Jurnal Informatika: Jurnal Pengembangan IT
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Rancang Bangun Aplikasi Bon Permintaan Dan Pengeluaran Barang Menggunakan Metode Prototype Berbasis Website
No full textGoods purchase requisitions and goods issue documents are receipts for purchase requisitions and goods issues for distribution goods from the unit of work to the warehouse. pt. Perkebunan Nusantara V still uses the manual method of registering and approving the Goods Request Form using a form filled out by a factory assistant and signed by multiple parties. Therefore, it takes 5-30 business days to collect all signatures. If all parties are present, the product request notification can be signed and approved immediately. However, if this is not the case, the bill of goods approval process will be delayed. For this reason, urgent needs often result in goods being released from the warehouse before the invoice has been fully approved. Therefore, there is a need for an application that helps companies manage good purchase requisitions from warehouses. The application is implemented as a website that allows users to approve notes step-by-step online. The prototyping method allows developers to design and build systems more efficiently because discussions take place between users and developers during the system development process. PHP Laravel is used as programming language and MySQL as database. The tests for this application are based on the ISO 9126 test standard and give the following results: According to the USE survey, functionality scored 100%, reliability scored A, usability scored 90.07 across the four factors, efficiency scored B, performance score 88%, The structural score was 87%. Maintainability was evaluated as A grade with a debt ratio of 2.6%, and portability was evaluated as 100%. This application reduced the approval time to less than 5 hours and test results showed that the application works well and is suitable for enterprise us
Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan
No full text Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used
Analisis Perbandingan Metode Fuzzy Logic Dan Metode SAW Dalam Pemilihan Keluarga Penerima Bantuan Sosial
No full textEnsuring and fulfilling the needs of the community isa form of government responsibility to reduce existing socialinequalities. One of the efforts that the government has made isto provide social assistance through the Non-Cash FoodAssistance program. However, the process of selecting recipientsof social assistance is often not on target. For this reason, it isnecessary to build a system that is able to support in determiningdecisions for the selection of families receiving social assistance.To help the selection process of social assistance recipients, ofcourse, it must use the right and appropriate method so that theselection process produces social assistance recipients who reallydeserve assistance. The selection process in this study uses twodecision support methods, namely Fuzzy Logic and SimpleAdditive Weighting (SAW) and has conducted accuracy tests onboth methods against the suitability of recipient eligibility data,so that it can be seen which method has the highest level ofaccuracy in the selection of social assistance recipients. Theresults of the accuracy test carried out in this study are that bothmethods produce the same high level of accuracy in thesuitability of prospective recipient eligibility results, namely100%, this means that both methods can be used in determiningrecipients of social assistanc
Deep Learning untuk Identifikasi Daun Tanaman Obat Menggunakan Transfer Learning MobileNetV2
No full textMedicinal plants are plants used as alternative medicines for healing or preventing various diseases due to their active substances. The utilization of medicinal plants in Indonesia has been widespread among the community since ancient times and is a heritage passed down from ancestors. Medicinal plants have leaf structures that are almost similar between one plant and another, which can lead to confusion for some people and require precision in identifying the leaves of medicinal plants. Incorrect identification can have negative consequences for the users. In recent years, deep learning has been used to identify objects because of its ability to interpret images. This study used a transfer learning method to identify medicinal plants. Transfer learning utilizes a pre-trained model to learn and perform new tasks, making it suitable for smaller datasets. The pre-trained model used in this study is MobileNetV2. MobileNetV2 has a lightweight architecture and high accuracy. Fine-tuning techniques were applied in this study to improve the model's performance. Several experiments were conducted with parameters such as epochs and fine-tuning layers to obtain the best results. The research yielded a training accuracy of 97%, validation accuracy of 96%, and testing accuracy of 93%
Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Mancanegara Di Prov. Sulawesi Selatan Dengan K-Means Dan SVM
No full textIndonesia'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
Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara
No full textDetecting breast cancer in early stage is not straightforward. This happens because biopsy test requires time to determine whether the type is benign or malignant. Data mining algorithm has been widely used to automate diagnosis of a disease. One of popular algorithms is nearest neighbor based because of its simplicity and low computation. However, too many features can cause low accuracy in nearest neighbor based models. In this research, nearest neighbor based with feature selection is developed to detect breast cancer. Conventional k-Nearest Neighbor (KNN) and Multi Local Means k-Harmonic Nearest Neighbor have been chosen as nearest neighbor based models to experiment. The feature selection method used in this study is filter based, namely Correlation based, Information Gain, and ReliefF. The experimental result shows that the highest recall metric of MLM-KHNN and Information Gain is 94% with 5 features. In brief, MLM-KHNN algorithm with Information Gain can increase the recall of the prediction of breast cancer compared with the conventional K-NN algorithm and have been deployed into website using Streamlit such that the model can be used to detect breast cancer from chosen Wisconsin dataset features
Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network
No full textThe science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods have good accuracy. We present five Convolutional Neural Network (CNN) architectures for identifying brain tumors (glioma, meningioma, no tumor, and pituitary) on MRI images. This study aims to develop an optimal CNN architecture for identifying tumors. We use the dataset from Kaggle with a total training data of 5712 and testing of 1311. Of the five proposed CNN architectures, architecture c has the highest accuracy of 82.2% with an unlimited number of parameters of 29605060. A good CNN architecture has many convolution layers. We also compare the proposed architecture with CNN transfer learning (Inception, ResNet-50, and VGG16), and with CNN transfer learning architecture, the accuracy is higher than our proposed architecture
Implementasi Metode Profile Matching Pada Sistem Pendukung Keputusan Pemilihan Distributor Alat Kesehatan
No full text Utilization of information technology in a company will assist in increasing the productivity of the company in carrying out its duties and responsibilities. Distributors are the most important part in a company that provides medical equipment in obtaining goods for the sustainability of the company. The evaluation process for distributors currently does not use a system, so the company has difficulty in evaluating distributors from the requirements that will be calculated and used as material for evaluating the distributor's performance. The decision support system in the selection of this distributor uses profile matching. Profile matching is a method in a decision support system that will provide recommendations based on a comparison of the profiles of each distributor with the highest score. The decision support system for selecting Medical device distributors helps companies to automate and computerize in determining the performance of distributors in meeting company needs. The results of manual calculations and web applications on the selection of medical device distributors show the same results, and there are no different calculations. Test results using blackbox get perfect results, namely 100% for all system functions created for the selection of medical device distributor
Analisis Sentimen Terhadap PERMENDIKBUD No.30 pada Media Sosial Twitter Menggunakan Metode Naive Bayes dan LSTM
No full textPenelitian Analisis Sentimen tanggapan masyarakat terhadap PERMENDIKBUD No. 30 di media sosial Twitter dapat menggunakan model Machine Learning dan Deep Learning. Penelitian ini menggunakan 2 metode yang diturunkan dari dua model yaitu metode Naïve Bayes dan metode Long Short-Term Memory. Pengumpulan data dengan cara crawling data menggunakan Twitter API yang menggunakan kata kunci berupa “permendikbud30” dan “Kekerasan seksual di kampus”. berisi "Negatif" dan "Positif" Namun, dataset yang telah diproses sebelumnya dikurangi menjadi 471 data. Setelah preprocessing dilakukan, selanjutnya dilakukan proses pembobotan dengan menggunakan metode TF-IDF dan dilanjutkan dengan metode perhitungan. Hasil penelitian ini menunjukkan bahwa metode LSTM mendapatkan nilai performansi yang lebih tinggi yaitu nilai Accuracy sebesar 77%, Precision sebesar 84%, Recall sebesar 75%, dan F1-Score 80%. pengujian metode Naïve Bayes diperoleh hasil akurasi 76%, presisi 75%, nilai recall 75% dan F1-Score 75%
Forensik WhatsApp Menggunakan Metode Digital Forensic Research Workshop (DFRWS)
No full textSmartphone technology makes cybercrime crimes increase from year to year, one of the smartphone applications used to commit crimes is WhatsApp. The WhatsApp application is one of the most widely used social media, especially in Indonesia. Criminal acts such as hate speech, fraud, and defamation often occur on WhatsApp social media. This research was conducted to find forensic evidence on the WhatsApp social media application using the Digital Forensics Research Workshop (DFRWS) method. The stages of digital forensics include identification, preservation, collection, examination, analysis and presentation in finding digital evidence of cybercrime using the MOBILedit Forensic Express and HashMyFiles software applications. The digital evidence sought on smartphones can be found using case scenarios with 13 parameters that have been created. The results of this study indicate that the digital forensic software MOBILedit Forensic Express can detect types of digital evidence with an accuracy rate of 84.6%, while Hashmyfiles can detect the authenticity of digital evidence by 100%