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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 20 Documents
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Search results for , issue "Vol 8, No 3 (2023): JPIT, September 2023" : 20 Documents clear
Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner Tupari Tupari; Syaukani Abdullah; Chairani Chairani
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5641

Abstract

The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively
Implementasi Algoritma Floyd Warshall Pada Aplikasi Dewan Masjid Indonesia (Dmi) Kota Semarang Untuk Menentukan Masjid Terdekat Muhammad Syaifur Rohman; Galuh Wilujeng Saraswati; Nurul Anisa Sri Winarsih
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4895

Abstract

Location Based Service (LBS) is a service on smartphones that functions as a navigation device based on the user's position to determine the location where the user is. LBS utilizes GPS capabilities in finding geolocation information and sometimes using Google maps to display a complete map of the location. But the results of previous research studies Google Map does not give shortest and accessible routes. Furthermore, to improve work of LBS, Floyd Warshall algorithm is used because the algorithm has the principle of optimality in calculating the total of all routes optimally. According to data recorded by the Ministry of Religion of the Republic of Indonesia there have been 1,304 Mosques in the City of Semarang, but with this much data it should be easier to find places of worship for Muslims. Most mosques that are visited are mosques on the highway because it is more visible even though there are many other mosques that can be accessed. By using the White Box and Black Box tests, finding shortest path to find places of worship in the city of Semarang can be given accurately. The result was the Floyd Warshall algorithm could provide shortest path route and it was more accessible better than Google Map navigation.
Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor Ikhsan Habib Kusuma; Nuri Cahyono
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5734

Abstract

Abstract − E-commerce's rapid growth has resulted in an increase in online transactions and shifts in consumer behavior. In Indonesia, the use of e-commerce has grown rapidly, with many online platforms emerging. Understanding public sentiment towards e-commerce in Indonesia is crucial for businesses to improve their services and maintain customer satisfaction. In this review, study propose a methodology for feeling investigation of popular assessment on the utilization of web-based business in Indonesia, utilizing directed learning calculations. The study involved collecting data from the website Google Play Store. The study performed data preprocessing, including removing stop words, tokenization, and stemming, before applying the K-Nearest Neighbor (K-NN) algorithm to classify sentiments into positive or negative. The evaluation was conducted using confusion matrix and classification report. The results showed that the proposed approach was effective in analyzing public sentiment towards e-commerce in Indonesia, with an accuracy rate of 82%. The study concluded that the proposed strategy could help businesses enhance their services and better satisfy customers' requirements and expectations.Keywords – Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN. Abstrak - Perkembangan e-commerce yang pesat telah menyebabkan peningkatan transaksi online dan perubahan perilaku konsumen. Di Indonesia, penggunaan e-commerce tumbuh pesat dengan banyak platform online bermunculan. Memahami sentimen masyarakat terhadap e-commerce di Indonesia sangat penting bagi bisnis untuk meningkatkan layanan dan menjaga kepuasan pelanggan. Oleh karena itu, dalam penelitian ini peneliti mengusulkan sebuah pendekatan untuk melakukan analisis sentimen opini publik mengenai penggunaan salah satu e-commerce di Indonesia dengan menggunakan algoritma K-Nearest Neighbor. Pengumpulan data dilakukan dari website Google Play Store dengan tujuan untuk memperoleh pandangan dan pengalaman masyarakat terkait penggunaan salah satu e-commerce di Indonesia. Setelah data terkumpul, dilakukan proses preprocessing untuk membersihkan data, termasuk menghilangkan stopwords, tokenisasi, dan stemming. Setelah itu, algoritma K-Nearest Neighbor (K-NN) digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Evaluasi dilakukan dengan menggunakan confusion matrix dan classification report untuk menilai keakuratan algoritma. Hasil penelitian menunjukan bahwa pendekatan yang diusulkan efektif dalam menganalisis sentimen masyarakat terhadap e-commerce di Indonesia, dengan tingkat akurasi 82%. Penelitian ini memiliki implikasi penting bagi bisnis e-commerce di Indonesia dalam meningkatkan layanan dan memenuhi kebutuhan serta harapan pelanggan secara lebih baik.Kata Kunci - Sentimen Analisis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.
Pengembangan Aplikasi Kamus Bahasa Bima-Inggris-Indonesia Menggunakan Rapid Application Development M. Julkarnain; Erwin Mardinata
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5692

Abstract

The dictionary is one of the solutions for learning about vocabulary and translating it. The use of dictionaries in the form of books is less effective and efficient, so it is necessary to develop electronic dictionaries in the form of dictionary applications available on smartphones. This study aims to develop an Android-based three-language dictionary application, namely Bima, Indonesian, and English, which includes a speech to text  feature. The software development method uses the Rapid Application Development (RAD) method. This method has four main stages: requirements planning, design, construction, and cutover. Data collection methods in this research include observation, interviews, documentation, and literature study. The data analysis technique used is qualitative data analysis of data resulting from observations, interviews, and literature studies. The application was built using the Flutter and Codeigniter frameworks. In the final stage of dictionary application development, testing was carried out on the application's functionality using the black box method. The results of the test show that the application runs very well; all buttons and features work as they should after fixing bugs and problems found in the final test before launch.
Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Mancanegara Di Prov. Sulawesi Selatan Dengan K-Means Dan SVM Nero Caesar Gosari; Rismayani Rismayani
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4554

Abstract

Indonesia's exchange rate can rise due to foreign tourist visits, which can also benefit the local economy. The provincial capital. South Sulawesi is Makassar which is one of the locations for tourist visits. There are 11 main tourist attractions in Prov. South Sulawesi according to sulselprov 1) Maritime Tourism, 2) Losari Beach, 3) Rotterdam Fort, 4) Somba opu Fort, 5) Takabonerate Marine Park, 6) Bantimurung National Park, 7) Malino, 8) Tanjung Bira Beach, 9) Kesu Tourism, 10) Londa Tourism, 11) Pallawa Tourism. The purpose of this study is to analyze the application of data mining in classifying the number of foreign tourists visiting the prefecture. South Sulawesi uses k-means. The data used comes from BPS Prov. South Sulawesi. The data is grouped into two clusters. That is, the most tourists as C1 with results from Malaysia, and low tourist arrivals as C0 with results from Singapore, Japan, South Korea, Taiwan, China, India, the Philippines, Hong Kong, Thailand, Australia, USA, UK, Netherlands, Germany, France, Russia, Saudi Arabia, Egypt, United Arab Emirates, Pearl of the Persian Gulf, and Switzerland then I use and process this data again with SVM to look for precision, precision and recall values and get 100.00% accuracy in the RapidMiner application.
Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost Firman Aziz; Benny Leonard Enrico Panggabean
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5675

Abstract

In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.
Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS Anisa Nur Syafia; Muhammad Fikri Hidayattullah; Wirmanto Suteddy
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5064

Abstract

Sentiment analysis of YouTube boy group BTS comments uses the NLP approach to detect emotional patterns based on two category labels, namely positive and negative. With NLP, positive or negative polarity in an entity can be allocated as well as predicted high and low performance from various classification sentiments. The machine learning algorithms used to measure the accuracy of sentiment analysis developed are the Support Vector Machine and Random Forest algorithms. The steps taken start from the data collection obtained from the BTS YouTube Comment dataset and then go through the data preprocessing stage. Then proceed to the feature extraction stage by converting text into digital vectors or Bag of Words (BOW) and classified using machine learning algorithms until the evaluation stage. From the results comparison of the evaluated algorithms, the accuracy value between the two algorithms is 96% for training data and 85% for data testing using the SVM algorithm, while for the Random Forest algorithm it is 82% for training data and 80% for data testing. This shows that the SVM algorithm produces a higher accuracy value than the Random Forest for sentiment analysis of YouTube boy group BTS comments.
Sistem Diagnosa Penyakit Liver Menggunakan Metode Artificial Neural Network: Studi Berdasarkan Dataset Indian Liver Patient Dataset Ashri Shabrina Afrah
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5346

Abstract

Penyakit Hati atau liver merupakan penyakit yang menyerang organ hati pada manusia dimana organ hati berfungsi dalam pengelolaan kolesterol atau lemak pada tubuh. Dampak yang diberikan oleh penyakit liver ini berbeda-beda tergantung pada tingkat keparahan dan respons pengobatan yang dilakukan oleh individu. Oleh karena itu, pengembangan sistem prediksi penyakit liver menjadi relevan dan bermanfaat dalam membantu dokter dan tenaga medis untuk mengambil tindakan yang tepat secara lebih cepat. Untuk dapat mengembangkan sistem ini maka dapat dilakukan dengan menggunakan metode Artificial Neural Network (ANN). Tujuan dilakukan klasifikasi ini adalah untuk membantu mengetahui keakuratan model ANN dalam mengklasifikasi dataset penyakit liver. Menggunakan metode tersebut dataset dibagi menjadi 3 tahapan yaitu preprocessing data, pemrosesan data, dan evaluasi data. Preprocessing data dilakukan perbaikan terhadap dataset dan melakukan split data sehingga dihasilkan dataset baru. Pada pemrosesan data dilakukan penentuan hidden layer, model aktivasi, dan normalisasi pada model. Pada tahap terakhir yaitu evaluasi dataset, terdapat nilai akurasi, confusion matrix, dan classification report. Pada model ini didapatkan sebuah prediksi true negatif 70, true positif 14, false negatif 16, dan false positif 17. Dengan menggunakan model ini didapatkan hasil akurasi 71,79% yang menandakan bahwa model baik dalam melakukan klasifikasi pada dataset.
A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction Permana langgeng wicaksono ellwid putra; Muhammad Naufal; Erwin Yudi Hidayat
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5703

Abstract

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.
Implementasi Algoritma Priority Scheduling Sistem Informasi Pelayanan Administrasi Kependudukan Desa Alifah Alfiatur Rohmah; Dedi Gunawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4891

Abstract

Pelayanan administrasi di desa seringkali masih menerapkan metode manual dalam pelaksanaanya yang mengharuskan masyarakat datang secara langsung ke kantor desa. Selain itu, pelayanan yang masih manual juga menyulitkan petugas kantor desa dalam menentukan urutan pemrosesan surat yang harus diverifikasi terlebih dahulu, akibatnya banyak surat yang seharusnya sudah diselesaikan namun prosesnya masih berjalan. Oleh sebab itu, tujuan dilakukan penelitian ini berfokus pada perancangan sistem informasi yang dapat membantu masyarakat dalam pengajuan pembuatan surat serta sistem yang dapat membantu petugas kantor desa dalam menentukan prioritas surat yang harus diverifikasi terlebih dahulu dengan mengimplementasikan algoritma priority scheduling. Dalam penelitian ini, metode yang digunakan melingkupi perancangan algoritma priority scheduling yang diimplementasikan ke dalam sistem serta perancangan perangkat lunak menggunakan metode SDLC waterfall. Perancangan algoritma priority scheduling berupa penentuan urutan prioritas serta pembuatan pseudocode dari algoritma. Hasil dari penelitian ini adalah sebuah sistem informasi pelayanan administrasi kependudukan yang mengimplementasikan algoritma priority scheduling dalam proses pengurutan surat. Berdasarkan hasil pengujian menggunakan metode blackbox aplikasi berjalan tanpa ada error, 0% kegagalan dan berjalan sesuai fungsinya. Sedangkan pengujian SUS menunjukkan bahwa aplikasi berada pada level good dengan skor 75,25.

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