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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer
Safitri, Sindy Eka
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

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Pengujian Website Sistem Pakar Penentuan Kepribadian Menggunakan Metode Forward Chaining dan Decision Table Sunniyyah Salma Fatihatur; Safitri, Sindy Eka; Rahmadani, Nurul; Anshor, Ibnu Kholil; Arsy S, Dessyaka
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14286

Abstract

Keputusan dalam aspek pendidikan, karir, dan hubungan sosial sering dipengaruhi oleh pemahaman terhadap kepribadian individu. Mengenali kepribadian biasanya membutuhkan bantuan psikolog, tetapi dengan kemajuan teknologi, alternatif yang lebih mudah diakses kini dapat dikembangkan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pakar berbasis web untuk membantu pengguna mengenali tipe kepribadiannya, seperti sanguinis, melankolis, plegmatis, atau koleris. Sistem ini menggunakan metode Forward Chaining, di mana kesimpulan diambil berdasarkan data atau jawaban yang diberikan pengguna melalui serangkaian pertanyaan. Antarmuka sistem dirancang sederhana dan user-friendly agar mudah digunakan oleh semua kalangan. Untuk memastikan keakuratan dan kehandalan, pengujian sistem dilakukan menggunakan metode black-box dengan pendekatan decision table. Metode ini digunakan untuk memverifikasi apakah sistem memproses input sesuai dengan aturan yang telah ditentukan. Hasil penelitian menunjukkan bahwa sistem berhasil mengidentifikasi tipe kepribadian pengguna dengan tingkat akurasi yang baik dan bekerja sesuai dengan aturan yang dirancang. Dengan pendekatan berbasis teknologi ini, diharapkan sistem pakar dapat menjadi solusi praktis bagi individu untuk mengenali potensi dirinya, mendukung pengambilan keputusan, serta meningkatkan pemahaman terhadap aspek psikologis yang relevan dalam kehidupan sehari-hari.
User Opinion Mining on the Maxim Application Reviews Using BERT-Base Multilingual Uncased Safitri, Sindy Eka; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2391

Abstract

Online transportation applications such as Maxim are increasingly used due to the convenience they offer in ordering services. As usage increases, the number of user reviews also grows, serving as a valuable source of information for evaluating customer satisfaction and service quality. Sentiment analysis of these reviews can help companies understand user perceptions and improve service quality. This study aims to analyze the sentiment of user reviews on the Maxim application using the BERT-Base Multilingual Uncased model. BERT was chosen for its ability to understand sentence context bidirectionally, and it has proven to outperform traditional models such as MultinomialNB and SVM in previous studies, with an accuracy of 75.6%. The dataset used consists of 10,000 user reviews with an imbalanced distribution: 4,000 negative, 2,000 neutral, and 4,000 positive reviews. The data was split into 90% training data (9,000 reviews) and 10% test data (1,000 reviews). From the 9,000 training data, 15% or 1,350 reviews were allocated as validation data, resulting in a final training set of 7,650 reviews. Evaluation results show that BERT is capable of classifying sentiment into three categories positive, neutral, and negative, with an accuracy of 94.7%. The highest F1-score was achieved in the positive class (0.9621), followed by the neutral class (0.9412), and the negative class (0.9246). The confusion matrix shows that most predictions match the actual labels. These findings indicate that BERT is an effective and reliable model for performing sentiment analysis on user reviews of online transportation applications such as Maxim.
Co-Authors Anshor, Ibnu Kholil Arsy S, Dessyaka Khothibul Umam Maya Rini Handayani Rahmadani, Nurul Sunniyyah Salma Fatihatur Wenty Dwi Yuniarti, Wenty Dwi