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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Jasmir Jasmir
Universitas Dinamika Bangsa
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document Jasmir Jasmir; Willy Riyadi; Silvia Rianti Agustini; Yulia Arvita; Despita Meisak; Lies Aryani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.414 KB) | DOI: 10.29207/resti.v6i4.4005

Abstract

In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4. meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5.
PENENTUAN HARGA JUAL PASIR SILIKA DENGAN METODE REGRESI LINIER SEDERHANA BERBASIS WEB Wiken Winata; Sharipuddin Sharipuddin; Jasmir Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 1 No 2 (2021): JAKAKOM Vol 1 No 2 September 2021
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (687.81 KB) | DOI: 10.33998/jakakom.2021.1.2.8

Abstract

ABSTRACT Batanghari Persada Makmur Jambi is a company located in the Jambi area and is engaged in the business of selling silica sand. The many types of sand sizes that have different selling prices cause problems, namely the difficulty in determining an accurate selling price for each type of sand size so that there will be uncertainty in determining the appropriate price, in addition if there are changes in production costs and profit determination, the manual way of pricing is very inefficient. Therefore, this study aims to provide a solution to the problems that occur by offering a decision support system to determine the selling price of sand using the PHP programming language and MySQL database. The author develops systems with the waterfall method and uses a system model approach to unified model language using usecase diagrams, activity diagrams, class diagrams and flowchart diagrams. The new system produces output that can display sand data, order data, admin profile data, customer data, sand price data, and sand price calculations using the Simple Linear Regression method. computerized system design will help company operations more efficiently. Keywords : Design, Decision, Price, Sand ABSTRAK Batanghari Persada Makmur Jambi merupakan salah satu perusahaan yang berlokasi di daerah Jambi dan bergerak dalam usaha penjualan pasir silika. Banyaknya jenis ukuran pasir yang memiliki harga jual yang berbeda-beda menyebabkan adanya permasalahan yaitu sulitnya menentukan harga jual yang akurat untuk setiap jenis ukuran pasir tersebut sehingga akan timbul ketidakpastian dalam menentukan harga yang sesuai, selain itu bila ada perubahan pada biaya produksi maupun penetapan laba maka cara penentuan harga secara manual sangat tidak efisien. Oleh karena itu, penelitian ini bertujuan memberkan sulusi untuk permasalahan yang terjadi dengan menawarkan sistem pendukung keputusan untuk menentukan harga jual pasir menggunakan bahasa pemrograman PHP dan database MySQL. Penulis melakukan pengembangan sistem dengan metode waterfall dan menggunakan pendekatan model sistem unified model language menggunakan usecase diagram, activity diagram, class diagram dan flowchart diagram. Sistem baru menghasilkan output yang dapat menampilkan data pasir, data pesanan, data profil admin, data pelanggan, data harga pasir, dan hasil perhitungan harga pasir dengan metode Regresi Linier Sederhana. perancangan sistem secara terkomputerisasi akan membantu operasional.perusahaan lebih efisien. Kata Kunci : Perancangan, Keputusan, Harga, Pasir
DESIGN OF SALES AND SERVICE INFORMATION SYSTEMS AT THE DELIMA BIKE SHOP Ferry Aldiansah; Jasmir Jasmir; Despita Meisak
International Conference on Business Management and Accounting Vol 1 No 1 (2022): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v1i1.2842

Abstract

Delima Bike Shop is a shop that sells various bicycles and bicycle accessories. In the current system, the Delima Bike Shop in recording sales and purchase data uses writing on paper, namely notes, while stock calculations still use the traditional system using a stock book. Therefore, this study aims to design a sales and service information system using the PHP programming language, MySQL database. This research has stages, namely problem identification, literature study, problem formulation, data collection, system design and report generation. So as to produce a system that can overcome the problems that occur at the Delima Bike Shop in managing its operational activities such as data on suppliers, users, stock of goods, buying and selling, and making reports, making it easier to control data so that it helps the Delima Bike Shop in managing data to be better and orderly.
PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS Willy Riyadi; Jasmir Jasmir
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4191

Abstract

The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic.
Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments Jasmir Jasmir; Nurhadi Nurhadi; Eni Rohaini; M Riza Pahlevi B; Daniel Sintong Pardamean Simanjuntak
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28799

Abstract

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values.
Comparative Analysis of Optimizer Effectiveness in GRU and CNN-GRU Models for Airport Traffic Prediction Willy Riyadi; Jasmir Jasmir
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29659

Abstract

The COVID-19 pandemic has posed significant challenges to airport traffic management, necessitating accurate predictive models. This research evaluates the effectiveness of various optimizers in enhancing airport traffic prediction using Deep Learning models, specifically Gated Recurrent Units (GRU) and Convolutional Neural Network-Gated Recurrent Units (CNN-GRU). We compare the performance of optimizers including RMSprop, Adam, Nadam, AdamW, Adamax, and Lion, and analyze the impact of their parameter tuning on model accuracy. Time series data from airports in the United States, Canada, Chile, and Australia were used, with preprocessing steps like filtering, cleaning, and applying a MinMax Scaler. The data was split into 80% for training and 20% for testing. Our findings reveal that the Adam optimizer paired with the GRU model achieved the lowest Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in the USA. The study underscores the importance of selecting and tuning optimizers, with ReduceLROnPlateau used to adjust the learning rate dynamically, preventing overfitting and improving model convergence. However, limitations include dataset imbalance and region-specific results, which may affect the generalizability of the findings. Future research should address these limitations by developing balanced datasets and exploring optimizer performance across a broader range of regions and conditions. This study lays the groundwork for further investigating sustainable and accurate airport traffic prediction models.
Comparison of word embedding features using deep learning in sentiment analysis Jasmir Jasmir; Errissya Rasywir; Herti Yani; Agus Nugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26223

Abstract

In this research, we use several deep learning methods with the word embedding feature to see their effect on increasing the evaluation value of classification performance from processing sentiment analysis data. The deep learning methods used are conditional random field (CRF), bidirectional long short term memory (BLSTM) and convolutional neural network (CNN). Our test uses social media data from Netflix application user comments. Through experimentation on different iterations of various deep learning techniques alongside multiple word embedding characteristics, the BLSTM algorithm achieved the most notable accuracy rate of 79.5% prior to integrating word embedding features. On the other hand, the highest accuracy value results when using the word embedding feature can be seen in the BLSTM algorithm which uses the word to vector (Word2Vec) feature with a value of 87.1%. Meanwhile, a very significant change in value increase was obtained from the FastText feature in the CNN algorithm. After all the evaluation processes were carried out, the best classification evaluation results were obtained, namely the BLSTM algorithm with stable values on all word embedding features.
An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir Jasmir; Riza Pahlevi; Gunardi Gunardi; Eni Rohaini; Tiko Nur Annisa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
Word Embedding Features to Improve Machine Learning Performance in Sentiment Analysis of the Honor of Kings Game Abdul Harris; Agus Nugroho; Yudi Novianto; Jasmir Jasmir; Dhea Fatma
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5850

Abstract

The rapid growth of social media has encouraged an increasing number of studies on sentiment analysis to better understand public perceptions and opinions. This study aims to evaluate the performance of three machine learning algorithms—Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest—in classifying user review sentiments toward the game Honor of Kings. The dataset was collected from the Google Play Store, consisting of 900 reviews. The data then underwent preprocessing steps including cleaning, case folding, tokenization, stopword removal, stemming, and sentiment labeling into positive and negative classes. Furthermore, three word embedding techniques were applied, namely Word2Vec, GloVe, and FastText, each of which was tested across the three machine learning algorithms. The experimental results indicate that the use of word embedding features significantly improves classification accuracy compared to models without embedding features. KNN combined with FastText achieved the best performance, reaching an accuracy of 87.55%, while Random Forest combined with FastText produced the lowest accuracy. FastText demonstrated superior performance due to its ability to represent words through subword information, making it more effective in handling rare vocabulary and large-scale datasets. This study confirms that combining machine learning classification methods with word embedding features plays a crucial role in improving sentiment analysis performance. Future research may focus on hyperparameter optimization, the application of more advanced preprocessing techniques, and dataset expansion to develop more robust models with better generalization capability.
Co-Authors Abdul Harris Agus Nugroho Daniel Sintong Pardamean Simanjuntak Despita Meisak Dhea Fatma Errissya Rasywir Ferry Aldiansah Gunardi Gunardi Herti Yani Lies Aryani M Riza Pahlevi B Muhammad Riza Pahlevi Nurhadi Nurhadi Rohaini, Eni Sharipuddin, Sharipuddin Silvia Rianti Agustini Tiko Nur Annisa Wiken Winata WILLY RIYADI Yudi Novianto Yulia Arvita