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All Journal SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Sistem Informasi Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta SPEED - Sentra Penelitian Engineering dan Edukasi Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pariwisata, Bisnis Digital dan Manajemen Journal Medical Informatics Technology International Journal of Computer Technology and Science
Daniati Uki Eka Saputri
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Techno Nusa Mandiri : Journal of Computing and Information Technology

 1 
IMPLEMENTATION OF DECISION TREE AND K-NN CLASSIFICATION OF INTEREST IN CONTINUING STUDENT SCHOOL Daniati Uki Eka Saputri; Fitra Septia Nugraha; Taopik Hidayat; Abdul Latif; Ade Suryadi; Achmad Baroqah Pohan
Jurnal Techno Nusa Mandiri Vol 17 No 1 (2020): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1075.117 KB) | DOI: 10.33480/techno.v17i1.1289

Abstract

Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school. This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measurement process. The test results show that the use of the Decision Tree algorithm produces an accuracy value of 97.30% while using the K-NN algorithm produces an accuracy of 89.60%. While the proposed method by combining the Decision Tree and K-NN algorithms produces an accuracy value of 98.07%. The results of evaluation measurements using the Area Under Curve (AUC) on the Decision Tree algorithm are 0.992 and the AUC on K-NN is 0.958 and on the combination of the Decision Tree and K-NN algorithms of 0.979. These results indicate that the proposed algorithm is very significant towards increasing accuracy in the classification of the interests of high school students continuing school
MEAT IMAGE CLASSIFICATION USING DEEP LEARNING WITH RESNET152V2 ARCHITECTURE Taopik Hidayat; Daniati Uki Eka Saputri; Faruq Aziz
Jurnal Techno Nusa Mandiri Vol 19 No 2 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i2.3932

Abstract

Meat is a food ingredient that can be consumed by humans and consists of essential nutrients, especially protein, which are needed for various physiological functions in the human body. Beef, goat and pork are meats that are commonly used by Indonesian people as daily processed foods. A very high level of meat consumption results in a high economic value of meat consumption. However, many people do not know how to distinguish between the types of beef, mutton and pork. This study aims to classify types of beef, goat and pork using the ResNet152V2 algorithm. The data used are 600 images with 200 images of beef, 200 images of mutton and 200 images of pork. The process carried out is pre-processing using 4 stages, namely image augmentation, image sharpness process, then the image is resized to adjust the size needed by the algorithm. The last pre-processing is to perform the image normalization process. After the pre-processing is done, then the data training stage is carried out using the ResNet152V2 algorithm to build a classification model and then the model is tested against data testing to get the results of the optimal classification of pork, goat and beef images by looking at the results of accuracy and loss values.
Co-Authors Abdul Latif Achmad Baroqah Pohan Ade Suryadi Agus Salim Annida Purnamawati Aziz, Faruq Cahyanti, F. Lia Dwi Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Faruq Aziz Faruq Aziz Firasari, Elly Fitra Septia Nugraha Gata, Windu Hasanah, Riyan Latifahul Hidayat, Taopik Juninisvianty, Tri Kekal, Harming Puja Khultsum, Umi Lili Novita M. Rangga Ramadhan Saelan Monikka Nur Winnarto Nugraha, Fitra Septia Nurfalah, Ridan Nurul Khasanah Nurul Khasanah Nurul Khasanah, Nurul Pratiwi, Risca Lusiana Rangga Pebrianto Risca Lusiana Pratiwi - Universitas Nusa Mandiri Rosiyadi, Didi Salsabila, Unik Hanifah Saputri, Elly Sardiarinto Sardiarinto Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Solly Aryza Sri Hadianti Taopik Hidayat Umi Khultsum Veithzal Rivai Zainal YANTO YANTO