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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
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Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 9 Documents
 1 
Search results for , issue "Vol 5 No 2 (2024): July 2024" : 9 Documents clear
Analisis dan Simulasi Rangkaian Konverter Buck untuk Efisiensi Tinggi dalam Pengisian Perangkat Portabel 5 Volt Nizar, Fahrun; Wiryajati, I Ketut; Nababan, Sabar
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5230

Abstract

Technological developments and advances have caused many electrical and electronic devices to almost all use DC voltage sources whose voltage can be adjusted. So a tool is needed to reduce the DC voltage to an adjusted voltage, namely a buck converter. A buck converter is a device that can reduce DC voltage to a lower voltage. This research aims to design an open and close loop buck converter circuit, determine the level of efficiency and the effect of the duty cycle on the output voltage and the error difference value. Investigation and use of MATLAB/SIMULINK software. In this research, the buck converter duty cycle was changed from 5% - 99% in the open loop and the input voltage was 12 volts - 24 volts in the closed loop which worked at a frequency of 50kHz. The output voltage value that approaches 5 volts is a duty cycle of 40% with the voltage obtained being V measured 5.067 volts, V calculated 4.8 volts with an error difference of 0.267 in the open loop circuit and in the closed loop the average difference in output voltage error is obtained from The setting point voltage is 5 volts, namely 1.102.
Klasifikasi Jenis Jerawat pada Data Citra Jerawat Wajah Menggunakan Convolutional Neural Network Putri, Chatarina Natassya; Qornain, Wafi Dzul; Bamahri, Fakhirah; Yuliastuti, Gusti Eka; Kurniawan, Muchamad
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5231

Abstract

Acne is a condition caused by pilosebaceous inflammation which affects 85% of skin conditions in adolescents and adults. Acne has an impact on the psychological and social health of sufferers. To treat acne, it is necessary to know the right type of acne so that sufferers can treat the type of acne according to how they are treated. This research was carried out to classify the types of acne in facial acne images using the Convolutional Neural Network (CNN) method. Based on previous research, it shows that the use of CNN is considered effective and appropriate in increasing classification accuracy. This research uses a dataset of acne types from Kaggle with a total of 351 data, divided into 5 classes, namely acne fulminans, acne nodules, fungal acne, papules and pustules which will be tested using 2 different optimizers, namely Adam and RMS- prop. From the results of this test, the highest accuracy was 100% using the Adam optimizer and the RMS-prop optimizer test obtained the highest accuracy value of 80%.
Implementasi Model Prototype untuk Perancangan Sistem Informasi Project Monitoring Berbasis Web Pratiwi, Rolita; Kholil, Ishak
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5307

Abstract

PT. Shuba Mitra Solusi is a business in the form of a limited liability company or PT, with a digital agency type of business. The company routinely receives projects from clients within a specified processing time. The large number of projects being carried out at the same time often makes the team overwhelmed, especially the target completion time. The project stages carried out are planning, work, monitoring and evaluation. Obstacles often faced by companies are that many team members report the results of their work manually so that the project leader does not update the information, team work targets are sometimes missed due to somewhat hampered coordination in reporting, team members do not know the next work after the work is completed, difficulty knowing progress of work carried out by the project team. Such obstacles mean that work plans and targets are not in accordance with initial planning, which results in delays in reporting project progress to clients by the project leader. The Project Monitoring Application Prototype that the author proposes tries to provide a solution as a support system to make it easier for teams to report work progress using the task menu and project menu. This facility also makes it easier to find solutions if there are problems or errors within the team, making the coordination and repair process easier. The application of this application at PT Shuba Mitra Solusi is to make it easier for the team to make work reports, provide information on the status of the work being done, and make it easier for leaders to monitor ongoing projects.
Optimasi Metode Support Vector Machine Menggunakan Seleksi Fitur Recursive Feature Elimination dan Forward Selection untuk Klasifikasi Kanker Payudara Septiany, Eva Senia; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Masruriyah, Anis Fitri Nur
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5324

Abstract

Cancer, the leading cause of global death, results from abnormal cell proliferation that spreads beyond the boundaries of normal tissue. Breast cancer is one of the most common types of cancer, with approximately 2.26 million cases reported in 2020. This research aims to develop a more effective Support Vector Machine (SVM) algorithm for breast cancer classification through efficient feature selection techniques. Previous research has used various algorithms such as K-Nearest Neighbor and Logistic Regression for breast cancer identification. This research focuses on improving accuracy by using alternative feature selection methods such as Recursive Feature Elimination (RFE) and Forward Selection. The dataset used consists of 569 instances with 32 features sourced from the UCI Machine Learning Repository, and classified into benign and malignant categories. Data pre-processing methods, including data cleaning, coding, and feature selection, were applied to the dataset. RFE and Forward Selection techniques were used to identify the most important features for model training. Evaluation of the improved SVM model shows a training accuracy of nearly 100% and a Cross Validation accuracy of 97%, demonstrating the effectiveness of the proposed approach in the context of breast cancer. In addition, the Learning Curve and testing showed the stability of the SVM model with no signs of overfitting or underfitting. Thus, this study developed an SVM algorithm with a feature selection method that produces better accuracy results in breast cancer classification.
Penerapan Metode CNN (Convolutional Neural Network) untuk Mengklasifikasikan Jenis Cacat pada Kulit Hewan Frannita, Eka Legya; Prananda, Alifia Revan
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5390

Abstract

Recently, leather industry was rapidly growth in several countries. In Indonesia, leather industry became one of the government's priority industries since there were quite a lot of leather industries developing in various regions in Indonesia. On the other hand, there were large number of consumer demand for leather products. Regarding to this fact, maintaining the quality of leather was strongly important. An alternative solution for maintaining leather quality is to conduct leather quality inspection process. However, currently the leather inspection process was still carried out manually by identifying directly the types of defects found on the surface of the leather. This manual inspection process certainly has several hurdles such as time consuming, requiring high accuracy, and requiring experienced operators. This research aimed to develop convolutional neural network architecture that can classify types of leather defects. This research was done by conducting four main processes which were literature study and data collection processes, develop CNN architecture, training process, and testing process. This research work used public dataset consisting of 3600 digital leather images distributed into six classes (folding mask, grain off, growth marks, loose grains, pinhole, non-defective). Based on the training and testing process, the model obtained training accuracy of 90.43% and testing accuracy of 88.47%.
Analisis Pola Penjualan Obat di Apotek Menggunakan Algoritma Apriori Untuk Optimalisasi Stok dan Penjualan Yulindawati, Yulindawati; Yusnita, Amelia; Mayasari, Renni; Melano, M Erick
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5407

Abstract

This research aims to identify product sales patterns at Teluk Bayur Pharmacy to optimize stock management and increase sales by using data mining techniques, especially the Apriori Algorithm. Pharmacies are very instrumental in providing drug-related information and are a form of retail trade that sells medicines at more affordable prices compared to hospital services. However, Teluk Bayur Pharmacy often faces difficulties in managing stock, analyzing product sales patterns and consumer behavior, which causes problems of over stock or under stock. Through the application of Association Rule Mining using the Apriori Algorithm, this research analyzes the correlation between products to find frequent purchase patterns. The methods used include literature study, data collection, data preprocessing, application of Apriori Algorithm, evaluation and interpretation of results, and application of conclusions and recommendations. To analyze sales patterns, the data collected exceeded 100 entries, and 12 transactions were selected that represented the most sales each month. The results of testing the analysis utilizing tanagra 1.4.41 software, by setting a minimum support of 40% and a minimum confidence of 70%, from the results of research and testing show that products that are often purchased together by customers are masks, vegeta, and antimo with a confidence value above 70%. The findings are expected to provide insight for Teluk Bayur Pharmacy in understanding consumer behavior and identifying new sales opportunities.
Optimasi Algoritma Machine Learning Menggunakan Seleksi Fitur Xgboost Untuk Klasifikasi Kanker Payudara Ramadhan, Naufal Cahya; H, Hanny Hikmayanti; Rohana, Tatang; Siregar, Amril Mutoi
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5408

Abstract

This research analyzes the performance of the K-Nearest Neighbors (KNN), Naïve Bayes, and Random Forest algorithms in the classification of breast cancer diagnosis using the Wisconsin Breast Cancer dataset. The problem discussed is how to improve the accuracy of breast cancer diagnosis classification through appropriate preprocessing techniques. The research objective is to evaluate and compare the performance of the three algorithms after the application of preprocessing which includes data cleaning, handling missing values, data duplication, and outliers, as well as feature selection using XGBoost and SMOTE oversampling. application of feature selection to identify the most relevant features and SMOTE to balance the class distribution in the dataset. Performance evaluation results using a confusion matrix show that Random Forest has the best performance with high accuracy, precision, recall, and F1-score, reaching an AUC of 98% after the application of SMOTE. The combination of feature selection and SMOTE was shown to significantly improve model performance, although KNN showed a decrease in performance with SMOTE, while Naïve Bayes experienced a considerable improvement. This study demonstrates the importance of preprocessing techniques in the development of machine learning models for medical applications, emphasizing that appropriate techniques can significantly improve classification performance and result in more accurate diagnoses.
Analisis Faktor Layanan, Reputasi dan Keamanan yang Mempengaruhi Keputusan Gen Z Menggunakan Perbankan Syariah Lauza, Atiqa; Rasyidin, M.; Saleh, M.; Zulfikar, Zulfikar; Nova, Nova; Rizkina, Azka
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5456

Abstract

TSharia banking is a bank that conducts business based on the principles of Islamic law or Sharia. Bank Shariah has introduced various products with features and amenities aimed at attracting Generation Z to use Bank Shariah's services. The objective of this study is to analyze the factors that influence decisions regarding the use of Islamic banking services. The subject of this research is Gen Z’s in Aceh. This type of research uses a quantitative approach. Data sources using online or electronic questionnaires with Google Forms Media. The data collection method uses an anonymous demographic technique with a sample size of 102 respondents. This study examines three factors of Shariah banking users' decisions to influence their decision to use Shariah banking services among Gen Z’s in Aceh, such as service, reputation and security variables. The results show that service, reputation and security variables influence Gen Z's decision of legitimate banking services in Aceh.
Identifikasi Rhodamin B pada Eyeshadow dengan Metode Kromatografi Lapis Tipis Kurniaty, Rina; Rejeki, Dwi Putri; Syarif, Cut Nurlaili
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5530

Abstract

Eyeshadow is a decorative cosmetic or makeup that contains color pigments used on the eyelids. Based on BPOM Decree No. 23 of 2019 concerning technical requirements for cosmetic ingredients, the Indonesian government has determined more than 20 hazardous dyes, and Rhodamin B is the most widely used hazardous synthetic dye as a dyes. This study aims to find out the presence of Rhodamin B dye in eyeshadow sold around the Aceh Market and does not have a registration number by BPOM. This research was conducted in the Multifunctional Laboratory of the Faculty of Science and Technology using the Thin Layer Chromatography method. The results of the study showed that the Rf value of the eyeshadow sample with code A was 0.5 while the standard Rf was 0.8, the Rf of the B code sample was 0.53 while the standard Rf was 0.83, the Rf of the C code sample is 0.83 while the standard Rf is 0.83, the D code sample is 0.41 while the standard Rf is 0.78, and the E code sample Rf is 0.5 while the standard Rf is 0.8. Where the code C the Rf value is equal to the Rf value of the Rhodamin B standard is also 0.83, the result is positive if the sample Rf is the same or close to each other with the difference in the price of the standard Rf ≤0.2. Based on the results of the study, it can be concluded that 1 out of 5 eyeshadow samples sold around Pasar Aceh by Thin Layer Chromatography obtained sample C positive for containing the synthetic dye Rhodamin B.

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