Achmad
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4041

Abstract

PT XYZ does not have a tested evaluation metric to measure the accuracy of procurement prediction models. This research aims to find a suitable metric by comparing two methods: Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS). Both methods were chosen based on the ability of ARIMA to handle time patterns and trends, and the flexibility of FTS in dealing with uncertainty in procurement values. The research uses Root Mean Squared Error (RMSE) values to measure prediction accuracy. The ARIMA implementation analyzes historical data to forecast patterns, while FTS accommodates fluctuations and uncertainties, allowing for more adaptive and accurate predictions. The analysis results show that the ARIMA model has an AIC value of 3953.57 and a residual value of 3351745.26, while FTS has a residual of -224.79. The RMSE evaluation shows the ARIMA value of 3351745.30 and FTS of 224793895.00. The predicted value of ARIMA is 440,326,255, while FTS is 668,471,895. Based on these results, FTS shows superior prediction accuracy compared to ARIMA. FTS is recommended to be implemented at PT XYZ due to its ability to effectively manage uncertainty and fluctuations in procurement values, enabling more accurate strategic decisions. Further research is needed to understand the factors that influence the performance difference between these two methods.

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