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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Buana Ilmu Jurnal Sisfotek Global Jurnal Buana Pengabdian INTERNAL (Information System Journal)
Adi Rizky Pratama
Universitas Buana Perjuangan Karawang
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RC4 Cryptography Implementation Analysis on Text Data Agung Susilo Yuda Irawan; Adi Rizky Pratama; Ryan Antono
JURNAL SISFOTEK GLOBAL Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (405.9 KB) | DOI: 10.38101/sisfotek.v11i2.408

Abstract

Security and confidentiality have become very important and continue to grow. In recent years, there have been several cases involving data security, such as the leaking of Facebook user account information. This is certainly a significant issue in the world of information technology and even the world of entrepreneurship because it involves one of the young entrepreneurs, Mark Zuckerberg. Main problem in this case is data security. who can know information and who should not know information. To overcome such things, research is made in the field of data and information security by analyzing one of the encryption and decryption techniques, namely RC4 cryptography. This study explains how RC4 cryptography works, the advantages and disadvantages of RC4 cryptography, and the effectiveness of RC4 cryptography. RC4 (River Ciper 4) is a decryption encryption technique that uses a key as a reference. The process consists of KSA, PRGA, and XOR. To find out the usefulness of RC4 cryptography, in this study, encryption and decryption were carried out on text data. There are several advantages and disadvantages to RC4 cryptography from a technical point of view. The advantage that needs to be underlined is that this RC4 cryptography uses a certain key as a reference, things like this can provide convenience to the encryption maker but can also be a threat. as a result RC4 can hide information very well while the secret key is not known by others. In addition to being used in text data, this cryptography may be used for other data such as audio and video.
The Utilization of Decision Tree Algorithm In Order to Predict Heart Disease Mia Mia; Anis Fitri Nur Masruriyah; Adi Rizky Pratama
JURNAL SISFOTEK GLOBAL Vol 12, No 2 (2022): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v12i2.551

Abstract

The data on heart disease patients obtained from the Ministry of Health of the Republic of Indonesia in 2020 explains that heart disease has increased every year and ranks as the highest cause of death in Indonesia, especially at productive ages. If people with heart disease are not treated properly, then in their effective period a patient can experience death more quickly. Thus, a predictive model that is able to help medical personnel solve health problems is built. This study employed the Random Forest and Decision Tree algorithm classification process by processing cardiac patient data to create a predictive model and based on the data obtained, showing that the data on heart disease was not balanced. Thus, to overcome the imbalance, an oversampling technique was carried out using ADASYN and SMOTE. This study proved that the performance of the ADASYN and SMOTE oversampling techniques on the C45 algorithm and the Random Forest Classifier had a significant effect on the prediction results. The usage of oversampling techniques to analyze data aims to handle unbalanced datasets, and the confusion matrix is used for testing Precision, Recall, and F1-SCORE, as well as Accuracy. Based on the results of research that has been carried out with the K-Fold 10 testing technique and oversampling technique, SMOTE + RF is one of the best oversampling techniques which has a greater Accuracy of 93.58% compared to Random Forest without SMOTE of 90.51% and SMOTE + ADASYN of 93.55%. The application of the SMOTE technique was proven to be able to overcome the problem of data imbalance and get better classification results than the application of the ADASYN technique.
SOSIALISASI DESAIN SISTEM RE GISTRASI PEMBUKUANDI KANTOR DESA CIKAMPEK UTARA Ayu Ratna Juwita; Cici Emilia Sukmawati; Tohirin Al Mudzakir; Adi Rizky Pratama
JURNAL BUANA PENGABDIAN Vol 5 No 1 (2023): JURNAL BUANA PENGABDIAN
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/jurnalbuanapengabdian.v5i1.3966

Abstract

Dalam era modern ini masyarakat diminta untuk mengetahui dan memahami perkembanganmengenai pentingnya suatu teknologi. Teknologi memberikan kemudahan untuk membantu setiapaktivitas manusia baik dalam dunia akademik, pembangunan dan lain sebagainya, terutama dalambidang pemerintahan. Dalam bidang pemerintahan pemanfaatan teknologi memiliki tujuan untukmeningkatkan kualitas pelayanan publik. Pelayanan publik ini dapat diterapkan dengan menggunakansistem berbasis web yang dapat lebih mudah dalam menyampaikan sebuah informasi maupunmeringankan pekerjaan dalam bidang pemerintahan, terutama pelayanan pada kantor desa CikampekUtara. Pada saat ini kantor desa Cikampek Utara masih menerapkan pencataan manual dalam mendatasurat atau registrasi surat pada buku register, sehingga data-data surat yang ditulis pada buku registermasih kurang efektif, karena apabila digunakan secara tidak baik, buku sangat rentan rusak dan sobek.Untuk meningkatkan kualitas dalam pencatatan data-data surat, sebuah sistem berbasis web sangatlahdibutuhkan karena data-data surat disimpan pada komputer (database). Mengacu pada hal tersebut,maka memberikan sauatu arahan mengenai sosialisasi sistem registrasi pembukuan berbasis webdengan tujuan agar dapat memudahkan dalam mendata surat tanpa harus menggunakan buku dan datadata surat dapat tersimpan secara baik.
KOMPARASI ALGORITMA NAïVE BAYES, SUPPORT VECTOR MACHINE, DAN LOGISTIC REGRESSION PADA ANALISIS SENTIMEN PENGGUNA APLIKASI TRANSPORTASI ONLINE Krisna Perdana Jaya Sitompul; Adi Rizky Pratama; Kiki Ahmad Baihaqi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 1 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i1.616

Abstract

Online transportation is one of the transportation that is increasingly in demand by the public at this time. Grab is an online transportation application that has many users in Indonesia. However, this system certainly has many shortcomings that are felt by users. One way to find out user satisfaction and disappointment with the application is to do sentiment analysis. By analyzing the deficiencies of the application, the company can find out the shortcomings of the application and how to fix it. The purpose of this study is to compare the accuracy between the Support Vector Machine, Naive Bayes, and Logistic Regression algorithms by conducting sentiment analysis on Grab application review data. The results of the comparative test found that the Naive Bayes algorithm has the best performance compared to other classification algorithms with an accuracy obtained by the Naive Bayes algorithm of 88.5%, while the Support Vector Machine algorithm has the lowest accuracy with an accuracy of 85.5%. So it can be concluded that the Naive Bayes algorithm has a better value than the Logistic Regression and Support Vector Machine algorithms. Keywords: Grab, Support Vector Machine, Naive Bayes, Logistic Regression Transportasi online adalah salah satu transportasi yang semakin diminati masyarakat pada saat ini. Grab adalah alah  satu  aplikasi  trasportasi online  yang  memiliki  pengguna  bisa  dikatakan  banyak  di  Indonesia. Namun  dalam  system  ini  pasti  memiliki banyak  kekurangan  yang  dirasakan  penggunanya. Salah satu cara untuk mengetahui kepuasan dan kekecewaan pengguna terhadap aplikasi tersebut yaitu melakukan analisis sentimen.  Dengan  menganalisis  kekurangan  dari  aplikasi  perusahaan dapat mengetahui kekurangan dari aplikasi dan bagaimana cara memperbaikinya. Tujuan penelitian ini untuk mengetahui perbandingan keakurasian antara algoritma Support Vector Machine, Naive Bayes, dan Logistic Regression dengan melakukan analisis sentimen pada data ulasan aplikasi Grab . Hasil pengujian komparasi ditemukan bahwa algoritma Naive bayes memiliki kinerja terbaik dibandingkan algoritma klasifikasi lainnya dengan akurasi yang di dapat algoritma Naive bayes sebesar 88.5%, sedangkan algoritma Support Vector Machine memiliki akurasi terendah dengan akurasi sebesar 85.5%. Sehingga dapat disimpulkan bahwa algoritma Naive bayes memiliki nilai yang lebih baik dibandingkan algoritma Logistic Regression dan Support Vector Machine.Kata kunci: Grab, Support Vector Machine, Naive Bayes, Logistic Regression
Analysis of Sentiment Adiraku App Reviews on Google Play Store Using Vector Machine Support Algorithm and Naïve Bayes Bayu Padilah; Adi Rizky Pratama; Ayu Ratna Juwita
JURNAL SISFOTEK GLOBAL Vol 13, No 1 (2023): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v13i1.2943

Abstract

The Adiraku application is considered to be able to facilitate and facilitate customers so that there is no need to come to the branch office to get information related to the number of installments that must be paid, due dates, credit simulations, and Adira Finance information offers to customers. A large number of reviews from users received makes it difficult for developers to read them, it will take too much time and effort if they have to read and analyze them manually. To find out which reviews are classified as positive or negative reviews. need a sentiment analysis of the review. This study aims to find out how the opinions or opinions of its users on the services of the application, by analyzing these sentiments through a classification process using two algorithms, namely Support Vector Machine and Naïve Bayes. The data used amounted to 2000 data obtained from Google Playstore. Data is labeled into 2 classes namely positive class and negative. Furthermore, the data is divided into 70% training data and 30% testing data and methods used for testing using Bernoulli Naïve Bayes and Linear Kernel. It was concluded that the number of user reviews of the Adiraku application on the Google Play Store showed more positive comments, amounting to 1412 positive and negative reviews, which was 588 reviews. The Support Vector Machine algorithm performs better by getting the best accuracy value of 96%, while the Naïve Bayes algorithm gets an accuracy value of 85%.
IMPLEMENTASI TEKNIK FAILOVER RECURSIVE GATEWAY Tohirin Al Mudzakir; Adi Rizky Pratama; Ayu Ratna Juwita
BUANA ILMU Vol 7 No 2 (2023): Buana Ilmu
Publisher : Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bi.v7i2.5365

Abstract

The development of information technology and the internet in Indonesia every year shows very rapid progress in terms of reliable infrastructure, users, hardware, software and information systems. At Buana Perjuangan University, Karawang has two internet lanes designated for users of all academics, both LAN and wifi, and one more lane for servers. These conditions sometimes create problems when one of the internet lines is down which results in hampering service activities. Based on the background above, the authors propose the implementation of a Failover Recursive Gateway, with the aim that there will be no more cessation of administrative services when one of the internet lines is down.
Deteksi Fake Review Menggunakan Metode Support Vector Machine dan Naïve Bayes Di Tokopedia Habib Alamsyah; Yana Cahyana; Adi Rizky Pratama
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i2.1222

Abstract

In the world of online business and services, product and service reviews can have a major influence on user trust and purchasing decisions. However, there is a risk of fake reviews that can affect user trust and purchase decisions. Therefore, detecting fake reviews is very important to avoid fraud and increase user trust. The techniques used in detecting fake reviews are Support vector machine (SVM) and Naïve Bayes. SVM and Naïve Bayes are machine learning algorithms used to classify data into positive and negative categories. In the implementation results using SVM on fake review detection, it appears that SVM and Naïve Bayes can classify reviews into two categories with fairly high accuracy. Through the implementation of SVM and Naïve Bayes, it has been identified that the patterns that are often found in fake reviews are excessive use of words and inconsistent with the actual user experience, so that they can help identify fake reviews more effectively. With the results of the implementation of SVM and Naïve Bayes on fake review detection, several stages in this study used the SVM and Naïve Bayes methods, namely preprocessing, word weighting using TF-IDF, which then implemented the SVM and Naïve Bayes methods. The SVM test results can detect reviews with an accuracy of up to 94.38% and Naïve Bayes produces an accuracy of 91.57%.Keywords: support vector machine; naïve bayes; fake review; detection; machine learning. AbstrakDalam dunia bisnis dan layanan online, review produk dan layanan dapat memberikan pengaruh yang besar terhadap kepercayaan dan keputusan pembelian pengguna. Namun, terdapat risiko review palsu atau fake review yang dapat mempengaruhi kepercayaan dan keputusan pembelian pengguna. Oleh karena itu, pendeteksian fake review sangat penting dilakukan untuk menghindari penipuan dan meningkatkan kepercayaan pengguna. Teknik yang digunakan dalam pendeteksian fake review adalah Support vector machine (SVM) dan Naïve bayes. SVM dan Naïve bayes adalah algoritma machine learning yang digunakan untuk mengklasifikasikan data ke dalam kategori positif dan negatif. Dalam hasil implementasi menggunakan SVM pada pendeteksian fake review, terlihat bahwa SVM dan Naïve bayes dapat mengklasifikasikan review ke dalam dua kategori dengan akurasi yang cukup tinggi. Melalui implementasi SVM dan Naïve bayes, berhasil teridentifikasi bahwa pola-pola yang sering terdapat pada fake review adalah penggunaan kata-kata berlebihan dan tidak konsisten dengan pengalaman pengguna sebenarnya, sehingga dapat membantu dalam mengidentifikasi review palsu dengan lebih efektif. Dengan adanya hasil implementasi SVM dan Naïve bayes pada pendeteksian fake review, Adapun beberapa tahapan dalam penelitian ini menggunakan metode SVM dan Naïve bayes yaitu preprocessing, pembobotan kata menggunakan TF-IDF, yang selanjutnya implementasi metode SVM dan Naïve Bayes. Hasil pengujian SVM dapat mendeteksi review dengan akurasi yang mencapai 94,38% serta Naïve Bayes menghasilkan akurasi sebesar 91,57%. 
The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease Chepy Sonjaya; Anis Fitri Nur Masruriyah; Dwi Sulistya Kusumaningrum; Adi Rizky Pratama
INTERNAL (Information System Journal) Vol. 5 No. 2 (2022)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Heart disease in Indonesia, especially in the productive age, there is always an increase in the number of cases. The main cause of the increase in the number of heart patients is an unhealthy lifestyle and diet. The increase in patients with heart disease also has an impact on decreasing the standard of living. With this in mind, there is a need for research related to comparing classification methods on heart disease datasets. The dataset obtained is not balanced so that an oversampling technique is needed. The oversampling technique used is SMOTE. This research method uses Support Vector Machine (SVM) and Logistic Regression (LR). In order for this research method to be applied successfully, the data acquisition, data pre-processing and data transformation techniques are used to ensure accurate results. The model evaluation technique used is K-Fold Cross Validation. Based on the results of the analysis, it showed that the data partition using k-fold cross validation without oversampling gets the same accuracy value but the precision value is quite low. Conversely, if using the SMOTE technique, the accuracy value is as good as the precision value. The results of the SVM accuracy value get a value of 91.69%. LR is 91.76%. While the results of the SVM precision value of 57.81% and LR 54.82%. If using the SVM oversampling technique, the score is 75.79% and the LR is 75.84%. Meanwhile, the precision value obtained in SVM is 75.74%. At LR by 74.77%.
Co-Authors Agung Susilo Yuda Irawan Anis Fitri Nur Masruriyah Ayu Ratna Juwita Baihaqi, Kiki Ahmad Bayu Padilah Chepy Sonjaya Dwi Sulistya Kusumaningrum Habib Alamsyah Krisna Perdana Jaya Sitompul Mia Mia Mudzakir, Tohirin Al Ryan Antono Sukmawati, Cici Emilia