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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naive Bayes Ramadhan, Bintang Zulfikar; Adam, Riza Ibnu; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4725

Abstract

The rapid development of E-commerce has given rise to many marketplaces in Indonesia such as Tokopedia, Shopee, Lazada. Tokopedia, Shopee and Lazada applications are applications that help sellers and buyers to make sales and purchase transactions for goods and services. Until now, of the three major E-Commerce applications, around 100 million users have downloaded the three E-Commerce applications. With the launch of some of these applications, it has caused a lot of opinions and criticisms from the public. Based on this, a sentiment analysis of the Naive Bayes algorithm was carried out to find out how the sentiment of users compares to the E-Commerce application on the Google Play Store. This research uses the Knowledge Discovery in Database (KDD) method which consists of 5 stages, namely data selection, preprocessing, transformation, data mining, and evaluation. The data used is a review of 500 E-Commerce applications per each application. At the data mining stage, it is carried out with 3 scenarios data sharing is 80:20, 70:30 and 60:40. The best results were obtained in scenario 1 (80:20) on the Shopee application using the Naive Bayes algorithm which resulted in an accuracy of 92%, precision of 92.13%, recall of 98.8% and f1-score of 95.35%.
Analisis Sentimen Aplikasi WETV di Google Play Store Menggunakan Algoritma Support Vector Machine Kulsum, Ummi; Jajuli, Mohamad; Sulistiyowati, Nina
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4802

Abstract

WeTV is an online streaming application widely used by Indonesia's people as an entertainment medium while at home. This application has been downloaded more than 50 million times on the official Google Play Store website. The number of users who use it makes the reviews of this application abundant as well. Large numbers of reviews are very difficult to read manually, sentiment analysis is needed to classify reviews into positive and negative classes. This study uses a support vector machine algorithm with a linear kernel to classify review data from the WeTV application. KDD was used as a method to complete this research. In the analysis process to obtain information, 4 scenarios were carried out, with the division in the first scenario consisting of 60% training data and 40% test data, the second scenario consisting of 70% training data and 30% test data, the third scenario 80% training data and 20% test data, and the last scenario 90% training data and 10% test data. The highest test results of 85% were obtained from the second scenario with the distribution of training data of 70% and 30% of test data, the third with the distribution of training data of 80% and 20% of test data, and the fourth with the distribution of training data of 90% and test 10% data. The confusion matrix is used as an evaluation of the model that has been made, the results show an accuracy in the first scenario of 83%, with a precision value of 83%, recall 89%, and an f1-score of 86%. The accuracy in the second scenario is 85%, precision is 86%, recall is 89%, f1-score is 87%, accuracy in the third scenario is 85%, precision is 85%, recall is 90%, and f1-score is 88%. And the fourth scenario gets an accuracy of 85%, precision 86%, recall 90%, and f1-score 90%.
Analisis Perbandingan Algoritma Untuk Prediksi Performa Akademik Mahasiswa Pada Pembelajaran Daring Herman, Herman; Christian, Yefta
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4854

Abstract

Student academic performance is one of the important factors for student graduation. Therefore, many studies have been conducted in the field of education to identify factors that affect student performance. This research focuses on academic performance in online learning conditions by studying cases at XYZ university. Data were collected using Machine Learning techniques with the application of the Distributed Random Forest model, Naïve Bayes, Generalized Linear Model, and Gradient Boosting Machine algorithms. The results of this study indicate that the Distributed Random Forest and Gradient Boosting Machine models have an average accuracy of 99.83%. Researchers found variables that affect student learning performance, especially online learning, are final exam scores, midterm scores, attendance, assignment scores, amount of material given, number of assignments given, and number of clicks on material. From these findings, the researcher recommends that to improve the performance of the next learning, the implementation of learning should focus on improving the implementation of the Final Exams and the material on the learning platform
Komparasi Teknik Hyperparameter Optimization pada SVM untuk Permasalahan Klasifikasi dengan Menggunakan Grid Search dan Random Search Fajri, Muhamad; Primajaya, Aji
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5004

Abstract

Classification is one of the important tasks in the field of Machine Learning. Classification can be viewed as an Optimization Problem (Optimization Problem) with the aim of finding the best model that can represent the relationship/pattern between data with labels. Support Vector Machine (SVM) Is an algorithm in Machine Learning used to solve problems such as Classification or Regression. The performance of the SVM algorithm is strongly influenced by parameters, for example error prediction in non-linear SVM results in parameters C and gamma. In this study, an analysis of the technique was carried out to obtain good parameter values using Grid search and Random Search on seven datasets. Evaluation is done by calculating the value of accuracy, memory usage and validity test time of the best model by the two techniques.
Teknologi Internet of Things (IoT) pada Tanaman Selada dan Pakcoy Hidroponik dengan Menggunakan Perhitungan MAPE Ramsari, Nopi; Hidayat, Teddy
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5011

Abstract

Hydroponics is a technique of cultivating plants without using soil, instead using water as a growing medium. One of the advantages of hydroponics is that it has a high selling value and does not require a large area. To make good quality plants, you must pay attention to factors such as: nutritional needs, pH, temperature and light intensity around the hydroponic environment. By using the Internet of Things (IoT), we can monitor and control these factors, so hydroponic plants can grow well. In this study we used two samples of different types of plants, such as pak choy and lettuce. Microcontroller as the main controller of all IoT components. The sensors used are TDS sensors, PH sensors, temperature sensors and light sensors. The softwares tools that we used are the Arduino IDE and the CodeIgniter Framework to develop the user interface display so that it is easy to use by users using web browsers or smartphone devices. Testing the concentration of nutrients with the TDS sensor uses MAPE to get a yield of 13,17 % for salad plants and 7,32 % for pak choy plants while for testing the pH of the water the results were 13,95% for salad plants and 13,91% for pak choy plants. Because the MAPE value is 10 "“ 20%, it shows the ability of the IoT for monitoring and controlling nutrient concentration and pH content in hydroponic systems is good. With IoT technology, we can monitor and control plants in real-time automatically so that hydroponic plants continue to grow properly with minimal human intervention.
Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma C4.5 dengan CRISP-DM Pratama, Suprayuandi; Iswandi, Iswandi; Sevtian, Andre; Anjani, Tsabita Putri
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.4998

Abstract

Because student data can be used to examine student academic accomplishment, or student achievement index data, student data is a very valuable database. Data on student performance is available at Muhammadiyah University of Bangka Belitung's Faculty of Engineering and Science, which houses study programs in computer science, civil engineering, and natural resource conservation. The data are analyzed using them. The C4.5 Algorithm is used in conjunction with a classification data mining technique on student data to forecast academic progress. A decision tree is constructed using algorithm C4.5. Decision trees are helpful for investigating data and uncovering undiscovered connections between numerous input factors and one goal variable. Performance outcomes are derived from the analysis results by categorizing student data. This serves as a resource for lecturers and students to enhance classroom learning and discipline among students.
Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta Ashari, Ilham Firman; Dwi Nugroho, Eko; Baraku, Randi; Novri Yanda, Ilham; Liwardana, Ridho
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.4947

Abstract

Jakarta is the capital city of Indonesia, which has a high population density, and is an area that is frequently hit by floods. This study aims to determine the classification of flood-affected areas in Jakarta between severe, moderate, and low. Design/method/approach: The study was conducted using the elbow, Silhouette, Davidson-Bouldin, and Calinski-Harabasz methods on the K-means algorithm, as well as the Rand method. index for evaluation. Grouping with 3 and 6 groups is the best grouping value based on Calinski-Harabasz. By using the davies bouldin index from the observations, the K value with a value of 6 has the smallest Davies-Bouldin value with a value of 0.2737. By using sillhoute, the experimental results obtained the best values sequentially, namely K=2, K=3, and K=6 with silhouette values of 0.866, 0.854, and 0.803. In this experiment, based on the elbow method, it was found that the best K value was K=3. This was obtained because it was based on observations on the appearance of the SSE data compared to the value of K. In the graph above, it can be seen that the largest decrease in data occurred at K=3 and after this decrease, the decline began to slope. The rand index is a method used to compare several cluster methods. If the value is >= 90 it is a very good result, if the value is in the range 80 to 90 it identifies a good index, whereas if it is below 80 it indicates a bad index. The results show that cluster three is verified as the best cluster with a value of 1, followed by a second alternative with cluster 2 of 0.9182. From several validation and evaluation methods it can be concluded that the best grouping can be done using 3 clusters. The results of the study yielded a value of 75.4% in low areas, 21.1% in moderate areas, and 3.5% in severe areas.
Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine Safitri, Tiara; Umaidah, Yuyun; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5039

Abstract

Twitter is often used as a source of public opinion and sentiment data for analysis, where the data can be used to understand public opinion about a topic. Sentiment analysis is widely used in various fields, one of which is in the marketing field. a company can carry out a sentiment analysis of the public figures they want to make Brand Ambassadors (BA), which later these sentiments can be taken into consideration for them to be able to determine the BA of their products. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.
Analisis Sentimen Pencitraan Perguruan Tinggi di Yogyakarta Menggunakan Metode Naive Bayes Classifier Marwanta, Y Yohakim; B, Badiyanto
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5103

Abstract

This research utilizes data from Twitter to analyze sentiment in Yogyakarta's universities using the Naive Bayes Classifier method. The Naive Bayes Classifier method is one of the text classification methods based on the probability of keywords in comparing training and testing documents. The data used consists of tweets in Indonesian language with keywords from the top 10 universities in Yogyakarta based on webometrics, as well as four other relevant keywords about Yogyakarta that are frequently searched through Google. From the conducted research, there are 1710 data collected from Twitter, which are used for classification and categorized into 3 labels: positive, negative, and neutral. The data is divided into 70% for training and 30% for testing randomly. The result of sentiment analysis classification from the test data shows that 82.1% of the data is categorized as neutral, 14.8% as positive, and 3.1% as negative, with an accuracy value of 73%.
Sistem Pakar Cara Mendidik Anak Pelajaran Agama Islam Sesuai Al-Qur'an Menggunakan Metode Forward Chaining Maulidan, Farid; Dwi Suhendra, Christian; Juita, Ratna
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5104

Abstract

The Al-Qur'an indirectly provides methods for educating children from an early age, as listed in Al-Ahqaf [46]: 30. The implementation of these educational methods requires an expert, specifically an Islamic religious teacher who can suggest appropriate methods to use. Hence, in this context, there is a need to develop an expert system application that can assist teachers in recommending suitable educational methods to parents or guardians based on the child's behavior input. The "expert-mendidik-islam.com" application is created using the forward chaining method, which is employed for reasoning in the database system through rules derived from interviews with experts. The existing child behaviors and rules can be added through the admin page. To evaluate the application, a Likert model questionnaire was administered to parents or guardians of students at SDN 44 Amban and SDN 48 Inggramui, Manokwari Regency. The questionnaire results consistently scored above 80%, indicating that the users strongly agreed with the questionnaire and the application's effectiveness.

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