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Menampilkan 11–16 dari 16 artikel
Klasifikasi Sentimen Ulasan Produk Olahraga di Tokopedia Menggunakan Metode Machine Learning dengan Pendekatan TF-IDF
Fransiskus Dapot Sihaloho
; Jasmir Jasmir
; Gunardi Gunardi
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Lo...
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Model Deteksi Mahasiswa Dropout Menggunakan Artificial Neural Network
Denia Igesti Nur Mellyati
; Kurniabudi Kurniabudi
; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education datas...
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Perancangan Sistem Pendukung Keputusan Untuk Seleksi Siswa Non-Akademik Cabang FLS3N di SMA Negeri 2 Muaro Jambi dengan Metode Topsis
Ayu Anggelina
; Fachruddin Fachruddin
; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to...
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Optimasi Software Effort Estimation Menggunakan Random Forest
Maria Rosario Borroek
; Jasmir Jasmir
; Fachruddin Fachruddin
; Marrylinteri Istoningtyas
; Yosefina Venus
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.
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Model Prediksi Pelunasan Haji Berbasis XGBoost Dengan Interpretasi Shap: Studi Prediksi Pelunasan Haji dengan XGBoost dan SHAP di Provinsi Jambi
Yan Apriadi
; Dodo Zaenal Abidin
; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was emplo...
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Perbandingan Akurasi BERT dan RNN pada Analisis Sentimen Komentar Hotel
Mahruzar, Mahruzar
; Setiawan Assegaff
; Jasmir Jasmir
; Yosefina Venus
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodolo...
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