Garuda - Garba Rujukan Digital

Moedjiono Moedjiono

Universitas Budi Luhur

Author-ID : 4240521

Computer Science & IT

Published : 3 Documents Claim Missing Document

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Articles

Selection of the Best Lecturers using the AHP (Analytical Hierarchy Process) and TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution): Case Study of STMIK Insan Pembangunan Winny Purbaratri; Moedjiono Moedjiono; Moch. Fajar Purnomo Alam
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

STMIK Insan Pembangunan is a College that was established in 1990, located in Tangerang Regency. Supported by 41 Lecturer staff. Lecturers have the position as professional staff at the higher education level who are appointed in accordance with the laws and regulations. Lecturers are educators who provide a number of knowledge to students in universities or universities. The best lecturer selection system is used to support learning and teaching activities in the campus so that students are competent in the field of concentration taken. So it is needed teaching staff or lecturers who are competent in their fields, in this case to meet the criteria of the competent lecturer is needed a system that supports in this case deciding which lecturers are considered the best. The process of selecting the Best Lecturers in the current system is that there is a shortage that takes a long time to process the results of the questionnaire data and only uses one of the criteria of the Tridarma of Higher Education, namely Education and Teaching. So that the resulting decision is not yet valid. In this study a Decision Support System (DSS) will be made where the decision support system can help a person in making accurate and well-targeted decisions. The method used is AHP to calculate the weight of each criterion and TOPSIS to rank each alternative based on each criterion. The results obtained in this study are a system that is able to produce the best rank of lecturers in STMIK Insan Pembangunan.

Penentuan Server Kritis Dengan Menggunakan Fuzzy Mamdani Dan Fuzzy Sugeno Pada PT SAMUDERA INDONESIA TBK suhaemi suhaemi; Moedjiono Moedjiono; Arriyadi Ade Sunarto
bit-Tech Vol. 1 No. 3 (2019): Learning Synchronous and Asynchronous in the Industry 4.0
Publisher : Komunitas Dosen Indonesia

CRITICAL SERVER DETERMINATION USING FUZZY MAMDANI AND FUZZY SUGENO METHODS :CASE STUDY PT SAMUDERA INDONESIA TBKBy : Suhaemi (1611601392)PT Samudera Indonesia Tbk is a company engaged in the field of logistics. As a service company, prioritizes services especially in providing information services to the customer or client. However, in the common problems of the technology information system which is used in particular is happening is the server down. This is due to several factors could not be predicted. Plus the company is still dependent on the role of the IT personnel to wait for handling server down occurrences. The effort to know the level of critical server is one of the prevention or mitigation of interference that are at risk of fatal to the business processes of the company then needed a decision support system. In this study, the author compares the mamdani fuzzy logic method calculations with fuzzy sugeno method to measure the level of critical servers used PT Samudera Indonesia Tbk. This research resulted in a prototype that is built with MATLAB R2013a, can be used to calculate the critical level on the server. The prototype uses two methods of fuzzy mamdani and sugeno fuzzy. Accuracy of Mamdani fuzzy method has a 85,21% average. Of the two methods of calculation, the highest accuracy rating results will serve as a reference in determining a critical server.Keywords: SPK, Server, Fuzzy Mamdani, Fuzzy Sugeno, Matlab R2013a

Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory Bahar Sobari; Moedjiono Moedjiono; M. Asep Rizkiawan
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports.

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