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Jurnal Teknologi Terpadu
Published by STT Terpadu Nurul Fikri
ISSN : 24770043     EISSN : 24607908     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
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Search results for , issue "Vol 10 No 1 (2024): Juli, 2024" : 10 Documents clear
Klasifikasi Penyakit Daun Pisang menggunakan Convolutional Neural Network (CNN) Pratama, M Duta; Gustriansyah, Rendra; Purnamasari, Evi
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1167

Abstract

Bananas are a fruit with promising economic value in Indonesia. They are an essential commodity for farmers, but diseases affecting banana plants can harm their livelihoods. Banana diseases initially attack the leaves, and in the early stages, they are difficult to differentiate with the naked eye due to farmers’ limited knowledge of pathogens. This research utilized the Convolutional Neural Network (CNN) method with transfer learning assistance using Google Colab to facilitate the classification of banana leaf diseases. The trained model experienced overfitting, so regularization was applied using dropout. The best model achieved an accuracy of 92%, precision of 92%, sensitivity of 91%, and an F1-score of 91% at a 70:20:10 ratio on epoch 80, as evaluated and validated using a confusion matrix. This study produced a reliable model for classifying banana leaf disease.
Penerapan Metode Combined Compromise Solution (CoCoSo) dalam Pemilihan Franchise Minuman Marito, Julita; Nainggolan, Wahyuni Betris; Mahendra, Gede Surya
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1183

Abstract

The research in this journal is motivated by the beverage franchise industry, which has become one of the most dynamic global business sectors. This article examines applying the Combined Compromise Solution (CoCoSo) method to the decision-making system (SPK) in selecting beverage franchises. The beverage franchise data that we use consists of 9 brands that are pretty famous among the public, such as Kopi Kenangan, Es Teh Indonesia, Teh Poci, Calais Tea, Puyo Puyo, Gulu Gulu, Kopi Kulo, Xi boba, Kopi Yor. In the process of data collection, we use observation and research. The data analysis process is carried out using the combined compromise solution method, one of the multi-criteria decision-making (MCDM) methods that can be used to select alternatives based on the calculation of criteria weights. This method can facilitate the determination of beverage franchises because it is more effective and efficient in calculating and ranking. Through the decision-making system that has been developed, the value of the beverage franchise can be generated based on predetermined criteria. Calculation of beverage franchises using CoCoSo shows the results of the calculation of the highest preference value obtained by the Puyo Puyo beverage franchise with a final value of 2.3436 and the lowest preference value obtained by the Kopi Kenangan beverage franchise with a final value of 1.3385.
Implementasi Metode Hybrid Filtering Technique pada Penentuan Rating Pestisida Ardimansyah, Ardimansyah; Husain, Husain; Herlinda, Herlinda; Kasmawaru, Kasmawaru; Nurdiansah, Nurdiansah; Marsa, Marsa
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1184

Abstract

Pesticides result from mixing organic chemicals that farmers use to protect their rice plants from disease. Farmers find it difficult to determine pesticide selection due to insufficient information.  So many pesticide products are available on the market, and their various advantages make it increasingly difficult for farmers to choose pesticides suitable for certain rice diseases. This research aims to provide farmers with recommendations on determining the best pesticide to eradicate rice diseases. The wrong choice of pesticide used can harm or reduce farmers' crop yields. This research used the Hybrid Filtering Technique combined with Content Based Filtering and Collaborative Filtering methods to search for weight values ​​and rating prediction values ​​using price criteria, pesticide ingredients, and form (liquid, solid, powder). The results of the calculation analysis of implementing the hybrid filtering technique method for each alternative criterion can simulate a ranking to recommend the best pesticide to eradicate the causes of rice disease. This research has concluded that the rating carried out by farmers who have used pesticides influences the determination of the rating value for each pesticide product. The system test results showed that the type of pesticide with the highest rating value was the enquity pesticide, with a value of 2,256.
Analisis Prediksi Kata Kunci Situs Web MonsterMAC dengan Metode Long Short-Term Memory (LSTM) Hanif Assalmi, Fityan; Syaifullah Jauharis Saputra, Wahyu; Muhaimin, Amri
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1187

Abstract

Amid increasingly fierce competition in the digital realm, many companies are striving to increase the number of visitors to their websites. One such competing company is MonsterMAC, a startup. This research aims to provide early warnings and analyze relevant keywords on the MonsterMAC website using the Long Short-Term Memory (LSTM) method. Visitor data from Google Analytics and keyword data from Google Trends for the period July 22, 2022, to July 15, 2023, have been collected and processed through several stages, such as preprocessing, model design, LSTM training, and testing, as well as visualization and interpretation of results. The modeling results show satisfactory performance, with MAE Train Real User = 0.0615, Vending Machine = 0.0218, IoT = 0.0284, Machine Learning = 0.0365, Digital Business = 0.0186, Business Intelligence = 0.0296. Furthermore, this research indicates that the number of visitors is predicted to increase but will also experience a sharp decline in the coming days. The use of the keyword "IoT" shows a significant increasing trend. Implementing the keyword "IoT" in SEO strategies has increased the number of visitors over the next seven days from 250 to 350. This research guides website owners in optimizing their content and SEO strategies to increase their visibility and competitiveness in a highly competitive digital environment. This research also emphasizes the importance of the LSTM method in keyword analysis and prediction to create more targeted SEO strategies.
Optimasi Parameter DBSCAN menggunakan Metode Differential Evolution untuk Deteksi Anomali pada Data Transaksi Bank Ibadirachman, Rifqi Karunia; Chrisnanto, Yulison Herry; Sabrina, Puspita Nurul
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1189

Abstract

Anomalies in bank transaction data often indicate fraudulent activity or errors. This research aims to detect anomalies in bank transaction data by optimizing DBSCAN parameters using the Differential Evolution (DE) method because there are shortcomings, namely the difficulty of determining the right parameters to create the right cluster in order to detect anomalies in bank transaction data properly. The data used is transaction data from Bank XYZ with more than 1011 data records. The research stages include data collection, data preprocessing (data cleaning, normalization, and transformation), system design, algorithm implementation, and analysis and testing using the Silhouette score and Z-score methods. The DE method is used to automatically determine the optimal parameters of MinPts and Epsilon. The results show that the use of DE can produce optimal parameters, with increased anomaly detection accuracy using DBSCAN. Evaluation with Silhouette score shows an average accuracy of 0.7916 and using DBI reaches 0.19791 at the lowest, while Z-score and MSE measurements show high cluster density with anomaly detection accuracy reaching 98.41% and 0.555537. The DE approach to parameter selection is effective in improving the performance of DBSCAN in detecting anomalies in bank transaction data. Suggestions for future research are to increase the number of data records and conduct experiments on a wider variety of data attributes.
Perbandingan Klasifikasi Label Tunggal untuk Soal Ujian Fisika menggunakan Naïve Bayes dan K-Fold Cross Validation Herijanto, Christopher Kevin; Wahyuningsih, Yulia
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1210

Abstract

This research evaluates the use of the Naïve Bayes algorithm in classifying Physics questions with single labels. The main objective is to identify the best algorithm for classifying Physics questions to assist high school students with difficulty understanding them. The research method involves using a dataset containing Physics questions that need to be classified to facilitate learning for high school students. The Naïve Bayes algorithm is implemented using Google Colab to train the classification model using features extracted from the text of the Physics questions. Additionally, several other classification algorithms, such as Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest, are tested, and their performance is compared. Experimental results show that Naïve Bayes provides competitive results in classifying single-label Physics questions. However, there are significant performance differences between Naïve Bayes and other algorithms, depending on the type and complexity of the classified Physics problems. In this study, SVM achieved higher accuracy, but Naïve Bayes excelled in training time. This research provides a deeper understanding of the strengths and weaknesses of Naïve Bayes in solving the task of classifying single-label Physics problems. These findings guide the development of more accurate classification models for application in the context of Physics learning.
Deteksi Citra Daun untuk Klasifikasi Penyakit Padi menggunakan Pendekatan Deep Learning dengan Model CNN Rijal, Muhammad; Yani, Andi Muhammad; Rahman, Abdul
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1224

Abstract

Agriculture is a vital sector related to food security. Rice is one of the productions that currently ranks third behind wheat and corn. However, in 2023, rice production in Indonesia will decrease 2022 by 1.12 million tons of GKG, and Diseases in plants are one of the causes of the reduced quantity of agricultural products. This research aims to detect disease in rice plants using leaf images with three classification classes and a test matrix to measure the model built. This research uses the Convolutional Neural Network (CNN) method to classify rice plants based on leaf images with 3 test scenarios using the Jupyter Notebook text editor tool for system coding. Research results with training show that the CNN model can classify diseases in rice based on leaf images. Of the 3 test scenarios carried out, scenario 2 shows the best results with Epoch 50 with training values ​​from the last Epoch, namely training accuracy 0.9905 and training loss 0.0280 while validation accuracy 0.8000 and The validation loss is 0.9222 with the confusion matrix showing the suitability of predictions based on class with the classification report good recall, precision and f1-score values, namely 1.00.
Implementasi Bi-LSTM dengan Ekstraksi Fitur Word2Vec untuk Pengembangan Analisis Sentimen Aplikasi Identitas Kependudukan Digital Onsu, Romario; Sengkey, Daniel Febrian; Kambey, Feisy Diane
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1225

Abstract

The Indonesian government is striving to enhance digital public services, including the Digital Identity Application (IKD) launched in 2022 by the Directorate General of Population and Civil Registration. Since its launch, IKD has received various responses from the public. User reviews on Google Play Store indicate a decline in ratings from June to December 2023. Review analysis is essential to understand user satisfaction, identify issues, and guide application improvements. This study aims to perform sentiment analysis on IKD user reviews using Bidirectional Long Short-Term Memory (Bi-LSTM) and Word2Vec methods. Bi-LSTM and Word2Vec are used to develop sentiment analysis from previous research that still used Machine Learning methods. This research is expected to contribute to the development of sentiment analysis models using Deep Learning for the IKD application. Review data was collected from the Google Play Store using scraping techniques for the period January-December 2023 and categorized into positive and negative. The Bi-LSTM model was trained with Word2Vec CBOW and Skip-Gram variations with dimensions of 100, 200, and 300. The results show that the combination of Bi-LSTM and Word2Vec CBOW with a dimension of 200 and a data split ratio of 80/20 produced the highest accuracy of 96.06%, with a precision of 96.44%, recall of 95.64%, and an f1-score of 96.04%. All combinations of Bi-LSTM and Word2Vec outperformed other Machine Learning algorithms.
Pengaruh Jarak Objek Citra pada Model Deteksi dan Klasifikasi Botol Plastik menggunakan YOLO Rosanti, Nurvelly; Latifah, Retnani; Munir, Sirojul; Maududi, Izzuddin Al Qossam
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1247

Abstract

Plastic bottle waste must be separated based on shape and size to facilitate recycling. Sorting plastic bottles can use object detection technology to facilitate classification using images. Image distance capture affects the classification of bottle waste because large bottles will look small when seen from a distance and vice versa. This study aims to create a plastic bottle detection and classification model using the YOLOv8 algorithm with the same bottle shape but different sizes and measure the effect of image distance on the model. Bottles consist of three sizes: large bottles measuring 1500 ml, medium bottles measuring 600 ml, and small sizes 330 ml. Pictures for the bottle image dataset were shot between 80 and 100 centimeters away. Robotoflow was used to produce the dataset. Model performance evaluation used Mean Average Precision, and model testing used a confusion matrix. The test results for the same model with an image capture distance had an accuracy value of 100%. Testing of 80 cm distance images applied to the 100 cm model had an accuracy of 67%. Testing for 100 cm distance images applied to the 80 cm model was still quite good, with an accuracy of 91.6%. The results obtained show that the image distance affects the results of the model that has been built, so use an image that matches the distance applied to the model.
Pemanfaatan Data Ulasan Pengguna untuk Membangun Sistem Klasterisasi berdasarkan Pain Points menggunakan Algoritma K-Means Ulummuddin, Ikhya; Sari, Anggraini Puspita; Swari, Made Hanindia Prami
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1252

Abstract

In design thinking, empathizing and defining stages are part of UX research. The goal is to analyze pain points or complaints experienced by users using qualitative data. However, this process is always done manually, which can be time-consuming and resource-intensive. The objective of this research is to develop a system for clustering qualitative data based on problem topics using K-Means clustering and several evaluation methods, namely silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index, implemented in Python programming language and run on Google Colaboratory. User review data for the Gojek app version 4.9.3 from November 2021 to January 2024, obtained from Kaggle and preprocessed, will be used as the object for system development. Based on testing for each cluster number, the results obtained are 14 clusters or problem topics with a silhouette score of 0.65, Davies-Bouldin Index of 0.35, and Calinski-Harabasz Index of 40.7, where each evaluation method has good accuracy. The system requires a computation time of 127.4 seconds. The K-Means algorithm is effective when clustered user review data based on complaint topics. UX researchers can utilize the system from this research to assist them in analyzing pain points more quickly and efficiently.

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