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Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Abwabul Jinan; Manutur Siregar; Vicky Rolanda; Dede Fika Suryani; Abdul Muis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3381

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.

Disguising Text Using Caesar Cipher, Reverse Cipher and Least Significant Bit (LSB) Algorithms in Video Siregar, Manutur; Jinan, Abwabul; Muhammad Raja Gunung, Tar
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4217

In communication, there is a process of transferring information from the sender to the recipient. The information sent must be the same as the information received. If there are differences, it means that there has been a data change process carried out by irresponsible parties. One technique for changing the content of information is man in the middle. The data changer will receive information from the sender, then change it and forward it to the recipient, so that the changed information appears to have come from the sender.To protect information, this can be done by utilizing the science of cryptography and steganography which aims to protect information by changing it to another form or by inserting the information into other media. In this research, to protect information the Caesar Cipher Algorithm is used, this algorithm will change the letters in plaintext to another letter (ciphertext) by using an alphabetical shift according to the number in the form of the key used, namely > 1 and < 26, then the Reverse Cipher algorithm is carried out, namely changing the position of the letters of the plaintext from the first order to the last order and so on. The encrypted information will then be inserted into a video using the Steganography Algorithm, namely Least Significant Bit (LSB). Before being inserted, the video will first be converted into several image frames, then in one frame the information will be inserted. This can be done because the frame is a collection of RGB arrays which have values 0-255 or 0 and 1. So the insertion is done in bit form. Frames containing information will then be converted back into a video.On the receiving side, the video will be converted into a frame, next is the process of retrieving the information that was previously inserted. The information that has been taken is then reversed in order and then shifted using the Caesar chipper algorithm according to the key used by the sender, then the first letter of each word is changed to capital, so that the information sent is the same as that received. The implication of this research is that it is a way to combine cryptography with steganography as an information security technique.

Implementasi Metode Case-Based Reasoning (CBR) dalam Sistem Pakar untuk Mendapatkan Diagnosis Anxiety Disorders Gunung, Tar Muhammad Raja; Lubis, Siti Sahara; Siregar, Manutur; Simanjuntak, Peter Jaya Negara; Jinan, Abwabul
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1480

This research aims to develop an expert system based on the case-based reasoning method for diagnosing anxiety disorders. Anxiety Disorder is a mental health disorder that is often experienced by the public but is often not detected correctly. The case-based reasoning method was chosen because of its ability to utilise previous cases to solve new problems that have similarities. Case-based reasoning uses four main stages: retrieval, reuse, revise, and retain. The case-based reasoning method is implemented using case data obtained from psychology clinics and interviews with mental health experts. Testing the case-based reasoning method shows a high level of accuracy in diagnosing various types of Anxiety Disorders, such as Generalised Anxiety Disorder, Panic Disorder, and Specific Phobias. The results of this study show that the case-based reasoning method can be an effective tool in helping mental health professionals diagnose Anxiety Disorders more quickly and accurately. After searching using the symptoms obtained, the percentage of each type of disease is the percentage of Generalised Anxiety Disorder 35.7%, the percentage of Panic Disorder 30.7%, and the percentage of Specific Phobias 65%.

Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Jinan, Abwabul; Siregar, Manutur; Rolanda, Vicky; Suryani, Dede Fika; Muis, Abdul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3381

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.

Disguising Text Using Caesar Cipher, Reverse Cipher and Least Significant Bit (LSB) Algorithms in Video Siregar, Manutur; Jinan, Abwabul; Muhammad Raja Gunung, Tar
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4217

In communication, there is a process of transferring information from the sender to the recipient. The information sent must be the same as the information received. If there are differences, it means that there has been a data change process carried out by irresponsible parties. One technique for changing the content of information is man in the middle. The data changer will receive information from the sender, then change it and forward it to the recipient, so that the changed information appears to have come from the sender.To protect information, this can be done by utilizing the science of cryptography and steganography which aims to protect information by changing it to another form or by inserting the information into other media. In this research, to protect information the Caesar Cipher Algorithm is used, this algorithm will change the letters in plaintext to another letter (ciphertext) by using an alphabetical shift according to the number in the form of the key used, namely > 1 and < 26, then the Reverse Cipher algorithm is carried out, namely changing the position of the letters of the plaintext from the first order to the last order and so on. The encrypted information will then be inserted into a video using the Steganography Algorithm, namely Least Significant Bit (LSB). Before being inserted, the video will first be converted into several image frames, then in one frame the information will be inserted. This can be done because the frame is a collection of RGB arrays which have values 0-255 or 0 and 1. So the insertion is done in bit form. Frames containing information will then be converted back into a video.On the receiving side, the video will be converted into a frame, next is the process of retrieving the information that was previously inserted. The information that has been taken is then reversed in order and then shifted using the Caesar chipper algorithm according to the key used by the sender, then the first letter of each word is changed to capital, so that the information sent is the same as that received. The implication of this research is that it is a way to combine cryptography with steganography as an information security technique.

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